Running on CPU 🥶 This demo does not work on CPU.
" - -if torch.cuda.is_available(): - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - model_id = "CompVis/stable-diffusion-v1-4" - ax_pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(model_id) - ax_pipe.to(device) - sd_pipe = StableDiffusionPipeline.from_pretrained(model_id) - sd_pipe.to(device) - - -MAX_INFERENCE_STEPS = 100 -MAX_SEED = np.iinfo(np.int32).max - - -def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: - if randomize_seed: - seed = random.randint(0, MAX_SEED) - return seed - - -def get_token_table(prompt: str) -> list[tuple[int, str]]: - tokens = [ax_pipe.tokenizer.decode(t) for t in ax_pipe.tokenizer(prompt)["input_ids"]] - tokens = tokens[1:-1] - return list(enumerate(tokens, start=1)) - - -@spaces.GPU -def run( - prompt: str, - indices_to_alter_str: str, - seed: int = 0, - apply_attend_and_excite: bool = True, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - scale_factor: int = 20, - thresholds: dict[int, float] = { - 10: 0.5, - 20: 0.8, - }, - max_iter_to_alter: int = 25, -) -> PIL.Image.Image: - if num_inference_steps > MAX_INFERENCE_STEPS: - raise gr.Error(f"Number of steps cannot exceed {MAX_INFERENCE_STEPS}.") - - generator = torch.Generator(device=device).manual_seed(seed) - if apply_attend_and_excite: - try: - token_indices = list(map(int, indices_to_alter_str.split(","))) - except Exception: - raise ValueError("Invalid token indices.") - out = ax_pipe( - prompt=prompt, - token_indices=token_indices, - guidance_scale=guidance_scale, - generator=generator, - num_inference_steps=num_inference_steps, - max_iter_to_alter=max_iter_to_alter, - thresholds=thresholds, - scale_factor=scale_factor, - ) - else: - out = sd_pipe( - prompt=prompt, - guidance_scale=guidance_scale, - generator=generator, - num_inference_steps=num_inference_steps, - ) - return out.images[0] - - -def process_example( - prompt: str, - indices_to_alter_str: str, - seed: int, - apply_attend_and_excite: bool, -) -> tuple[list[tuple[int, str]], PIL.Image.Image]: - token_table = get_token_table(prompt) - result = run( - prompt=prompt, - indices_to_alter_str=indices_to_alter_str, - seed=seed, - apply_attend_and_excite=apply_attend_and_excite, - ) - return token_table, result - - -with gr.Blocks(css="style.css") as demo: - gr.Markdown(DESCRIPTION) - gr.DuplicateButton( - value="Duplicate Space for private use", - elem_id="duplicate-button", - visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", - ) - - with gr.Row(): - with gr.Column(): - prompt = gr.Text( - label="Prompt", - max_lines=1, - placeholder="A pod of dolphins leaping out of the water in an ocean with a ship on the background", - ) - with gr.Accordion(label="Check token indices", open=False): - show_token_indices_button = gr.Button("Show token indices") - token_indices_table = gr.Dataframe(label="Token indices", headers=["Index", "Token"], col_count=2) - token_indices_str = gr.Text( - label="Token indices (a comma-separated list indices of the tokens you wish to alter)", - max_lines=1, - placeholder="4,16", - ) - apply_attend_and_excite = gr.Checkbox(label="Apply Attend-and-Excite", value=True) - seed = gr.Slider( - label="Seed", - minimum=0, - maximum=MAX_SEED, - step=1, - value=0, - ) - randomize_seed = gr.Checkbox(label="Randomize seed", value=True) - num_inference_steps = gr.Slider( - label="Number of inference steps", - minimum=1, - maximum=MAX_INFERENCE_STEPS, - step=1, - value=50, - ) - guidance_scale = gr.Slider( - label="Guidance scale", - minimum=0, - maximum=50, - step=0.1, - value=7.5, - ) - run_button = gr.Button("Generate") - with gr.Column(): - result = gr.Image(label="Result") - - with gr.Row(): - examples = [ - [ - "A mouse and a red car", - "2,6", - 2098, - True, - ], - [ - "A mouse and a red car", - "2,6", - 2098, - False, - ], - [ - "A horse and a dog", - "2,5", - 123, - True, - ], - [ - "A horse and a dog", - "2,5", - 123, - False, - ], - [ - "A painting of an elephant with glasses", - "5,7", - 123, - True, - ], - [ - "A painting of an elephant with glasses", - "5,7", - 123, - False, - ], - [ - "A playful kitten chasing a butterfly in a wildflower meadow", - "3,6,10", - 123, - True, - ], - [ - "A playful kitten chasing a butterfly in a wildflower meadow", - "3,6,10", - 123, - False, - ], - [ - "A grizzly bear catching a salmon in a crystal clear river surrounded by a forest", - "2,6,15", - 123, - True, - ], - [ - "A grizzly bear catching a salmon in a crystal clear river surrounded by a forest", - "2,6,15", - 123, - False, - ], - [ - "A pod of dolphins leaping out of the water in an ocean with a ship on the background", - "4,16", - 123, - True, - ], - [ - "A pod of dolphins leaping out of the water in an ocean with a ship on the background", - "4,16", - 123, - False, - ], - ] - gr.Examples( - examples=examples, - inputs=[ - prompt, - token_indices_str, - seed, - apply_attend_and_excite, - ], - outputs=[ - token_indices_table, - result, - ], - fn=process_example, - cache_examples=os.getenv("CACHE_EXAMPLES") == "1", - examples_per_page=20, - ) - - show_token_indices_button.click( - fn=get_token_table, - inputs=prompt, - outputs=token_indices_table, - queue=False, - api_name="get-token-table", - ) - - gr.on( - triggers=[prompt.submit, token_indices_str.submit, run_button.click], - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - queue=False, - api_name=False, - ).then( - fn=get_token_table, - inputs=prompt, - outputs=token_indices_table, - queue=False, - api_name=False, - ).then( - fn=run, - inputs=[ - prompt, - token_indices_str, - seed, - apply_attend_and_excite, - num_inference_steps, - guidance_scale, - ], - outputs=result, - api_name="run", - ) - -if __name__ == "__main__": - demo.queue(max_size=20).launch() diff --git a/spaces/AvinashRamesh23/AIEditor/app.py b/spaces/AvinashRamesh23/AIEditor/app.py deleted file mode 100644 index 27775f6315de44aaafe185222f053815d2e5747d..0000000000000000000000000000000000000000 --- a/spaces/AvinashRamesh23/AIEditor/app.py +++ /dev/null @@ -1,435 +0,0 @@ -import streamlit as st -import whisper -import re -from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip -from moviepy.editor import * -import math -from stable_whisper import modify_model,results_to_word_srt -import asyncio -from deepgram import Deepgram -from typing import Dict -import os -import moviepy.editor as mp -from pytube import YouTube -from time import sleep -import pandas as pd - -import calendar -import time - -current_GMT = time.gmtime() - -time_stamp = calendar.timegm(current_GMT) - -st.title('AI Editor for Content Creators!') - -@st.cache(suppress_st_warning=True) -#load whisper model -def load_model(model_selected): - #load medium model - model = whisper.load_model(model_selected) - # modify model to get word timestamp - modify_model(model) - return model - -#transcribe -@st.cache(suppress_st_warning=True) -def transcribe_video(vid,model_selected): - model = load_model(model_selected) - options = whisper.DecodingOptions(fp16=False,language="English") - result = model.transcribe(vid, **options.__dict__) - result['srt'] = whisper_result_to_srt(result) - return result - -#srt generation -def whisper_result_to_srt(result): - text = [] - for i,s in enumerate(result['segments']): - text.append(str(i+1)) - time_start = s['start'] - hours, minutes, seconds = int(time_start/3600), (time_start/60) % 60, (time_start) % 60 - timestamp_start = "%02d:%02d:%06.3f" % (hours, minutes, seconds) - timestamp_start = timestamp_start.replace('.',',') - time_end = s['end'] - hours, minutes, seconds = int(time_end/3600), (time_end/60) % 60, (time_end) % 60 - timestamp_end = "%02d:%02d:%06.3f" % (hours, minutes, seconds) - timestamp_end = timestamp_end.replace('.',',') - text.append(timestamp_start + " --> " + timestamp_end) - text.append(s['text'].strip() + "\n") - return "\n".join(text) - -#compute speaking_time -async def compute_speaking_time(transcript_data: Dict,data:str) -> None: - if 'results' in transcript_data: - transcript = transcript_data['results']['channels'][0]['alternatives'][0]['words'] - total_speaker_time = {} - speaker_words = [] - current_speaker = -1 - - for speaker in transcript: - speaker_number = speaker["speaker"] - - if speaker_number is not current_speaker: - current_speaker = speaker_number - speaker_words.append([speaker_number, [], 0]) - - try: - total_speaker_time[speaker_number][1] += 1 - except KeyError: - total_speaker_time[speaker_number] = [0,1] - - get_word = speaker["word"] - speaker_words[-1][1].append(get_word) - - total_speaker_time[speaker_number][0] += speaker["end"] - speaker["start"] - speaker_words[-1][2] += speaker["end"] - speaker["start"] - - for speaker, words, time_amount in speaker_words: - print(f"Speaker {speaker}: {' '.join(words)}") - data+=f"\nSpeaker {speaker}: {' '.join(words)}" - print(f"Speaker {speaker}: {time_amount}") - data+=f"\nSpeaker {speaker}: {time_amount}" - - - for speaker, (total_time, amount) in total_speaker_time.items(): - print(f"Speaker {speaker} avg time per phrase: {total_time/amount} ") - data+=f"\nSpeaker {speaker} avg time per phrase: {total_time/amount} " - print(f"Total time of conversation: {total_time}") - data+=f"\nTotal time of conversation: {total_time}" - return transcript,data - -#extract audio from video -def extract_write_audio(vd): - my_clip = mp.VideoFileClip(f'{vd}') - my_clip.audio.write_audiofile(f"audio.wav") - -#speaker diarization workflow -async def speaker_diarization_flow(PATH_TO_FILE): - audio = extract_write_audio(PATH_TO_FILE) - data = '' - DEEPGRAM_API_KEY = "3dc39bf904babb858390455b1a1399e221bf87f8" - deepgram = Deepgram(DEEPGRAM_API_KEY) - with open(PATH_TO_FILE, 'rb') as audio: - source = {'buffer': audio, 'mimetype': 'audio/wav'} - transcription = await deepgram.transcription.prerecorded(source, {'punctuate': True, 'diarize': True}) - transcript,final_data = await compute_speaking_time(transcription,data) - return final_data - -# speaker diarization main funciton -async def speaker_diarization(PATH_TO_FILE): - data = await speaker_diarization_flow(PATH_TO_FILE) - print("data is", data) - return data - -#find filler words -def filler_words_finder(result_data): - word_map_prior_edit=set() - word_map_after_edit=set() - #my filler words sample - filler_words={'um','ah','you know','mmm','mmm','er','uh','Hmm','actually','basically','seriously','mhm','uh huh','uh','huh','ooh','aah','ooh'} - filler_words_timestamp=set() - for keys in result_data: - if keys == 'segments': - prev=0 - for i in result_data[keys]: - for word in i['whole_word_timestamps']: - lower_case = re.sub(r'\W','',word['word'].lower()) - word_map_prior_edit.add(word['timestamp']) - if lower_case in filler_words or lower_case.startswith(('hm','aa','mm','oo')): - st.write(word['word'].lower(),word['timestamp']) - print(word['word'].lower(),word['timestamp']) - filler_words_timestamp.add(word['timestamp']) - prev=word['timestamp'] - continue - word_map_after_edit.add((prev,word['timestamp'])) - prev=word['timestamp'] - return word_map_after_edit, filler_words_timestamp - -def merge_overlapping_time_intervals(intervals): - stack = [] - result=[intervals[0]] - - for interval in intervals: - interval2=result[-1] - - if overlap(interval,interval2): - result[-1] = [min(interval[0],interval2[0]),max(interval[1],interval2[1])] - else: - result.append(interval) - - return result - -def overlap(interval1,interval2): - return min(interval1[1],interval2[1])-max(interval1[0],interval2[0]) >= 0 - -#assembly ai endpoints -import requests -transcript_endpoint = "https://api.assemblyai.com/v2/transcript" -upload_endpoint = "https://api.assemblyai.com/v2/upload" - -headers = { - "authorization": "05e515bf6b474966bc48bbdd1448b3cf", - "content-type": "application/json" -} - -def upload_to_AssemblyAI(save_location): - CHUNK_SIZE = 5242880 - def read_file(filename): - with open(filename, 'rb') as _file: - while True: - print("chunk uploaded") - data = _file.read(CHUNK_SIZE) - if not data: - break - yield data - - upload_response = requests.post( - upload_endpoint, - headers=headers, data=read_file(save_location) - ) - print(upload_response.json()) - audio_url = upload_response.json()['upload_url'] - print('Uploaded to', audio_url) - return audio_url - - -def start_analysis(audio_url,type): - ## Start transcription job of audio file - data = { - 'audio_url': audio_url, - 'iab_categories': True, - 'content_safety': True, - "summarization": True, - "summary_type": "bullets", - "summary_model":type - } - if type=='conversational': - data["speaker_labels"]= True - - transcript_response = requests.post(transcript_endpoint, json=data, headers=headers) - print(transcript_response.json()) - transcript_id = transcript_response.json()['id'] - polling_endpoint = transcript_endpoint + "/" + transcript_id - print("Transcribing at", polling_endpoint) - return polling_endpoint - -def get_analysis_results(polling_endpoint): - status = 'submitted' - - while True: - print(status) - polling_response = requests.get(polling_endpoint, headers=headers) - status = polling_response.json()['status'] - # st.write(polling_response.json()) - # st.write(status) - if status == 'submitted' or status == 'processing' or status == 'queued': - print('not ready yet') - sleep(10) - - elif status == 'completed': - print('creating transcript') - return polling_response - break - - else: - print('error') - return False - break - -def pii_redact(audiourl,options): - print(options,audiourl) - endpoint = "https://api.assemblyai.com/v2/transcript" - json = { - "audio_url": audiourl, - "redact_pii": True, - "redact_pii_audio": True, - "redact_pii_policies": options - } - - headers = { - "authorization": "05e515bf6b474966bc48bbdd1448b3cf", - "content-type": "application/json", - } - - response = requests.post(endpoint, json=json, headers=headers) - print(response.json()) - transcript_id = response.json()['id'] - polling_endpoint = endpoint + "/" + transcript_id - return polling_endpoint - -def pii_redact_audio(polling_endpoint): - status = 'submitted' - headers = { - "authorization": "05e515bf6b474966bc48bbdd1448b3cf", - "content-type": "application/json", - } - while True: - print(status) - polling_response = requests.get(polling_endpoint, headers=headers) - status = polling_response.json()['status'] - if status == 'submitted' or status == 'processing' or status == 'queued': - print('not ready yet') - sleep(10) - - elif status == 'completed': - print('creating transcript') - return polling_response - break - - else: - print('error') - return False - break - -def download_redact_audio(pooling_enpoint): - headers = { - "authorization": "05e515bf6b474966bc48bbdd1448b3cf", - "content-type": "application/json", - } - - redacted_audio_response = requests.get(pooling_enpoint + "/redacted-audio",headers=headers) - print(redacted_audio_response.json()) - redacted_audio = requests.get(redacted_audio_response.json()['redacted_audio_url']) - with open('redacted_audio.mp3', 'wb') as f: - f.write(redacted_audio.content) - -def redact_audio_video_display(vd,audio): - audioclip = AudioFileClip(audio) - clip = VideoFileClip(vd) - videoclip = clip.set_audio(audioclip) - videoclip.write_videofile("Redacted_video.mp4") - st.video("Redacted_video.mp4") - -async def main(uploaded_video,model_selected): - try: - vid = uploaded_video.name - with open(vid, mode='wb') as f: - f.write(uploaded_video.read()) # save video to disk - except: - with st.spinner('Downloading Yotube Video'): - yt = YouTube(uploaded_video) - title=yt.title - vid = f"{title}.mp4" - yt.streams.filter(file_extension="mp4").get_by_resolution("360p").download(filename=vid) - finally: - name = vid.split('.')[0] - preview = st.video(vid) - #extracting the transcription result - with st.spinner('Transcribing Video, Wait for it...'): - result = transcribe_video(vid,model_selected) - st.text_area("Edit Transcript",result["text"]) - col1, col2, col3, col4, col5, col6 = st.columns([1,1,1,1,1,1]) - tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Remove Filler Words","Edit Video" ,"Download SRT", "Perform Speaker Diarization","Content Analyzer","PII redactation"]) - - with tab1: - filler_word = st.button('Edit/Remove Filler Words with a click of a button') - if filler_word: - with st.spinner(text="In progress..."): - word_map_after_edit, filler_words_timestamp = filler_words_finder(result) - final_intervals = merge_overlapping_time_intervals(sorted(list(word_map_after_edit))) - subclips=[] - for start,end in final_intervals: - clip = VideoFileClip(vid) - tmp = clip.subclip(start,(end - end*0.1)) - subclips.append(tmp) - #concatenate subclips without filler words - final_clip = concatenate_videoclips(subclips) - final_clip.write_videofile(f"remove_{vid}") - preview = st.video(f"remove_{vid}") - - with tab2: - save = st.button('Edit') - - with tab3: - download = st.download_button('Download SRT', result['srt'],f'{name}.srt') - if download: - st.write('Thanks for downloading!') - - with tab4: - identify_download_speaker = st.button('Perform Speaker Diarization') - if identify_download_speaker: - with st.spinner(text="In progress..."): - results = await speaker_diarization(vid) - download_speaker = st.download_button("download speaker_diarization",results,'diarization_stats.txt') - if download_speaker: - st.write('Thanks for downloading!') - - with tab5: - type = st.selectbox('Summary Type?',('informative', 'conversational', 'catchy')) - Analyze_content = st.button("Start Content Analysis") - if Analyze_content: - with st.spinner(text="In progress..."): - audio = extract_write_audio(vid) - audio_url = upload_to_AssemblyAI("audio.wav") - # start analysis of the file - polling_endpoint = start_analysis(audio_url,type) - # receive the results - results = get_analysis_results(polling_endpoint) - - # separate analysis results - summary = results.json()['summary'] - content_moderation = results.json()["content_safety_labels"] - topic_labels = results.json()["iab_categories_result"] - - my_expander1 = st.expander(label='Summary') - my_expander2 = st.expander(label='Content Moderation') - my_expander3 = st.expander(label='Topic Discussed') - - with my_expander1: - st.header("Video summary") - st.write(summary) - - with my_expander2: - st.header("Sensitive content") - if content_moderation['summary'] != {}: - st.subheader('🚨 Mention of the following sensitive topics detected.') - moderation_df = pd.DataFrame(content_moderation['summary'].items()) - moderation_df.columns = ['topic','confidence'] - st.dataframe(moderation_df, use_container_width=True) - else: - st.subheader('✅ All clear! No sensitive content detected.') - - with my_expander3: - st.header("Topics discussed") - topics_df = pd.DataFrame(topic_labels['summary'].items()) - topics_df.columns = ['topic','confidence'] - topics_df["topic"] = topics_df["topic"].str.split(">") - expanded_topics = topics_df.topic.apply(pd.Series).add_prefix('topic_level_') - topics_df = topics_df.join(expanded_topics).drop('topic', axis=1).sort_values(['confidence'], ascending=False).fillna('') - st.dataframe(topics_df, use_container_width=True) - - with tab6: - options = st.multiselect('Select Policies to redact from video',["medical_process","medical_condition","blood_type","drug","injury","number_sequence","email_address","date_of_birth","phone_number","us_social_security_number","credit_card_number","credit_card_expiration","credit_card_cvv","date","nationality","event","language","location","money_amount","person_name","person_age","organization","political_affiliation","occupation","religion","drivers_license","banking_information"],["person_name", 'credit_card_number']) - Perform_redact = st.button("Start PII Redaction") - if Perform_redact: - with st.spinner(text="In progress..."): - audio = extract_write_audio(vid) - audio_url = upload_to_AssemblyAI("audio.wav") - print(audio_url) - print([ x for x in options ]) - polling_endpoint = pii_redact(audio_url,options) - results = pii_redact_audio(polling_endpoint) - download_redact_audio(polling_endpoint) - redact_audio_video_display(vid,"redacted_audio.mp3") - -Model_type = st.sidebar.selectbox("Choose Model",('Tiny - Best for Srt generation', 'Base - Best suited for various AI services', 'Medium - Use this model for filler word removal'),0) -upload_video = st.sidebar.file_uploader("Upload mp4 file",type=["mp4","mpeg"]) -youtube_url = st.sidebar.text_input("Enter a youtube video url") -# submit_button = st.sidebar.button("Extract Youtube Video") - -if Model_type.startswith("Tiny"): - model_selected = 'tiny.en' -if Model_type.startswith("Base"): - model_selected = 'base.en' -if Model_type.startswith("Small"): - model_selected = 'small.en' -if Model_type.startswith("Medium"): - model_selected = 'medium.en' - -if youtube_url: - asyncio.run(main(youtube_url,model_selected)) - -if upload_video: - asyncio.run(main(upload_video,model_selected)) - -st.sidebar.write("Kindly upload or provide youtube link with less a minute of video for faster performance and avoid excess usage of the free tier.") diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py deleted file mode 100644 index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py +++ /dev/null @@ -1,864 +0,0 @@ - -import math -import json -import copy -from typing import List, Dict -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY -from detectron2.layers import ShapeSpec, cat -from detectron2.structures import Instances, Boxes -from detectron2.modeling import detector_postprocess -from detectron2.utils.comm import get_world_size -from detectron2.config import configurable - -from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit -from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss -from ..layers.iou_loss import IOULoss -from ..layers.ml_nms import ml_nms -from ..debug import debug_train, debug_test -from .utils import reduce_sum, _transpose -from .centernet_head import CenterNetHead - -__all__ = ["CenterNet"] - -INF = 100000000 - -@PROPOSAL_GENERATOR_REGISTRY.register() -class CenterNet(nn.Module): - @configurable - def __init__(self, - # input_shape: Dict[str, ShapeSpec], - in_channels=256, - *, - num_classes=80, - in_features=("p3", "p4", "p5", "p6", "p7"), - strides=(8, 16, 32, 64, 128), - score_thresh=0.05, - hm_min_overlap=0.8, - loc_loss_type='giou', - min_radius=4, - hm_focal_alpha=0.25, - hm_focal_beta=4, - loss_gamma=2.0, - reg_weight=2.0, - not_norm_reg=True, - with_agn_hm=False, - only_proposal=False, - as_proposal=False, - not_nms=False, - pos_weight=1., - neg_weight=1., - sigmoid_clamp=1e-4, - ignore_high_fp=-1., - center_nms=False, - sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]], - more_pos=False, - more_pos_thresh=0.2, - more_pos_topk=9, - pre_nms_topk_train=1000, - pre_nms_topk_test=1000, - post_nms_topk_train=100, - post_nms_topk_test=100, - nms_thresh_train=0.6, - nms_thresh_test=0.6, - no_reduce=False, - debug=False, - vis_thresh=0.5, - pixel_mean=[103.530,116.280,123.675], - pixel_std=[1.0,1.0,1.0], - device='cuda', - centernet_head=None, - ): - super().__init__() - self.num_classes = num_classes - self.in_features = in_features - self.strides = strides - self.score_thresh = score_thresh - self.min_radius = min_radius - self.hm_focal_alpha = hm_focal_alpha - self.hm_focal_beta = hm_focal_beta - self.loss_gamma = loss_gamma - self.reg_weight = reg_weight - self.not_norm_reg = not_norm_reg - self.with_agn_hm = with_agn_hm - self.only_proposal = only_proposal - self.as_proposal = as_proposal - self.not_nms = not_nms - self.pos_weight = pos_weight - self.neg_weight = neg_weight - self.sigmoid_clamp = sigmoid_clamp - self.ignore_high_fp = ignore_high_fp - self.center_nms = center_nms - self.sizes_of_interest = sizes_of_interest - self.more_pos = more_pos - self.more_pos_thresh = more_pos_thresh - self.more_pos_topk = more_pos_topk - self.pre_nms_topk_train = pre_nms_topk_train - self.pre_nms_topk_test = pre_nms_topk_test - self.post_nms_topk_train = post_nms_topk_train - self.post_nms_topk_test = post_nms_topk_test - self.nms_thresh_train = nms_thresh_train - self.nms_thresh_test = nms_thresh_test - self.no_reduce = no_reduce - self.debug = debug - self.vis_thresh = vis_thresh - if self.center_nms: - self.not_nms = True - self.iou_loss = IOULoss(loc_loss_type) - assert (not self.only_proposal) or self.with_agn_hm - # delta for rendering heatmap - self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap) - if centernet_head is None: - self.centernet_head = CenterNetHead( - in_channels=in_channels, - num_levels=len(in_features), - with_agn_hm=with_agn_hm, - only_proposal=only_proposal) - else: - self.centernet_head = centernet_head - if self.debug: - pixel_mean = torch.Tensor(pixel_mean).to( - torch.device(device)).view(3, 1, 1) - pixel_std = torch.Tensor(pixel_std).to( - torch.device(device)).view(3, 1, 1) - self.denormalizer = lambda x: x * pixel_std + pixel_mean - - @classmethod - def from_config(cls, cfg, input_shape): - ret = { - # 'input_shape': input_shape, - 'in_channels': input_shape[ - cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels, - 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES, - 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES, - 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES, - 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH, - 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE, - 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP, - 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS, - 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA, - 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA, - 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA, - 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT, - 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG, - 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM, - 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL, - 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL, - 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS, - 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT, - 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT, - 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP, - 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP, - 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS, - 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI, - 'more_pos': cfg.MODEL.CENTERNET.MORE_POS, - 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH, - 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK, - 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN, - 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST, - 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN, - 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST, - 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN, - 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST, - 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE, - 'debug': cfg.DEBUG, - 'vis_thresh': cfg.VIS_THRESH, - 'pixel_mean': cfg.MODEL.PIXEL_MEAN, - 'pixel_std': cfg.MODEL.PIXEL_STD, - 'device': cfg.MODEL.DEVICE, - 'centernet_head': CenterNetHead( - cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]), - } - return ret - - - def forward(self, images, features_dict, gt_instances): - features = [features_dict[f] for f in self.in_features] - clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \ - self.centernet_head(features) - grids = self.compute_grids(features) - shapes_per_level = grids[0].new_tensor( - [(x.shape[2], x.shape[3]) for x in reg_pred_per_level]) - - if not self.training: - return self.inference( - images, clss_per_level, reg_pred_per_level, - agn_hm_pred_per_level, grids) - else: - pos_inds, labels, reg_targets, flattened_hms = \ - self._get_ground_truth( - grids, shapes_per_level, gt_instances) - # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M - logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs( - clss_per_level, reg_pred_per_level, agn_hm_pred_per_level) - - if self.more_pos: - # add more pixels as positive if \ - # 1. they are within the center3x3 region of an object - # 2. their regression losses are small (Cómo descargar Dr Fone Unlock para PC
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\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_functools.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_functools.py deleted file mode 100644 index 71f66bd03cb713a2190853bdf7170c4ea80d2425..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_functools.py +++ /dev/null @@ -1,104 +0,0 @@ -import types -import functools - - -# from jaraco.functools 3.3 -def method_cache(method, cache_wrapper=None): - """ - Wrap lru_cache to support storing the cache data in the object instances. - - Abstracts the common paradigm where the method explicitly saves an - underscore-prefixed protected property on first call and returns that - subsequently. - - >>> class MyClass: - ... calls = 0 - ... - ... @method_cache - ... def method(self, value): - ... self.calls += 1 - ... return value - - >>> a = MyClass() - >>> a.method(3) - 3 - >>> for x in range(75): - ... res = a.method(x) - >>> a.calls - 75 - - Note that the apparent behavior will be exactly like that of lru_cache - except that the cache is stored on each instance, so values in one - instance will not flush values from another, and when an instance is - deleted, so are the cached values for that instance. - - >>> b = MyClass() - >>> for x in range(35): - ... res = b.method(x) - >>> b.calls - 35 - >>> a.method(0) - 0 - >>> a.calls - 75 - - Note that if method had been decorated with ``functools.lru_cache()``, - a.calls would have been 76 (due to the cached value of 0 having been - flushed by the 'b' instance). - - Clear the cache with ``.cache_clear()`` - - >>> a.method.cache_clear() - - Same for a method that hasn't yet been called. - - >>> c = MyClass() - >>> c.method.cache_clear() - - Another cache wrapper may be supplied: - - >>> cache = functools.lru_cache(maxsize=2) - >>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache) - >>> a = MyClass() - >>> a.method2() - 3 - - Caution - do not subsequently wrap the method with another decorator, such - as ``@property``, which changes the semantics of the function. - - See also - http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/ - for another implementation and additional justification. - """ - cache_wrapper = cache_wrapper or functools.lru_cache() - - def wrapper(self, *args, **kwargs): - # it's the first call, replace the method with a cached, bound method - bound_method = types.MethodType(method, self) - cached_method = cache_wrapper(bound_method) - setattr(self, method.__name__, cached_method) - return cached_method(*args, **kwargs) - - # Support cache clear even before cache has been created. - wrapper.cache_clear = lambda: None - - return wrapper - - -# From jaraco.functools 3.3 -def pass_none(func): - """ - Wrap func so it's not called if its first param is None - - >>> print_text = pass_none(print) - >>> print_text('text') - text - >>> print_text(None) - """ - - @functools.wraps(func) - def wrapper(param, *args, **kwargs): - if param is not None: - return func(param, *args, **kwargs) - - return wrapper diff --git a/spaces/BraydenMoore/MARCI-NFL-Betting/Source/Predict/predict.py b/spaces/BraydenMoore/MARCI-NFL-Betting/Source/Predict/predict.py deleted file mode 100644 index 9a4cf4088fcb2114f9988edef842e4296d145570..0000000000000000000000000000000000000000 --- a/spaces/BraydenMoore/MARCI-NFL-Betting/Source/Predict/predict.py +++ /dev/null @@ -1,166 +0,0 @@ -import xgboost as xgb -import numpy as np -import pandas as pd -import pickle as pkl -import os -import requests -from bs4 import BeautifulSoup -import warnings -warnings.filterwarnings("ignore") -from datetime import datetime - -# set dirs for other files -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(data_directory, 'gbg_this_year.csv') -gbg = pd.read_csv(file_path, low_memory=False) - -file_path = os.path.join(data_directory, 'results.csv') -results = pd.read_csv(file_path, low_memory=False) - -# get team abbreviations -file_path = os.path.join(pickle_directory, 'team_name_to_abbreviation.pkl') -with open(file_path, 'rb') as f: - team_name_to_abbreviation = pkl.load(f) - -file_path = os.path.join(pickle_directory, 'team_abbreviation_to_name.pkl') -with open(file_path, 'rb') as f: - team_abbreviation_to_name = pkl.load(f) - -# get schedule -file_path = os.path.join(pickle_directory, 'schedule.pkl') -with open(file_path, 'rb') as f: - schedule = pkl.load(f) - -# load models -# moneyline -model = 'xgboost_ML_no_odds_71.4%' -file_path = os.path.join(model_directory, f'{model}.json') -xgb_ml = xgb.Booster() -xgb_ml.load_model(file_path) - -# over/under -model = 'xgboost_OU_no_odds_59.8%' -file_path = os.path.join(model_directory, f'{model}.json') -xgb_ou = xgb.Booster() -xgb_ou.load_model(file_path) - - -def get_week(): - headers = { - 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', - 'Accept-Encoding': 'gzip, deflate', - 'Accept-Language': 'en-US,en;q=0.9', - 'Cache-Control': 'max-age=0', - 'Connection': 'keep-alive', - 'Dnt': '1', - 'Upgrade-Insecure-Requests': '1', - 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36' - } - url = 'https://www.nfl.com/schedules/' - resp = requests.get(url,headers=headers) - soup = BeautifulSoup(resp.text, 'html.parser') - h2_tags = soup.find_all('h2') - year = h2_tags[0].getText().split(' ')[0] - week = h2_tags[0].getText().split(' ')[-1] - return int(week), int(year) - - -def get_games(week): - # pull from NBC - #url = 'https://www.nbcsports.com/nfl/schedule' - #df = pd.read_html(url)[week-1] - df = schedule[week-1] - df['Away Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Away TeamAway Team']] - df['Home Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Home TeamHome Team']] - df['Date'] = pd.to_datetime(df['Game TimeGame Time']) - df['Date'] = df['Date'].dt.strftime('%A %d/%m %I:%M %p') - df['Date'] = df['Date'].apply(lambda x: f"{x.split()[0]} {int(x.split()[1].split('/')[1])}/{int(x.split()[1].split('/')[0])} {x.split()[2]}".capitalize()) - - return df[['Away Team','Home Team','Date']] - - -def get_one_week(home,away,season,week): - try: - max_GP_home = gbg.loc[((gbg['home_team'] == home) | (gbg['away_team'] == home)) & (gbg['GP'] < week)]['GP'].max() - max_GP_away = gbg.loc[((gbg['home_team'] == away) | (gbg['away_team'] == away)) & (gbg['GP'] < week)]['GP'].max() - - home_df = gbg.loc[((gbg['away_team']==home) | (gbg['home_team']==home)) & (gbg['Season']==season) & (gbg['GP']==max_GP_home)] - gbg_home_team = home_df['home_team'].item() - home_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True) - home_df = home_df[[i for i in home_df.columns if '.Away' not in i] if gbg_home_team==home else [i for i in home_df.columns if '.Away' in i]] - home_df.columns = [i.replace('.Away','') for i in home_df.columns] - - away_df = gbg.loc[((gbg['away_team']==away) | (gbg['home_team']==away)) & (gbg['Season']==season) & (gbg['GP']==max_GP_away)] - gbg_home_team = away_df['home_team'].item() - away_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True) - away_df = away_df[[i for i in away_df.columns if '.Away' not in i] if gbg_home_team==away else [i for i in away_df.columns if '.Away' in i]] - away_df.columns = [i.replace('.Away','') + '.Away' for i in away_df.columns] - - df = home_df.reset_index(drop=True).merge(away_df.reset_index(drop=True), left_index=True, right_index=True) - return df - except ValueError: - return pd.DataFrame() - - -def predict(home,away,season,week,total): - global results - - # finish preparing data - if len(home)>4: - home_abbrev = team_name_to_abbreviation[home] - else: - home_abbrev = home - - if len(away)>4: - away_abbrev = team_name_to_abbreviation[away] - else: - away_abbrev = away - - data = get_one_week(home_abbrev,away_abbrev,season,week) - data['Total Score Close'] = total - matrix = xgb.DMatrix(data.astype(float).values) - - # create game id - game_id = str(season) + '_0' + str(int(week)) + '_' + away_abbrev + '_' + home_abbrev - - try: - moneyline_result = results.loc[results['game_id']==game_id, 'winner'].item() - except: - moneyline_result = 'N/A' - - try: - ml_predicted_proba = xgb_ml.predict(matrix)[0][1] - winner_proba = max([ml_predicted_proba, 1-ml_predicted_proba]).item() - moneyline = {'Winner': [home if ml_predicted_proba>0.5 else away if ml_predicted_proba<0.5 else 'Toss-Up'], - 'Probabilities':[winner_proba], - 'Result': moneyline_result} - except: - moneyline = {'Winner': 'NA', - 'Probabilities':['N/A'], - 'Result': moneyline_result} - - try: - result = results.loc[results['game_id']==game_id, 'total'].item() - over_under_result = 'Over' if float(result)>float(total) else 'Push' if float(result)==float(total) else 'Under' - print(float(result), float(total)) - except: - over_under_result = 'N/A' - - try: - ou_predicted_proba = xgb_ou.predict(matrix)[0][1] - ou_proba = max([ou_predicted_proba, 1-ou_predicted_proba]).item() - - over_under = {'Over/Under': ['Over' if ou_predicted_proba>0.5 else 'Under'], - 'Probability': [ou_proba], - 'Result': over_under_result} - except: - over_under = {'Over/Under': 'N/A', - 'Probability': ['N/A'], - 'Result': over_under_result} - - return game_id, moneyline, over_under diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_roi_pooler.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_roi_pooler.py deleted file mode 100644 index 9aa3825c0196e4a6d89162e3d7c797e3d77b23bd..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_roi_pooler.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import logging -import unittest -import torch - -from detectron2.modeling.poolers import ROIPooler -from detectron2.structures import Boxes, RotatedBoxes - -logger = logging.getLogger(__name__) - - -class TestROIPooler(unittest.TestCase): - def _rand_boxes(self, num_boxes, x_max, y_max): - coords = torch.rand(num_boxes, 4) - coords[:, 0] *= x_max - coords[:, 1] *= y_max - coords[:, 2] *= x_max - coords[:, 3] *= y_max - boxes = torch.zeros(num_boxes, 4) - boxes[:, 0] = torch.min(coords[:, 0], coords[:, 2]) - boxes[:, 1] = torch.min(coords[:, 1], coords[:, 3]) - boxes[:, 2] = torch.max(coords[:, 0], coords[:, 2]) - boxes[:, 3] = torch.max(coords[:, 1], coords[:, 3]) - return boxes - - def _test_roialignv2_roialignrotated_match(self, device): - pooler_resolution = 14 - canonical_level = 4 - canonical_scale_factor = 2 ** canonical_level - pooler_scales = (1.0 / canonical_scale_factor,) - sampling_ratio = 0 - - N, C, H, W = 2, 4, 10, 8 - N_rois = 10 - std = 11 - mean = 0 - feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean - - features = [feature.to(device)] - - rois = [] - rois_rotated = [] - for _ in range(N): - boxes = self._rand_boxes( - num_boxes=N_rois, x_max=W * canonical_scale_factor, y_max=H * canonical_scale_factor - ) - - rotated_boxes = torch.zeros(N_rois, 5) - rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0 - rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0 - rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0] - rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1] - rois.append(Boxes(boxes).to(device)) - rois_rotated.append(RotatedBoxes(rotated_boxes).to(device)) - - roialignv2_pooler = ROIPooler( - output_size=pooler_resolution, - scales=pooler_scales, - sampling_ratio=sampling_ratio, - pooler_type="ROIAlignV2", - ) - - roialignv2_out = roialignv2_pooler(features, rois) - - roialignrotated_pooler = ROIPooler( - output_size=pooler_resolution, - scales=pooler_scales, - sampling_ratio=sampling_ratio, - pooler_type="ROIAlignRotated", - ) - - roialignrotated_out = roialignrotated_pooler(features, rois_rotated) - - self.assertTrue(torch.allclose(roialignv2_out, roialignrotated_out, atol=1e-4)) - - def test_roialignv2_roialignrotated_match_cpu(self): - self._test_roialignv2_roialignrotated_match(device="cpu") - - @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") - def test_roialignv2_roialignrotated_match_cuda(self): - self._test_roialignv2_roialignrotated_match(device="cuda") - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/default_construct_range.h b/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/default_construct_range.h deleted file mode 100644 index 6c3856c142990a3230c3bc4f805c0cb0a5fbcb73..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/default_construct_range.h +++ /dev/null @@ -1,37 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include- -namespace thrust -{ -namespace detail -{ - - -template -__host__ __device__ -inline void default_construct_range(Allocator &a, Pointer p, Size n); - - -} // end detail -} // end thrust - -#include - - diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/sort.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/sort.h deleted file mode 100644 index 863189a1ea6bc39e5ae9c15a088cffab8060a1b9..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/sort.h +++ /dev/null @@ -1,55 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include - -namespace thrust -{ -namespace system -{ -namespace tbb -{ -namespace detail -{ - -template - void stable_sort(execution_policy &exec, - RandomAccessIterator first, - RandomAccessIterator last, - StrictWeakOrdering comp); - -template - void stable_sort_by_key(execution_policy &exec, - RandomAccessIterator1 keys_first, - RandomAccessIterator1 keys_last, - RandomAccessIterator2 values_first, - StrictWeakOrdering comp); - -} // end namespace detail -} // end namespace tbb -} // end namespace system -} // end namespace thrust - -#include - diff --git a/spaces/CVPR/WALT/mmdet/models/backbones/detectors_resnext.py b/spaces/CVPR/WALT/mmdet/models/backbones/detectors_resnext.py deleted file mode 100644 index 57d032fe37ed82d5ba24e761bdc014cc0ee5ac64..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/mmdet/models/backbones/detectors_resnext.py +++ /dev/null @@ -1,122 +0,0 @@ -import math - -from mmcv.cnn import build_conv_layer, build_norm_layer - -from ..builder import BACKBONES -from .detectors_resnet import Bottleneck as _Bottleneck -from .detectors_resnet import DetectoRS_ResNet - - -class Bottleneck(_Bottleneck): - expansion = 4 - - def __init__(self, - inplanes, - planes, - groups=1, - base_width=4, - base_channels=64, - **kwargs): - """Bottleneck block for ResNeXt. - - If style is "pytorch", the stride-two layer is the 3x3 conv layer, if - it is "caffe", the stride-two layer is the first 1x1 conv layer. - """ - super(Bottleneck, self).__init__(inplanes, planes, **kwargs) - - if groups == 1: - width = self.planes - else: - width = math.floor(self.planes * - (base_width / base_channels)) * groups - - self.norm1_name, norm1 = build_norm_layer( - self.norm_cfg, width, postfix=1) - self.norm2_name, norm2 = build_norm_layer( - self.norm_cfg, width, postfix=2) - self.norm3_name, norm3 = build_norm_layer( - self.norm_cfg, self.planes * self.expansion, postfix=3) - - self.conv1 = build_conv_layer( - self.conv_cfg, - self.inplanes, - width, - kernel_size=1, - stride=self.conv1_stride, - bias=False) - self.add_module(self.norm1_name, norm1) - fallback_on_stride = False - self.with_modulated_dcn = False - if self.with_dcn: - fallback_on_stride = self.dcn.pop('fallback_on_stride', False) - if self.with_sac: - self.conv2 = build_conv_layer( - self.sac, - width, - width, - kernel_size=3, - stride=self.conv2_stride, - padding=self.dilation, - dilation=self.dilation, - groups=groups, - bias=False) - elif not self.with_dcn or fallback_on_stride: - self.conv2 = build_conv_layer( - self.conv_cfg, - width, - width, - kernel_size=3, - stride=self.conv2_stride, - padding=self.dilation, - dilation=self.dilation, - groups=groups, - bias=False) - else: - assert self.conv_cfg is None, 'conv_cfg must be None for DCN' - self.conv2 = build_conv_layer( - self.dcn, - width, - width, - kernel_size=3, - stride=self.conv2_stride, - padding=self.dilation, - dilation=self.dilation, - groups=groups, - bias=False) - - self.add_module(self.norm2_name, norm2) - self.conv3 = build_conv_layer( - self.conv_cfg, - width, - self.planes * self.expansion, - kernel_size=1, - bias=False) - self.add_module(self.norm3_name, norm3) - - -@BACKBONES.register_module() -class DetectoRS_ResNeXt(DetectoRS_ResNet): - """ResNeXt backbone for DetectoRS. - - Args: - groups (int): The number of groups in ResNeXt. - base_width (int): The base width of ResNeXt. - """ - - arch_settings = { - 50: (Bottleneck, (3, 4, 6, 3)), - 101: (Bottleneck, (3, 4, 23, 3)), - 152: (Bottleneck, (3, 8, 36, 3)) - } - - def __init__(self, groups=1, base_width=4, **kwargs): - self.groups = groups - self.base_width = base_width - super(DetectoRS_ResNeXt, self).__init__(**kwargs) - - def make_res_layer(self, **kwargs): - return super().make_res_layer( - groups=self.groups, - base_width=self.base_width, - base_channels=self.base_channels, - **kwargs) diff --git a/spaces/CVPR/WALT/mmdet/models/detectors/yolo.py b/spaces/CVPR/WALT/mmdet/models/detectors/yolo.py deleted file mode 100644 index 240aab20f857befe25e64114300ebb15a66c6a70..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/mmdet/models/detectors/yolo.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) 2019 Western Digital Corporation or its affiliates. - -from ..builder import DETECTORS -from .single_stage import SingleStageDetector - - -@DETECTORS.register_module() -class YOLOV3(SingleStageDetector): - - def __init__(self, - backbone, - neck, - bbox_head, - train_cfg=None, - test_cfg=None, - pretrained=None): - super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg, - test_cfg, pretrained) diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/cyan/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/cyan/__init__.py deleted file mode 100644 index 62e511971bf32f7c928b17dc977ab3df4066544b..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/cyan/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -from typing import List - -from pil_utils import BuildImage - -from meme_generator import add_meme - - -def cyan(images: List[BuildImage], texts, args): - color = (78, 114, 184) - frame = images[0].convert("RGB").square().resize((500, 500)).color_mask(color) - frame.draw_text( - (400, 40, 480, 280), - "群\n青", - max_fontsize=80, - weight="bold", - fill="white", - stroke_ratio=0.04, - stroke_fill=color, - ).draw_text( - (200, 270, 480, 350), - "YOASOBI", - halign="right", - max_fontsize=40, - fill="white", - stroke_ratio=0.06, - stroke_fill=color, - ) - return frame.save_jpg() - - -add_meme("cyan", cyan, min_images=1, max_images=1, keywords=["群青"]) diff --git a/spaces/CloudOrc/SolidUI/README.md b/spaces/CloudOrc/SolidUI/README.md deleted file mode 100644 index e2a490b6a52aed6bb6c3f5c081611270fd173851..0000000000000000000000000000000000000000 --- a/spaces/CloudOrc/SolidUI/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: SolidUI -emoji: 🐠 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.36.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/encodings/StandardEncoding.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/encodings/StandardEncoding.py deleted file mode 100644 index bf1388624bef4763d26656497b7f3068cb23e307..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/encodings/StandardEncoding.py +++ /dev/null @@ -1,258 +0,0 @@ -StandardEncoding = [ - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - "space", - "exclam", - "quotedbl", - "numbersign", - "dollar", - "percent", - "ampersand", - "quoteright", - "parenleft", - "parenright", - "asterisk", - "plus", - "comma", - "hyphen", - "period", - "slash", - "zero", - "one", - "two", - "three", - "four", - "five", - "six", - "seven", - "eight", - "nine", - "colon", - "semicolon", - "less", - "equal", - "greater", - "question", - "at", - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "J", - "K", - "L", - "M", - "N", - "O", - "P", - "Q", - "R", - "S", - "T", - "U", - "V", - "W", - "X", - "Y", - "Z", - "bracketleft", - "backslash", - "bracketright", - "asciicircum", - "underscore", - "quoteleft", - "a", - "b", - "c", - "d", - "e", - "f", - "g", - "h", - "i", - "j", - "k", - "l", - "m", - "n", - "o", - "p", - "q", - "r", - "s", - "t", - "u", - "v", - "w", - "x", - "y", - "z", - "braceleft", - "bar", - "braceright", - "asciitilde", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - "exclamdown", - "cent", - "sterling", - "fraction", - "yen", - "florin", - "section", - "currency", - "quotesingle", - "quotedblleft", - "guillemotleft", - "guilsinglleft", - "guilsinglright", - "fi", - "fl", - ".notdef", - "endash", - "dagger", - "daggerdbl", - "periodcentered", - ".notdef", - "paragraph", - "bullet", - "quotesinglbase", - "quotedblbase", - "quotedblright", - "guillemotright", - "ellipsis", - "perthousand", - ".notdef", - "questiondown", - ".notdef", - "grave", - "acute", - "circumflex", - "tilde", - "macron", - "breve", - "dotaccent", - "dieresis", - ".notdef", - "ring", - "cedilla", - ".notdef", - "hungarumlaut", - "ogonek", - "caron", - "emdash", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - "AE", - ".notdef", - "ordfeminine", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - "Lslash", - "Oslash", - "OE", - "ordmasculine", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - ".notdef", - "ae", - ".notdef", - ".notdef", - ".notdef", - "dotlessi", - ".notdef", - ".notdef", - "lslash", - "oslash", - "oe", - "germandbls", - ".notdef", - ".notdef", - ".notdef", - ".notdef", -] diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/svgLib/path/arc.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/svgLib/path/arc.py deleted file mode 100644 index 3e0a211e043a9f52954a29ce95de9d2a9f1858d4..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/svgLib/path/arc.py +++ /dev/null @@ -1,153 +0,0 @@ -"""Convert SVG Path's elliptical arcs to Bezier curves. - -The code is mostly adapted from Blink's SVGPathNormalizer::DecomposeArcToCubic -https://github.com/chromium/chromium/blob/93831f2/third_party/ -blink/renderer/core/svg/svg_path_parser.cc#L169-L278 -""" -from fontTools.misc.transform import Identity, Scale -from math import atan2, ceil, cos, fabs, isfinite, pi, radians, sin, sqrt, tan - - -TWO_PI = 2 * pi -PI_OVER_TWO = 0.5 * pi - - -def _map_point(matrix, pt): - # apply Transform matrix to a point represented as a complex number - r = matrix.transformPoint((pt.real, pt.imag)) - return r[0] + r[1] * 1j - - -class EllipticalArc(object): - def __init__(self, current_point, rx, ry, rotation, large, sweep, target_point): - self.current_point = current_point - self.rx = rx - self.ry = ry - self.rotation = rotation - self.large = large - self.sweep = sweep - self.target_point = target_point - - # SVG arc's rotation angle is expressed in degrees, whereas Transform.rotate - # uses radians - self.angle = radians(rotation) - - # these derived attributes are computed by the _parametrize method - self.center_point = self.theta1 = self.theta2 = self.theta_arc = None - - def _parametrize(self): - # convert from endopoint to center parametrization: - # https://www.w3.org/TR/SVG/implnote.html#ArcConversionEndpointToCenter - - # If rx = 0 or ry = 0 then this arc is treated as a straight line segment (a - # "lineto") joining the endpoints. - # http://www.w3.org/TR/SVG/implnote.html#ArcOutOfRangeParameters - rx = fabs(self.rx) - ry = fabs(self.ry) - if not (rx and ry): - return False - - # If the current point and target point for the arc are identical, it should - # be treated as a zero length path. This ensures continuity in animations. - if self.target_point == self.current_point: - return False - - mid_point_distance = (self.current_point - self.target_point) * 0.5 - - point_transform = Identity.rotate(-self.angle) - - transformed_mid_point = _map_point(point_transform, mid_point_distance) - square_rx = rx * rx - square_ry = ry * ry - square_x = transformed_mid_point.real * transformed_mid_point.real - square_y = transformed_mid_point.imag * transformed_mid_point.imag - - # Check if the radii are big enough to draw the arc, scale radii if not. - # http://www.w3.org/TR/SVG/implnote.html#ArcCorrectionOutOfRangeRadii - radii_scale = square_x / square_rx + square_y / square_ry - if radii_scale > 1: - rx *= sqrt(radii_scale) - ry *= sqrt(radii_scale) - self.rx, self.ry = rx, ry - - point_transform = Scale(1 / rx, 1 / ry).rotate(-self.angle) - - point1 = _map_point(point_transform, self.current_point) - point2 = _map_point(point_transform, self.target_point) - delta = point2 - point1 - - d = delta.real * delta.real + delta.imag * delta.imag - scale_factor_squared = max(1 / d - 0.25, 0.0) - - scale_factor = sqrt(scale_factor_squared) - if self.sweep == self.large: - scale_factor = -scale_factor - - delta *= scale_factor - center_point = (point1 + point2) * 0.5 - center_point += complex(-delta.imag, delta.real) - point1 -= center_point - point2 -= center_point - - theta1 = atan2(point1.imag, point1.real) - theta2 = atan2(point2.imag, point2.real) - - theta_arc = theta2 - theta1 - if theta_arc < 0 and self.sweep: - theta_arc += TWO_PI - elif theta_arc > 0 and not self.sweep: - theta_arc -= TWO_PI - - self.theta1 = theta1 - self.theta2 = theta1 + theta_arc - self.theta_arc = theta_arc - self.center_point = center_point - - return True - - def _decompose_to_cubic_curves(self): - if self.center_point is None and not self._parametrize(): - return - - point_transform = Identity.rotate(self.angle).scale(self.rx, self.ry) - - # Some results of atan2 on some platform implementations are not exact - # enough. So that we get more cubic curves than expected here. Adding 0.001f - # reduces the count of sgements to the correct count. - num_segments = int(ceil(fabs(self.theta_arc / (PI_OVER_TWO + 0.001)))) - for i in range(num_segments): - start_theta = self.theta1 + i * self.theta_arc / num_segments - end_theta = self.theta1 + (i + 1) * self.theta_arc / num_segments - - t = (4 / 3) * tan(0.25 * (end_theta - start_theta)) - if not isfinite(t): - return - - sin_start_theta = sin(start_theta) - cos_start_theta = cos(start_theta) - sin_end_theta = sin(end_theta) - cos_end_theta = cos(end_theta) - - point1 = complex( - cos_start_theta - t * sin_start_theta, - sin_start_theta + t * cos_start_theta, - ) - point1 += self.center_point - target_point = complex(cos_end_theta, sin_end_theta) - target_point += self.center_point - point2 = target_point - point2 += complex(t * sin_end_theta, -t * cos_end_theta) - - point1 = _map_point(point_transform, point1) - point2 = _map_point(point_transform, point2) - target_point = _map_point(point_transform, target_point) - - yield point1, point2, target_point - - def draw(self, pen): - for point1, point2, target_point in self._decompose_to_cubic_curves(): - pen.curveTo( - (point1.real, point1.imag), - (point2.real, point2.imag), - (target_point.real, target_point.imag), - ) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/unicodedata/OTTags.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/unicodedata/OTTags.py deleted file mode 100644 index 859a3bcdcf29bcdda827edad766ffeab2f0b636a..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/unicodedata/OTTags.py +++ /dev/null @@ -1,50 +0,0 @@ -# Data updated to OpenType 1.8.2 as of January 2018. - -# Complete list of OpenType script tags at: -# https://www.microsoft.com/typography/otspec/scripttags.htm - -# Most of the script tags are the same as the ISO 15924 tag but lowercased, -# so we only have to handle the exceptional cases: -# - KATAKANA and HIRAGANA both map to 'kana'; -# - spaces at the end are preserved, unlike ISO 15924; -# - we map special script codes for Inherited, Common and Unknown to DFLT. - -DEFAULT_SCRIPT = "DFLT" - -SCRIPT_ALIASES = { - "jamo": "hang", -} - -SCRIPT_EXCEPTIONS = { - "Hira": "kana", - "Hrkt": "kana", - "Laoo": "lao ", - "Yiii": "yi ", - "Nkoo": "nko ", - "Vaii": "vai ", - "Zmth": "math", - "Zinh": DEFAULT_SCRIPT, - "Zyyy": DEFAULT_SCRIPT, - "Zzzz": DEFAULT_SCRIPT, -} - -SCRIPT_EXCEPTIONS_REVERSED = { - "math": "Zmth", -} - -NEW_SCRIPT_TAGS = { - "Beng": ("bng2",), - "Deva": ("dev2",), - "Gujr": ("gjr2",), - "Guru": ("gur2",), - "Knda": ("knd2",), - "Mlym": ("mlm2",), - "Orya": ("ory2",), - "Taml": ("tml2",), - "Telu": ("tel2",), - "Mymr": ("mym2",), -} - -NEW_SCRIPT_TAGS_REVERSED = { - value: key for key, values in NEW_SCRIPT_TAGS.items() for value in values -} diff --git a/spaces/Datasculptor/AIart_sources_of_inspiration/app.py b/spaces/Datasculptor/AIart_sources_of_inspiration/app.py deleted file mode 100644 index be317f11b6d5252cf35832a4f84db8f56d180371..0000000000000000000000000000000000000000 --- a/spaces/Datasculptor/AIart_sources_of_inspiration/app.py +++ /dev/null @@ -1,29 +0,0 @@ -import tensorflow as tf -import pandas as pd -import gradio as gr - -authors_df = pd.read_csv('authors.csv') -labels = sorted(list(authors_df.name)) - -model = tf.keras.models.load_model('efficientnetb0.h5') - -description = 'This is a DEMO that attempts to recognize the inspirations used by the AI art generator. After uploading a picture of an image, the application displays the predicted artist along with the probability of predicting the top three authors.The DEMO uses EfficientNetB0 convolutional neural network as a base model whose classifier was modified and trained the 8,000+ paintings from [Kaggle](https://www.kaggle.com/datasets/ikarus777/best-artworks-of-all-time) dataset. Model trained by osydorchuk89. Given the dataset limitations, the model only recognizes paintings of [50 artists](https://huggingface.co/spaces/osydorchuk/painting_authors/blob/main/authors.csv).' - -def predict_author(input): - if input is None: - return 'Please upload an image' - input = input.reshape((-1, 224, 224, 3)) - prediction = model.predict(input).flatten() - confidences = {labels[i]: float(prediction[i]) for i in range(50)} - return confidences - -demo = gr.Interface( - title='the AI art generator sources of inspiration', - description=description, - fn=predict_author, - inputs=gr.Image(shape=(224, 224)), - outputs=gr.Label(num_top_classes=3), - examples=['test_pics/eva_miro.jpg', 'test_pics/eva_bosch.jpg', 'test_pics/eva_miro_2.jpg', 'test_pics/eva_rtology.jpg'] - ) - -demo.launch() \ No newline at end of file diff --git a/spaces/DhruvShek/chatlm/models.py b/spaces/DhruvShek/chatlm/models.py deleted file mode 100644 index 8f9b1e6aa592f570bfc610cf98c98c454cf2b7b7..0000000000000000000000000000000000000000 --- a/spaces/DhruvShek/chatlm/models.py +++ /dev/null @@ -1,162 +0,0 @@ -import torch -import torch.nn as nn -import math -import torch.nn.functional as F - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -class Embeddings(nn.Module): - """ - Implements embeddings of the words and adds their positional encodings. - """ - def __init__(self, vocab_size, d_model, max_len = 50): - super(Embeddings, self).__init__() - self.d_model = d_model - self.dropout = nn.Dropout(0.1) - self.embed = nn.Embedding(vocab_size, d_model) - self.pe = self.create_positinal_encoding(max_len, self.d_model) - self.dropout = nn.Dropout(0.1) - - def create_positinal_encoding(self, max_len, d_model): - pe = torch.zeros(max_len, d_model).to(device) - for pos in range(max_len): # for each position of the word - for i in range(0, d_model, 2): # for each dimension of the each position - pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model))) - pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model))) - pe = pe.unsqueeze(0) # include the batch size - return pe - - def forward(self, encoded_words): - embedding = self.embed(encoded_words) * math.sqrt(self.d_model) - embedding += self.pe[:, :embedding.size(1)] # pe will automatically be expanded with the same batch size as encoded_words - embedding = self.dropout(embedding) - return embedding - - - -class MultiHeadAttention(nn.Module): - - def __init__(self, heads, d_model): - - super(MultiHeadAttention, self).__init__() - assert d_model % heads == 0 - self.d_k = d_model // heads - self.heads = heads - self.dropout = nn.Dropout(0.1) - self.query = nn.Linear(d_model, d_model) - self.key = nn.Linear(d_model, d_model) - self.value = nn.Linear(d_model, d_model) - self.concat = nn.Linear(d_model, d_model) - - def forward(self, query, key, value, mask): - """ - query, key, value of shape: (batch_size, max_len, 512) - mask of shape: (batch_size, 1, 1, max_words) - """ - # (batch_size, max_len, 512) - query = self.query(query) - key = self.key(key) - value = self.value(value) - - # (batch_size, max_len, 512) --> (batch_size, max_len, h, d_k) --> (batch_size, h, max_len, d_k) - query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3) - key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3) - value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3) - - # (batch_size, h, max_len, d_k) matmul (batch_size, h, d_k, max_len) --> (batch_size, h, max_len, max_len) - scores = torch.matmul(query, key.permute(0,1,3,2)) / math.sqrt(query.size(-1)) - scores = scores.masked_fill(mask == 0, -1e9) # (batch_size, h, max_len, max_len) - weights = F.softmax(scores, dim = -1) # (batch_size, h, max_len, max_len) - weights = self.dropout(weights) - # (batch_size, h, max_len, max_len) matmul (batch_size, h, max_len, d_k) --> (batch_size, h, max_len, d_k) - context = torch.matmul(weights, value) - # (batch_size, h, max_len, d_k) --> (batch_size, max_len, h, d_k) --> (batch_size, max_len, h * d_k) - context = context.permute(0,2,1,3).contiguous().view(context.shape[0], -1, self.heads * self.d_k) - # (batch_size, max_len, h * d_k) - interacted = self.concat(context) - return interacted - - - -class FeedForward(nn.Module): - - def __init__(self, d_model, middle_dim = 2048): - super(FeedForward, self).__init__() - - self.fc1 = nn.Linear(d_model, middle_dim) - self.fc2 = nn.Linear(middle_dim, d_model) - self.dropout = nn.Dropout(0.1) - - def forward(self, x): - out = F.relu(self.fc1(x)) - out = self.fc2(self.dropout(out)) - return out - - -class EncoderLayer(nn.Module): - - def __init__(self, d_model, heads): - super(EncoderLayer, self).__init__() - self.layernorm = nn.LayerNorm(d_model) - self.self_multihead = MultiHeadAttention(heads, d_model) - self.feed_forward = FeedForward(d_model) - self.dropout = nn.Dropout(0.1) - - def forward(self, embeddings, mask): - interacted = self.dropout(self.self_multihead(embeddings, embeddings, embeddings, mask)) - interacted = self.layernorm(interacted + embeddings) - feed_forward_out = self.dropout(self.feed_forward(interacted)) - encoded = self.layernorm(feed_forward_out + interacted) - return encoded - - -class DecoderLayer(nn.Module): - - def __init__(self, d_model, heads): - super(DecoderLayer, self).__init__() - self.layernorm = nn.LayerNorm(d_model) - self.self_multihead = MultiHeadAttention(heads, d_model) - self.src_multihead = MultiHeadAttention(heads, d_model) - self.feed_forward = FeedForward(d_model) - self.dropout = nn.Dropout(0.1) - - def forward(self, embeddings, encoded, src_mask, target_mask): - query = self.dropout(self.self_multihead(embeddings, embeddings, embeddings, target_mask)) - query = self.layernorm(query + embeddings) - interacted = self.dropout(self.src_multihead(query, encoded, encoded, src_mask)) - interacted = self.layernorm(interacted + query) - feed_forward_out = self.dropout(self.feed_forward(interacted)) - decoded = self.layernorm(feed_forward_out + interacted) - return decoded - - -class Transformer(nn.Module): - - def __init__(self, d_model, heads, num_layers, word_map): - super(Transformer, self).__init__() - - self.d_model = d_model - self.vocab_size = len(word_map) - self.embed = Embeddings(self.vocab_size, d_model) - self.encoder = nn.ModuleList([EncoderLayer(d_model, heads) for _ in range(num_layers)]) - self.decoder = nn.ModuleList([DecoderLayer(d_model, heads) for _ in range(num_layers)]) - self.logit = nn.Linear(d_model, self.vocab_size) - - def encode(self, src_words, src_mask): - src_embeddings = self.embed(src_words) - for layer in self.encoder: - src_embeddings = layer(src_embeddings, src_mask) - return src_embeddings - - def decode(self, target_words, target_mask, src_embeddings, src_mask): - tgt_embeddings = self.embed(target_words) - for layer in self.decoder: - tgt_embeddings = layer(tgt_embeddings, src_embeddings, src_mask, target_mask) - return tgt_embeddings - - def forward(self, src_words, src_mask, target_words, target_mask): - encoded = self.encode(src_words, src_mask) - decoded = self.decode(target_words, target_mask, encoded, src_mask) - out = F.log_softmax(self.logit(decoded), dim = 2) - return out diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.h b/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.h deleted file mode 100644 index d0246aa06c3dcd5919111fdc914136014b9044b5..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.h +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright (c) SenseTime Research. All rights reserved. - -// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct bias_act_kernel_params -{ - const void* x; // [sizeX] - const void* b; // [sizeB] or NULL - const void* xref; // [sizeX] or NULL - const void* yref; // [sizeX] or NULL - const void* dy; // [sizeX] or NULL - void* y; // [sizeX] - - int grad; - int act; - float alpha; - float gain; - float clamp; - - int sizeX; - int sizeB; - int stepB; - int loopX; -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template void* choose_bias_act_kernel(const bias_act_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/spaces/DunnBC22/Password_Strength_Classifier_with_CodeBERT/app.py b/spaces/DunnBC22/Password_Strength_Classifier_with_CodeBERT/app.py deleted file mode 100644 index 82a417e4b8879f246c55fe6e0769923f0e820cf3..0000000000000000000000000000000000000000 --- a/spaces/DunnBC22/Password_Strength_Classifier_with_CodeBERT/app.py +++ /dev/null @@ -1,46 +0,0 @@ -import os - -os.system('python -m pip install --upgrade pip') -os.system('pip install transformers torch') - -import gradio as gr -from transformers import pipeline - -unique_classes = [ - "Weak", - "Medium", - "Strong" - ] - -id2label = {f"LABEL_{idx}":label for idx, label in enumerate(unique_classes)} - -def classify_password(text): - password_clf = pipeline(model="DunnBC22/codebert-base-Password_Strength_Classifier") - password_result = password_clf(text) - return f"The password is {id2label[password_result[0]['label']]} with a probability of {password_result[0]['score']*100:.2f}" - -title = "Classify Password Strength" -description = """ -This is a demo of a password classifier. The feedback should not be taken as a guarantee of a password's strength. -""" - -article = """ - -| CodeBERT: A Pre-Trained Model for Programming & Natural Languages -| Microsoft CodeBERT-Base Documentation -| My Code for this Fune-Tuned Project -| Dataset Source -|
-""" - -examples = ['94311163nobp', 'mpompo1', 'dK4dWOjM1OAPeisw'] - -gr.Interface(fn=classify_password, - inputs=gr.inputs.Textbox(), - outputs=gr.outputs.Textbox(), - title=title, - article=article, - description=description, - examples=examples, - theme='abidlabs/dracula_revamped' - ).launch() \ No newline at end of file diff --git a/spaces/EXPOSUREEE/Ai-Image-Enhancer/Training.md b/spaces/EXPOSUREEE/Ai-Image-Enhancer/Training.md deleted file mode 100644 index 64704e1d2e1f334984232afd12b245235b274a9e..0000000000000000000000000000000000000000 --- a/spaces/EXPOSUREEE/Ai-Image-Enhancer/Training.md +++ /dev/null @@ -1,100 +0,0 @@ -# :computer: How to Train Real-ESRGAN - -The training codes have been released.
-Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report issues and I will also retrain the models. - -## Overview - -The training has been divided into two stages. These two stages have the same data synthesis process and training pipeline, except for the loss functions. Specifically, - -1. We first train Real-ESRNet with L1 loss from the pre-trained model ESRGAN. -1. We then use the trained Real-ESRNet model as an initialization of the generator, and train the Real-ESRGAN with a combination of L1 loss, perceptual loss and GAN loss. - -## Dataset Preparation - -We use DF2K (DIV2K and Flickr2K) + OST datasets for our training. Only HR images are required.
-You can download from : - -1. DIV2K: http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip -2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar -3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip - -For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales. - -We then crop DF2K images into sub-images for faster IO and processing. - -You need to prepare a txt file containing the image paths. The following are some examples in `meta_info_DF2Kmultiscale+OST_sub.txt` (As different users may have different sub-images partitions, this file is not suitable for your purpose and you need to prepare your own txt file): - -```txt -DF2K_HR_sub/000001_s001.png -DF2K_HR_sub/000001_s002.png -DF2K_HR_sub/000001_s003.png -... -``` - -## Train Real-ESRNet - -1. Download pre-trained model [ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth) into `experiments/pretrained_models`. - ```bash - wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models - ``` -1. Modify the content in the option file `options/train_realesrnet_x4plus.yml` accordingly: - ```yml - train: - name: DF2K+OST - type: RealESRGANDataset - dataroot_gt: datasets/DF2K # modify to the root path of your folder - meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # modify to your own generate meta info txt - io_backend: - type: disk - ``` -1. If you want to perform validation during training, uncomment those lines and modify accordingly: - ```yml - # Uncomment these for validation - # val: - # name: validation - # type: PairedImageDataset - # dataroot_gt: path_to_gt - # dataroot_lq: path_to_lq - # io_backend: - # type: disk - - ... - - # Uncomment these for validation - # validation settings - # val: - # val_freq: !!float 5e3 - # save_img: True - - # metrics: - # psnr: # metric name, can be arbitrary - # type: calculate_psnr - # crop_border: 4 - # test_y_channel: false - ``` -1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training: - ```bash - CUDA_VISIBLE_DEVICES=0,1,2,3 \ - python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug - ``` -1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary. - ```bash - CUDA_VISIBLE_DEVICES=0,1,2,3 \ - python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume - ``` - -## Train Real-ESRGAN - -1. After the training of Real-ESRNet, you now have the file `experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth`. If you need to specify the pre-trained path to other files, modify the `pretrain_network_g` value in the option file `train_realesrgan_x4plus.yml`. -1. Modify the option file `train_realesrgan_x4plus.yml` accordingly. Most modifications are similar to those listed above. -1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training: - ```bash - CUDA_VISIBLE_DEVICES=0,1,2,3 \ - python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug - ``` -1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary. - ```bash - CUDA_VISIBLE_DEVICES=0,1,2,3 \ - python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume - ``` diff --git a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/nets.py b/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/nets.py deleted file mode 100644 index 5da3948c2f2e9edcc3cdac49bdf9f738e403de40..0000000000000000000000000000000000000000 --- a/spaces/Eddycrack864/Applio-Inference/infer/lib/uvr5_pack/lib_v5/nets.py +++ /dev/null @@ -1,123 +0,0 @@ -import layers -import torch -import torch.nn.functional as F -from torch import nn - -from . import spec_utils - - -class BaseASPPNet(nn.Module): - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 16) - self.stg1_high_band_net = BaseASPPNet(2, 16) - - self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(8, 16) - - self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(16, 32) - - self.out = nn.Conv2d(32, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat( - [ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]), - ], - dim=2, - ) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode="replicate", - ) - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode="replicate", - ) - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, : aggressiveness["split_bin"]] = torch.pow( - mask[:, :, : aggressiveness["split_bin"]], - 1 + aggressiveness["value"] / 3, - ) - mask[:, :, aggressiveness["split_bin"] :] = torch.pow( - mask[:, :, aggressiveness["split_bin"] :], - 1 + aggressiveness["value"], - ) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset : -self.offset] - assert h.size()[3] > 0 - - return h diff --git a/spaces/Eddycrack864/Applio-Inference/lib/uvr5_pack/lib_v5/nets_537227KB.py b/spaces/Eddycrack864/Applio-Inference/lib/uvr5_pack/lib_v5/nets_537227KB.py deleted file mode 100644 index a1bb530e006482704f234c2e739a695174142941..0000000000000000000000000000000000000000 --- a/spaces/Eddycrack864/Applio-Inference/lib/uvr5_pack/lib_v5/nets_537227KB.py +++ /dev/null @@ -1,123 +0,0 @@ -import torch -import numpy as np -from torch import nn -import torch.nn.functional as F - -from . import layers_537238KB as layers - - -class BaseASPPNet(nn.Module): - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 64) - self.stg1_high_band_net = BaseASPPNet(2, 64) - - self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(32, 64) - - self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(64, 128) - - self.out = nn.Conv2d(128, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(64, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(64, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat( - [ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]), - ], - dim=2, - ) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode="replicate", - ) - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode="replicate", - ) - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, : aggressiveness["split_bin"]] = torch.pow( - mask[:, :, : aggressiveness["split_bin"]], - 1 + aggressiveness["value"] / 3, - ) - mask[:, :, aggressiveness["split_bin"] :] = torch.pow( - mask[:, :, aggressiveness["split_bin"] :], - 1 + aggressiveness["value"], - ) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset : -self.offset] - assert h.size()[3] > 0 - - return h diff --git a/spaces/Epoching/3D_Photo_Inpainting/download.sh b/spaces/Epoching/3D_Photo_Inpainting/download.sh deleted file mode 100644 index a2ce8b2686dd62791510af335e306ca8de4ea28d..0000000000000000000000000000000000000000 --- a/spaces/Epoching/3D_Photo_Inpainting/download.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/sh -fb_status=$(wget --spider -S https://filebox.ece.vt.edu/ 2>&1 | grep "HTTP/1.1 200 OK") - -mkdir checkpoints - -echo "downloading from filebox ..." -wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/color-model.pth -wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/depth-model.pth -wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/edge-model.pth -wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/model.pt - -mv color-model.pth checkpoints/. -mv depth-model.pth checkpoints/. -mv edge-model.pth checkpoints/. -mv model.pt MiDaS/. - -echo "cloning from BoostingMonocularDepth ..." -git clone https://github.com/compphoto/BoostingMonocularDepth.git -mkdir -p BoostingMonocularDepth/pix2pix/checkpoints/mergemodel/ - -echo "downloading mergenet weights ..." -wget https://filebox.ece.vt.edu/~jbhuang/project/3DPhoto/model/latest_net_G.pth -mv latest_net_G.pth BoostingMonocularDepth/pix2pix/checkpoints/mergemodel/ -wget https://github.com/intel-isl/MiDaS/releases/download/v2/model-f46da743.pt -mv model-f46da743.pt BoostingMonocularDepth/midas/model.pt diff --git a/spaces/EronSamez/RVC_HFmeu/tensorlowest.py b/spaces/EronSamez/RVC_HFmeu/tensorlowest.py deleted file mode 100644 index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000 --- a/spaces/EronSamez/RVC_HFmeu/tensorlowest.py +++ /dev/null @@ -1,123 +0,0 @@ -from tensorboard.backend.event_processing import event_accumulator - -import os -from shutil import copy2 -from re import search as RSearch -import pandas as pd -from ast import literal_eval as LEval - -weights_dir = 'weights/' - -def find_biggest_tensorboard(tensordir): - try: - files = [f for f in os.listdir(tensordir) if f.endswith('.0')] - if not files: - print("No files with the '.0' extension found!") - return - - max_size = 0 - biggest_file = "" - - for file in files: - file_path = os.path.join(tensordir, file) - if os.path.isfile(file_path): - file_size = os.path.getsize(file_path) - if file_size > max_size: - max_size = file_size - biggest_file = file - - return biggest_file - - except FileNotFoundError: - print("Couldn't find your model!") - return - -def main(model_name, save_freq, lastmdls): - global lowestval_weight_dir, scl - - tensordir = os.path.join('logs', model_name) - lowestval_weight_dir = os.path.join(tensordir, "lowestvals") - - latest_file = find_biggest_tensorboard(tensordir) - - if latest_file is None: - print("Couldn't find a valid tensorboard file!") - return - - tfile = os.path.join(tensordir, latest_file) - - ea = event_accumulator.EventAccumulator(tfile, - size_guidance={ - event_accumulator.COMPRESSED_HISTOGRAMS: 500, - event_accumulator.IMAGES: 4, - event_accumulator.AUDIO: 4, - event_accumulator.SCALARS: 0, - event_accumulator.HISTOGRAMS: 1, - }) - - ea.Reload() - ea.Tags() - - scl = ea.Scalars('loss/g/total') - - listwstep = {} - - for val in scl: - if (val.step // save_freq) * save_freq in [val.step for val in scl]: - listwstep[float(val.value)] = (val.step // save_freq) * save_freq - - lowest_vals = sorted(listwstep.keys())[:lastmdls] - - sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals} - - return sorted_dict - -def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir): - os.makedirs(lowestval_weight_dir, exist_ok=True) - logdir = [] - files = [] - lbldict = { - 'Values': {}, - 'Names': {} - } - weights_dir_path = os.path.join(weights_dir, "") - low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, "")) - - try: - file_dict = LEval(file_dict) - except Exception as e: - print(f"Error! {e}") - return f"Couldn't load tensorboard file! {e}" - - weights = [f for f in os.scandir(weights_dir)] - for key, value in file_dict.items(): - pattern = fr"^{model_name}_.*_s{value}\.pth$" - matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)] - for weight in matching_weights: - source_path = weights_dir_path + weight - destination_path = os.path.join(lowestval_weight_dir, weight) - - copy2(source_path, destination_path) - - logdir.append(f"File = {weight} Value: {key}, Step: {value}") - - lbldict['Names'][weight] = weight - lbldict['Values'][weight] = key - - files.append(low_val_path + weight) - - print(f"File = {weight} Value: {key}, Step: {value}") - - yield ('\n'.join(logdir), files, pd.DataFrame(lbldict)) - - - return ''.join(logdir), files, pd.DataFrame(lbldict) - - -if __name__ == "__main__": - model = str(input("Enter the name of the model: ")) - sav_freq = int(input("Enter save frequency of the model: ")) - ds = main(model, sav_freq) - - if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir) - \ No newline at end of file diff --git a/spaces/EuroPython2022/mmocr-demo/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py b/spaces/EuroPython2022/mmocr-demo/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py deleted file mode 100644 index 251b7bc2faaaa254766e0902c4238b2917f0d230..0000000000000000000000000000000000000000 --- a/spaces/EuroPython2022/mmocr-demo/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py +++ /dev/null @@ -1,35 +0,0 @@ -_base_ = [ - '../../_base_/default_runtime.py', - '../../_base_/schedules/schedule_sgd_1200e.py', - '../../_base_/det_models/dbnet_r50dcnv2_fpnc.py', - '../../_base_/det_datasets/icdar2015.py', - '../../_base_/det_pipelines/dbnet_pipeline.py' -] - -train_list = {{_base_.train_list}} -test_list = {{_base_.test_list}} - -train_pipeline_r50dcnv2 = {{_base_.train_pipeline_r50dcnv2}} -test_pipeline_4068_1024 = {{_base_.test_pipeline_4068_1024}} - -load_from = 'checkpoints/textdet/dbnet/res50dcnv2_synthtext.pth' - -data = dict( - samples_per_gpu=8, - workers_per_gpu=4, - val_dataloader=dict(samples_per_gpu=1), - test_dataloader=dict(samples_per_gpu=1), - train=dict( - type='UniformConcatDataset', - datasets=train_list, - pipeline=train_pipeline_r50dcnv2), - val=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline_4068_1024), - test=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline_4068_1024)) - -evaluation = dict(interval=100, metric='hmean-iou') diff --git a/spaces/GAIR/Factool/plugin_config/main.py b/spaces/GAIR/Factool/plugin_config/main.py deleted file mode 100644 index 5cae0ed7d4d4584a5dd35d303ab9f2899cf04be1..0000000000000000000000000000000000000000 --- a/spaces/GAIR/Factool/plugin_config/main.py +++ /dev/null @@ -1,91 +0,0 @@ -import json -from fastapi import FastAPI, Request -from fastapi.responses import JSONResponse, FileResponse -from fastapi.staticfiles import StaticFiles -from fastapi.middleware.cors import CORSMiddleware -from pydantic import BaseModel -from typing import Optional, List, Dict, Union -from factool.factool import Factool - -foundation_model = 'gpt-4' -factool_instance = Factool(foundation_model) - -app = FastAPI() - -app.add_middleware( - CORSMiddleware, - allow_origins=["https://chat.openai.com"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - -class FactCheckRequest(BaseModel): - prompt: str - response: str - entry_point: Optional[str] - -class FactCheckResponse(BaseModel): - fact_check_result: List[Dict[str, Union[str, List[str]]]] - -fact_checks = {} - -@app.post("/fact_check_kbqa") -async def fact_check_kbqa(request_data: FactCheckRequest): - request_obj = FactCheckRequest(**request_data.dict()) - fact_check_result = await factool_instance.run_for_plugin([{'prompt': request_obj.prompt, 'response': request_obj.response, 'category': 'kbqa'}]) - fact_check_id = len(fact_checks) + 1 - fact_checks[fact_check_id] = fact_check_result - return JSONResponse(content={"fact_check_id": fact_check_id, "fact_check_result": fact_check_result}) - -@app.post("/fact_check_code") -async def fact_check_code(request_data: FactCheckRequest): - request_obj = FactCheckRequest(**request_data.dict()) - fact_check_result = await factool_instance.run_for_plugin([{'prompt': request_obj.prompt, 'response': request_obj.response, 'category': 'code', 'entry_point': request_obj.entry_point}]) - fact_check_id = len(fact_checks) + 1 - fact_checks[fact_check_id] = fact_check_result - return JSONResponse(content={"fact_check_id": fact_check_id, "fact_check_result": fact_check_result}) - -@app.post("/fact_check_math") -async def fact_check_math(request_data: FactCheckRequest): - request_obj = FactCheckRequest(**request_data.dict()) - fact_check_result = await factool_instance.run_for_plugin([{'prompt': request_obj.prompt, 'response': request_obj.response, 'category': 'math'}]) - fact_check_id = len(fact_checks) + 1 - fact_checks[fact_check_id] = fact_check_result - return JSONResponse(content={"fact_check_id": fact_check_id, "fact_check_result": fact_check_result}) - -@app.post("/fact_check_scientific_literature") -async def fact_check_scientific_literature(request_data: FactCheckRequest): - request_obj = FactCheckRequest(**request_data.dict()) - fact_check_result = await factool_instance.run_for_plugin([{'prompt': request_obj.prompt, 'response': request_obj.response, 'category': 'scientific'}]) - fact_check_id = len(fact_checks) + 1 - fact_checks[fact_check_id] = fact_check_result - return JSONResponse(content={"fact_check_id": fact_check_id, "fact_check_result": fact_check_result}) - -@app.get("/get_fact_check/{fact_check_id}") -async def get_fact_check(fact_check_id: int): - if fact_check_id in fact_checks: - fact_check_result = fact_checks[fact_check_id] - return JSONResponse(content={"fact_check_id": fact_check_id, "fact_check_result": fact_check_result}) - else: - return JSONResponse(content={"error": "Fact check not found"}) - -@app.get("/logo.png") -async def plugin_logo(): - filename = "logo.png" - return FileResponse(filename, media_type="image/png") - -@app.get("/.well-known/ai-plugin.json") -async def read_plugin_manifest(): - return FileResponse(".well-known/ai-plugin.json") - -@app.get("/openapi.yaml") -async def openapi_spec(): - return FileResponse("./openapi.yaml") - -def main(): - import uvicorn - uvicorn.run(app, host="0.0.0.0", port=5003, log_level="info") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/spaces/GIZ/SDSN-demo/appStore/info.py b/spaces/GIZ/SDSN-demo/appStore/info.py deleted file mode 100644 index 9f27362233aaa4da23013ddc67d7d7d33a8ea997..0000000000000000000000000000000000000000 --- a/spaces/GIZ/SDSN-demo/appStore/info.py +++ /dev/null @@ -1,72 +0,0 @@ -import streamlit as st - -def app(): - - - with open('style.css') as f: - st.markdown(f"", unsafe_allow_html=True) - - st.markdown("Policy Action Tracker
", - unsafe_allow_html=True) - - - st.markdown("The Policy Action Tracker is an open-source\ - digital tool which aims to assist policy analysts and \ - other users in extracting and filtering relevant \ - information from policy documents.", - unsafe_allow_html=True) - footer = """ - - """ - st.markdown(footer, unsafe_allow_html=True) - - c1, c2, c3 = st.columns([8,1,12]) - with c1: - st.image("docStore/img/ndc.png") - with c3: - st.markdown('The manual extraction \ - of relevant information from text documents is a \ - time-consuming task for any policy analyst. As the amount and length of \ - public policy documents in relation to sustainable development (such as \ - National Development Plans and Nationally Determined Contributions) \ - continuously increases, a major challenge for policy action tracking – the \ - evaluation of stated goals and targets and their actual implementation on \ - the ground – arises. Luckily, Artificial Intelligence (AI) and Natural \ - Language Processing (NLP) methods can help in shortening and easing this \ - task for policy analysts.
', - unsafe_allow_html=True) - - intro = """ -- - For this purpose, the United Nations Sustainable Development Solutions \ - Network (SDSN) and the Deutsche Gesellschaft für Internationale \ - Zusammenarbeit (GIZ) GmbH are collaborated in the development \ - of this AI-powered open-source web application that helps find and extract \ - relevant information from public policy documents faster to facilitate \ - evidence-based decision-making processes in sustainable development and beyond. - - This tool allows policy analysts and other users the possibility to rapidly \ - search for relevant information/paragraphs in the document according to the \ - user’s interest, classify the document’s content according to the Sustainable \ - Development Goals (SDGs), and compare climate-related policy documents and NDCs \ - across countries using open data from the German Institute of Development and \ - Sustainability’s (IDOS) NDC Explorer. - To understand the application's functionalities and learn more about ß - the project, see the attached concept note. We hope you like our application 😊 - - --
- """ - st.markdown(intro, unsafe_allow_html=True) - # st.image("docStore/img/paris.png") diff --git a/spaces/GXSA/bingo/src/pages/api/kblob.ts b/spaces/GXSA/bingo/src/pages/api/kblob.ts deleted file mode 100644 index 0ce7e6063cdc06838e76f1cff1d5982d34ef52de..0000000000000000000000000000000000000000 --- a/spaces/GXSA/bingo/src/pages/api/kblob.ts +++ /dev/null @@ -1,56 +0,0 @@ -'use server' - -import { NextApiRequest, NextApiResponse } from 'next' -import FormData from 'form-data' -import { fetch } from '@/lib/isomorphic' -import { KBlobRequest } from '@/lib/bots/bing/types' - -const API_DOMAIN = 'https://bing.vcanbb.top' - -export const config = { - api: { - bodyParser: { - sizeLimit: '10mb' // Set desired value here - } - } -} - -export default async function handler(req: NextApiRequest, res: NextApiResponse) { - try { - const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest - - const formData = new FormData() - formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest)) - if (imageBase64) { - formData.append('imageBase64', imageBase64) - } - - const response = await fetch(`${API_DOMAIN}/images/kblob`, - { - method: 'POST', - body: formData.getBuffer(), - headers: { - "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"", - "sec-ch-ua-mobile": "?0", - "sec-ch-ua-platform": "\"Windows\"", - "Referer": `${API_DOMAIN}/web/index.html`, - "Referrer-Policy": "origin-when-cross-origin", - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - ...formData.getHeaders() - } - } - ).then(res => res.text()) - - res.writeHead(200, { - 'Content-Type': 'application/json', - }) - res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } })) - } catch (e) { - return res.json({ - result: { - value: 'UploadFailed', - message: `${e}` - } - }) - } -} diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py deleted file mode 100644 index 4b28a59280e6701d31afeeaae7ae12cdbd4fb95e..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py +++ /dev/null @@ -1,86 +0,0 @@ -_base_ = [ - '../_base_/models/cascade_rcnn_r50_fpn.py', - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' -] -# model settings -model = dict( - roi_head=dict(bbox_head=[ - dict( - type='SABLHead', - num_classes=80, - cls_in_channels=256, - reg_in_channels=256, - roi_feat_size=7, - reg_feat_up_ratio=2, - reg_pre_kernel=3, - reg_post_kernel=3, - reg_pre_num=2, - reg_post_num=1, - cls_out_channels=1024, - reg_offset_out_channels=256, - reg_cls_out_channels=256, - num_cls_fcs=1, - num_reg_fcs=0, - reg_class_agnostic=True, - norm_cfg=None, - bbox_coder=dict( - type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox_cls=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), - loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, - loss_weight=1.0)), - dict( - type='SABLHead', - num_classes=80, - cls_in_channels=256, - reg_in_channels=256, - roi_feat_size=7, - reg_feat_up_ratio=2, - reg_pre_kernel=3, - reg_post_kernel=3, - reg_pre_num=2, - reg_post_num=1, - cls_out_channels=1024, - reg_offset_out_channels=256, - reg_cls_out_channels=256, - num_cls_fcs=1, - num_reg_fcs=0, - reg_class_agnostic=True, - norm_cfg=None, - bbox_coder=dict( - type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox_cls=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), - loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, - loss_weight=1.0)), - dict( - type='SABLHead', - num_classes=80, - cls_in_channels=256, - reg_in_channels=256, - roi_feat_size=7, - reg_feat_up_ratio=2, - reg_pre_kernel=3, - reg_post_kernel=3, - reg_pre_num=2, - reg_post_num=1, - cls_out_channels=1024, - reg_offset_out_channels=256, - reg_cls_out_channels=256, - num_cls_fcs=1, - num_reg_fcs=0, - reg_class_agnostic=True, - norm_cfg=None, - bbox_coder=dict( - type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3), - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox_cls=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), - loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0)) - ])) diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/regnet.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/regnet.py deleted file mode 100644 index 91a602a952226cebb5fd0e3e282c6f98ae4fa455..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/regnet.py +++ /dev/null @@ -1,325 +0,0 @@ -import numpy as np -import torch.nn as nn -from mmcv.cnn import build_conv_layer, build_norm_layer - -from ..builder import BACKBONES -from .resnet import ResNet -from .resnext import Bottleneck - - -@BACKBONES.register_module() -class RegNet(ResNet): - """RegNet backbone. - - More details can be found in `paperllama.cpp - chat - - - - - - - - - - - - diff --git a/spaces/JammyMachina/the-jam-machine-app/constants.py b/spaces/JammyMachina/the-jam-machine-app/constants.py deleted file mode 100644 index 067778dc63be191289d7a90cbd124b7e835c28e4..0000000000000000000000000000000000000000 --- a/spaces/JammyMachina/the-jam-machine-app/constants.py +++ /dev/null @@ -1,128 +0,0 @@ -# fmt: off -# Instrument mapping and mapping functions -INSTRUMENT_CLASSES = [ - {"name": "Piano", "program_range": range(0, 8), "family_number": 0}, - {"name": "Chromatic Percussion", "program_range": range(8, 16), "family_number": 1}, - {"name": "Organ", "program_range": range(16, 24), "family_number": 2}, - {"name": "Guitar", "program_range": range(24, 32), "family_number": 3}, - {"name": "Bass", "program_range": range(32, 40), "family_number": 4}, - {"name": "Strings", "program_range": range(40, 48), "family_number": 5}, - {"name": "Ensemble", "program_range": range(48, 56), "family_number": 6}, - {"name": "Brass", "program_range": range(56, 64), "family_number": 7}, - {"name": "Reed", "program_range": range(64, 72), "family_number": 8}, - {"name": "Pipe", "program_range": range(72, 80), "family_number": 9}, - {"name": "Synth Lead", "program_range": range(80, 88), "family_number": 10}, - {"name": "Synth Pad", "program_range": range(88, 96), "family_number": 11}, - {"name": "Synth Effects", "program_range": range(96, 104), "family_number": 12}, - {"name": "Ethnic", "program_range": range(104, 112), "family_number": 13}, - {"name": "Percussive", "program_range": range(112, 120), "family_number": 14}, - {"name": "Sound Effects", "program_range": range(120, 128), "family_number": 15,}, -] -# fmt: on - -# Instrument mapping for decodiing our midi sequence into midi instruments of our choice -INSTRUMENT_TRANSFER_CLASSES = [ - { - "name": "Piano", - "program_range": [4], - "family_number": 0, - "transfer_to": "Electric Piano 1", - }, - { - "name": "Chromatic Percussion", - "program_range": [11], - "family_number": 1, - "transfer_to": "Vibraphone", - }, - { - "name": "Organ", - "program_range": [17], - "family_number": 2, - "transfer_to": "Percussive Organ", - }, - { - "name": "Guitar", - "program_range": [80], - "family_number": 3, - "transfer_to": "Synth Lead Square", - }, - { - "name": "Bass", - "program_range": [38], - "family_number": 4, - "transfer_to": "Synth Bass 1", - }, - { - "name": "Strings", - "program_range": [50], - "family_number": 5, - "transfer_to": "Synth Strings 1", - }, - { - "name": "Ensemble", - "program_range": [51], - "family_number": 6, - "transfer_to": "Synth Strings 2", - }, - { - "name": "Brass", - "program_range": [63], - "family_number": 7, - "transfer_to": "Synth Brass 1", - }, - { - "name": "Reed", - "program_range": [64], - "family_number": 8, - "transfer_to": "Synth Brass 2", - }, - { - "name": "Pipe", - "program_range": [82], - "family_number": 9, - "transfer_to": "Synth Lead Calliope", - }, - { - "name": "Synth Lead", - "program_range": [81], # Synth Lead Sawtooth - "family_number": 10, - "transfer_to": "Synth Lead Sawtooth", - }, - { - "name": "Synth Pad", - "program_range": range(88, 96), - "family_number": 11, - "transfer_to": "Synth Pad", - }, - { - "name": "Synth Effects", - "program_range": range(96, 104), - "family_number": 12, - "transfer_to": "Synth Effects", - }, - { - "name": "Ethnic", - "program_range": range(104, 112), - "family_number": 13, - "transfer_to": "Ethnic", - }, - { - "name": "Percussive", - "program_range": range(112, 120), - "family_number": 14, - "transfer_to": "Percussive", - }, - { - "name": "Sound Effects", - "program_range": range(120, 128), - "family_number": 15, - "transfer_to": "Sound Effects", - }, -] - - -"Encoding and decoding constants" - -DRUMS_BEAT_QUANTIZATION = 4 # 8th notes per beat -NONE_DRUMS_BEAT_QUANTIZATION = 4 # 4th notes per beat -BEATS_PER_BAR = 4 # 4/4 time diff --git a/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/yolov5face/models/experimental.py b/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/yolov5face/models/experimental.py deleted file mode 100644 index 37ba4c4420789c92dc0e2aaeb3d5b64859ec728c..0000000000000000000000000000000000000000 --- a/spaces/Jasonyoyo/CodeFormer/CodeFormer/facelib/detection/yolov5face/models/experimental.py +++ /dev/null @@ -1,45 +0,0 @@ -# # This file contains experimental modules - -import numpy as np -import torch -from torch import nn - -from facelib.detection.yolov5face.models.common import Conv - - -class CrossConv(nn.Module): - # Cross Convolution Downsample - def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): - # ch_in, ch_out, kernel, stride, groups, expansion, shortcut - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, (1, k), (1, s)) - self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class MixConv2d(nn.Module): - # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 - def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): - super().__init__() - groups = len(k) - if equal_ch: # equal c_ per group - i = torch.linspace(0, groups - 1e-6, c2).floor() # c2 indices - c_ = [(i == g).sum() for g in range(groups)] # intermediate channels - else: # equal weight.numel() per group - b = [c2] + [0] * groups - a = np.eye(groups + 1, groups, k=-1) - a -= np.roll(a, 1, axis=1) - a *= np.array(k) ** 2 - a[0] = 1 - c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b - - self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.LeakyReLU(0.1, inplace=True) - - def forward(self, x): - return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) diff --git a/spaces/Javtor/Biomedical-topic-categorization/README.md b/spaces/Javtor/Biomedical-topic-categorization/README.md deleted file mode 100644 index bceab279bc9267d01a3cd7d0c98a69951171b3ec..0000000000000000000000000000000000000000 --- a/spaces/Javtor/Biomedical-topic-categorization/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Biomedical Topic Categorization -emoji: 💻 -colorFrom: pink -colorTo: pink -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/JunchuanYu/SegRS/segment_anything/modeling/prompt_encoder.py b/spaces/JunchuanYu/SegRS/segment_anything/modeling/prompt_encoder.py deleted file mode 100644 index c3143f4f8e02ddd7ca8587b40ff5d47c3a6b7ef3..0000000000000000000000000000000000000000 --- a/spaces/JunchuanYu/SegRS/segment_anything/modeling/prompt_encoder.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -from torch import nn - -from typing import Any, Optional, Tuple, Type - -from .common import LayerNorm2d - - -class PromptEncoder(nn.Module): - def __init__( - self, - embed_dim: int, - image_embedding_size: Tuple[int, int], - input_image_size: Tuple[int, int], - mask_in_chans: int, - activation: Type[nn.Module] = nn.GELU, - ) -> None: - """ - Encodes prompts for input to SAM's mask decoder. - - Arguments: - embed_dim (int): The prompts' embedding dimension - image_embedding_size (tuple(int, int)): The spatial size of the - image embedding, as (H, W). - input_image_size (int): The padded size of the image as input - to the image encoder, as (H, W). - mask_in_chans (int): The number of hidden channels used for - encoding input masks. - activation (nn.Module): The activation to use when encoding - input masks. - """ - super().__init__() - self.embed_dim = embed_dim - self.input_image_size = input_image_size - self.image_embedding_size = image_embedding_size - self.pe_layer = PositionEmbeddingRandom(embed_dim // 2) - - self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners - point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)] - self.point_embeddings = nn.ModuleList(point_embeddings) - self.not_a_point_embed = nn.Embedding(1, embed_dim) - - self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1]) - self.mask_downscaling = nn.Sequential( - nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), - LayerNorm2d(mask_in_chans // 4), - activation(), - nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), - LayerNorm2d(mask_in_chans), - activation(), - nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1), - ) - self.no_mask_embed = nn.Embedding(1, embed_dim) - - def get_dense_pe(self) -> torch.Tensor: - """ - Returns the positional encoding used to encode point prompts, - applied to a dense set of points the shape of the image encoding. - - Returns: - torch.Tensor: Positional encoding with shape - 1x(embed_dim)x(embedding_h)x(embedding_w) - """ - return self.pe_layer(self.image_embedding_size).unsqueeze(0) - - def _embed_points( - self, - points: torch.Tensor, - labels: torch.Tensor, - pad: bool, - ) -> torch.Tensor: - """Embeds point prompts.""" - points = points + 0.5 # Shift to center of pixel - if pad: - padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device) - padding_label = -torch.ones((labels.shape[0], 1), device=labels.device) - points = torch.cat([points, padding_point], dim=1) - labels = torch.cat([labels, padding_label], dim=1) - point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size) - point_embedding[labels == -1] = 0.0 - point_embedding[labels == -1] += self.not_a_point_embed.weight - point_embedding[labels == 0] += self.point_embeddings[0].weight - point_embedding[labels == 1] += self.point_embeddings[1].weight - return point_embedding - - def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: - """Embeds box prompts.""" - boxes = boxes + 0.5 # Shift to center of pixel - coords = boxes.reshape(-1, 2, 2) - corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size) - corner_embedding[:, 0, :] += self.point_embeddings[2].weight - corner_embedding[:, 1, :] += self.point_embeddings[3].weight - return corner_embedding - - def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor: - """Embeds mask inputs.""" - mask_embedding = self.mask_downscaling(masks) - return mask_embedding - - def _get_batch_size( - self, - points: Optional[Tuple[torch.Tensor, torch.Tensor]], - boxes: Optional[torch.Tensor], - masks: Optional[torch.Tensor], - ) -> int: - """ - Gets the batch size of the output given the batch size of the input prompts. - """ - if points is not None: - return points[0].shape[0] - elif boxes is not None: - return boxes.shape[0] - elif masks is not None: - return masks.shape[0] - else: - return 1 - - def _get_device(self) -> torch.device: - return self.point_embeddings[0].weight.device - - def forward( - self, - points: Optional[Tuple[torch.Tensor, torch.Tensor]], - boxes: Optional[torch.Tensor], - masks: Optional[torch.Tensor], - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Embeds different types of prompts, returning both sparse and dense - embeddings. - - Arguments: - points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates - and labels to embed. - boxes (torch.Tensor or none): boxes to embed - masks (torch.Tensor or none): masks to embed - - Returns: - torch.Tensor: sparse embeddings for the points and boxes, with shape - BxNx(embed_dim), where N is determined by the number of input points - and boxes. - torch.Tensor: dense embeddings for the masks, in the shape - Bx(embed_dim)x(embed_H)x(embed_W) - """ - bs = self._get_batch_size(points, boxes, masks) - sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device()) - if points is not None: - coords, labels = points - point_embeddings = self._embed_points(coords, labels, pad=(boxes is None)) - sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1) - if boxes is not None: - box_embeddings = self._embed_boxes(boxes) - sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1) - - if masks is not None: - dense_embeddings = self._embed_masks(masks) - else: - dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( - bs, -1, self.image_embedding_size[0], self.image_embedding_size[1] - ) - - return sparse_embeddings, dense_embeddings - - -class PositionEmbeddingRandom(nn.Module): - """ - Positional encoding using random spatial frequencies. - """ - - def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: - super().__init__() - if scale is None or scale <= 0.0: - scale = 1.0 - self.register_buffer( - "positional_encoding_gaussian_matrix", - scale * torch.randn((2, num_pos_feats)), - ) - - def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: - """Positionally encode points that are normalized to [0,1].""" - # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape - coords = 2 * coords - 1 - coords = coords @ self.positional_encoding_gaussian_matrix - coords = 2 * np.pi * coords - # outputs d_1 x ... x d_n x C shape - return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) - - def forward(self, size: Tuple[int, int]) -> torch.Tensor: - """Generate positional encoding for a grid of the specified size.""" - h, w = size - device: Any = self.positional_encoding_gaussian_matrix.device - grid = torch.ones((h, w), device=device, dtype=torch.float32) - y_embed = grid.cumsum(dim=0) - 0.5 - x_embed = grid.cumsum(dim=1) - 0.5 - y_embed = y_embed / h - x_embed = x_embed / w - - pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) - return pe.permute(2, 0, 1) # C x H x W - - def forward_with_coords( - self, coords_input: torch.Tensor, image_size: Tuple[int, int] - ) -> torch.Tensor: - """Positionally encode points that are not normalized to [0,1].""" - coords = coords_input.clone() - coords[:, :, 0] = coords[:, :, 0] / image_size[1] - coords[:, :, 1] = coords[:, :, 1] / image_size[0] - return self._pe_encoding(coords.to(torch.float)) # B x N x C diff --git a/spaces/KenjieDec/RemBG/rembg/sessions/silueta.py b/spaces/KenjieDec/RemBG/rembg/sessions/silueta.py deleted file mode 100644 index 50094f15a1c4c24c1f03c8d79d9430dc29b485ad..0000000000000000000000000000000000000000 --- a/spaces/KenjieDec/RemBG/rembg/sessions/silueta.py +++ /dev/null @@ -1,51 +0,0 @@ -import os -from typing import List - -import numpy as np -import pooch -from PIL import Image -from PIL.Image import Image as PILImage - -from .base import BaseSession - - -class SiluetaSession(BaseSession): - def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]: - ort_outs = self.inner_session.run( - None, - self.normalize( - img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (320, 320) - ), - ) - - pred = ort_outs[0][:, 0, :, :] - - ma = np.max(pred) - mi = np.min(pred) - - pred = (pred - mi) / (ma - mi) - pred = np.squeeze(pred) - - mask = Image.fromarray((pred * 255).astype("uint8"), mode="L") - mask = mask.resize(img.size, Image.LANCZOS) - - return [mask] - - @classmethod - def download_models(cls, *args, **kwargs): - fname = f"{cls.name()}.onnx" - pooch.retrieve( - "https://github.com/danielgatis/rembg/releases/download/v0.0.0/silueta.onnx", - None - if cls.checksum_disabled(*args, **kwargs) - else "md5:55e59e0d8062d2f5d013f4725ee84782", - fname=fname, - path=cls.u2net_home(*args, **kwargs), - progressbar=True, - ) - - return os.path.join(cls.u2net_home(), fname) - - @classmethod - def name(cls, *args, **kwargs): - return "silueta" diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/indicnlp/langinfo.py b/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/indicnlp/langinfo.py deleted file mode 100644 index efb7e372feeb67d7106eb5c443de2e14053fd204..0000000000000000000000000000000000000000 --- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/indicnlp/langinfo.py +++ /dev/null @@ -1,488 +0,0 @@ -# -# Copyright (c) 2013-present, Anoop Kunchukuttan -# All rights reserved. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -# - -## language codes -LC_TA='ta' - -SCRIPT_RANGES={ - 'pa':[0x0a00,0x0a7f] , - 'gu':[0x0a80,0x0aff] , - 'or':[0x0b00,0x0b7f] , - 'ta':[0x0b80,0x0bff] , - 'te':[0x0c00,0x0c7f] , - 'kn':[0x0c80,0x0cff] , - 'ml':[0x0d00,0x0d7f] , - 'si':[0x0d80,0x0dff] , - 'hi':[0x0900,0x097f] , - 'mr':[0x0900,0x097f] , - 'kK':[0x0900,0x097f] , - 'sa':[0x0900,0x097f] , - 'ne':[0x0900,0x097f] , - 'sd':[0x0900,0x097f] , - 'bn':[0x0980,0x09ff] , - 'as':[0x0980,0x09ff] , - } - -DRAVIDIAN_LANGUAGES=['ta', 'te', 'kn', 'ml',] -IE_LANGUAGES=['hi', 'mr', 'kK', 'sa', 'ne', 'sd', 'bn', 'as', 'pa', 'gu', 'or', 'si', ] -DANDA_DELIM_LANGUAGES=['as','bn','hi','ne','or','pa','sa','sd'] - -URDU_RANGES=[ - [0x0600,0x06ff], - [0x0750,0x077f], - [0xfb50,0xfdff], - [0xfe70,0xfeff], - ] - -COORDINATED_RANGE_START_INCLUSIVE=0 -COORDINATED_RANGE_END_INCLUSIVE=0x6f - -NUMERIC_OFFSET_START=0x66 -NUMERIC_OFFSET_END=0x6f - -HALANTA_OFFSET=0x4d -AUM_OFFSET=0x50 -NUKTA_OFFSET=0x3c - -RUPEE_SIGN=0x20b9 - -DANDA=0x0964 -DOUBLE_DANDA=0x0965 - -#TODO: add missing fricatives and approximants -VELAR_RANGE=[0x15,0x19] -PALATAL_RANGE=[0x1a,0x1e] -RETROFLEX_RANGE=[0x1f,0x23] -DENTAL_RANGE=[0x24,0x29] -LABIAL_RANGE=[0x2a,0x2e] - -# verify -VOICED_LIST=[0x17,0x18,0x1c,0x1d,0x21,0x22,0x26,0x27,0x2c,0x2d] -UNVOICED_LIST=[0x15,0x16,0x1a,0x1b,0x1f,0x20,0x24,0x25,0x2a,0x2b] #TODO: add sibilants/sonorants -ASPIRATED_LIST=[0x16,0x18,0x1b,0x1d,0x20,0x22,0x25,0x27,0x2b,0x2d] -UNASPIRATED_LIST=[0x15,0x17,0x1a,0x1c,0x1f,0x21,0x24,0x26,0x2a,0x2c] -NASAL_LIST=[0x19,0x1e,0x23,0x28,0x29,0x2d] -FRICATIVE_LIST=[0x36,0x37,0x38] -APPROXIMANT_LIST=[0x2f,0x30,0x31,0x32,0x33,0x34,0x35] - -#TODO: ha has to be properly categorized - -def is_danda_delim(lang): - """ - Returns True if danda/double danda is a possible delimiter for the language - """ - return lang in DANDA_DELIM_LANGUAGES - -def get_offset(c,lang): - """ - Applicable to Brahmi derived Indic scripts - """ - return ord(c)-SCRIPT_RANGES[lang][0] - -def offset_to_char(c,lang): - """ - Applicable to Brahmi derived Indic scripts - """ - return chr(c+SCRIPT_RANGES[lang][0]) - -def in_coordinated_range(c_offset): - """ - Applicable to Brahmi derived Indic scripts - """ - return (c_offset>=COORDINATED_RANGE_START_INCLUSIVE and c_offset<=COORDINATED_RANGE_END_INCLUSIVE) - -def is_indiclang_char(c,lang): - """ - Applicable to Brahmi derived Indic scripts - """ - o=get_offset(c,lang) - return (o>=0 and o<=0x7f) or ord(c)==DANDA or ord(c)==DOUBLE_DANDA - -# def is_vowel(c,lang): -# """ -# Is the character a vowel -# """ -# o=get_offset(c,lang) -# return (o>=0x04 and o<=0x14) - -# def is_vowel_sign(c,lang): -# """ -# Is the character a vowel sign (maatraa) -# """ -# o=get_offset(c,lang) -# return (o>=0x3e and o<=0x4c) - -# def is_halanta(c,lang): -# """ -# Is the character the halanta character -# """ -# o=get_offset(c,lang) -# return (o==HALANTA_OFFSET) - -# def is_nukta(c,lang): -# """ -# Is the character the halanta character -# """ -# o=get_offset(c,lang) -# return (o==NUKTA_OFFSET) - -# def is_aum(c,lang): -# """ -# Is the character a vowel sign (maatraa) -# """ -# o=get_offset(c,lang) -# return (o==AUM_OFFSET) - -# def is_consonant(c,lang): -# """ -# Is the character a consonant -# """ -# o=get_offset(c,lang) -# return (o>=0x15 and o<=0x39) - -# def is_velar(c,lang): -# """ -# Is the character a velar -# """ -# o=get_offset(c,lang) -# return (o>=VELAR_RANGE[0] and o<=VELAR_RANGE[1]) - -# def is_palatal(c,lang): -# """ -# Is the character a palatal -# """ -# o=get_offset(c,lang) -# return (o>=PALATAL_RANGE[0] and o<=PALATAL_RANGE[1]) - -# def is_retroflex(c,lang): -# """ -# Is the character a retroflex -# """ -# o=get_offset(c,lang) -# return (o>=RETROFLEX_RANGE[0] and o<=RETROFLEX_RANGE[1]) - -# def is_dental(c,lang): -# """ -# Is the character a dental -# """ -# o=get_offset(c,lang) -# return (o>=DENTAL_RANGE[0] and o<=DENTAL_RANGE[1]) - -# def is_labial(c,lang): -# """ -# Is the character a labial -# """ -# o=get_offset(c,lang) -# return (o>=LABIAL_RANGE[0] and o<=LABIAL_RANGE[1]) - -# def is_voiced(c,lang): -# """ -# Is the character a voiced consonant -# """ -# o=get_offset(c,lang) -# return o in VOICED_LIST - -# def is_unvoiced(c,lang): -# """ -# Is the character a unvoiced consonant -# """ -# o=get_offset(c,lang) -# return o in UNVOICED_LIST - -# def is_aspirated(c,lang): -# """ -# Is the character a aspirated consonant -# """ -# o=get_offset(c,lang) -# return o in ASPIRATED_LIST - -# def is_unaspirated(c,lang): -# """ -# Is the character a unaspirated consonant -# """ -# o=get_offset(c,lang) -# return o in UNASPIRATED_LIST - -# def is_nasal(c,lang): -# """ -# Is the character a nasal consonant -# """ -# o=get_offset(c,lang) -# return o in NASAL_LIST - -# def is_fricative(c,lang): -# """ -# Is the character a fricative consonant -# """ -# o=get_offset(c,lang) -# return o in FRICATIVE_LIST - -# def is_approximant(c,lang): -# """ -# Is the character an approximant consonant -# """ -# o=get_offset(c,lang) -# return o in APPROXIMANT_LIST - -# def is_number(c,lang): -# """ -# Is the character a number -# """ -# o=get_offset(c,lang) -# return (o>=0x66 and o<=0x6f) - - -def is_vowel(c,lang): - """ - Is the character a vowel - """ - o=get_offset(c,lang) - return (o>=0x04 and o<=0x14) - -def is_vowel_sign(c,lang): - """ - Is the character a vowel sign (maatraa) - """ - o=get_offset(c,lang) - return (o>=0x3e and o<=0x4c) - -def is_halanta(c,lang): - """ - Is the character the halanta character - """ - o=get_offset(c,lang) - return (o==HALANTA_OFFSET) - -def is_nukta(c,lang): - """ - Is the character the halanta character - """ - o=get_offset(c,lang) - return (o==NUKTA_OFFSET) - -def is_aum(c,lang): - """ - Is the character a vowel sign (maatraa) - """ - o=get_offset(c,lang) - return (o==AUM_OFFSET) - -def is_consonant(c,lang): - """ - Is the character a consonant - """ - o=get_offset(c,lang) - return (o>=0x15 and o<=0x39) - -def is_velar(c,lang): - """ - Is the character a velar - """ - o=get_offset(c,lang) - return (o>=VELAR_RANGE[0] and o<=VELAR_RANGE[1]) - -def is_palatal(c,lang): - """ - Is the character a palatal - """ - o=get_offset(c,lang) - return (o>=PALATAL_RANGE[0] and o<=PALATAL_RANGE[1]) - -def is_retroflex(c,lang): - """ - Is the character a retroflex - """ - o=get_offset(c,lang) - return (o>=RETROFLEX_RANGE[0] and o<=RETROFLEX_RANGE[1]) - -def is_dental(c,lang): - """ - Is the character a dental - """ - o=get_offset(c,lang) - return (o>=DENTAL_RANGE[0] and o<=DENTAL_RANGE[1]) - -def is_labial(c,lang): - """ - Is the character a labial - """ - o=get_offset(c,lang) - return (o>=LABIAL_RANGE[0] and o<=LABIAL_RANGE[1]) - -def is_voiced(c,lang): - """ - Is the character a voiced consonant - """ - o=get_offset(c,lang) - return o in VOICED_LIST - -def is_unvoiced(c,lang): - """ - Is the character a unvoiced consonant - """ - o=get_offset(c,lang) - return o in UNVOICED_LIST - -def is_aspirated(c,lang): - """ - Is the character a aspirated consonant - """ - o=get_offset(c,lang) - return o in ASPIRATED_LIST - -def is_unaspirated(c,lang): - """ - Is the character a unaspirated consonant - """ - o=get_offset(c,lang) - return o in UNASPIRATED_LIST - -def is_nasal(c,lang): - """ - Is the character a nasal consonant - """ - o=get_offset(c,lang) - return o in NASAL_LIST - -def is_fricative(c,lang): - """ - Is the character a fricative consonant - """ - o=get_offset(c,lang) - return o in FRICATIVE_LIST - -def is_approximant(c,lang): - """ - Is the character an approximant consonant - """ - o=get_offset(c,lang) - return o in APPROXIMANT_LIST - -def is_number(c,lang): - """ - Is the character a number - """ - o=get_offset(c,lang) - return (o>=0x66 and o<=0x6f) - - -################################################## - -def is_vowel_offset(c_offset): - """ - Is the offset a vowel - """ - return (c_offset>=0x04 and c_offset<=0x14) - -def is_vowel_sign_offset(c_offset): - """ - Is the offset a vowel sign (maatraa) - """ - return (c_offset>=0x3e and c_offset<=0x4c) - -def is_halanta_offset(c_offset): - """ - Is the offset the halanta offset - """ - return (c_offset==HALANTA_OFFSET) - -def is_nukta_offset(c_offset): - """ - Is the offset the halanta offset - """ - return (c_offset==NUKTA_OFFSET) - -def is_aum_offset(c_offset): - """ - Is the offset a vowel sign (maatraa) - """ - return (c_offset==AUM_OFFSET) - -def is_consonant_offset(c_offset): - """ - Is the offset a consonant - """ - return (c_offset>=0x15 and c_offset<=0x39) - -def is_velar_offset(c_offset): - """ - Is the offset a velar - """ - return (c_offset>=VELAR_RANGE[0] and c_offset<=VELAR_RANGE[1]) - -def is_palatal_offset(c_offset): - """ - Is the offset a palatal - """ - return (c_offset>=PALATAL_RANGE[0] and c_offset<=PALATAL_RANGE[1]) - -def is_retroflex_offset(c_offset): - """ - Is the offset a retroflex - """ - return (c_offset>=RETROFLEX_RANGE[0] and c_offset<=RETROFLEX_RANGE[1]) - -def is_dental_offset(c_offset): - """ - Is the offset a dental - """ - return (c_offset>=DENTAL_RANGE[0] and c_offset<=DENTAL_RANGE[1]) - -def is_labial_offset(c_offset): - """ - Is the offset a labial - """ - return (c_offset>=LABIAL_RANGE[0] and c_offset<=LABIAL_RANGE[1]) - -def is_voiced_offset(c_offset): - """ - Is the offset a voiced consonant - """ - return c_offset in VOICED_LIST - -def is_unvoiced_offset(c_offset): - """ - Is the offset a unvoiced consonant - """ - return c_offset in UNVOICED_LIST - -def is_aspirated_offset(c_offset): - """ - Is the offset a aspirated consonant - """ - return c_offset in ASPIRATED_LIST - -def is_unaspirated_offset(c_offset): - """ - Is the offset a unaspirated consonant - """ - return c_offset in UNASPIRATED_LIST - -def is_nasal_offset(c_offset): - """ - Is the offset a nasal consonant - """ - return c_offset in NASAL_LIST - -def is_fricative_offset(c_offset): - """ - Is the offset a fricative consonant - """ - return c_offset in FRICATIVE_LIST - -def is_approximant_offset(c_offset): - """ - Is the offset an approximant consonant - """ - return c_offset in APPROXIMANT_LIST - -def is_number_offset(c_offset): - """ - Is the offset a number - """ - return (c_offset>=0x66 and c_offset<=0x6f) diff --git a/spaces/KyanChen/FunSR/models/baselines/basis.py b/spaces/KyanChen/FunSR/models/baselines/basis.py deleted file mode 100644 index 95ebfd2feaed08bd2df2ac9aa0d47d8bf57a76c1..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/FunSR/models/baselines/basis.py +++ /dev/null @@ -1,79 +0,0 @@ -import math -from argparse import Namespace - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from models import register - - -class gen_basis(nn.Module): - def __init__(self, args): - super(gen_basis, self).__init__() - self.basis_num = args.basis_num - self.hidden = args.hidden - self.state = args.state - self.path=args.path - - def init_basis_bias(self): - self.w0 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden*580), requires_grad=True) - nn.init.kaiming_uniform_(self.w0, a=math.sqrt(5)) - self.w1 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden*self.hidden), requires_grad=True) - nn.init.kaiming_uniform_(self.w1, a=math.sqrt(5)) - self.w2 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden*self.hidden), requires_grad=True) - nn.init.kaiming_uniform_(self.w2, a=math.sqrt(5)) - self.w3 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden*self.hidden), requires_grad=True) - nn.init.kaiming_uniform_(self.w3, a=math.sqrt(5)) - self.w4 = nn.Parameter(torch.Tensor(self.basis_num,3*self.hidden), requires_grad=True) - nn.init.kaiming_uniform_(self.w4, a=math.sqrt(5)) - basis = [self.w0, self.w1, self.w2, self.w3, self.w4] - self.bias1 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden), requires_grad=True) - self.bias2 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden), requires_grad=True) - self.bias3 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden), requires_grad=True) - self.bias4 = nn.Parameter(torch.Tensor(self.basis_num,self.hidden), requires_grad=True) - self.bias5 = nn.Parameter(torch.Tensor(self.basis_num,3), requires_grad=True) - bias = [self.bias1,self.bias2,self.bias3,self.bias4,self.bias5] - - for i in range(len(bias)): - fan_in, _ = nn.init._calculate_fan_in_and_fan_out(basis[i]) - bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 - nn.init.uniform_(bias[i], -bound, bound) - - - - return basis,bias - - - def load_basis_for_test_kaiming(self,path): - model_spec = torch.load(path)['model'] - w0 = model_spec['sd']['basis.w0'] - w1 = model_spec['sd']['basis.w1'] - w2 = model_spec['sd']['basis.w2'] - w3 = model_spec['sd']['basis.w3'] - w4 = model_spec['sd']['basis.w4'] - b0 = model_spec['sd']['basis.bias1'] - b1 = model_spec['sd']['basis.bias2'] - b2 = model_spec['sd']['basis.bias3'] - b3 = model_spec['sd']['basis.bias4'] - b4 = model_spec['sd']['basis.bias5'] - torch.cuda.empty_cache() - return [w0,w1,w2,w3,w4],[b0,b1,b2,b3,b4] - - def forward(self): - if self.state=='train': - print('init_basis_use_kaiming') - res=self.init_basis_bias() - else: - print('load_basis_from_model') - res=self.load_basis_for_test_kaiming(self.path) - return res - -@register('basis') -def make_basis(basis_num=10,hidden=16,state=None,path=None): - args = Namespace() - args.basis_num = basis_num - args.hidden = hidden - args.state = state - args.path = path - return gen_basis(args) diff --git a/spaces/KyanChen/FunSR/tools/paper_vis_tools/rearange_x4_PSNR_SSIM.py b/spaces/KyanChen/FunSR/tools/paper_vis_tools/rearange_x4_PSNR_SSIM.py deleted file mode 100644 index f2f6ec0b60dd323327a440cd8f640b6efa017173..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/FunSR/tools/paper_vis_tools/rearange_x4_PSNR_SSIM.py +++ /dev/null @@ -1,152 +0,0 @@ -all = '''1 & 27.96/0.7853 & 28.24/0.7950 & 28.31/0.7961 & 28.25/0.7947 & 28.42/0.7987 & 28.32/0.7960 -\\ -2 & 35.39/0.8411 & 35.43/0.8424 & 35.43/0.8429 & 35.41/0.8424 & 35.45/0.8434 & 35.43/0.8426 -\\ -3 & 34.87/0.8769 & 35.12/0.8815 & 35.15/0.8818 & 35.11/0.8812 & 35.22/0.8831 & 35.18/0.8825 -\\ -4 & 32.33/0.8027 & 32.43/0.8048 & 32.43/0.8054 & 32.42/0.8051 & 32.47/0.8065 & 32.43/0.8056 -\\ -5 & 30.93/0.8502 & 31.46/0.8619 & 31.49/0.8627 & 31.44/0.8615 & 31.63/0.8658 & 31.49/0.8629 -\\ -6 & 28.98/0.8123 & 29.59/0.8283 & 29.64/0.8314 & 29.56/0.8281 & 29.78/0.8354 & 29.63/0.8303 -\\ -7 & 23.31/0.6780 & 23.60/0.6920 & 23.62/0.6950 & 23.60/0.6923 & 23.74/0.7005 & 23.66/0.6966 -\\ -8 & 25.97/0.7552 & 26.24/0.7662 & 26.26/0.7672 & 26.21/0.7653 & 26.32/0.7695 & 26.26/0.7672 -\\ -9 & 23.06/0.6636 & 23.27/0.6754 & 23.31/0.6784 & 23.26/0.6757 & 23.36/0.6822 & 23.30/0.6789 -\\ -10 & 37.39/0.8860 & 37.48/0.8877 & 37.51/0.8884 & 37.50/0.8883 & 37.52/0.8885 & 37.50/0.8882 -\\ -11 & 34.65/0.8620 & 34.90/0.8673 & 34.92/0.8676 & 34.90/0.8670 & 34.97/0.8688 & 34.93/0.8679 -\\ -12 & 27.50/0.6394 & 27.61/0.6457 & 27.62/0.6482 & 27.60/0.6452 & 27.66/0.6519 & 27.64/0.6495 -\\ -13 & 26.16/0.7576 & 26.53/0.7741 & 26.55/0.7762 & 26.50/0.7731 & 26.65/0.7807 & 26.56/0.7763 -\\ -14 & 33.27/0.7518 & 33.35/0.7539 & 33.38/0.7551 & 33.33/0.7546 & 33.35/0.7555 & 33.36/0.7545 -\\ -15 & 25.48/0.6900 & 25.80/0.7014 & 25.84/0.7043 & 25.80/0.7025 & 25.90/0.7069 & 25.83/0.7055 -\\ -16 & 27.97/0.7213 & 28.06/0.7250 & 28.07/0.7255 & 28.06/0.7251 & 28.10/0.7272 & 28.08/0.7258 -\\ -17 & 28.74/0.7631 & 28.89/0.7689 & 28.91/0.7699 & 28.88/0.7689 & 28.95/0.7719 & 28.91/0.7702 -\\ -18 & 23.36/0.7927 & 24.24/0.8179 & 24.31/0.8203 & 24.20/0.8171 & 24.62/0.8280 & 24.36/0.8209 -\\ -19 & 36.85/0.8920 & 37.28/0.8992 & 37.30/0.8995 & 37.23/0.8987 & 37.43/0.9015 & 37.25/0.8989 -\\ -20 & 38.02/0.9129 & 38.18/0.9146 & 38.17/0.9147 & 38.15/0.9147 & 38.26/0.9156 & 38.17/0.9149 -\\ -21 & 26.89/0.8433 & 27.38/0.8545 & 27.39/0.8554 & 27.33/0.8541 & 27.48/0.8579 & 27.39/0.8562 -\\ -22 & 27.68/0.8091 & 28.19/0.8263 & 28.21/0.8265 & 28.13/0.8239 & 28.31/0.8300 & 28.20/0.8256 -\\ -23 & 26.26/0.7492 & 26.51/0.7588 & 26.55/0.7606 & 26.50/0.7589 & 26.64/0.7645 & 26.54/0.7611 -\\ -24 & 30.09/0.7624 & 30.22/0.7659 & 30.23/0.7670 & 30.22/0.7665 & 30.26/0.7680 & 30.23/0.7666 -\\ -25 & 25.07/0.7363 & 25.35/0.7471 & 25.41/0.7499 & 25.36/0.7475 & 25.47/0.7525 & 25.41/0.7504 -\\ -26 & 24.39/0.6501 & 24.61/0.6580 & 24.64/0.6604 & 24.61/0.6588 & 24.72/0.6637 & 24.66/0.6616 -\\ -27 & 29.42/0.8154 & 29.74/0.8258 & 29.81/0.8276 & 29.72/0.8255 & 29.86/0.8306 & 29.80/0.8272 -\\ -28 & 33.46/0.8813 & 33.99/0.8921 & 34.09/0.8930 & 33.94/0.8905 & 34.20/0.8944 & 33.99/0.8898 -\\ -29 & 24.60/0.7345 & 25.04/0.7488 & 25.08/0.7505 & 25.02/0.7481 & 25.16/0.7544 & 25.06/0.7507 -\\ -30 & 27.60/0.7737 & 28.12/0.7944 & 28.17/0.7966 & 28.10/0.7934 & 28.26/0.8005 & 28.17/0.7964 -\\ -''' -all = all.split('\n') - -results = '''psnr -Airport: 28.57 -BareLand: 35.46 -BaseballField: 35.29 -Beach: 32.51 -Bridge: 31.74 -Center: 29.99 -Church: 23.83 -Commercial: 26.39 -DenseResidential: 23.50 -Desert: 37.67 -Farmland: 35.00 -Forest: 27.67 -Industrial: 26.76 -Meadow: 33.40 -MediumResidential: 25.95 -Mountain: 28.14 -Park: 28.98 -Parking: 24.89 -Playground: 37.54 -Pond: 38.28 -Port: 27.63 -RailwayStation: 28.43 -Resort: 26.67 -River: 30.28 -School: 25.57 -SparseResidential: 24.75 -Square: 29.98 -Stadium: 34.48 -StorageTanks: 25.29 -Viaduct: 28.34 -all: 29.78 -ssim -Airport: 0.8013 -BareLand: 0.8439 -BaseballField: 0.8848 -Beach: 0.8085 -Bridge: 0.8668 -Center: 0.8403 -Church: 0.7053 -Commercial: 0.7717 -DenseResidential: 0.6879 -Desert: 0.8926 -Farmland: 0.8697 -Forest: 0.6541 -Industrial: 0.7845 -Meadow: 0.7578 -MediumResidential: 0.7100 -Mountain: 0.7296 -Park: 0.7738 -Parking: 0.8344 -Playground: 0.9032 -Pond: 0.9158 -Port: 0.8612 -RailwayStation: 0.8327 -Resort: 0.7658 -River: 0.7694 -School: 0.7561 -SparseResidential: 0.6652 -Square: 0.8330 -Stadium: 0.8983 -StorageTanks: 0.7574 -Viaduct: 0.8030 -all: 0.8013''' -results = results.split('\n') -n_len = len(results) -total_psnr = 0 -total_ssim = 0 -all_data = '' -idx_all = 0 -for i in range(1, n_len//2): - name = results[i].split(':')[0] - name_tmp = results[i+n_len//2].split(':')[0] - assert name == name_tmp - psnr_val = results[i].split(':')[-1].strip() - psnr_val = float(psnr_val) - ssim_val = results[i+n_len//2].split(':')[-1].strip() - ssim_val = float(ssim_val) - if name == 'all': - all_data = name+':'+str(psnr_val)+'/'+str(ssim_val) - continue - total_psnr += psnr_val - total_ssim += ssim_val - print(all[idx_all] +' & ' +f'{psnr_val:.2f}'+'/'+f'{ssim_val:.4f}') - idx_all += 2 - print('\\\\') -print(all_data) -print(total_psnr/(n_len//2-2)) -print(total_ssim/(n_len//2-2)) diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/single_stage_instance_seg.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/single_stage_instance_seg.py deleted file mode 100644 index acb5f0d2f8e4636b86b4b66cbf5c4916d0dae16f..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/single_stage_instance_seg.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -from typing import Tuple - -from torch import Tensor - -from mmdet.registry import MODELS -from mmdet.structures import OptSampleList, SampleList -from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig -from .base import BaseDetector - -INF = 1e8 - - -@MODELS.register_module() -class SingleStageInstanceSegmentor(BaseDetector): - """Base class for single-stage instance segmentors.""" - - def __init__(self, - backbone: ConfigType, - neck: OptConfigType = None, - bbox_head: OptConfigType = None, - mask_head: OptConfigType = None, - train_cfg: OptConfigType = None, - test_cfg: OptConfigType = None, - data_preprocessor: OptConfigType = None, - init_cfg: OptMultiConfig = None) -> None: - super().__init__( - data_preprocessor=data_preprocessor, init_cfg=init_cfg) - self.backbone = MODELS.build(backbone) - if neck is not None: - self.neck = MODELS.build(neck) - else: - self.neck = None - if bbox_head is not None: - bbox_head.update(train_cfg=copy.deepcopy(train_cfg)) - bbox_head.update(test_cfg=copy.deepcopy(test_cfg)) - self.bbox_head = MODELS.build(bbox_head) - else: - self.bbox_head = None - - assert mask_head, f'`mask_head` must ' \ - f'be implemented in {self.__class__.__name__}' - mask_head.update(train_cfg=copy.deepcopy(train_cfg)) - mask_head.update(test_cfg=copy.deepcopy(test_cfg)) - self.mask_head = MODELS.build(mask_head) - - self.train_cfg = train_cfg - self.test_cfg = test_cfg - - def extract_feat(self, batch_inputs: Tensor) -> Tuple[Tensor]: - """Extract features. - - Args: - batch_inputs (Tensor): Image tensor with shape (N, C, H ,W). - - Returns: - tuple[Tensor]: Multi-level features that may have different - resolutions. - """ - x = self.backbone(batch_inputs) - if self.with_neck: - x = self.neck(x) - return x - - def _forward(self, - batch_inputs: Tensor, - batch_data_samples: OptSampleList = None, - **kwargs) -> tuple: - """Network forward process. Usually includes backbone, neck and head - forward without any post-processing. - - Args: - batch_inputs (Tensor): Inputs with shape (N, C, H, W). - - Returns: - tuple: A tuple of features from ``bbox_head`` forward. - """ - outs = () - # backbone - x = self.extract_feat(batch_inputs) - # bbox_head - positive_infos = None - if self.with_bbox: - assert batch_data_samples is not None - bbox_outs = self.bbox_head.forward(x) - outs = outs + (bbox_outs, ) - # It is necessary to use `bbox_head.loss` to update - # `_raw_positive_infos` which will be used in `get_positive_infos` - # positive_infos will be used in the following mask head. - _ = self.bbox_head.loss(x, batch_data_samples, **kwargs) - positive_infos = self.bbox_head.get_positive_infos() - # mask_head - if positive_infos is None: - mask_outs = self.mask_head.forward(x) - else: - mask_outs = self.mask_head.forward(x, positive_infos) - outs = outs + (mask_outs, ) - return outs - - def loss(self, batch_inputs: Tensor, batch_data_samples: SampleList, - **kwargs) -> dict: - """ - Args: - batch_inputs (Tensor): Input images of shape (N, C, H, W). - These should usually be mean centered and std scaled. - batch_data_samples (list[:obj:`DetDataSample`]): The batch - data samples. It usually includes information such - as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`. - - Returns: - dict: A dictionary of loss components. - """ - x = self.extract_feat(batch_inputs) - losses = dict() - - positive_infos = None - # CondInst and YOLACT have bbox_head - if self.with_bbox: - bbox_losses = self.bbox_head.loss(x, batch_data_samples, **kwargs) - losses.update(bbox_losses) - # get positive information from bbox head, which will be used - # in the following mask head. - positive_infos = self.bbox_head.get_positive_infos() - - mask_loss = self.mask_head.loss( - x, batch_data_samples, positive_infos=positive_infos, **kwargs) - # avoid loss override - assert not set(mask_loss.keys()) & set(losses.keys()) - - losses.update(mask_loss) - return losses - - def predict(self, - batch_inputs: Tensor, - batch_data_samples: SampleList, - rescale: bool = True, - **kwargs) -> SampleList: - """Perform forward propagation of the mask head and predict mask - results on the features of the upstream network. - - Args: - batch_inputs (Tensor): Inputs with shape (N, C, H, W). - batch_data_samples (List[:obj:`DetDataSample`]): The Data - Samples. It usually includes information such as - `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`. - rescale (bool): Whether to rescale the results. - Defaults to False. - - Returns: - list[:obj:`DetDataSample`]: Detection results of the - input images. Each DetDataSample usually contain - 'pred_instances'. And the ``pred_instances`` usually - contains following keys. - - - scores (Tensor): Classification scores, has a shape - (num_instance, ) - - labels (Tensor): Labels of bboxes, has a shape - (num_instances, ). - - bboxes (Tensor): Has a shape (num_instances, 4), - the last dimension 4 arrange as (x1, y1, x2, y2). - - masks (Tensor): Has a shape (num_instances, H, W). - """ - x = self.extract_feat(batch_inputs) - if self.with_bbox: - # the bbox branch does not need to be scaled to the original - # image scale, because the mask branch will scale both bbox - # and mask at the same time. - bbox_rescale = rescale if not self.with_mask else False - results_list = self.bbox_head.predict( - x, batch_data_samples, rescale=bbox_rescale) - else: - results_list = None - - results_list = self.mask_head.predict( - x, batch_data_samples, rescale=rescale, results_list=results_list) - - batch_data_samples = self.add_pred_to_datasample( - batch_data_samples, results_list) - return batch_data_samples diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/nets_new.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/nets_new.py deleted file mode 100644 index bfaf72e48b31cc1130f2892b0973c9aa06f195a3..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/nets_new.py +++ /dev/null @@ -1,132 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F -from . import layers_new - - -class BaseNet(nn.Module): - def __init__( - self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6)) - ): - super(BaseNet, self).__init__() - self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1) - self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1) - self.enc3 = layers_new.Encoder(nout * 2, nout * 4, 3, 2, 1) - self.enc4 = layers_new.Encoder(nout * 4, nout * 6, 3, 2, 1) - self.enc5 = layers_new.Encoder(nout * 6, nout * 8, 3, 2, 1) - - self.aspp = layers_new.ASPPModule(nout * 8, nout * 8, dilations, dropout=True) - - self.dec4 = layers_new.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1) - self.dec3 = layers_new.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1) - self.dec2 = layers_new.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1) - self.lstm_dec2 = layers_new.LSTMModule(nout * 2, nin_lstm, nout_lstm) - self.dec1 = layers_new.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1) - - def __call__(self, x): - e1 = self.enc1(x) - e2 = self.enc2(e1) - e3 = self.enc3(e2) - e4 = self.enc4(e3) - e5 = self.enc5(e4) - - h = self.aspp(e5) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = torch.cat([h, self.lstm_dec2(h)], dim=1) - h = self.dec1(h, e1) - - return h - - -class CascadedNet(nn.Module): - def __init__(self, n_fft, nout=32, nout_lstm=128): - super(CascadedNet, self).__init__() - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - self.nin_lstm = self.max_bin // 2 - self.offset = 64 - - self.stg1_low_band_net = nn.Sequential( - BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm), - layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0), - ) - - self.stg1_high_band_net = BaseNet( - 2, nout // 4, self.nin_lstm // 2, nout_lstm // 2 - ) - - self.stg2_low_band_net = nn.Sequential( - BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), - layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0), - ) - self.stg2_high_band_net = BaseNet( - nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2 - ) - - self.stg3_full_band_net = BaseNet( - 3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm - ) - - self.out = nn.Conv2d(nout, 2, 1, bias=False) - self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False) - - def forward(self, x): - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - l1_in = x[:, :, :bandw] - h1_in = x[:, :, bandw:] - l1 = self.stg1_low_band_net(l1_in) - h1 = self.stg1_high_band_net(h1_in) - aux1 = torch.cat([l1, h1], dim=2) - - l2_in = torch.cat([l1_in, l1], dim=1) - h2_in = torch.cat([h1_in, h1], dim=1) - l2 = self.stg2_low_band_net(l2_in) - h2 = self.stg2_high_band_net(h2_in) - aux2 = torch.cat([l2, h2], dim=2) - - f3_in = torch.cat([x, aux1, aux2], dim=1) - f3 = self.stg3_full_band_net(f3_in) - - mask = torch.sigmoid(self.out(f3)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux = torch.cat([aux1, aux2], dim=1) - aux = torch.sigmoid(self.aux_out(aux)) - aux = F.pad( - input=aux, - pad=(0, 0, 0, self.output_bin - aux.size()[2]), - mode="replicate", - ) - return mask, aux - else: - return mask - - def predict_mask(self, x): - mask = self.forward(x) - - if self.offset > 0: - mask = mask[:, :, :, self.offset : -self.offset] - assert mask.size()[3] > 0 - - return mask - - def predict(self, x, aggressiveness=None): - mask = self.forward(x) - pred_mag = x * mask - - if self.offset > 0: - pred_mag = pred_mag[:, :, :, self.offset : -self.offset] - assert pred_mag.size()[3] > 0 - - return pred_mag diff --git a/spaces/LaynzKunz/RVC-Inference-webui-grado-colab-huggingafce/README.md b/spaces/LaynzKunz/RVC-Inference-webui-grado-colab-huggingafce/README.md deleted file mode 100644 index 034bbddd35159e85625f10ca3ea19555ed2020eb..0000000000000000000000000000000000000000 --- a/spaces/LaynzKunz/RVC-Inference-webui-grado-colab-huggingafce/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Advanced RVC Inference -emoji: ⚡ -colorFrom: purple -colorTo: purple -sdk: gradio -sdk_version: 3.44.4 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/LegacyLeague/Legacy_League/app.py b/spaces/LegacyLeague/Legacy_League/app.py deleted file mode 100644 index 4529dd6c56ab0bbe0ad519aa62bbd11b2ede7ac1..0000000000000000000000000000000000000000 --- a/spaces/LegacyLeague/Legacy_League/app.py +++ /dev/null @@ -1,77 +0,0 @@ -import gradio as gr -import fastai -from fastai.vision import * -from fastai.utils.mem import * -from fastai.vision import open_image, load_learner, image, torch -import numpy as np4 -import urllib.request -import PIL.Image -from io import BytesIO -import torchvision.transforms as T -from PIL import Image -import requests -from io import BytesIO -import fastai -from fastai.vision import * -from fastai.utils.mem import * -from fastai.vision import open_image, load_learner, image, torch -import numpy as np -import urllib.request -from urllib.request import urlretrieve -import PIL.Image -from io import BytesIO -import torchvision.transforms as T -import torchvision.transforms as tfms - -class FeatureLoss(nn.Module): - def __init__(self, m_feat, layer_ids, layer_wgts): - super().__init__() - self.m_feat = m_feat - self.loss_features = [self.m_feat[i] for i in layer_ids] - self.hooks = hook_outputs(self.loss_features, detach=False) - self.wgts = layer_wgts - self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids)) - ] + [f'gram_{i}' for i in range(len(layer_ids))] - - def make_features(self, x, clone=False): - self.m_feat(x) - return [(o.clone() if clone else o) for o in self.hooks.stored] - - def forward(self, input, target): - out_feat = self.make_features(target, clone=True) - in_feat = self.make_features(input) - self.feat_losses = [base_loss(input,target)] - self.feat_losses += [base_loss(f_in, f_out)*w - for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] - self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3 - for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] - self.metrics = dict(zip(self.metric_names, self.feat_losses)) - return sum(self.feat_losses) - - def __del__(self): self.hooks.remove() - -MODEL_URL = "https://www.dropbox.com/s/daf70v42oo93kym/Legacy_best.pkl?dl=1" -urllib.request.urlretrieve(MODEL_URL, "Legacy_best.pkl") -path = Path(".") -learn=load_learner(path, 'Legacy_best.pkl') - -urlretrieve("https://s.hdnux.com/photos/01/07/33/71/18726490/5/1200x0.jpg","soccer1.jpg") -urlretrieve("https://cdn.vox-cdn.com/thumbor/4J8EqJBsS2qEQltIBuFOJWSn8dc=/1400x1400/filters:format(jpeg)/cdn.vox-cdn.com/uploads/chorus_asset/file/22466347/1312893179.jpg","soccer2.jpg") -urlretrieve("https://cdn.vox-cdn.com/thumbor/VHa7adj0Oie2Ao12RwKbs40i58s=/0x0:2366x2730/1200x800/filters:focal(1180x774:1558x1152)/cdn.vox-cdn.com/uploads/chorus_image/image/69526697/E5GnQUTWEAEK445.0.jpg","baseball.jpg") -urlretrieve("https://baseball.ca/uploads/images/content/Diodati(1).jpeg","baseball2.jpeg") - -sample_images = [["soccer1.jpg"], - ["soccer2.jpg"], - ["baseball.jpg"], - ["baseball2.jpeg"]] - - -def predict(input): - img_t = T.ToTensor()(input) - img_fast = Image(img_t) - p,img_hr,b = learn.predict(img_fast) - x = np.minimum(np.maximum(image2np(img_hr.data*255), 0), 255).astype(np.uint8) - img = PIL.Image.fromarray(x) - return img - -gr_interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(), outputs="image", title='Legacy-League',examples=sample_images).launch(); diff --git a/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/utils/datasets.py b/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/utils/datasets.py deleted file mode 100644 index e672b136f56fd6b05038e24377908361a54fe519..0000000000000000000000000000000000000000 --- a/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/utils/datasets.py +++ /dev/null @@ -1,35 +0,0 @@ -import cv2 -import numpy as np - - -def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale_fill=False, scaleup=True): - # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 - shape = img.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - if not scaleup: # only scale down, do not scale up (for better test mAP) - r = min(r, 1.0) - - # Compute padding - ratio = r, r # width, height ratios - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - if auto: # minimum rectangle - dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding - elif scale_fill: # stretch - dw, dh = 0.0, 0.0 - new_unpad = (new_shape[1], new_shape[0]) - ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border - return img, ratio, (dw, dh) diff --git a/spaces/LuxOAI/guanaco-playground-tgi/dialogue.py b/spaces/LuxOAI/guanaco-playground-tgi/dialogue.py deleted file mode 100644 index 6ca26d2d887c6b683c5bd6240a09f9a55a046a3a..0000000000000000000000000000000000000000 --- a/spaces/LuxOAI/guanaco-playground-tgi/dialogue.py +++ /dev/null @@ -1,239 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -import os -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional, Type, TypeVar, Union - -from huggingface_hub import ModelHubMixin, hf_hub_download - -# Generic variable that is either ModelHubMixin or a subclass thereof -T = TypeVar("T", bound="ModelHubMixin") - -TEMPLATE_FILENAME = "dialogue_template.json" -IGNORE_INDEX = -100 - - -@dataclass -class DialogueTemplate(ModelHubMixin): - """Converts all turns of a dialogue between a user and assistant to a standardized format. - Adapted from OpenAI's ChatML (https://github.com/openai/openai-python/blob/main/chatml.md) and Vicuna (https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) - """ - - system: str - messages: List[Dict[str, str]] = None - system_token: str = "<|system|>" - user_token: str = "<|user|>" - assistant_token: str = "<|assistant|>" - end_token: str = "<|end|>" - - def get_training_prompt(self) -> str: - prompt = self.system_token + "\n" + self.system + self.end_token + "\n" - if self.messages is None: - raise ValueError("Dialogue template must have at least one message.") - for message in self.messages: - if message["role"] == "user": - prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n" - else: - prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n" - return prompt - - def get_inference_prompt(self) -> str: - prompt = self.system_token + "\n" + self.system + self.end_token + "\n" - if self.messages is None: - raise ValueError("Dialogue template must have at least one message.") - for message in self.messages: - if message["role"] == "user": - prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n" - else: - prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n" - prompt += self.assistant_token - return prompt - - def get_dialogue(self): - """Helper function to format the messages as an easy-to-read dialogue.""" - prompt = "" - if self.messages is None: - raise ValueError("Dialogue template must have at least one message.") - for message in self.messages: - if message["role"] == "user": - prompt += "\n\nHuman: " + message["content"] - else: - prompt += "\n\nAssistant: " + message["content"] - return prompt - - def get_special_tokens(self) -> List[str]: - return [self.system_token, self.user_token, self.assistant_token, self.end_token] - - def copy(self): - return DialogueTemplate( - system=self.system, - messages=self.messages, - system_token=self.system_token, - user_token=self.user_token, - assistant_token=self.assistant_token, - end_token=self.end_token, - ) - - def to_dict(self) -> Dict[str, Any]: - return {k: v for k, v in asdict(self).items()} - - @classmethod - def from_dict(cls, data): - return DialogueTemplate( - system=data["system"] if "system" in data else "", - messages=data["messages"] if "messages" in data else None, - system_token=data["system_token"] if "system_token" in data else "<|system|>", - user_token=data["user_token"] if "user_token" in data else "<|user|>", - assistant_token=data["assistant_token"] if "assistant_token" in data else "<|assistant|>", - end_token=data["end_token"] if "end_token" in data else "<|end|>", - ) - - def _save_pretrained(self, save_directory: Union[str, Path]) -> None: - save_directory = Path(save_directory) - save_directory.mkdir(exist_ok=True) - with open(save_directory / "dialogue_template.json", "w") as f: - json.dump(self.to_dict(), f, indent=2) - - @classmethod - def _from_pretrained( - cls: Type[T], - *, - model_id: str, - revision: Optional[str], - cache_dir: Optional[Union[str, Path]], - force_download: bool, - proxies: Optional[Dict], - resume_download: bool, - local_files_only: bool, - token: Optional[Union[str, bool]], - **model_kwargs, - ) -> T: - """Loads the dialogue template from a local directory or the Huggingface Hub. - Args: - model_id (`str`): - ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`). - revision (`str`, *optional*): - Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the - latest commit on `main` branch. - force_download (`bool`, *optional*, defaults to `False`): - Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding - the existing cache. - resume_download (`bool`, *optional*, defaults to `False`): - Whether to delete incompletely received files. Will attempt to resume the download if such a file exists. - proxies (`Dict[str, str]`, *optional*): - A dictionary of proxy servers to use by protocol or endpoint (e.g., `{'http': 'foo.bar:3128', - 'http://hostname': 'foo.bar:4012'}`). - token (`str` or `bool`, *optional*): - The token to use as HTTP bearer authorization for remote files. By default, it will use the token - cached when running `huggingface-cli login`. - cache_dir (`str`, `Path`, *optional*): - Path to the folder where cached files are stored. - local_files_only (`bool`, *optional*, defaults to `False`): - If `True`, avoid downloading the file and return the path to the local cached file if it exists. - model_kwargs: - Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method. - """ - if os.path.isdir(model_id): # Can either be a local directory - print("Loading dialogue template from local directory") - template_file = os.path.join(model_id, TEMPLATE_FILENAME) - else: # Or a template on the Hub - template_file = hf_hub_download( # Download from the hub, passing same input args - repo_id=model_id, - filename=TEMPLATE_FILENAME, - revision=revision, - cache_dir=cache_dir, - force_download=force_download, - proxies=proxies, - resume_download=resume_download, - token=token, - local_files_only=local_files_only, - ) - - # Load template - with open(template_file, "r") as f: - data = json.load(f) - return cls.from_dict(data=data) - - -# A shortened version of the system message in Anthropic's HHH prompt: https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt -default_template = DialogueTemplate( - system="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.", -) - -# OpenAI and OpenAssistant train on few to no system messages. -# TODO: consider defining this as the `default` template -no_system_template = DialogueTemplate( - system="", -) - -alpaca_template = DialogueTemplate( - system="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.", - user_token="### Instruction:", - assistant_token="### Response:", -) - -SUPPORTED_DIALOGUE_TEMPLATES = { - "default": default_template, - "no_system": no_system_template, - "alpaca": alpaca_template, -} - - -def get_dialogue_template(template: str) -> DialogueTemplate: - if template not in SUPPORTED_DIALOGUE_TEMPLATES.keys(): - raise ValueError(f"Template {template} is not supported!") - return SUPPORTED_DIALOGUE_TEMPLATES[template].copy() - - -def prepare_dialogue(example, dialogue_template, is_train=True): - """Format example to single- or multi-turn dialogue.""" - # TODO: make this simpler by just ensuring every dataset has a messages column - if "messages" in example.keys() and example["messages"] is not None: - dialogue_template.messages = example["messages"] - elif all(k in example.keys() for k in ("prompt", "completion")): - # Construct single-turn dialogue from prompt and completion - dialogue_template.messages = [ - {"role": "user", "content": example["prompt"]}, - {"role": "assistant", "content": example["completion"]}, - ] - elif "prompt" in example.keys(): - # Construct single-turn dialogue from prompt (inference only) - dialogue_template.messages = [ - {"role": "user", "content": example["prompt"]}, - ] - else: - raise ValueError( - f"Could not format example as dialogue! Require either `messages` or `[prompt, completion]` or `[prompt]` keys but found {list(example.keys())}" - ) - if is_train: - example["text"] = dialogue_template.get_training_prompt() - else: - example["text"] = dialogue_template.get_inference_prompt() - return example - - -def mask_user_labels(tokenizer, dialogue_template, labels): - """Masks the user turns of a dialogue from the loss""" - user_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.user_token) - assistant_token_id = tokenizer.convert_tokens_to_ids(dialogue_template.assistant_token) - for idx, label_id in enumerate(labels): - if label_id == user_token_id: - current_idx = idx - while labels[current_idx] != assistant_token_id and current_idx < len(labels): - labels[current_idx] = IGNORE_INDEX - current_idx += 1 \ No newline at end of file diff --git a/spaces/MCkernick/Image_Restoration_Colorization/CODE_OF_CONDUCT.md b/spaces/MCkernick/Image_Restoration_Colorization/CODE_OF_CONDUCT.md deleted file mode 100644 index f9ba8cf65f3e3104dd061c178066ec8247811f33..0000000000000000000000000000000000000000 --- a/spaces/MCkernick/Image_Restoration_Colorization/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,9 +0,0 @@ -# Microsoft Open Source Code of Conduct - -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). - -Resources: - -- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) -- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) -- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns diff --git a/spaces/MLVKU/Human_Object_Interaction/hotr/engine/evaluator_vcoco.py b/spaces/MLVKU/Human_Object_Interaction/hotr/engine/evaluator_vcoco.py deleted file mode 100644 index 716ca22ab52d29d763f732cddd1d9e90ec85d2c9..0000000000000000000000000000000000000000 --- a/spaces/MLVKU/Human_Object_Interaction/hotr/engine/evaluator_vcoco.py +++ /dev/null @@ -1,87 +0,0 @@ -# ------------------------------------------------------------------------ -# HOTR official code : hotr/engine/evaluator_vcoco.py -# Copyright (c) Kakao Brain, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ -# Modified from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -# ------------------------------------------------------------------------ -import os -import torch -import time -import datetime - -import hotr.util.misc as utils -import hotr.util.logger as loggers -from hotr.data.evaluators.vcoco_eval import VCocoEvaluator -from hotr.util.box_ops import rescale_bboxes, rescale_pairs - -import wandb - -@torch.no_grad() -def vcoco_evaluate(model, criterion, postprocessors, data_loader, device, output_dir, thr,args=None): - model.eval() - criterion.eval() - - metric_logger = loggers.MetricLogger(mode="test", delimiter=" ") - header = 'Evaluation Inference (V-COCO)' - - print_freq = 1 # len(data_loader) - res = {} - hoi_recognition_time = [] - - for samples, targets in metric_logger.log_every(data_loader, print_freq, header): - samples = samples.to(device) - targets = [{k: v.to(device) for k, v in t.items()} for t in targets] - - outputs = model(samples) - loss_dict = criterion(outputs, targets) - loss_dict_reduced = utils.reduce_dict(loss_dict) # ddp gathering - - orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0) - results = postprocessors['hoi'](outputs, orig_target_sizes, threshold=thr, dataset='vcoco',args=args) - targets = process_target(targets, orig_target_sizes) - hoi_recognition_time.append(results[0]['hoi_recognition_time'] * 1000) - - res.update( - {target['image_id'].item():\ - {'target': target, 'prediction': output} for target, output in zip(targets, results) - } - ) - print(f"[stats] HOI Recognition Time (avg) : {sum(hoi_recognition_time)/len(hoi_recognition_time):.4f} ms") - - start_time = time.time() - gather_res = utils.all_gather(res) - total_res = {} - for dist_res in gather_res: - total_res.update(dist_res) - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print(f"[stats] Distributed Gathering Time : {total_time_str}") - - return total_res - -def vcoco_accumulate(total_res, args, print_results, wandb_log): - vcoco_evaluator = VCocoEvaluator(args) - vcoco_evaluator.update(total_res) - print(f"[stats] Score Matrix Generation completed!! ") - - scenario1 = vcoco_evaluator.role_eval1.evaluate(print_results) - scenario2 = vcoco_evaluator.role_eval2.evaluate(print_results) - - if wandb_log: - wandb.log({ - 'scenario1': scenario1, - 'scenario2': scenario2 - }) - - return scenario1, scenario2 - -def process_target(targets, target_sizes): - for idx, (target, target_size) in enumerate(zip(targets, target_sizes)): - labels = target['labels'] - valid_boxes_inds = (labels > 0) - - targets[idx]['boxes'] = rescale_bboxes(target['boxes'], target_size) # boxes - targets[idx]['pair_boxes'] = rescale_pairs(target['pair_boxes'], target_size) # pairs - - return targets \ No newline at end of file diff --git a/spaces/Maharaja36/myGenAIApp/README.md b/spaces/Maharaja36/myGenAIApp/README.md deleted file mode 100644 index 0bfbca57d6add7d9c4c8376cb2fe68689b1cc955..0000000000000000000000000000000000000000 --- a/spaces/Maharaja36/myGenAIApp/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: MyGenAIApp -emoji: 🐠 -colorFrom: blue -colorTo: yellow -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/modeling/ocr.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/modeling/ocr.py deleted file mode 100644 index df3b4f67959fc6a088b93ee7a34b15c1e07402df..0000000000000000000000000000000000000000 --- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/modeling/ocr.py +++ /dev/null @@ -1,141 +0,0 @@ -import torch -import torch.nn as nn -import torch._utils -import torch.nn.functional as F - - -class SpatialGather_Module(nn.Module): - """ - Aggregate the context features according to the initial - predicted probability distribution. - Employ the soft-weighted method to aggregate the context. - """ - - def __init__(self, cls_num=0, scale=1): - super(SpatialGather_Module, self).__init__() - self.cls_num = cls_num - self.scale = scale - - def forward(self, feats, probs): - batch_size, c, h, w = probs.size(0), probs.size(1), probs.size(2), probs.size(3) - probs = probs.view(batch_size, c, -1) - feats = feats.view(batch_size, feats.size(1), -1) - feats = feats.permute(0, 2, 1) # batch x hw x c - probs = F.softmax(self.scale * probs, dim=2) # batch x k x hw - ocr_context = torch.matmul(probs, feats) \ - .permute(0, 2, 1).unsqueeze(3) # batch x k x c - return ocr_context - - -class SpatialOCR_Module(nn.Module): - """ - Implementation of the OCR module: - We aggregate the global object representation to update the representation for each pixel. - """ - - def __init__(self, - in_channels, - key_channels, - out_channels, - scale=1, - dropout=0.1, - norm_layer=nn.BatchNorm2d, - align_corners=True): - super(SpatialOCR_Module, self).__init__() - self.object_context_block = ObjectAttentionBlock2D(in_channels, key_channels, scale, - norm_layer, align_corners) - _in_channels = 2 * in_channels - - self.conv_bn_dropout = nn.Sequential( - nn.Conv2d(_in_channels, out_channels, kernel_size=1, padding=0, bias=False), - nn.Sequential(norm_layer(out_channels), nn.ReLU(inplace=True)), - nn.Dropout2d(dropout) - ) - - def forward(self, feats, proxy_feats): - context = self.object_context_block(feats, proxy_feats) - - output = self.conv_bn_dropout(torch.cat([context, feats], 1)) - - return output - - -class ObjectAttentionBlock2D(nn.Module): - ''' - The basic implementation for object context block - Input: - N X C X H X W - Parameters: - in_channels : the dimension of the input feature map - key_channels : the dimension after the key/query transform - scale : choose the scale to downsample the input feature maps (save memory cost) - bn_type : specify the bn type - Return: - N X C X H X W - ''' - - def __init__(self, - in_channels, - key_channels, - scale=1, - norm_layer=nn.BatchNorm2d, - align_corners=True): - super(ObjectAttentionBlock2D, self).__init__() - self.scale = scale - self.in_channels = in_channels - self.key_channels = key_channels - self.align_corners = align_corners - - self.pool = nn.MaxPool2d(kernel_size=(scale, scale)) - self.f_pixel = nn.Sequential( - nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels, - kernel_size=1, stride=1, padding=0, bias=False), - nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True)), - nn.Conv2d(in_channels=self.key_channels, out_channels=self.key_channels, - kernel_size=1, stride=1, padding=0, bias=False), - nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True)) - ) - self.f_object = nn.Sequential( - nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels, - kernel_size=1, stride=1, padding=0, bias=False), - nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True)), - nn.Conv2d(in_channels=self.key_channels, out_channels=self.key_channels, - kernel_size=1, stride=1, padding=0, bias=False), - nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True)) - ) - self.f_down = nn.Sequential( - nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels, - kernel_size=1, stride=1, padding=0, bias=False), - nn.Sequential(norm_layer(self.key_channels), nn.ReLU(inplace=True)) - ) - self.f_up = nn.Sequential( - nn.Conv2d(in_channels=self.key_channels, out_channels=self.in_channels, - kernel_size=1, stride=1, padding=0, bias=False), - nn.Sequential(norm_layer(self.in_channels), nn.ReLU(inplace=True)) - ) - - def forward(self, x, proxy): - batch_size, h, w = x.size(0), x.size(2), x.size(3) - if self.scale > 1: - x = self.pool(x) - - query = self.f_pixel(x).view(batch_size, self.key_channels, -1) - query = query.permute(0, 2, 1) - key = self.f_object(proxy).view(batch_size, self.key_channels, -1) - value = self.f_down(proxy).view(batch_size, self.key_channels, -1) - value = value.permute(0, 2, 1) - - sim_map = torch.matmul(query, key) - sim_map = (self.key_channels ** -.5) * sim_map - sim_map = F.softmax(sim_map, dim=-1) - - # add bg context ... - context = torch.matmul(sim_map, value) - context = context.permute(0, 2, 1).contiguous() - context = context.view(batch_size, self.key_channels, *x.size()[2:]) - context = self.f_up(context) - if self.scale > 1: - context = F.interpolate(input=context, size=(h, w), - mode='bilinear', align_corners=self.align_corners) - - return context diff --git a/spaces/MarioWasTaken/BackroomsIG/index.html b/spaces/MarioWasTaken/BackroomsIG/index.html deleted file mode 100644 index db5b2cdad6c826d9024a906638cbbd34e30165ab..0000000000000000000000000000000000000000 --- a/spaces/MarioWasTaken/BackroomsIG/index.html +++ /dev/null @@ -1,15 +0,0 @@ - - - - - -BackroomsIG - - - -- Backrooms Image Generator - -- - diff --git a/spaces/MathysL/AutoGPT4/autogpt/commands/google_search.py b/spaces/MathysL/AutoGPT4/autogpt/commands/google_search.py deleted file mode 100644 index 7d38ce7568d2de207d521b077cfebd72527c9795..0000000000000000000000000000000000000000 --- a/spaces/MathysL/AutoGPT4/autogpt/commands/google_search.py +++ /dev/null @@ -1,87 +0,0 @@ -"""Google search command for Autogpt.""" -from __future__ import annotations - -import json - -from duckduckgo_search import ddg - -from autogpt.config import Config - -CFG = Config() - - -def google_search(query: str, num_results: int = 8) -> str: - """Return the results of a Google search - - Args: - query (str): The search query. - num_results (int): The number of results to return. - - Returns: - str: The results of the search. - """ - search_results = [] - if not query: - return json.dumps(search_results) - - results = ddg(query, max_results=num_results) - if not results: - return json.dumps(search_results) - - for j in results: - search_results.append(j) - - return json.dumps(search_results, ensure_ascii=False, indent=4) - - -def google_official_search(query: str, num_results: int = 8) -> str | list[str]: - """Return the results of a Google search using the official Google API - - Args: - query (str): The search query. - num_results (int): The number of results to return. - - Returns: - str: The results of the search. - """ - - from googleapiclient.discovery import build - from googleapiclient.errors import HttpError - - try: - # Get the Google API key and Custom Search Engine ID from the config file - api_key = CFG.google_api_key - custom_search_engine_id = CFG.custom_search_engine_id - - # Initialize the Custom Search API service - service = build("customsearch", "v1", developerKey=api_key) - - # Send the search query and retrieve the results - result = ( - service.cse() - .list(q=query, cx=custom_search_engine_id, num=num_results) - .execute() - ) - - # Extract the search result items from the response - search_results = result.get("items", []) - - # Create a list of only the URLs from the search results - search_results_links = [item["link"] for item in search_results] - - except HttpError as e: - # Handle errors in the API call - error_details = json.loads(e.content.decode()) - - # Check if the error is related to an invalid or missing API key - if error_details.get("error", {}).get( - "code" - ) == 403 and "invalid API key" in error_details.get("error", {}).get( - "message", "" - ): - return "Error: The provided Google API key is invalid or missing." - else: - return f"Error: {e}" - - # Return the list of search result URLs - return search_results_links diff --git a/spaces/MathysL/AutoGPT4/autogpt/spinner.py b/spaces/MathysL/AutoGPT4/autogpt/spinner.py deleted file mode 100644 index 4e33d74213881352546f334ccb1eb4772b8b7b70..0000000000000000000000000000000000000000 --- a/spaces/MathysL/AutoGPT4/autogpt/spinner.py +++ /dev/null @@ -1,65 +0,0 @@ -"""A simple spinner module""" -import itertools -import sys -import threading -import time - - -class Spinner: - """A simple spinner class""" - - def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None: - """Initialize the spinner class - - Args: - message (str): The message to display. - delay (float): The delay between each spinner update. - """ - self.spinner = itertools.cycle(["-", "/", "|", "\\"]) - self.delay = delay - self.message = message - self.running = False - self.spinner_thread = None - - def spin(self) -> None: - """Spin the spinner""" - while self.running: - sys.stdout.write(f"{next(self.spinner)} {self.message}\r") - sys.stdout.flush() - time.sleep(self.delay) - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - - def __enter__(self): - """Start the spinner""" - self.running = True - self.spinner_thread = threading.Thread(target=self.spin) - self.spinner_thread.start() - - return self - - def __exit__(self, exc_type, exc_value, exc_traceback) -> None: - """Stop the spinner - - Args: - exc_type (Exception): The exception type. - exc_value (Exception): The exception value. - exc_traceback (Exception): The exception traceback. - """ - self.running = False - if self.spinner_thread is not None: - self.spinner_thread.join() - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - sys.stdout.flush() - - def update_message(self, new_message, delay=0.1): - """Update the spinner message - Args: - new_message (str): New message to display - delay: Delay in seconds before updating the message - """ - time.sleep(delay) - sys.stdout.write( - f"\r{' ' * (len(self.message) + 2)}\r" - ) # Clear the current message - sys.stdout.flush() - self.message = new_message diff --git a/spaces/MechaXYZ/Audio-to-Text/README.md b/spaces/MechaXYZ/Audio-to-Text/README.md deleted file mode 100644 index 39e4257d2d8cf7b195fbe6b388d9bb62ed1b6974..0000000000000000000000000000000000000000 --- a/spaces/MechaXYZ/Audio-to-Text/README.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -title: Audio-to-Text Playground -emoji: 🤫 -colorFrom: indigo -colorTo: red -sdk: gradio -sdk_version: 3.9.1 -app_file: app.py -pinned: false -tags: -- whisper-event -duplicated_from: NeuralInternet/Audio-to-Text_Playground ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/utils/sync_bn.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/utils/sync_bn.py deleted file mode 100644 index f78f39181d75bb85c53e8c7c8eaf45690e9f0bee..0000000000000000000000000000000000000000 --- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/utils/sync_bn.py +++ /dev/null @@ -1,59 +0,0 @@ -import torch - -import annotator.uniformer.mmcv as mmcv - - -class _BatchNormXd(torch.nn.modules.batchnorm._BatchNorm): - """A general BatchNorm layer without input dimension check. - - Reproduced from @kapily's work: - (https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547) - The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc - is `_check_input_dim` that is designed for tensor sanity checks. - The check has been bypassed in this class for the convenience of converting - SyncBatchNorm. - """ - - def _check_input_dim(self, input): - return - - -def revert_sync_batchnorm(module): - """Helper function to convert all `SyncBatchNorm` (SyncBN) and - `mmcv.ops.sync_bn.SyncBatchNorm`(MMSyncBN) layers in the model to - `BatchNormXd` layers. - - Adapted from @kapily's work: - (https://github.com/pytorch/pytorch/issues/41081#issuecomment-783961547) - - Args: - module (nn.Module): The module containing `SyncBatchNorm` layers. - - Returns: - module_output: The converted module with `BatchNormXd` layers. - """ - module_output = module - module_checklist = [torch.nn.modules.batchnorm.SyncBatchNorm] - if hasattr(mmcv, 'ops'): - module_checklist.append(mmcv.ops.SyncBatchNorm) - if isinstance(module, tuple(module_checklist)): - module_output = _BatchNormXd(module.num_features, module.eps, - module.momentum, module.affine, - module.track_running_stats) - if module.affine: - # no_grad() may not be needed here but - # just to be consistent with `convert_sync_batchnorm()` - with torch.no_grad(): - module_output.weight = module.weight - module_output.bias = module.bias - module_output.running_mean = module.running_mean - module_output.running_var = module.running_var - module_output.num_batches_tracked = module.num_batches_tracked - module_output.training = module.training - # qconfig exists in quantized models - if hasattr(module, 'qconfig'): - module_output.qconfig = module.qconfig - for name, child in module.named_children(): - module_output.add_module(name, revert_sync_batchnorm(child)) - del module - return module_output diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/speech/__init__.py b/spaces/MetaWabbit/Auto-GPT/autogpt/speech/__init__.py deleted file mode 100644 index 2ff0d2bf48dc356bf810cb5a2063d6774e5fec6e..0000000000000000000000000000000000000000 --- a/spaces/MetaWabbit/Auto-GPT/autogpt/speech/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -"""This module contains the speech recognition and speech synthesis functions.""" -from autogpt.speech.say import say_text - -__all__ = ["say_text"] diff --git a/spaces/MiloSobral/PortiloopDemo/.github/ISSUE_TEMPLATE/feature_request.md b/spaces/MiloSobral/PortiloopDemo/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index bbcbbe7d61558adde3cbfd0c7a63a67c27ed6d30..0000000000000000000000000000000000000000 --- a/spaces/MiloSobral/PortiloopDemo/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: '' -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context or screenshots about the feature request here. diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/model_utils_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/model_utils_test.py deleted file mode 100644 index a8c4a15c9aba8dbff043088a392fe415f22206ca..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/model_utils_test.py +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Test Transformer model helper methods.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from official.nlp.transformer import model_utils - -NEG_INF = -1e9 - - -class ModelUtilsTest(tf.test.TestCase): - - def test_get_padding(self): - x = tf.constant([[1, 0, 0, 0, 2], [3, 4, 0, 0, 0], [0, 5, 6, 0, 7]]) - padding = model_utils.get_padding(x, padding_value=0) - - self.assertAllEqual([[0, 1, 1, 1, 0], [0, 0, 1, 1, 1], [1, 0, 0, 1, 0]], - padding) - - def test_get_padding_bias(self): - x = tf.constant([[1, 0, 0, 0, 2], [3, 4, 0, 0, 0], [0, 5, 6, 0, 7]]) - bias = model_utils.get_padding_bias(x) - bias_shape = tf.shape(bias) - flattened_bias = tf.reshape(bias, [3, 5]) - - self.assertAllEqual([[0, NEG_INF, NEG_INF, NEG_INF, 0], - [0, 0, NEG_INF, NEG_INF, NEG_INF], - [NEG_INF, 0, 0, NEG_INF, 0]], - flattened_bias) - self.assertAllEqual([3, 1, 1, 5], bias_shape) - - def test_get_decoder_self_attention_bias(self): - length = 5 - bias = model_utils.get_decoder_self_attention_bias(length) - - self.assertAllEqual([[[[0, NEG_INF, NEG_INF, NEG_INF, NEG_INF], - [0, 0, NEG_INF, NEG_INF, NEG_INF], - [0, 0, 0, NEG_INF, NEG_INF], - [0, 0, 0, 0, NEG_INF], - [0, 0, 0, 0, 0]]]], - bias) - - -if __name__ == "__main__": - tf.test.main() diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/README.md b/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/README.md deleted file mode 100644 index 9675f01a57fd26a83ed5103e116257b3664396cb..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/nlp/xlnet/README.md +++ /dev/null @@ -1,16 +0,0 @@ -# XLNet: Generalized Autoregressive Pretraining for Language Understanding - -The academic paper which describes XLNet in detail and provides full results on -a number of tasks can be found here: https://arxiv.org/abs/1906.08237. - -**Instructions and user guide will be added soon.** - -XLNet is a generalized autoregressive BERT-like pretraining language model that -enables learning bidirectional contexts by maximizing the expected likelihood -over all permutations of the factorization order. It can learn dependency beyond -a fixed length without disrupting temporal coherence by using segment-level -recurrence mechanism and relative positional encoding scheme introduced in -[Transformer-XL](https://arxiv.org/pdf/1901.02860.pdf). XLNet outperforms BERT -on 20 NLP benchmark tasks and achieves state-of-the-art results on 18 tasks -including question answering, natural language inference, sentiment analysis, -and document ranking. diff --git a/spaces/NSect/multitrack-midi-music-generator/utils.py b/spaces/NSect/multitrack-midi-music-generator/utils.py deleted file mode 100644 index 22c140ff963af1fa884267c280b0a2c6933a3f77..0000000000000000000000000000000000000000 --- a/spaces/NSect/multitrack-midi-music-generator/utils.py +++ /dev/null @@ -1,244 +0,0 @@ -from typing import List, Tuple - -import gradio as gr -from transformers import AutoTokenizer, AutoModelForCausalLM -import note_seq -from matplotlib.figure import Figure -from numpy import ndarray -import torch - -from constants import GM_INSTRUMENTS, SAMPLE_RATE -from string_to_notes import token_sequence_to_note_sequence -from model import get_model_and_tokenizer - - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -# Load the tokenizer and the model -model, tokenizer = get_model_and_tokenizer() - - -def create_seed_string(genre: str = "OTHER") -> str: - """ - Creates a seed string for generating a new piece. - - Args: - genre (str, optional): The genre of the piece. Defaults to "OTHER". - - Returns: - str: The seed string. - """ - if genre == "RANDOM": - seed_string = "PIECE_START" - else: - seed_string = f"PIECE_START GENRE={genre} TRACK_START" - return seed_string - - -def get_instruments(text_sequence: str) -> List[str]: - """ - Extracts the list of instruments from a text sequence. - - Args: - text_sequence (str): The text sequence. - - Returns: - List[str]: The list of instruments. - """ - instruments = [] - parts = text_sequence.split() - for part in parts: - if part.startswith("INST="): - if part[5:] == "DRUMS": - instruments.append("Drums") - else: - index = int(part[5:]) - instruments.append(GM_INSTRUMENTS[index]) - return instruments - - -def generate_new_instrument(seed: str, temp: float = 0.75) -> str: - """ - Generates a new instrument sequence from a given seed and temperature. - - Args: - seed (str): The seed string for the generation. - temp (float, optional): The temperature for the generation, which controls the randomness. Defaults to 0.75. - - Returns: - str: The generated instrument sequence. - """ - seed_length = len(tokenizer.encode(seed)) - - while True: - # Encode the conditioning tokens. - input_ids = tokenizer.encode(seed, return_tensors="pt") - - # Move the input_ids tensor to the same device as the model - input_ids = input_ids.to(model.device) - - # Generate more tokens. - eos_token_id = tokenizer.encode("TRACK_END")[0] - generated_ids = model.generate( - input_ids, - max_new_tokens=2048, - do_sample=True, - temperature=temp, - eos_token_id=eos_token_id, - ) - generated_sequence = tokenizer.decode(generated_ids[0]) - - # Check if the generated sequence contains "NOTE_ON" beyond the seed - new_generated_sequence = tokenizer.decode(generated_ids[0][seed_length:]) - if "NOTE_ON" in new_generated_sequence: - return generated_sequence - - -def get_outputs_from_string( - generated_sequence: str, qpm: int = 120 -) -> Tuple[ndarray, str, Figure, str, str]: - """ - Converts a generated sequence into various output formats including audio, MIDI, plot, etc. - - Args: - generated_sequence (str): The generated sequence of tokens. - qpm (int, optional): The quarter notes per minute. Defaults to 120. - - Returns: - Tuple[ndarray, str, Figure, str, str]: The audio waveform, MIDI file name, plot figure, - instruments string, and number of tokens string. - """ - instruments = get_instruments(generated_sequence) - instruments_str = "\n".join(f"- {instrument}" for instrument in instruments) - note_sequence = token_sequence_to_note_sequence(generated_sequence, qpm=qpm) - - synth = note_seq.fluidsynth - array_of_floats = synth(note_sequence, sample_rate=SAMPLE_RATE) - int16_data = note_seq.audio_io.float_samples_to_int16(array_of_floats) - fig = note_seq.plot_sequence(note_sequence, show_figure=False) - num_tokens = str(len(generated_sequence.split())) - audio = gr.make_waveform((SAMPLE_RATE, int16_data)) - note_seq.note_sequence_to_midi_file(note_sequence, "midi_ouput.mid") - return audio, "midi_ouput.mid", fig, instruments_str, num_tokens - - -def remove_last_instrument( - text_sequence: str, qpm: int = 120 -) -> Tuple[ndarray, str, Figure, str, str, str]: - """ - Removes the last instrument from a song string and returns the various output formats. - - Args: - text_sequence (str): The song string. - qpm (int, optional): The quarter notes per minute. Defaults to 120. - - Returns: - Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure, - instruments string, new song string, and number of tokens string. - """ - # We split the song into tracks by splitting on 'TRACK_START' - tracks = text_sequence.split("TRACK_START") - # We keep all tracks except the last one - modified_tracks = tracks[:-1] - # We join the tracks back together, adding back the 'TRACK_START' that was removed by split - new_song = "TRACK_START".join(modified_tracks) - - if len(tracks) == 2: - # There is only one instrument, so start from scratch - audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song( - text_sequence=new_song - ) - elif len(tracks) == 1: - # No instrument so start from empty sequence - audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song( - text_sequence="" - ) - else: - audio, midi_file, fig, instruments_str, num_tokens = get_outputs_from_string( - new_song, qpm - ) - - return audio, midi_file, fig, instruments_str, new_song, num_tokens - - -def regenerate_last_instrument( - text_sequence: str, qpm: int = 120 -) -> Tuple[ndarray, str, Figure, str, str, str]: - """ - Regenerates the last instrument in a song string and returns the various output formats. - - Args: - text_sequence (str): The song string. - qpm (int, optional): The quarter notes per minute. Defaults to 120. - - Returns: - Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure, - instruments string, new song string, and number of tokens string. - """ - last_inst_index = text_sequence.rfind("INST=") - if last_inst_index == -1: - # No instrument so start from empty sequence - audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song( - text_sequence="", qpm=qpm - ) - else: - # Take it from the last instrument and continue generation - next_space_index = text_sequence.find(" ", last_inst_index) - new_seed = text_sequence[:next_space_index] - audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song( - text_sequence=new_seed, qpm=qpm - ) - return audio, midi_file, fig, instruments_str, new_song, num_tokens - - -def change_tempo( - text_sequence: str, qpm: int -) -> Tuple[ndarray, str, Figure, str, str, str]: - """ - Changes the tempo of a song string and returns the various output formats. - - Args: - text_sequence (str): The song string. - qpm (int): The new quarter notes per minute. - - Returns: - Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure, - instruments string, text sequence, and number of tokens string. - """ - audio, midi_file, fig, instruments_str, num_tokens = get_outputs_from_string( - text_sequence, qpm=qpm - ) - return audio, midi_file, fig, instruments_str, text_sequence, num_tokens - - -def generate_song( - genre: str = "OTHER", - temp: float = 0.75, - text_sequence: str = "", - qpm: int = 120, -) -> Tuple[ndarray, str, Figure, str, str, str]: - """ - Generates a song given a genre, temperature, initial text sequence, and tempo. - - Args: - model (AutoModelForCausalLM): The pretrained model used for generating the sequences. - tokenizer (AutoTokenizer): The tokenizer used to encode and decode the sequences. - genre (str, optional): The genre of the song. Defaults to "OTHER". - temp (float, optional): The temperature for the generation, which controls the randomness. Defaults to 0.75. - text_sequence (str, optional): The initial text sequence for the song. Defaults to "". - qpm (int, optional): The quarter notes per minute. Defaults to 120. - - Returns: - Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure, - instruments string, generated song string, and number of tokens string. - """ - if text_sequence == "": - seed_string = create_seed_string(genre) - else: - seed_string = text_sequence - - generated_sequence = generate_new_instrument(seed=seed_string, temp=temp) - audio, midi_file, fig, instruments_str, num_tokens = get_outputs_from_string( - generated_sequence, qpm - ) - return audio, midi_file, fig, instruments_str, generated_sequence, num_tokens diff --git a/spaces/NataKaichkina/PredictSalary/app.py b/spaces/NataKaichkina/PredictSalary/app.py deleted file mode 100644 index b2efc16441c240e718ffc94bc421c59b4b2787e9..0000000000000000000000000000000000000000 --- a/spaces/NataKaichkina/PredictSalary/app.py +++ /dev/null @@ -1,138 +0,0 @@ -import streamlit as st -import numpy as np -import pandas as pd -import re -import pickle -from sentence_transformers import SentenceTransformer -from PIL import Image -COUNT = 768 - - -image = Image.open('images.jpg') -st.sidebar.image(image) -st.sidebar.header('Предсказание заработной платы по описанию вакансии') - - -categories = [ - 'Accounting & Finance Jobs', - 'Admin Jobs', - 'Charity & Voluntary Jobs', - 'Consultancy Jobs', - 'Creative & Design Jobs', - 'Customer Services Jobs', - 'Domestic help & Cleaning Jobs', - 'Energy, Oil & Gas Jobs', - 'Engineering Jobs', - 'Graduate Jobs', - 'HR & Recruitment Jobs', - 'Healthcare & Nursing Jobs', - 'Hospitality & Catering Jobs', - 'IT Jobs', - 'Legal Jobs', - 'Logistics & Warehouse Jobs', - 'Maintenance Jobs', - 'Manufacturing Jobs', - 'Other/General Jobs', - 'PR, Advertising & Marketing Jobs', - 'Part time Jobs', - 'Property Jobs', - 'Retail Jobs', - 'Sales Jobs', - 'Scientific & QA Jobs', - 'Social work Jobs', - 'Teaching Jobs', - 'Trade & Construction Jobs', - 'Travel Jobs'] - - -def normilize_text(text): - text = str(text) - return re.sub('[^a-zA-Zа-яА-Я\s]+', '', text.lower().strip()) - - -def predict_by_model(Title, FullDescription, model, embedding_model): - title, full_description = normilize_text(Title), normilize_text(FullDescription) - temp = np.array((*embedding_model.encode(title), *embedding_model.encode(full_description))) - return model.predict(temp.reshape((1, COUNT * 2)))[0] - - -@st.cache(allow_output_mutation=True) -def load_models(): - embedding_model = SentenceTransformer('all-mpnet-base-v2') - with open("pickle_model_final.pkl", 'rb') as file: - pickle_model = pickle.load(file) - with open("pickle_model_try20.pkl", 'rb') as file: - pickle_model20 = pickle.load(file) - return embedding_model, pickle_model, pickle_model20 - - -def pd_to_emb_pd(pd, embedding_model): # подразумевается, что нужные два столбца идут первыми - title_emb, descr_emb = embedding_model.encode(pd["Title"]), embedding_model.encode(pd['FullDescription']) - return title_emb, descr_emb - - -def GetResultV(description, text, model, embedding_model): - prediction = predict_by_model(description, text, model, embedding_model) - return int((prediction // 500) * 500) - - -def GetResultN(title, description, category, contract_type, contract_time, model, embedding_model): - title123 = [] - for i in range(COUNT): - title123.append('Title' + str(i)) - - descr123 = [] - for i in range(COUNT): - descr123.append('Descr' + str(i)) - - d = {'Title': [normilize_text(title)], - 'FullDescription': [normilize_text(description)], - 'ContractTime' + '_contract': [contract_time == 'contract'], - 'ContractTime' + '_permanent': [contract_time == 'permanent'], - 'ContractTime' + '_unknown': [contract_time != 'contract' and contract_time != 'permanent'], - 'ContractType' + '_full_time': [contract_type == 'full_time'], - 'ContractType' + '_part_time': [contract_type == 'part_time'], - 'ContractType' + '_unknown': [contract_type != 'full_time' and contract_time != 'part_time'], - } - for i in categories: - d[i] = 0 - d[category] = 1 - - valid = pd.DataFrame.from_dict(d) - title_emb, descr_emb = pd_to_emb_pd(valid, embedding_model) - valid = valid.join(pd.DataFrame(title_emb, columns=title123)).join(pd.DataFrame(descr_emb, columns=descr123)) - del valid['Title'] - del valid['FullDescription'] - - predict = model.predict(valid) - return int((predict // 500) * 500) - - -def main(): - embedding_model, pickle_model, pickle_model20 = load_models() - title = st.text_input('Введите название вакансии') - description = st.text_area('Введите описание вакансии') - category = st.selectbox('Введите категорию вакансии', ['', *categories]) - contractType = st.selectbox('Введите тип занятости', ['part_time', 'full_time', 'other']) - contractTime = st.selectbox('Введите тип договора', ['contract', 'permanent', 'other']) - - if st.button('Предсказать зарплату'): - if description != '' and title != '' and category != '': - if len(description) < 50 or len(title) < 10: - st.error('Поле с названием и описанием вакансии имеют слишком короткие значения') - else: - result = (GetResultN(title, description, category, contractType, contractTime, pickle_model20, embedding_model) +\ - GetResultV(description, category, pickle_model, embedding_model)) / 2. - st.write('Результат: ' + str(result)) - else: - st.error('Поле с названием, описанием вакансии и категорией должны быть обязательно заполнены') - else: - st.write('Результат:') - if description != '' and title != '': - st.warning('Для получения предсказания нажмите кнопку "Предсказать зарплату"') - else: - st.warning('Введите название и описание вакансии') - - -if __name__ == "__main__": - main() diff --git a/spaces/Nee001/bing0/src/components/ui/dropdown-menu.tsx b/spaces/Nee001/bing0/src/components/ui/dropdown-menu.tsx deleted file mode 100644 index 184d4e6007ef85187446362f69532ab077897fea..0000000000000000000000000000000000000000 --- a/spaces/Nee001/bing0/src/components/ui/dropdown-menu.tsx +++ /dev/null @@ -1,128 +0,0 @@ -'use client' - -import * as React from 'react' -import * as DropdownMenuPrimitive from '@radix-ui/react-dropdown-menu' - -import { cn } from '@/lib/utils' - -const DropdownMenu = DropdownMenuPrimitive.Root - -const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger - -const DropdownMenuGroup = DropdownMenuPrimitive.Group - -const DropdownMenuPortal = DropdownMenuPrimitive.Portal - -const DropdownMenuSub = DropdownMenuPrimitive.Sub - -const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup - -const DropdownMenuSubContent = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -DropdownMenuSubContent.displayName = - DropdownMenuPrimitive.SubContent.displayName - -const DropdownMenuContent = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, sideOffset = 4, ...props }, ref) => ( - - -)) -DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName - -const DropdownMenuItem = React.forwardRef< - React.ElementRef- , - React.ComponentPropsWithoutRef & { - inset?: boolean - } ->(({ className, inset, ...props }, ref) => ( - -)) -DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName - -const DropdownMenuLabel = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef & { - inset?: boolean - } ->(({ className, inset, ...props }, ref) => ( - -)) -DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName - -const DropdownMenuSeparator = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName - -const DropdownMenuShortcut = ({ - className, - ...props -}: React.HTMLAttributes ) => { - return ( - - ) -} -DropdownMenuShortcut.displayName = 'DropdownMenuShortcut' - -export { - DropdownMenu, - DropdownMenuTrigger, - DropdownMenuContent, - DropdownMenuItem, - DropdownMenuLabel, - DropdownMenuSeparator, - DropdownMenuShortcut, - DropdownMenuGroup, - DropdownMenuPortal, - DropdownMenuSub, - DropdownMenuSubContent, - DropdownMenuRadioGroup -} diff --git a/spaces/Nepmods/kawaiiAI/README.md b/spaces/Nepmods/kawaiiAI/README.md deleted file mode 100644 index 9f475824e5bb0693b756d96a3056f9e14afc04a4..0000000000000000000000000000000000000000 --- a/spaces/Nepmods/kawaiiAI/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: KawaiiAI -emoji: 📉 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.19.1 -app_file: app.py -pinned: false -license: other ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/OAOA/DifFace/basicsr/models/sr_model.py b/spaces/OAOA/DifFace/basicsr/models/sr_model.py deleted file mode 100644 index 787f1fd2eab5963579c764c1bfb87199b7dd196f..0000000000000000000000000000000000000000 --- a/spaces/OAOA/DifFace/basicsr/models/sr_model.py +++ /dev/null @@ -1,279 +0,0 @@ -import torch -from collections import OrderedDict -from os import path as osp -from tqdm import tqdm - -from basicsr.archs import build_network -from basicsr.losses import build_loss -from basicsr.metrics import calculate_metric -from basicsr.utils import get_root_logger, imwrite, tensor2img -from basicsr.utils.registry import MODEL_REGISTRY -from .base_model import BaseModel - - -@MODEL_REGISTRY.register() -class SRModel(BaseModel): - """Base SR model for single image super-resolution.""" - - def __init__(self, opt): - super(SRModel, self).__init__(opt) - - # define network - self.net_g = build_network(opt['network_g']) - self.net_g = self.model_to_device(self.net_g) - self.print_network(self.net_g) - - # load pretrained models - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - param_key = self.opt['path'].get('param_key_g', 'params') - self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) - - if self.is_train: - self.init_training_settings() - - def init_training_settings(self): - self.net_g.train() - train_opt = self.opt['train'] - - self.ema_decay = train_opt.get('ema_decay', 0) - if self.ema_decay > 0: - logger = get_root_logger() - logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') - # define network net_g with Exponential Moving Average (EMA) - # net_g_ema is used only for testing on one GPU and saving - # There is no need to wrap with DistributedDataParallel - self.net_g_ema = build_network(self.opt['network_g']).to(self.device) - # load pretrained model - load_path = self.opt['path'].get('pretrain_network_g', None) - if load_path is not None: - self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') - else: - self.model_ema(0) # copy net_g weight - self.net_g_ema.eval() - - # define losses - if train_opt.get('pixel_opt'): - self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) - else: - self.cri_pix = None - - if train_opt.get('perceptual_opt'): - self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) - else: - self.cri_perceptual = None - - if self.cri_pix is None and self.cri_perceptual is None: - raise ValueError('Both pixel and perceptual losses are None.') - - # set up optimizers and schedulers - self.setup_optimizers() - self.setup_schedulers() - - def setup_optimizers(self): - train_opt = self.opt['train'] - optim_params = [] - for k, v in self.net_g.named_parameters(): - if v.requires_grad: - optim_params.append(v) - else: - logger = get_root_logger() - logger.warning(f'Params {k} will not be optimized.') - - optim_type = train_opt['optim_g'].pop('type') - self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) - self.optimizers.append(self.optimizer_g) - - def feed_data(self, data): - self.lq = data['lq'].to(self.device) - if 'gt' in data: - self.gt = data['gt'].to(self.device) - - def optimize_parameters(self, current_iter): - self.optimizer_g.zero_grad() - self.output = self.net_g(self.lq) - - l_total = 0 - loss_dict = OrderedDict() - # pixel loss - if self.cri_pix: - l_pix = self.cri_pix(self.output, self.gt) - l_total += l_pix - loss_dict['l_pix'] = l_pix - # perceptual loss - if self.cri_perceptual: - l_percep, l_style = self.cri_perceptual(self.output, self.gt) - if l_percep is not None: - l_total += l_percep - loss_dict['l_percep'] = l_percep - if l_style is not None: - l_total += l_style - loss_dict['l_style'] = l_style - - l_total.backward() - self.optimizer_g.step() - - self.log_dict = self.reduce_loss_dict(loss_dict) - - if self.ema_decay > 0: - self.model_ema(decay=self.ema_decay) - - def test(self): - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - self.output = self.net_g_ema(self.lq) - else: - self.net_g.eval() - with torch.no_grad(): - self.output = self.net_g(self.lq) - self.net_g.train() - - def test_selfensemble(self): - # TODO: to be tested - # 8 augmentations - # modified from https://github.com/thstkdgus35/EDSR-PyTorch - - def _transform(v, op): - # if self.precision != 'single': v = v.float() - v2np = v.data.cpu().numpy() - if op == 'v': - tfnp = v2np[:, :, :, ::-1].copy() - elif op == 'h': - tfnp = v2np[:, :, ::-1, :].copy() - elif op == 't': - tfnp = v2np.transpose((0, 1, 3, 2)).copy() - - ret = torch.Tensor(tfnp).to(self.device) - # if self.precision == 'half': ret = ret.half() - - return ret - - # prepare augmented data - lq_list = [self.lq] - for tf in 'v', 'h', 't': - lq_list.extend([_transform(t, tf) for t in lq_list]) - - # inference - if hasattr(self, 'net_g_ema'): - self.net_g_ema.eval() - with torch.no_grad(): - out_list = [self.net_g_ema(aug) for aug in lq_list] - else: - self.net_g.eval() - with torch.no_grad(): - out_list = [self.net_g_ema(aug) for aug in lq_list] - self.net_g.train() - - # merge results - for i in range(len(out_list)): - if i > 3: - out_list[i] = _transform(out_list[i], 't') - if i % 4 > 1: - out_list[i] = _transform(out_list[i], 'h') - if (i % 4) % 2 == 1: - out_list[i] = _transform(out_list[i], 'v') - output = torch.cat(out_list, dim=0) - - self.output = output.mean(dim=0, keepdim=True) - - def dist_validation(self, dataloader, current_iter, tb_logger, save_img): - if self.opt['rank'] == 0: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): - dataset_name = dataloader.dataset.opt['name'] - with_metrics = self.opt['val'].get('metrics') is not None - use_pbar = self.opt['val'].get('pbar', False) - - if with_metrics: - if not hasattr(self, 'metric_results'): # only execute in the first run - self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} - # initialize the best metric results for each dataset_name (supporting multiple validation datasets) - self._initialize_best_metric_results(dataset_name) - # zero self.metric_results - if with_metrics: - self.metric_results = {metric: 0 for metric in self.metric_results} - - metric_data = dict() - if use_pbar: - pbar = tqdm(total=len(dataloader), unit='image') - - for idx, val_data in enumerate(dataloader): - img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] - self.feed_data(val_data) - self.test() - - visuals = self.get_current_visuals() - sr_img = tensor2img([visuals['result']]) - metric_data['img'] = sr_img - if 'gt' in visuals: - gt_img = tensor2img([visuals['gt']]) - metric_data['img2'] = gt_img - del self.gt - - # tentative for out of GPU memory - del self.lq - del self.output - torch.cuda.empty_cache() - - if save_img: - if self.opt['is_train']: - save_img_path = osp.join(self.opt['path']['visualization'], img_name, - f'{img_name}_{current_iter}.png') - else: - if self.opt['val']['suffix']: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["val"]["suffix"]}.png') - else: - save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, - f'{img_name}_{self.opt["name"]}.png') - imwrite(sr_img, save_img_path) - - if with_metrics: - # calculate metrics - for name, opt_ in self.opt['val']['metrics'].items(): - self.metric_results[name] += calculate_metric(metric_data, opt_) - if use_pbar: - pbar.update(1) - pbar.set_description(f'Test {img_name}') - if use_pbar: - pbar.close() - - if with_metrics: - for metric in self.metric_results.keys(): - self.metric_results[metric] /= (idx + 1) - # update the best metric result - self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) - - self._log_validation_metric_values(current_iter, dataset_name, tb_logger) - - def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): - log_str = f'Validation {dataset_name}\n' - for metric, value in self.metric_results.items(): - log_str += f'\t # {metric}: {value:.4f}' - if hasattr(self, 'best_metric_results'): - log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' - f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') - log_str += '\n' - - logger = get_root_logger() - logger.info(log_str) - if tb_logger: - for metric, value in self.metric_results.items(): - tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) - - def get_current_visuals(self): - out_dict = OrderedDict() - out_dict['lq'] = self.lq.detach().cpu() - out_dict['result'] = self.output.detach().cpu() - if hasattr(self, 'gt'): - out_dict['gt'] = self.gt.detach().cpu() - return out_dict - - def save(self, epoch, current_iter): - if hasattr(self, 'net_g_ema'): - self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) - else: - self.save_network(self.net_g, 'net_g', current_iter) - self.save_training_state(epoch, current_iter) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/linformer/linformer_src/modules/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/linformer/linformer_src/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/language_pair_dataset.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/language_pair_dataset.py deleted file mode 100644 index ff3e14bf14770638524ef6067b558e455dbe5f2b..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/language_pair_dataset.py +++ /dev/null @@ -1,471 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging - -import numpy as np -import torch -from fairseq.data import FairseqDataset, data_utils - - -logger = logging.getLogger(__name__) - - -def collate( - samples, - pad_idx, - eos_idx, - left_pad_source=True, - left_pad_target=False, - input_feeding=True, - pad_to_length=None, - pad_to_multiple=1, -): - if len(samples) == 0: - return {} - - def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None): - return data_utils.collate_tokens( - [s[key] for s in samples], - pad_idx, - eos_idx, - left_pad, - move_eos_to_beginning, - pad_to_length=pad_to_length, - pad_to_multiple=pad_to_multiple, - ) - - def check_alignment(alignment, src_len, tgt_len): - if alignment is None or len(alignment) == 0: - return False - if ( - alignment[:, 0].max().item() >= src_len - 1 - or alignment[:, 1].max().item() >= tgt_len - 1 - ): - logger.warning("alignment size mismatch found, skipping alignment!") - return False - return True - - def compute_alignment_weights(alignments): - """ - Given a tensor of shape [:, 2] containing the source-target indices - corresponding to the alignments, a weight vector containing the - inverse frequency of each target index is computed. - For e.g. if alignments = [[5, 7], [2, 3], [1, 3], [4, 2]], then - a tensor containing [1., 0.5, 0.5, 1] should be returned (since target - index 3 is repeated twice) - """ - align_tgt = alignments[:, 1] - _, align_tgt_i, align_tgt_c = torch.unique( - align_tgt, return_inverse=True, return_counts=True - ) - align_weights = align_tgt_c[align_tgt_i[np.arange(len(align_tgt))]] - return 1.0 / align_weights.float() - - id = torch.LongTensor([s["id"] for s in samples]) - src_tokens = merge( - "source", - left_pad=left_pad_source, - pad_to_length=pad_to_length["source"] if pad_to_length is not None else None, - ) - # sort by descending source length - src_lengths = torch.LongTensor( - [s["source"].ne(pad_idx).long().sum() for s in samples] - ) - src_lengths, sort_order = src_lengths.sort(descending=True) - id = id.index_select(0, sort_order) - src_tokens = src_tokens.index_select(0, sort_order) - - prev_output_tokens = None - target = None - if samples[0].get("target", None) is not None: - target = merge( - "target", - left_pad=left_pad_target, - pad_to_length=pad_to_length["target"] - if pad_to_length is not None - else None, - ) - target = target.index_select(0, sort_order) - tgt_lengths = torch.LongTensor( - [s["target"].ne(pad_idx).long().sum() for s in samples] - ).index_select(0, sort_order) - ntokens = tgt_lengths.sum().item() - - if samples[0].get("prev_output_tokens", None) is not None: - prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target) - elif input_feeding: - # we create a shifted version of targets for feeding the - # previous output token(s) into the next decoder step - prev_output_tokens = merge( - "target", - left_pad=left_pad_target, - move_eos_to_beginning=True, - pad_to_length=pad_to_length["target"] - if pad_to_length is not None - else None, - ) - else: - ntokens = src_lengths.sum().item() - - batch = { - "id": id, - "nsentences": len(samples), - "ntokens": ntokens, - "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths,}, - "target": target, - } - if prev_output_tokens is not None: - batch["net_input"]["prev_output_tokens"] = prev_output_tokens.index_select( - 0, sort_order - ) - - if samples[0].get("alignment", None) is not None: - bsz, tgt_sz = batch["target"].shape - src_sz = batch["net_input"]["src_tokens"].shape[1] - - offsets = torch.zeros((len(sort_order), 2), dtype=torch.long) - offsets[:, 1] += torch.arange(len(sort_order), dtype=torch.long) * tgt_sz - if left_pad_source: - offsets[:, 0] += src_sz - src_lengths - if left_pad_target: - offsets[:, 1] += tgt_sz - tgt_lengths - - alignments = [ - alignment + offset - for align_idx, offset, src_len, tgt_len in zip( - sort_order, offsets, src_lengths, tgt_lengths - ) - for alignment in [samples[align_idx]["alignment"].view(-1, 2)] - if check_alignment(alignment, src_len, tgt_len) - ] - - if len(alignments) > 0: - alignments = torch.cat(alignments, dim=0) - align_weights = compute_alignment_weights(alignments) - - batch["alignments"] = alignments - batch["align_weights"] = align_weights - - if samples[0].get("constraints", None) is not None: - # Collate the packed constraints across the samples, padding to - # the length of the longest sample. - lens = [sample.get("constraints").size(0) for sample in samples] - max_len = max(lens) - constraints = torch.zeros((len(samples), max(lens))).long() - for i, sample in enumerate(samples): - constraints[i, 0 : lens[i]] = samples[i].get("constraints") - batch["constraints"] = constraints.index_select(0, sort_order) - - return batch - - -class LanguagePairDataset(FairseqDataset): - """ - A pair of torch.utils.data.Datasets. - - Args: - src (torch.utils.data.Dataset): source dataset to wrap - src_sizes (List[int]): source sentence lengths - src_dict (~fairseq.data.Dictionary): source vocabulary - tgt (torch.utils.data.Dataset, optional): target dataset to wrap - tgt_sizes (List[int], optional): target sentence lengths - tgt_dict (~fairseq.data.Dictionary, optional): target vocabulary - left_pad_source (bool, optional): pad source tensors on the left side - (default: True). - left_pad_target (bool, optional): pad target tensors on the left side - (default: False). - shuffle (bool, optional): shuffle dataset elements before batching - (default: True). - input_feeding (bool, optional): create a shifted version of the targets - to be passed into the model for teacher forcing (default: True). - remove_eos_from_source (bool, optional): if set, removes eos from end - of source if it's present (default: False). - append_eos_to_target (bool, optional): if set, appends eos to end of - target if it's absent (default: False). - align_dataset (torch.utils.data.Dataset, optional): dataset - containing alignments. - constraints (Tensor, optional): 2d tensor with a concatenated, zero- - delimited list of constraints for each sentence. - append_bos (bool, optional): if set, appends bos to the beginning of - source/target sentence. - num_buckets (int, optional): if set to a value greater than 0, then - batches will be bucketed into the given number of batch shapes. - src_lang_id (int, optional): source language ID, if set, the collated batch - will contain a field 'src_lang_id' in 'net_input' which indicates the - source language of the samples. - tgt_lang_id (int, optional): target language ID, if set, the collated batch - will contain a field 'tgt_lang_id' which indicates the target language - of the samples. - """ - - def __init__( - self, - src, - src_sizes, - src_dict, - tgt=None, - tgt_sizes=None, - tgt_dict=None, - left_pad_source=True, - left_pad_target=False, - shuffle=True, - input_feeding=True, - remove_eos_from_source=False, - append_eos_to_target=False, - align_dataset=None, - constraints=None, - append_bos=False, - eos=None, - num_buckets=0, - src_lang_id=None, - tgt_lang_id=None, - pad_to_multiple=1, - ): - if tgt_dict is not None: - assert src_dict.pad() == tgt_dict.pad() - assert src_dict.eos() == tgt_dict.eos() - assert src_dict.unk() == tgt_dict.unk() - if tgt is not None: - assert len(src) == len( - tgt - ), "Source and target must contain the same number of examples" - self.src = src - self.tgt = tgt - self.src_sizes = np.array(src_sizes) - self.tgt_sizes = np.array(tgt_sizes) if tgt_sizes is not None else None - self.sizes = ( - np.vstack((self.src_sizes, self.tgt_sizes)).T - if self.tgt_sizes is not None - else self.src_sizes - ) - self.src_dict = src_dict - self.tgt_dict = tgt_dict - self.left_pad_source = left_pad_source - self.left_pad_target = left_pad_target - self.shuffle = shuffle - self.input_feeding = input_feeding - self.remove_eos_from_source = remove_eos_from_source - self.append_eos_to_target = append_eos_to_target - self.align_dataset = align_dataset - if self.align_dataset is not None: - assert ( - self.tgt_sizes is not None - ), "Both source and target needed when alignments are provided" - self.constraints = constraints - self.append_bos = append_bos - self.eos = eos if eos is not None else src_dict.eos() - self.src_lang_id = src_lang_id - self.tgt_lang_id = tgt_lang_id - if num_buckets > 0: - from fairseq.data import BucketPadLengthDataset - - self.src = BucketPadLengthDataset( - self.src, - sizes=self.src_sizes, - num_buckets=num_buckets, - pad_idx=self.src_dict.pad(), - left_pad=self.left_pad_source, - ) - self.src_sizes = self.src.sizes - logger.info("bucketing source lengths: {}".format(list(self.src.buckets))) - if self.tgt is not None: - self.tgt = BucketPadLengthDataset( - self.tgt, - sizes=self.tgt_sizes, - num_buckets=num_buckets, - pad_idx=self.tgt_dict.pad(), - left_pad=self.left_pad_target, - ) - self.tgt_sizes = self.tgt.sizes - logger.info( - "bucketing target lengths: {}".format(list(self.tgt.buckets)) - ) - - # determine bucket sizes using self.num_tokens, which will return - # the padded lengths (thanks to BucketPadLengthDataset) - num_tokens = np.vectorize(self.num_tokens, otypes=[np.compat.long]) - self.bucketed_num_tokens = num_tokens(np.arange(len(self.src))) - self.buckets = [ - (None, num_tokens) for num_tokens in np.unique(self.bucketed_num_tokens) - ] - else: - self.buckets = None - self.pad_to_multiple = pad_to_multiple - - def get_batch_shapes(self): - return self.buckets - - def __getitem__(self, index): - tgt_item = self.tgt[index] if self.tgt is not None else None - src_item = self.src[index] - # Append EOS to end of tgt sentence if it does not have an EOS and remove - # EOS from end of src sentence if it exists. This is useful when we use - # use existing datasets for opposite directions i.e., when we want to - # use tgt_dataset as src_dataset and vice versa - if self.append_eos_to_target: - eos = self.tgt_dict.eos() if self.tgt_dict else self.src_dict.eos() - if self.tgt and self.tgt[index][-1] != eos: - tgt_item = torch.cat([self.tgt[index], torch.LongTensor([eos])]) - - if self.append_bos: - bos = self.tgt_dict.bos() if self.tgt_dict else self.src_dict.bos() - if self.tgt and self.tgt[index][0] != bos: - tgt_item = torch.cat([torch.LongTensor([bos]), self.tgt[index]]) - - bos = self.src_dict.bos() - if self.src[index][0] != bos: - src_item = torch.cat([torch.LongTensor([bos]), self.src[index]]) - - if self.remove_eos_from_source: - eos = self.src_dict.eos() - if self.src[index][-1] == eos: - src_item = self.src[index][:-1] - - example = { - "id": index, - "source": src_item, - "target": tgt_item, - } - if self.align_dataset is not None: - example["alignment"] = self.align_dataset[index] - if self.constraints is not None: - example["constraints"] = self.constraints[index] - return example - - def __len__(self): - return len(self.src) - - def collater(self, samples, pad_to_length=None): - """Merge a list of samples to form a mini-batch. - - Args: - samples (List[dict]): samples to collate - pad_to_length (dict, optional): a dictionary of - {'source': source_pad_to_length, 'target': target_pad_to_length} - to indicate the max length to pad to in source and target respectively. - - Returns: - dict: a mini-batch with the following keys: - - - `id` (LongTensor): example IDs in the original input order - - `ntokens` (int): total number of tokens in the batch - - `net_input` (dict): the input to the Model, containing keys: - - - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in - the source sentence of shape `(bsz, src_len)`. Padding will - appear on the left if *left_pad_source* is ``True``. - - `src_lengths` (LongTensor): 1D Tensor of the unpadded - lengths of each source sentence of shape `(bsz)` - - `prev_output_tokens` (LongTensor): a padded 2D Tensor of - tokens in the target sentence, shifted right by one - position for teacher forcing, of shape `(bsz, tgt_len)`. - This key will not be present if *input_feeding* is - ``False``. Padding will appear on the left if - *left_pad_target* is ``True``. - - `src_lang_id` (LongTensor): a long Tensor which contains source - language IDs of each sample in the batch - - - `target` (LongTensor): a padded 2D Tensor of tokens in the - target sentence of shape `(bsz, tgt_len)`. Padding will appear - on the left if *left_pad_target* is ``True``. - - `tgt_lang_id` (LongTensor): a long Tensor which contains target language - IDs of each sample in the batch - """ - res = collate( - samples, - pad_idx=self.src_dict.pad(), - eos_idx=self.eos, - left_pad_source=self.left_pad_source, - left_pad_target=self.left_pad_target, - input_feeding=self.input_feeding, - pad_to_length=pad_to_length, - pad_to_multiple=self.pad_to_multiple, - ) - if self.src_lang_id is not None or self.tgt_lang_id is not None: - src_tokens = res["net_input"]["src_tokens"] - bsz = src_tokens.size(0) - if self.src_lang_id is not None: - res["net_input"]["src_lang_id"] = ( - torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens) - ) - if self.tgt_lang_id is not None: - res["tgt_lang_id"] = ( - torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens) - ) - return res - - def num_tokens(self, index): - """Return the number of tokens in a sample. This value is used to - enforce ``--max-tokens`` during batching.""" - return max( - self.src_sizes[index], - self.tgt_sizes[index] if self.tgt_sizes is not None else 0, - ) - - def num_tokens_vec(self, indices): - """Return the number of tokens for a set of positions defined by indices. - This value is used to enforce ``--max-tokens`` during batching.""" - sizes = self.src_sizes[indices] - if self.tgt_sizes is not None: - sizes = np.maximum(sizes, self.tgt_sizes[indices]) - return sizes - - def size(self, index): - """Return an example's size as a float or tuple. This value is used when - filtering a dataset with ``--max-positions``.""" - return ( - self.src_sizes[index], - self.tgt_sizes[index] if self.tgt_sizes is not None else 0, - ) - - def ordered_indices(self): - """Return an ordered list of indices. Batches will be constructed based - on this order.""" - if self.shuffle: - indices = np.random.permutation(len(self)).astype(np.int64) - else: - indices = np.arange(len(self), dtype=np.int64) - if self.buckets is None: - # sort by target length, then source length - if self.tgt_sizes is not None: - indices = indices[np.argsort(self.tgt_sizes[indices], kind="mergesort")] - return indices[np.argsort(self.src_sizes[indices], kind="mergesort")] - else: - # sort by bucketed_num_tokens, which is: - # max(padded_src_len, padded_tgt_len) - return indices[ - np.argsort(self.bucketed_num_tokens[indices], kind="mergesort") - ] - - @property - def supports_prefetch(self): - return getattr(self.src, "supports_prefetch", False) and ( - getattr(self.tgt, "supports_prefetch", False) or self.tgt is None - ) - - def prefetch(self, indices): - self.src.prefetch(indices) - if self.tgt is not None: - self.tgt.prefetch(indices) - if self.align_dataset is not None: - self.align_dataset.prefetch(indices) - - def filter_indices_by_size(self, indices, max_sizes): - """Filter a list of sample indices. Remove those that are longer - than specified in max_sizes. - - Args: - indices (np.array): original array of sample indices - max_sizes (int or list[int] or tuple[int]): max sample size, - can be defined separately for src and tgt (then list or tuple) - - Returns: - np.array: filtered sample array - list: list of removed indices - """ - return data_utils.filter_paired_dataset_indices_by_size( - self.src_sizes, self.tgt_sizes, indices, max_sizes, - ) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/ema/ema.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/ema/ema.py deleted file mode 100644 index 010b60ba2fd766340d2c5b8ba96f9e57c6fe25b5..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/ema/ema.py +++ /dev/null @@ -1,200 +0,0 @@ -#!/usr/bin/env python3 - -""" -This module has the EMA class used to store a copy of the exponentially decayed -model params. - -Typical usage of EMA class involves initializing an object using an existing -model (random or from a seed model) and setting the config like ema_decay, -ema_start_update which determine how the EMA model is updated. After every -update of the model i.e. at the end of the train_step, the EMA should be updated -by passing the new model to the EMA.step function. The EMA model state dict -can be stored in the extra state under the key of "ema" and dumped -into a checkpoint and loaded. The EMA object can be passed to tasks -by setting task.uses_ema property. -EMA is a smoothed/ensemble model which might have better performance -when used for inference or further fine-tuning. EMA class has a -reverse function to load the EMA params into a model and use it -like a regular model. -""" - -import copy -import logging - -import torch -from fairseq import checkpoint_utils - - -class EMA(object): - """Exponential Moving Average of Fairseq Models - EMA keeps a copy of the exponentially decayed model params. - The set of params should include both gradient-descent and - non-gradient descent params, such as batch mean/var and buffers. - This is a modified implementation of - the open source code in https://github.com/zhawe01/fairseq-gec.git, - and internal source code in - fbcode/mobile-vision/projects/classification_pytorch/lib/utils/model_ema.py. - - Similar to TF EMA. - https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage. - EMA provides a averaged and smoothed set of model weights, and has been shown to - improve vision models. EMA class does all necessary functions to update, reload, - or init EMA methods. - - EMA object is initialized from an arbitrary model. By default, it is stored in - the same device (unless device specified at initialization) and with the - same precision as the model (unless ema_fp32 is True). ema_fp32 is recommended. - This stores the EMA parameters in fp32 only for the EMA update step, and - is used at the default precision otherwise. - EMA is usually enabled using EMAConfig with store_ema=True. Some important - parameters to configure EMA are - 1) ema_decay - The decay of EMA - 2) ema_update_freq - EMA is updated every this many model updates. - 3) ema_start_update - Start EMA update after this many model updates [default 0] - - Key methods: - 1) step - One update of EMA using new model - 2) restore - Update EMA from a state dict - 3) reverse - Load EMA into a model - 4) get_decay, _set_decay - Used to get or set the decay. Note _set_decay is - called from step. - 5) build_fp32_params - Used to initialize or update the fp32 copy of EMA params. - Note this is enabled only when ema_fp32=True - """ - - def __init__(self, model, config, device=None): - """ - @param model model to initialize the EMA with - @param config EMAConfig object with configuration like - ema_decay, ema_update_freq, ema_fp32 - @param device If provided, copy EMA to this device (e.g. gpu). - Otherwise EMA is in the same device as the model. - """ - - self.decay = config.ema_decay - self.model = copy.deepcopy(model) - self.model.requires_grad_(False) - self.config = config - self.fp32_params = {} - - if self.config.ema_seed_model is not None: - state = checkpoint_utils.load_ema_from_checkpoint(self.config.ema_seed_model) - self.model.load_state_dict(state["model"], strict=True) - - if device is not None: - logging.info(f"Copying EMA model to device {device}") - self.model = self.model.to(device=device) - - if self.config.ema_fp32: - self.build_fp32_params() - - self.update_freq_counter = 0 - - def get_model(self): - return self.model - - def build_fp32_params(self, state_dict=None): - """ - Store a copy of the EMA params in fp32. - If state dict is passed, the EMA params is copied from - the provided state dict. Otherwise, it is copied from the - current EMA model parameters. - """ - if not self.config.ema_fp32: - raise RuntimeError( - "build_fp32_params should not be called if ema_fp32=False. " - "Use ema_fp32=True if this is really intended." - ) - - if state_dict is None: - state_dict = self.model.state_dict() - - def _to_float(t): - return t.float() if torch.is_floating_point(t) else t - - # for non-float params (like registered symbols), they are copied into this dict and covered in each update - for param_key in state_dict: - if param_key in self.fp32_params: - self.fp32_params[param_key].copy_(state_dict[param_key]) - else: - self.fp32_params[param_key] = _to_float(state_dict[param_key]) - - def restore(self, state_dict, build_fp32_params=False): - """ Load data from a model spec into EMA model """ - self.model.load_state_dict(state_dict, strict=False) - if build_fp32_params: - self.build_fp32_params(state_dict) - - def _set_decay(self, decay): - self.decay = decay - - def get_decay(self): - return self.decay - - def _step_internal(self, new_model, updates=None): - """ One update of the EMA model based on new model weights """ - decay = self.decay - - ema_state_dict = {} - ema_params = self.fp32_params if self.config.ema_fp32 else self.model.state_dict() - for key, param in new_model.state_dict().items(): - try: - ema_param = ema_params[key] - except KeyError: - ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param) - - if param.shape != ema_param.shape: - raise ValueError( - "incompatible tensor shapes between model param and ema param" - + "{} vs. {}".format(param.shape, ema_param.shape) - ) - if "version" in key: - # Do not decay a model.version pytorch param - continue - - # for non-float params (like registered symbols), they are covered in each update - if not torch.is_floating_point(ema_param): - if ema_param.dtype != param.dtype: - raise ValueError( - "incompatible tensor dtypes between model param and ema param" - + "{} vs. {}".format(param.dtype, ema_param.dtype) - ) - ema_param.copy_(param) - else: - ema_param.mul_(decay) - ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1-decay) - ema_state_dict[key] = ema_param - self.restore(ema_state_dict, build_fp32_params=False) - - def step(self, new_model, updates=None): - """ - One update of EMA which is done every self.config.ema_update_freq - updates of the model. - - @param updates The current number of model updates done. - Decay is set of 0 if model updates < ema_start_update, which means - the model will be simply copied over to the EMA. - When model updates >= ema_start_updates, then EMA is updated with - a decay of self.config.ema_decay. - """ - self._set_decay( - 0 - if updates is not None - and updates < self.config.ema_start_update - else self.config.ema_decay - ) - if updates is not None and self.config.ema_update_freq > 1: - self.update_freq_counter += 1 - if self.update_freq_counter >= self.config.ema_update_freq: - self._step_internal(new_model, updates) - self.update_freq_counter = 0 - else: - self._step_internal(new_model, updates) - - def reverse(self, model): - """ - Load the model parameters from EMA model. - Useful for inference or fine-tuning from the EMA model. - """ - model.load_state_dict(self.model.state_dict(), strict=False) - return model diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/sparse_multihead_attention.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/sparse_multihead_attention.py deleted file mode 100644 index 3cbd9d6785886e319aab0601517e27df733b6f97..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/sparse_multihead_attention.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch - -from .multihead_attention import MultiheadAttention - - -class SparseMultiheadAttention(MultiheadAttention): - """Sparse Multi-Headed Attention. - - "Generating Long Sequences with Sparse Transformers". Implements - fixed factorized self attention, where l=stride and c=expressivity. - A(1) includes all words in the stride window and A(2) takes a summary of c - words from the end of each stride window. - If is_bidirectional=False, we do not include any words past the current word, - as in the paper. - """ - - def __init__( - self, - embed_dim, - num_heads, - kdim=None, - vdim=None, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - self_attention=False, - encoder_decoder_attention=False, - stride=32, - expressivity=8, - is_bidirectional=True, - ): - - super().__init__( - embed_dim, - num_heads, - kdim, - vdim, - dropout, - bias, - add_bias_kv, - add_zero_attn, - self_attention, - encoder_decoder_attention, - ) - - self.is_bidirectional = is_bidirectional - self.stride = stride - self.expressivity = expressivity - assert self.stride > 0 and self.stride >= self.expressivity - - # Used for Ai(2) calculations - beginning of [l-c, l] range - def compute_checkpoint(self, word_index): - if word_index % self.stride == 0 and word_index != 0: - checkpoint_index = word_index - self.expressivity - else: - checkpoint_index = ( - math.floor(word_index / self.stride) * self.stride - + self.stride - - self.expressivity - ) - return checkpoint_index - - # Computes Ai(2) - def compute_subset_summaries(self, absolute_max): - checkpoint_index = self.compute_checkpoint(0) - subset_two = set() - while checkpoint_index <= absolute_max - 1: - summary = set( - range( - checkpoint_index, - min(checkpoint_index + self.expressivity + 1, absolute_max), - ) - ) - subset_two = subset_two.union(summary) - checkpoint_index = self.compute_checkpoint(checkpoint_index + self.stride) - return subset_two - - # Sparse Transformer Fixed Attention Pattern: https://arxiv.org/pdf/1904.10509.pdf - def compute_fixed_attention_subset(self, word_index, tgt_len): - # +1s account for range function; [min, max) -> [min, max] - if not self.is_bidirectional: - absolute_max = word_index + 1 - else: - absolute_max = tgt_len - - # Subset 1 - whole window - rounded_index = ( - math.floor((word_index + self.stride) / self.stride) * self.stride - ) - if word_index % self.stride == 0 and word_index != 0: - subset_one = set( - range(word_index - self.stride, min(absolute_max, word_index + 1)) - ) - else: - subset_one = set( - range( - max(0, rounded_index - self.stride), - min(absolute_max, rounded_index + 1), - ) - ) - - # Subset 2 - summary per window - # If bidirectional, subset 2 is the same for every index - subset_two = set() - if not self.is_bidirectional: - subset_two = self.compute_subset_summaries(absolute_max) - - return subset_one.union(subset_two) - - # Compute sparse mask - if bidirectional, can pre-compute and store - def buffered_sparse_mask(self, tensor, tgt_len, src_len): - assert tgt_len > self.stride - sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float("-inf")) - - # If bidirectional, subset 2 is the same for every index - subset_summaries = set() - if self.is_bidirectional: - subset_summaries = self.compute_subset_summaries(tgt_len) - - for i in range(tgt_len): - fixed_attention_subset = self.compute_fixed_attention_subset(i, tgt_len) - fixed_attention_subset = fixed_attention_subset.union(subset_summaries) - included_word_indices = torch.LongTensor(list(fixed_attention_subset)) - sparse_mask[i].index_fill_(0, included_word_indices, 0) - return sparse_mask.type_as(tensor) - - def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz): - sparse_mask = self.buffered_sparse_mask(attn_weights, tgt_len, src_len) - sparse_mask = sparse_mask.unsqueeze(0).expand( - bsz * self.num_heads, tgt_len, src_len - ) - attn_weights += sparse_mask diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/optim/shard.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/optim/shard.py deleted file mode 100644 index 9d7f2eb9e5de6086fe2435d432bde7521ebb8155..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/optim/shard.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Any, Dict - -from fairseq.distributed import utils - - -try: - from fairscale.optim import OSS - - _has_fairscale = True -except ImportError: - _has_fairscale = False - - -def shard_(optimizer, group): - if not _has_fairscale: - raise ImportError( - "\n\nPlease install the fairscale package:" "\n\n pip install fairscale" - ) - - class FairseqOSS(OSS): - @property - def disable_mem_eff_fp16_loading_hack(self): - return True - - def __getattr__(self, name): - if name.startswith("supports") and hasattr(self.optim, name): - return getattr(self.optim, name) - raise AttributeError( - "'FairseqOSS' object has no attribute {0!r}".format(name) - ) - - def broadcast_global_state_dict( - self, state_dict: Dict[str, Any] - ) -> Dict[str, Any]: - """ - Broadcasts the entire state_dict to all other ranks - each rank is responsible to load their own partition of data - """ - return utils.broadcast_object( - state_dict, - src_rank=0, - group=self.group, - ) - - torch_optimizer = optimizer.optimizer - optim_cls = type(torch_optimizer) - - optimizer.optimizer = FairseqOSS( - torch_optimizer.param_groups, - optim_cls, - group=group, - **optimizer.optimizer_config - ) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/resnet.py b/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/resnet.py deleted file mode 100644 index 9ad8ee87de4bb579d745ab8302a368ca1749a1fe..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/resnet.py +++ /dev/null @@ -1,225 +0,0 @@ -import torch -import torch.nn as nn - - -def drop_path(x, drop_prob: float = 0., training: bool = False): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, - the original name is misleading as 'Drop Connect' is a.sh different form of dropout in a.sh separate paper... - See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for - changing the layer and argument names to 'drop path' rather than mix DropConnect as a.sh layer name and use - 'survival rate' as the argument. - """ - if drop_prob == 0. or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - """ - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) - - -def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=dilation, groups=groups, bias=False, dilation=dilation) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution""" - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, - base_width=64, dilation=1, norm_layer=None): - super(BasicBlock, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - if groups != 1 or base_width != 64: - raise ValueError('BasicBlock only supports groups=1 and base_width=64') - if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") - # Both self.conv1 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = norm_layer(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = norm_layer(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - assert False - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) - # while original implementation places the stride at the first 1x1 convolution(self.conv1) - # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. - # This variant is also known as ResNet V1.5 and improves accuracy according to - # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. - - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, - base_width=64, dilation=1, norm_layer=None, drop_path_rate=0.0): - super(Bottleneck, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - width = int(planes * (base_width / 64.)) * groups - # Both self.conv2 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv1x1(inplanes, width) - self.bn1 = norm_layer(width) - self.conv2 = conv3x3(width, width, stride, groups, dilation) - self.bn2 = norm_layer(width) - self.conv3 = conv1x1(width, planes * self.expansion) - self.bn3 = norm_layer(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity() - - def forward(self, x): - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out = identity + self.drop_path(out) - out = self.relu(out) - - return out - - -class ResNet(nn.Module): - - def __init__(self, layers, zero_init_residual=False, - groups=1, width_per_group=64, replace_stride_with_dilation=None, - norm_layer=None, drop_path_rate=0.0): - super(ResNet, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - self._norm_layer = norm_layer - - self.inplanes = 64 - self.dilation = 1 - if replace_stride_with_dilation is None: - # each element in the tuple indicates if we should replace - # the 2x2 stride with a dilated convolution instead - replace_stride_with_dilation = [False, False, False] - if len(replace_stride_with_dilation) != 3: - raise ValueError("replace_stride_with_dilation should be None " - "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) - self.groups = groups - self.base_width = width_per_group - self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, - bias=False) - self.bn1 = norm_layer(self.inplanes) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(Bottleneck, 64, layers[0], drop_path_rate=drop_path_rate) - self.layer2 = self._make_layer(Bottleneck, 128, layers[1], stride=2, - dilate=replace_stride_with_dilation[0], drop_path_rate=drop_path_rate) - self.layer3 = self._make_layer(Bottleneck, 256, layers[2], stride=2, - dilate=replace_stride_with_dilation[1], drop_path_rate=drop_path_rate) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, (nn.SyncBatchNorm, nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - # Zero-initialize the last BN in each residual branch, - # so that the residual branch starts with zeros, and each residual block behaves like an identity. - # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 - if zero_init_residual: - for m in self.modules(): - if isinstance(m, Bottleneck): - nn.init.constant_(m.bn3.weight, 0) - elif isinstance(m, BasicBlock): - nn.init.constant_(m.bn2.weight, 0) - - def _make_layer(self, block, planes, blocks, stride=1, dilate=False, drop_path_rate=0.0): - norm_layer = self._norm_layer - downsample = None - previous_dilation = self.dilation - if dilate: - self.dilation *= stride - stride = 1 - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - conv1x1(self.inplanes, planes * block.expansion, stride), - norm_layer(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample, self.groups, - self.base_width, previous_dilation, norm_layer)) - self.inplanes = planes * block.expansion - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, blocks)] - for i in range(1, blocks): - layers.append(block(self.inplanes, planes, groups=self.groups, - base_width=self.base_width, dilation=self.dilation, - norm_layer=norm_layer, drop_path_rate=dpr[i])) - - return nn.Sequential(*layers) - - def _forward_impl(self, x): - # See note [TorchScript super()] - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - - return x - - def forward(self, x): - return self._forward_impl(x) \ No newline at end of file diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/model_parallel/models/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/model_parallel/models/__init__.py deleted file mode 100644 index 3532479e52a0e1f1ba204c6f5d51c71c98ee5df0..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/model_parallel/models/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import importlib -import os - - -# automatically import any Python files in the models/ directory -models_dir = os.path.dirname(__file__) -for file in os.listdir(models_dir): - path = os.path.join(models_dir, file) - if ( - not file.startswith("_") - and not file.startswith(".") - and (file.endswith(".py") or os.path.isdir(path)) - ): - model_name = file[: file.find(".py")] if file.endswith(".py") else file - module = importlib.import_module("fairseq.model_parallel.models." + model_name) diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/m2m_100/tokenizers/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/m2m_100/tokenizers/README.md deleted file mode 100644 index e116932bc80572f221cff6472a7b1eea7032925d..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/m2m_100/tokenizers/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# M2M-100 Tokenization - -We apply different tokenization strategies for different languages following the existing literature. Here we provide tok.sh a tokenizer that can be used to reproduce our results. - -To reproduce the results, follow these steps: - -``` -tgt_lang=... -reference_translation=... -cat generation_output | grep -P "^H" | sort -V | cut -f 3- | sh tok.sh $tgt_lang > hyp -cat $reference_translation |sh tok.sh $tgt_lang > ref -sacrebleu -tok 'none' ref < hyp -``` - -## Installation - -Tools needed for all the languages except Arabic can be installed by running install_dependencies.sh -If you want to evaluate Arabic models, please follow the instructions provided here: http://alt.qcri.org/tools/arabic-normalizer/ to install diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/quantization/pq/pq.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/quantization/pq/pq.py deleted file mode 100644 index eddc2eb34602403f10979f54cd23a45bc2f104d5..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/quantization/pq/pq.py +++ /dev/null @@ -1,128 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .em import EM, EmptyClusterResolveError - - -class PQ(EM): - """ - Quantizes the layer weights W with the standard Product Quantization - technique. This learns a codebook of codewords or centroids of size - block_size from W. For further reference on using PQ to quantize - neural networks, see "And the Bit Goes Down: Revisiting the Quantization - of Neural Networks", Stock et al., ICLR 2020. - - PQ is performed in two steps: - (1) The matrix W (weights or fully-connected or convolutional layer) - is reshaped to (block_size, -1). - - If W is fully-connected (2D), its columns are split into - blocks of size block_size. - - If W is convolutional (4D), its filters are split along the - spatial dimension. - (2) We apply the standard EM/k-means algorithm to the resulting reshaped matrix. - - Args: - - W: weight matrix to quantize of size (in_features x out_features) - - block_size: size of the blocks (subvectors) - - n_centroids: number of centroids - - n_iter: number of k-means iterations - - eps: for cluster reassignment when an empty cluster is found - - max_tentatives for cluster reassignment when an empty cluster is found - - verbose: print information after each iteration - - Remarks: - - block_size be compatible with the shape of W - """ - - def __init__( - self, - W, - block_size, - n_centroids=256, - n_iter=20, - eps=1e-6, - max_tentatives=30, - verbose=True, - ): - self.block_size = block_size - W_reshaped = self._reshape(W) - super(PQ, self).__init__( - W_reshaped, - n_centroids=n_centroids, - n_iter=n_iter, - eps=eps, - max_tentatives=max_tentatives, - verbose=verbose, - ) - - def _reshape(self, W): - """ - Reshapes the matrix W as expained in step (1). - """ - - # fully connected: by convention the weight has size out_features x in_features - if len(W.size()) == 2: - self.out_features, self.in_features = W.size() - assert ( - self.in_features % self.block_size == 0 - ), "Linear: n_blocks must be a multiple of in_features" - return ( - W.reshape(self.out_features, -1, self.block_size) - .permute(2, 1, 0) - .flatten(1, 2) - ) - - # convolutional: we reshape along the spatial dimension - elif len(W.size()) == 4: - self.out_channels, self.in_channels, self.k_h, self.k_w = W.size() - assert ( - self.in_channels * self.k_h * self.k_w - ) % self.block_size == 0, ( - "Conv2d: n_blocks must be a multiple of in_channels * k_h * k_w" - ) - return ( - W.reshape(self.out_channels, -1, self.block_size) - .permute(2, 1, 0) - .flatten(1, 2) - ) - # not implemented - else: - raise NotImplementedError(W.size()) - - def encode(self): - """ - Performs self.n_iter EM steps. - """ - - self.initialize_centroids() - for i in range(self.n_iter): - try: - self.step(i) - except EmptyClusterResolveError: - break - - def decode(self): - """ - Returns the encoded full weight matrix. Must be called after - the encode function. - """ - - # fully connected case - if "k_h" not in self.__dict__: - return ( - self.centroids[self.assignments] - .reshape(-1, self.out_features, self.block_size) - .permute(1, 0, 2) - .flatten(1, 2) - ) - - # convolutional case - else: - return ( - self.centroids[self.assignments] - .reshape(-1, self.out_channels, self.block_size) - .permute(1, 0, 2) - .reshape(self.out_channels, self.in_channels, self.k_h, self.k_w) - ) diff --git a/spaces/ORI-Muchim/MarinTTS/models.py b/spaces/ORI-Muchim/MarinTTS/models.py deleted file mode 100644 index fe004e94bbe9074ec736f14325268f4515a53420..0000000000000000000000000000000000000000 --- a/spaces/ORI-Muchim/MarinTTS/models.py +++ /dev/null @@ -1,540 +0,0 @@ -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules -import attentions -import monotonic_align - -from torch.nn import Conv1d, ConvTranspose1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from commons import init_weights, get_padding - - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]) - logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout): - super().__init__() - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - if self.n_vocab != 0: - self.emb = nn.Embedding(n_vocab, hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) - - self.encoder = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths): - if self.n_vocab != 0: - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return x, m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, - gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, - upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)), - k, u, padding=(k - u) // 2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, - upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, - gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - if use_sdp: - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - else: - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers > 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def forward(self, x, x_lengths, y, y_lengths, sid=None): - - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) - if self.n_speakers > 1: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - - with torch.no_grad(): - # negative cross-entropy - s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] - neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] - neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), - s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] - neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 - - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() - - w = attn.sum(2) - if self.use_sdp: - l_length = self.dp(x, x_mask, w, g=g) - l_length = l_length / torch.sum(x_mask) - else: - logw_ = torch.log(w + 1e-6) * x_mask - logw = self.dp(x, x_mask, g=g) - l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging - - # expand prior - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) - - z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) - o = self.dec(z_slice, g=g) - return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) - if self.n_speakers > 1: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - if self.use_sdp: - logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) - else: - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, - 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:, :, :max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) - - def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): - assert self.n_speakers > 1, "n_speakers have to be larger than 1." - g_src = self.emb_g(sid_src).unsqueeze(-1) - g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) - z_p = self.flow(z, y_mask, g=g_src) - z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) - o_hat = self.dec(z_hat * y_mask, g=g_tgt) - return o_hat, y_mask, (z, z_p, z_hat) diff --git a/spaces/OdiaGenAI/Olive_Farm/README.md b/spaces/OdiaGenAI/Olive_Farm/README.md deleted file mode 100644 index ab646bb4e2fb246ce9002aacf174c83ec6fb0e0a..0000000000000000000000000000000000000000 --- a/spaces/OdiaGenAI/Olive_Farm/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Olive Farm -emoji: 🐠 -colorFrom: indigo -colorTo: green -sdk: streamlit -sdk_version: 1.27.0 -app_file: app.py -pinned: false -license: cc-by-nc-sa-4.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/themes/yhd/index.tsx b/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/themes/yhd/index.tsx deleted file mode 100644 index 1f7a9c8574d739d958775a3ce916dc2111866f66..0000000000000000000000000000000000000000 --- a/spaces/OpenDILabCommunity/DI-sheep/DI-sheep/ui/src/themes/yhd/index.tsx +++ /dev/null @@ -1,26 +0,0 @@ -import React from 'react'; -import { Theme } from '../interface'; -import { DefaultSoundNames, defaultSounds } from '../default'; - -const imagesUrls = import.meta.glob('./images/*.png', { - import: 'default', - eager: true, -}); - -const yhds = Object.entries(imagesUrls).map(([key, value]) => ({ - name: key.slice(9, -4), - // eslint-disable-next-line @typescript-eslint/ban-ts-comment - // @ts-ignore - content: , -})); - -export const yhdTheme: Theme
= { - name: 'YHD', - icons: yhds.map(({ name, content }) => ({ - name, - content, - clickSound: 'button-click', - tripleSound: 'triple', - })), - sounds: defaultSounds, -}; diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/image_dense_captions.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/image_dense_captions.py deleted file mode 100644 index 527a72eb75b75d2d636c9ae4e9d6e04d0346044a..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/image_dense_captions.py +++ /dev/null @@ -1,83 +0,0 @@ -import argparse -import multiprocessing as mp -import os -import time -import cv2 -import tqdm -import sys - -from detectron2.config import get_cfg -from detectron2.data.detection_utils import read_image -from detectron2.utils.logger import setup_logger - -sys.path.insert(0, 'iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/') -from centernet.config import add_centernet_config -from ..grit_src.grit.config import add_grit_config - -from ..grit_src.grit.predictor import VisualizationDemo - - -# constants -WINDOW_NAME = "GRiT" - - -def dense_pred_to_caption(predictions): - boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None - object_description = predictions["instances"].pred_object_descriptions.data - new_caption = "" - for i in range(len(object_description)): - new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; " - return new_caption - -def dense_pred_to_caption_only_name(predictions): - # boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None - object_description = predictions["instances"].pred_object_descriptions.data - new_caption = ",".join(object_description) - del predictions - # for i in range(len(object_description)): - # new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; " - return new_caption - -def setup_cfg(args): - cfg = get_cfg() - # if args["cpu"]: - # cfg.MODEL.DEVICE="cpu" - add_centernet_config(cfg) - add_grit_config(cfg) - cfg.merge_from_file(args["config_file"]) - cfg.merge_from_list(args["opts"]) - # Set score_threshold for builtin models - cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args["confidence_threshold"] - cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args["confidence_threshold"] - if args["test_task"]: - cfg.MODEL.TEST_TASK = args["test_task"] - cfg.MODEL.BEAM_SIZE = 1 - cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False - cfg.USE_ACT_CHECKPOINT = False - cfg.freeze() - return cfg - -def get_parser(device): - arg_dict = {'config_file': "iGPT/models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml", 'device': device, 'confidence_threshold': 0.5, 'test_task': 'DenseCap', 'opts': ["MODEL.WEIGHTS", "model_zoo/grit_b_densecap_objectdet.pth"]} - return arg_dict - -def image_caption_api(image_src, device): - args2 = get_parser(device) - cfg = setup_cfg(args2) - demo = VisualizationDemo(cfg) - if image_src: - img = read_image(image_src, format="BGR") - predictions, visualized_output = demo.run_on_image(img) - new_caption = dense_pred_to_caption(predictions) - return new_caption - -def init_demo(device): - args2 = get_parser(device) - cfg = setup_cfg(args2) - demo = VisualizationDemo(cfg) - return demo - -if __name__=="__main__": - import os - os.environ['CUDA_VISIBLE_DEVICES']='7' - print(image_caption_api("images/dancing_example_4.mp4_20230417_135359.263.jpg",'cuda')) \ No newline at end of file diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/test_events.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/test_events.py deleted file mode 100644 index c1b03e4d1a703a417a83c2805be1ca15a4e458ed..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/test_events.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import json -import os -import tempfile -import unittest - -from detectron2.utils.events import CommonMetricPrinter, EventStorage, JSONWriter - - -class TestEventWriter(unittest.TestCase): - def testScalar(self): - with tempfile.TemporaryDirectory( - prefix="detectron2_tests" - ) as dir, EventStorage() as storage: - json_file = os.path.join(dir, "test.json") - writer = JSONWriter(json_file) - for k in range(60): - storage.put_scalar("key", k, smoothing_hint=False) - if (k + 1) % 20 == 0: - writer.write() - storage.step() - writer.close() - with open(json_file) as f: - data = [json.loads(l) for l in f] - self.assertTrue([int(k["key"]) for k in data] == [19, 39, 59]) - - def testScalarMismatchedPeriod(self): - with tempfile.TemporaryDirectory( - prefix="detectron2_tests" - ) as dir, EventStorage() as storage: - json_file = os.path.join(dir, "test.json") - - writer = JSONWriter(json_file) - for k in range(60): - if k % 17 == 0: # write in a differnt period - storage.put_scalar("key2", k, smoothing_hint=False) - storage.put_scalar("key", k, smoothing_hint=False) - if (k + 1) % 20 == 0: - writer.write() - storage.step() - writer.close() - with open(json_file) as f: - data = [json.loads(l) for l in f] - self.assertTrue([int(k.get("key2", 0)) for k in data] == [17, 0, 34, 0, 51, 0]) - self.assertTrue([int(k.get("key", 0)) for k in data] == [0, 19, 0, 39, 0, 59]) - self.assertTrue([int(k["iteration"]) for k in data] == [17, 19, 34, 39, 51, 59]) - - def testPrintETA(self): - with EventStorage() as s: - p1 = CommonMetricPrinter(10) - p2 = CommonMetricPrinter() - - s.put_scalar("time", 1.0) - s.step() - s.put_scalar("time", 1.0) - s.step() - - with self.assertLogs("detectron2.utils.events") as logs: - p1.write() - self.assertIn("eta", logs.output[0]) - - with self.assertLogs("detectron2.utils.events") as logs: - p2.write() - self.assertNotIn("eta", logs.output[0]) diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/models/mgpt.py b/spaces/OpenMotionLab/MotionGPT/mGPT/models/mgpt.py deleted file mode 100644 index c8db4a45978020bf712bfae2757b20fc283b13de..0000000000000000000000000000000000000000 --- a/spaces/OpenMotionLab/MotionGPT/mGPT/models/mgpt.py +++ /dev/null @@ -1,494 +0,0 @@ -import numpy as np -import os -import random -import torch -import time -from mGPT.config import instantiate_from_config -from os.path import join as pjoin -from mGPT.losses.mgpt import GPTLosses -from mGPT.models.base import BaseModel -from .base import BaseModel -import json -import mGPT.render.matplot.plot_3d_global as plot_3d - - -class MotionGPT(BaseModel): - """ - Stage 1 Motion Tokenizer - Stage 2 Motion-language pretrian - Stage 3 Motion-language instruction tuning - """ - - def __init__(self, - cfg, - datamodule, - lm, - motion_vae, - codebook_size=512, - stage='vae', - debug=True, - condition='text', - task='t2m', - metrics_dict=['TM2TMetrics'], - **kwargs): - - self.save_hyperparameters(ignore='datamodule', logger=False) - self.datamodule = datamodule - super().__init__() - - # Instantiate motion tokenizer - if motion_vae != None: - self.vae = instantiate_from_config(motion_vae) - - # Instantiate motion-language model - self.lm = instantiate_from_config(lm) - - # Freeze the motion tokenizer for lm training - if 'lm' in self.hparams.stage: - self.vae.training = False - for p in self.vae.parameters(): - p.requires_grad = False - - # Instantiate the losses - self._losses = torch.nn.ModuleDict({ - split: GPTLosses(cfg, self.hparams.stage, self.datamodule.njoints) - for split in ["losses_train", "losses_test", "losses_val"] - }) - - # Data transform - self.feats2joints = datamodule.feats2joints - - # Count codebook frequency - self.codePred = [] - self.codeFrequency = torch.zeros((self.hparams.codebook_size, )) - - def forward(self, batch, task="t2m"): - texts = batch["text"] - lengths_ref = batch["length"] - - # Forward - # texts = ['Generate motion: ' + text for text in texts] - outputs, output_texts = self.lm.generate_direct(texts, do_sample=True) - - # Motion Decode - feats_rst_lst = [] - lengths = [] - max_len = 0 - - for i in range(len(texts)): - if task == "pred": - motion = self.vae.decode( - torch.cat((batch["motion"][i], outputs[i]))) - elif task in ["t2m", "m2t", "inbetween"]: - motion = self.vae.decode(outputs[i]) - # motion = self.datamodule.denormalize(motion) - lengths.append(motion.shape[1]) - else: - raise NotImplementedError - - if motion.shape[1] > max_len: - max_len = motion.shape[1] - - if task in ["t2m", "m2t", "pred"]: - feats_rst_lst.append(motion) - - elif task == "inbetween": - motion = torch.cat( - (batch["motion_heading"][i][None], - motion[:, lengths_ref[i] // 4:lengths_ref[i] // 4 * 3, - ...], batch["motion_tailing"][i][None]), - dim=1) - feats_rst_lst.append(motion) - - feats_rst = torch.zeros( - (len(feats_rst_lst), max_len, motion.shape[-1])).to(self.device) - - # padding and concat - for i in range(len(feats_rst_lst)): - feats_rst[i, :feats_rst_lst[i].shape[1], ...] = feats_rst_lst[i] - - # Recover joints for evaluation - joints_rst = self.feats2joints(feats_rst) - - # return set - outputs = { - "texts": output_texts, - "feats": feats_rst, - "joints": joints_rst, - "length": lengths - } - - return outputs - - def train_lm_forward(self, batch): - tokens_ref = batch["motion"] - texts = batch["text"] - lengths = batch["length"] - tasks = batch["tasks"] - all_captions = batch['all_captions'] - if self.hparams.condition == 'caption': - texts = [random.choice(all_captions[i]) for i in range(len(texts))] - - # LLM Forward - outputs = self.lm(texts, tokens_ref, lengths, tasks) - # outputs = self.t2m_gpt.generate(texts) - return {'outputs': outputs} - - @torch.no_grad() - def val_t2m_forward(self, batch): - feats_ref = batch["motion"] - texts = batch["text"] - lengths = batch["length"] - tasks = None - if self.trainer.datamodule.is_mm: - texts = texts * self.hparams.cfg.METRIC.MM_NUM_REPEATS - feats_ref = feats_ref.repeat_interleave( - self.hparams.cfg.METRIC.MM_NUM_REPEATS, dim=0) - lengths = lengths * self.hparams.cfg.METRIC.MM_NUM_REPEATS - instructions = pjoin(self.datamodule.hparams.data_root, - 'template_instructions.json') - instructions = json.load(open(instructions, 'r')) - tasks = [instructions["Text-to-Motion"]["caption"]] * len(texts) - - if self.hparams.condition == 'caption': - tasks = [{ - 'input': [' '], - 'output': [''] - }] * len(texts) - - if self.hparams.cfg.DATASET.TASK_PATH: - instructions = pjoin(self.hparams.cfg.DATASET.TASK_PATH) - instructions = json.load(open(instructions, 'r')) - tasks = [instructions["Text-to-Motion"]["t2m"]] * len(texts) - - min_len = lengths.copy() - # Forward - outputs = self.lm.generate_conditional(texts, - lengths=lengths, - stage='test', - tasks=tasks) - - # Motion Decode - feats_rst = torch.zeros_like(feats_ref) - - for i in range(len(texts)): - outputs[i] = torch.clamp(outputs[i], - 0, - self.hparams.codebook_size - 1, - out=None) - - if len(outputs[i]) > 1: - motion = self.vae.decode(outputs[i]) - else: - motion = torch.zeros_like(feats_ref[i:i + 1, ...]) - - min_len[i] = min(motion.shape[1], lengths[i]) - - # Cut Motion - feats_rst[i:i + 1, :min_len[i], ...] = motion[:, :lengths[i]] - - # Recover joints for evaluation - joints_ref = self.feats2joints(feats_ref) - joints_rst = self.feats2joints(feats_rst) - - # Renorm for evaluation - feats_ref = self.datamodule.renorm4t2m(feats_ref) - feats_rst = self.datamodule.renorm4t2m(feats_rst) - - # return set - rs_set = { - "m_ref": feats_ref, - "m_rst": feats_rst, - "joints_ref": joints_ref, - "joints_rst": joints_rst, - "length": min_len - # "length": lengths - } - - return rs_set - - @torch.no_grad() - def val_m2t_forward(self, batch): - self.hparams.metrics_dict = [] - - feats_ref = batch["motion"] - texts = batch["text"] - lengths = batch["length"] - all_captions = batch['all_captions'] - - # Motion Encode - motion_tokens = [] - lengths_tokens = [] - for i in range(len(feats_ref)): - motion_token, _ = self.vae.encode(feats_ref[i:i + 1]) - motion_tokens.append(motion_token[0]) - lengths_tokens.append(motion_token.shape[1]) - - # Forward - outputs = self.lm.generate_conditional(motion_tokens=motion_tokens, - lengths=lengths_tokens, - task="m2t", - stage='test') - - # return set - rs_set = { - "m_ref": feats_ref, - "t_ref": all_captions, - # "t_ref": texts, - "t_pred": outputs, - "length": lengths - } - - return rs_set - - @torch.no_grad() - def val_m2m_forward(self, batch, task="pred"): - feats_ref = batch["motion"] - lengths = batch["length"] - - # Motion Encode - motion_tokens = [] - lengths_tokens = [] - for i in range(len(feats_ref)): - motion_token, _ = self.vae.encode(feats_ref[i:i + 1]) - motion_tokens.append(motion_token[0]) - - # Forward - outputs = self.lm.generate_conditional(motion_tokens=motion_tokens, - lengths=lengths, - task=task, - stage='test') - - # Motion Decode - feats_rst = torch.zeros_like(feats_ref) - min_len = lengths.copy() - - for i in range(len(lengths)): - outputs[i] = torch.clamp(outputs[i], - 0, - self.hparams.codebook_size - 1, - out=None) - - if len(outputs[i]) > 1: - motion = self.vae.decode(outputs[i]) - else: - motion = torch.zeros_like(feats_ref[i:i + 1, ...]) - - min_len[i] = min(motion.shape[1], lengths[i]) - - # Cut Motion - feats_rst[i:i + 1, :min_len[i], ...] = motion[:, :lengths[i]] - - # Recover joints for evaluation - joints_ref = self.feats2joints(feats_ref) - joints_rst = self.feats2joints(feats_rst) - - # Renorm for evaluation - feats_ref = self.datamodule.renorm4t2m(feats_ref) - feats_rst = self.datamodule.renorm4t2m(feats_rst) - - # return set - rs_set = { - "m_ref": feats_ref, - "m_rst": feats_rst, - "joints_ref": joints_ref, - "joints_rst": joints_rst, - "length": min_len - # "length": lengths - } - - return rs_set - - def train_vae_forward(self, batch): - # batch detach - feats_ref = batch["motion"] - joints_ref = self.feats2joints(feats_ref) - # motion encode & decode - feats_rst, loss_commit, perplexity = self.vae(feats_ref) - joints_rst = self.feats2joints(feats_rst) - # return set - rs_set = { - "m_ref": feats_ref, - "joints_ref": joints_ref, - "m_rst": feats_rst, - "joints_rst": joints_rst, - "loss_commit": loss_commit, - "perplexity": perplexity, - } - return rs_set - - @torch.no_grad() - def val_vae_forward(self, batch, split="train"): - # Detach batch - feats_ref = batch["motion"] - lengths = batch["length"] - - # Repeat for multimodal evaluation - if self.trainer.datamodule.is_mm: - feats_ref = feats_ref.repeat_interleave( - self.hparams.cfg.METRIC.MM_NUM_REPEATS, dim=0) - lengths = lengths * self.hparams.cfg.METRIC.MM_NUM_REPEATS - - # Motion encode & decode - feats_rst = torch.zeros_like(feats_ref) - - for i in range(len(feats_ref)): - if lengths[i] == 0: - continue - feats_pred, _, _ = self.vae(feats_ref[i:i + 1, :lengths[i]]) - feats_rst[i:i + 1, :feats_pred.shape[1], :] = feats_pred - - code_pred, _ = self.vae.encode(feats_ref[i:i + 1, :lengths[i]]) - - # codeFre_pred = torch.bincount(code_pred[0], - # minlength=self.hparams.codebook_size).to( - # self.codeFrequency.device) - # self.codePred.append(code_pred[0]) - # self.codeFrequency += codeFre_pred - - # np.save('../memData/results/codeFrequency.npy', - # self.codeFrequency.cpu().numpy()) - - # Recover joints for evaluation - joints_ref = self.feats2joints(feats_ref) - joints_rst = self.feats2joints(feats_rst) - - # Renorm for evaluation - feats_ref = self.datamodule.renorm4t2m(feats_ref) - feats_rst = self.datamodule.renorm4t2m(feats_rst) - - # Return set - rs_set = { - "m_ref": feats_ref, - "joints_ref": joints_ref, - "m_rst": feats_rst, - "joints_rst": joints_rst, - "length": lengths, - } - - return rs_set - - - def allsplit_step(self, split: str, batch, batch_idx): - # Compute the losses - loss = None - - if self.hparams.stage == "vae" and split in ["train", "val"]: - rs_set = self.train_vae_forward(batch) - loss = self._losses['losses_' + split].update(rs_set) - elif self.hparams.stage in ["lm_instruct", "lm_pretrain" - ] and split in ["train"]: - rs_set = self.train_lm_forward(batch) - loss = self._losses['losses_' + split].update(rs_set) - elif self.hparams.stage == 'lm_rl' and split in ['train']: - rs_set = self.train_rl_forward(batch) - loss = None - - # Compute the metrics - if split in ["val", "test"]: - if self.hparams.stage == "vae": - rs_set = self.val_vae_forward(batch, split) - elif self.hparams.stage in ["lm_instruct", "lm_pretrain", "lm_rl"]: - if self.hparams.task == "t2m": - rs_set = self.val_t2m_forward(batch) - elif self.hparams.task == "m2t": - rs_set = self.val_m2t_forward(batch) - elif self.hparams.task in ["m2m", "pred", "inbetween"]: - rs_set = self.val_m2m_forward(batch, self.hparams.task) - - if self.hparams.task not in ["m2t"]: - # MultiModality evaluation sperately - if self.trainer.datamodule.is_mm: - metrics_dicts = ['MMMetrics'] - else: - metrics_dicts = self.hparams.metrics_dict - - if self.hparams.task not in ['pred', 'inbetween']: - metrics_dicts.remove('PredMetrics') - - for metric in metrics_dicts: - lengths = batch['length'] - if metric == "TemosMetric": - getattr(self.metrics, - metric).update(rs_set["joints_rst"], - rs_set["joints_ref"], lengths) - elif metric == "TM2TMetrics": - if self.hparams.stage in [ - "lm_instruct", "lm_pretrain", "lm_rl" - ]: - word_embs = batch['word_embs'] - pos_ohot = batch['pos_ohot'] - text_lengths = batch['text_len'] - if self.trainer.datamodule.is_mm: - word_embs = word_embs.repeat_interleave( - self.hparams.cfg.METRIC.MM_NUM_REPEATS, - dim=0) - pos_ohot = pos_ohot.repeat_interleave( - self.hparams.cfg.METRIC.MM_NUM_REPEATS, - dim=0) - text_lengths = text_lengths.repeat_interleave( - self.hparams.cfg.METRIC.MM_NUM_REPEATS, - dim=0) - else: - word_embs = None - pos_ohot = None - text_lengths = None - - getattr(self.metrics, metric).update( - feats_ref=rs_set["m_ref"], - feats_rst=rs_set["m_rst"], - lengths_ref=lengths, - lengths_rst=rs_set['length'], - word_embs=word_embs, - pos_ohot=pos_ohot, - text_lengths=text_lengths, - ) - elif metric == "UncondMetrics": - getattr(self.metrics, metric).update( - recmotion_embeddings=rs_set["lat_rm"], - gtmotion_embeddings=rs_set["lat_m"], - lengths=lengths, - ) - elif metric == "MRMetrics": - getattr(self.metrics, - metric).update(rs_set["joints_rst"], - rs_set["joints_ref"], lengths) - elif metric == "PredMetrics": - getattr(self.metrics, - metric).update(rs_set["joints_rst"], - rs_set["joints_ref"], lengths) - elif metric == "MMMetrics": - # pass - getattr(self.metrics, - metric).update(rs_set["m_rst"], - rs_set['length']) - else: - raise TypeError(f"Not support this metric {metric}") - - elif self.hparams.task == "m2t" and self.hparams.stage in [ - "lm_instruct", "lm_pretrain", "lm_rl" - ]: - self.hparams.metrics_dict = metrics_dicts = ['M2TMetrics'] - for metric in metrics_dicts: - if metric == "M2TMetrics": - getattr(self.metrics, metric).update( - feats_ref=rs_set["m_ref"], - pred_texts=rs_set["t_pred"], - gt_texts=batch["all_captions"], - lengths=rs_set['length'], - word_embs=batch["word_embs"], - pos_ohot=batch["pos_ohot"], - text_lengths=batch["text_len"], - ) - - # return forward output rather than loss during test - if split in ["test"]: - if self.hparams.task == "t2m": - return rs_set["joints_rst"], rs_set["length"], rs_set[ - "joints_ref"] - # pass - elif self.hparams.task == "m2t": - return rs_set["t_pred"], batch["length"] - # return batch["length"] - - return loss diff --git a/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/diffusionmodules/__init__.py b/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/diffusionmodules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/pmatch.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/pmatch.go deleted file mode 100644 index 3f048637be52641d10cc7cee13c148261dd74df5..0000000000000000000000000000000000000000 Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/pmatch.go and /dev/null differ diff --git a/spaces/Paulraj916/paulraj916/newScrapCss.py b/spaces/Paulraj916/paulraj916/newScrapCss.py deleted file mode 100644 index e5115d1be192670830f4e5ee63b0f78b7753216e..0000000000000000000000000000000000000000 --- a/spaces/Paulraj916/paulraj916/newScrapCss.py +++ /dev/null @@ -1,58 +0,0 @@ -import os -import requests -from bs4 import BeautifulSoup, Tag -from urllib.parse import urljoin -import cssbeautifier - -class NewScrapCss: - def __init__(self, link): - self.link = link - - def scrap_css(self): - try: - # Send an HTTP GET request to the webpage - response = requests.get(self.link) - response.raise_for_status() - - # Get the HTML content of the page - html_content = response.text - - # Extract CSS file URLs from the webpage and convert to absolute URLs - base_url = response.url - soup = BeautifulSoup(html_content, 'html.parser') - css_urls = [urljoin(base_url, link['href']) for link in soup.find_all('link', rel='stylesheet')] - - # Create an "output" folder if it doesn't exist - output_path = "output" - if not os.path.exists(output_path): - os.makedirs(output_path) - - # Download and store CSS files in the "output" folder - for css_url in css_urls: - # Check if the URL ends with ".css" - if not css_url.lower().endswith(".css"): - # Append ".css" to the URL if it doesn't end with it - css_url += ".css" - - folder_name = os.path.dirname(css_url.replace(base_url, "").replace("http://", "").replace("https://", "")) - if folder_name.startswith("/"): - folder_name = folder_name[1:] - folder_path = os.path.join(output_path, folder_name) - try: - os.makedirs(folder_path, exist_ok=True) - filename = os.path.basename(css_url) - try: - css_content = requests.get(css_url).text - # Beautify CSS content - css_content = cssbeautifier.beautify(css_content) - with open(os.path.join(folder_path, filename), 'w', encoding='utf-8') as file: - file.write(css_content) - print("Downloaded and beautified:", css_url) - except Exception as e: - print(f"Failed to download {css_url}: {e}") - except Exception as e: - print(f"Failed to download {css_url}: {e}") - - print("CSS files downloaded and saved successfully.") - except requests.exceptions.RequestException as e: - print(f"Failed to fetch content from {self.link}: {e}") diff --git a/spaces/PeepDaSlan9/Llama-2-AWS/README.md b/spaces/PeepDaSlan9/Llama-2-AWS/README.md deleted file mode 100644 index c75c53aac9edff5a2f4b79e0ca644b58f41f81c3..0000000000000000000000000000000000000000 --- a/spaces/PeepDaSlan9/Llama-2-AWS/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Llama 2 AWS -emoji: 😻 -colorFrom: indigo -colorTo: pink -sdk: streamlit -sdk_version: 1.27.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Plurigrid/LifeSim/src/app/main.tsx b/spaces/Plurigrid/LifeSim/src/app/main.tsx deleted file mode 100644 index 3b89e11ab88283e7a4ee85919d6d95e26b258bde..0000000000000000000000000000000000000000 --- a/spaces/Plurigrid/LifeSim/src/app/main.tsx +++ /dev/null @@ -1,93 +0,0 @@ -"use client" - -import { useEffect, useRef, useState, useTransition } from "react" - -import { VideoPlayer } from "@/components/business/video-player" - -import { - Select, - SelectContent, - SelectItem, - SelectTrigger, - SelectValue, -} from "@/components/ui/select" - -import { render } from "./render" -import { Agent, AgentType, Scene } from "./agents/types" -import { agents, defaultAgent, getAgent } from "./agents" - -export default function Main() { - const [url, setUrl] = useState () - const [isPending, startTransition] = useTransition() - const [scene, setScene] = useState () - const ref = useRef (defaultAgent) - - useEffect(() => { - - const updateView = async () => { - // console.log(`update view..`) - - await startTransition(async () => { - - // console.log(`getting agent..`) - const type = ref?.current - const agent = getAgent(type) - - // console.log(`asking agent to determine things..`) - const scene = agent.simulate() - - // console.log(`rendering scene..`) - const newUrl = await render(scene.prompt) - - if (type !== ref?.current) { - console.log("agent type changed! reloading scene") - setTimeout(() => { updateView() }, 0) - return - } - - if (newUrl) { - // console.log(`got a new url: ${newUrl}`) - setUrl(newUrl) - setScene(scene) - setTimeout(() => { updateView()}, 1000) - } else { - // console.log(`going to wait a bit more: ${newUrl}`) - setTimeout(() => { updateView()}, 3000) - } - }) - } - - updateView() - - }, []) - - return ( - -- ) -} \ No newline at end of file diff --git a/spaces/Pranay009/FACE2COMIC/src/Generator.py b/spaces/Pranay009/FACE2COMIC/src/Generator.py deleted file mode 100644 index ac144d25729ead4d02ab55369652c706bd478ee8..0000000000000000000000000000000000000000 --- a/spaces/Pranay009/FACE2COMIC/src/Generator.py +++ /dev/null @@ -1,97 +0,0 @@ -import keras -import tensorflow as tf -from keras.models import Model -from keras.models import Input -from keras.layers import Conv2D -from keras.layers import Conv2DTranspose -from keras.layers import LeakyReLU -from keras.layers import Activation -from keras.layers import Concatenate -from keras.layers import Dropout -from keras.layers import BatchNormalization -from matplotlib import pyplot as plt -from keras.initializers import RandomNormal -from keras.layers import concatenate,MaxPooling2D - -#model -def Generator(x=256,y=256,z=3): - inputs=Input(shape=(x,y,z)) - init = RandomNormal(stddev=0.02) - c1=Conv2D(64,(4,4),strides=(2,2),activation="relu",kernel_initializer=init,padding="same")(inputs) - c1=LeakyReLU(alpha=0.2)(c1)#(64,64,64) - - c2 = Conv2D(128, (4,4),strides=(2,2), kernel_initializer=init, padding='same')(c1) - c2=BatchNormalization()(c2,training=True)#(128,32,32) - c2=LeakyReLU(alpha=0.2)(c2) - - c3 = Conv2D(256 ,(4,4), strides=(2,2) ,kernel_initializer=init, padding='same')(c2) - c3=BatchNormalization()(c3,training=True)#(256,16,16) - c3=LeakyReLU(alpha=0.2)(c3) - - c4 = Conv2D(512, (4,4), strides=(2,2), kernel_initializer=init, padding='same')(c3) - c4=BatchNormalization()(c4,training=True)#(512,8,8) - c4=LeakyReLU(alpha=0.2)(c4) - - c5 = Conv2D(512, (4,4),strides=(2,2), kernel_initializer=init, padding='same')(c4) - c5=BatchNormalization()(c5,training=True)#512,4,4 - c5=LeakyReLU(alpha=0.2)(c5) - - c6 = Conv2D(512, (4,4), strides=(2,2),kernel_initializer=init, padding='same')(c5) - c6=BatchNormalization()(c6,training=True)#512,2,2 - c6=LeakyReLU(alpha=0.2)(c6) - - c7 = Conv2D(512, (4,4), strides=(2,2), kernel_initializer=init, padding='same')(c6) - c7=BatchNormalization()(c7,training=True)#512,1,1 - c7=LeakyReLU(alpha=0.2)(c7) - - #b = Conv2D(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(c7) - #b = Activation('relu')(c7) - - d1 = Conv2DTranspose(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(c7) - d1=BatchNormalization()(d1,training=True)#512*2*2 - d1=Dropout(0.5)(d1,training=True) - d1=Concatenate()([d1, c6]) - d1=Activation("relu")(d1) - - d2 =Conv2DTranspose(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d1) - d2=BatchNormalization()(d2,training=True)#512*4*4 - d2=Dropout(0.5)(d2,training=True) - d2=Concatenate()([d2,c5]) - d2=Activation("relu")(d2) - - d3 = Conv2DTranspose(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d2) - d3=BatchNormalization()(d3,training=True)#512*8*8 - d3=Dropout(0.5)(d3,training=True) - d3=Concatenate()([d3, c4]) - d3=Activation("relu")(d3) - - d4 = Conv2DTranspose(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d3) - d4=BatchNormalization()(d4,training=True)#512*16*16 - #d4=Dropout(0.5)(d4,training=True) - d4=Concatenate()([d4, c3]) - d4=Activation("relu")(d4) - - d5 = Conv2DTranspose(256, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d4) - d5=BatchNormalization()(d5,training=True)#256*32*32 - #d5=Dropout(0.5)(d5,training=True) - d5=Concatenate()([d5, c2]) - d5=Activation("relu")(d5) - - d6 = Conv2DTranspose(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d5) - d6=BatchNormalization()(d6,training=True)#128*64*64 - #d6=Dropout(0.5)(d6,training=True) - d6=Concatenate()([d6, c1]) - d6=Activation("relu")(d6) - - #d7 = Conv2DTranspose(64, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d6) - #d7=BatchNormalization()(d7,training=True) - #d7=Concatenate()([d7, c1]) - #$d7=Activation("relu")(d7) - - f = Conv2DTranspose(3, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d6) - out_image = Activation('tanh')(f) #3,128,128 - - model = Model(inputs, out_image) - return model - - \ No newline at end of file diff --git a/spaces/Pranjal2041/SemSup-XC/cleaned_code/src/__init__.py b/spaces/Pranjal2041/SemSup-XC/cleaned_code/src/__init__.py deleted file mode 100644 index 757de134d8725e0bdceb03cc1521df1eedf82983..0000000000000000000000000000000000000000 --- a/spaces/Pranjal2041/SemSup-XC/cleaned_code/src/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .constants import * -from .training_arguments import * -from .metrics import * -from .dataloaders import * -from .models import * -from .utils import * diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/install_lib.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/install_lib.py deleted file mode 100644 index 2e9d8757a582b1dcdb47a34c35c6cfb3ed23ba90..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/install_lib.py +++ /dev/null @@ -1,122 +0,0 @@ -import os -import sys -from itertools import product, starmap -import distutils.command.install_lib as orig - - -class install_lib(orig.install_lib): - """Don't add compiled flags to filenames of non-Python files""" - - def run(self): - self.build() - outfiles = self.install() - if outfiles is not None: - # always compile, in case we have any extension stubs to deal with - self.byte_compile(outfiles) - - def get_exclusions(self): - """ - Return a collections.Sized collections.Container of paths to be - excluded for single_version_externally_managed installations. - """ - all_packages = ( - pkg - for ns_pkg in self._get_SVEM_NSPs() - for pkg in self._all_packages(ns_pkg) - ) - - excl_specs = product(all_packages, self._gen_exclusion_paths()) - return set(starmap(self._exclude_pkg_path, excl_specs)) - - def _exclude_pkg_path(self, pkg, exclusion_path): - """ - Given a package name and exclusion path within that package, - compute the full exclusion path. - """ - parts = pkg.split('.') + [exclusion_path] - return os.path.join(self.install_dir, *parts) - - @staticmethod - def _all_packages(pkg_name): - """ - >>> list(install_lib._all_packages('foo.bar.baz')) - ['foo.bar.baz', 'foo.bar', 'foo'] - """ - while pkg_name: - yield pkg_name - pkg_name, sep, child = pkg_name.rpartition('.') - - def _get_SVEM_NSPs(self): - """ - Get namespace packages (list) but only for - single_version_externally_managed installations and empty otherwise. - """ - # TODO: is it necessary to short-circuit here? i.e. what's the cost - # if get_finalized_command is called even when namespace_packages is - # False? - if not self.distribution.namespace_packages: - return [] - - install_cmd = self.get_finalized_command('install') - svem = install_cmd.single_version_externally_managed - - return self.distribution.namespace_packages if svem else [] - - @staticmethod - def _gen_exclusion_paths(): - """ - Generate file paths to be excluded for namespace packages (bytecode - cache files). - """ - # always exclude the package module itself - yield '__init__.py' - - yield '__init__.pyc' - yield '__init__.pyo' - - if not hasattr(sys, 'implementation'): - return - - base = os.path.join( - '__pycache__', '__init__.' + sys.implementation.cache_tag) - yield base + '.pyc' - yield base + '.pyo' - yield base + '.opt-1.pyc' - yield base + '.opt-2.pyc' - - def copy_tree( - self, infile, outfile, - preserve_mode=1, preserve_times=1, preserve_symlinks=0, level=1 - ): - assert preserve_mode and preserve_times and not preserve_symlinks - exclude = self.get_exclusions() - - if not exclude: - return orig.install_lib.copy_tree(self, infile, outfile) - - # Exclude namespace package __init__.py* files from the output - - from setuptools.archive_util import unpack_directory - from distutils import log - - outfiles = [] - - def pf(src, dst): - if dst in exclude: - log.warn("Skipping installation of %s (namespace package)", - dst) - return False - - log.info("copying %s -> %s", src, os.path.dirname(dst)) - outfiles.append(dst) - return dst - - unpack_directory(infile, outfile, pf) - return outfiles - - def get_outputs(self): - outputs = orig.install_lib.get_outputs(self) - exclude = self.get_exclusions() - if exclude: - return [f for f in outputs if f not in exclude] - return outputs diff --git a/spaces/Reeve/Ohayou_Face/model_build.py b/spaces/Reeve/Ohayou_Face/model_build.py deleted file mode 100644 index 475f8f36ea5c7b2ceed1c2cf4eac7127118d9f17..0000000000000000000000000000000000000000 --- a/spaces/Reeve/Ohayou_Face/model_build.py +++ /dev/null @@ -1,95 +0,0 @@ -import os -import glob - -import numpy as np -from numpy import linalg -import PIL.Image as Image -import torch -from torchvision import transforms -from tqdm import tqdm -from argparse import Namespace -import easydict - -import legacy -import dnnlib - -from opensimplex import OpenSimplex - -from configs import data_configs -from models.psp import pSp - - -def build_stylegan2( - increment = 0.01, - network_pkl = 'pretrained/ohayou_face2.pkl', - process = 'image', #['image', 'interpolation','truncation','interpolation-truncation'] - random_seed = 0, - diameter = 100.0, - scale_type = 'pad', #['pad', 'padside', 'symm','symmside'] - size = [512, 512], - seeds = [0], - space = 'z', #['z', 'w'] - fps = 24, - frames = 240, - noise_mode = 'none', #['const', 'random', 'none'] - outdir = 'path', - projected_w = 'path', - easing = 'linear', - device = 'cpu' - - ): - - G_kwargs = dnnlib.EasyDict() - G_kwargs.size = size - G_kwargs.scale_type = scale_type - - device = torch.device(device) - with dnnlib.util.open_url(network_pkl) as f: - # G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore - G = legacy.load_network_pkl(f, custom=True, **G_kwargs)['G_ema'].to(device) # type: ignore - - return G.synthesis - - -def build_psp(): - test_opts = easydict.EasyDict({ - # arguments for inference script - 'checkpoint_path' : 'pretrained/ohayou_face.pt', - 'couple_outputs' : False, - 'resize_outputs' : False, - - 'test_batch_size' : 1, - 'test_workers' : 1, - - # arguments for style-mixing script - 'n_images' : None, - 'n_outputs_to_generate' : 5, - 'mix_alpha' : None, - 'latent_mask' : None, - - # arguments for super-resolution - 'resize_factors' : None, - }) - - # update test options with options used during training - ckpt = torch.load(test_opts.checkpoint_path, map_location='cpu') - opts = ckpt['opts'] - opts.update(vars(test_opts)) - if 'learn_in_w' not in opts: - opts['learn_in_w'] = False - opts = Namespace(**opts) - opts.device = 'cpu' - net = pSp(opts) - net.eval() - return net - -def img_preprocess(img, transform): - if (img.mode == 'RGBA') or (img.mode == 'P'): - img.load() - background = Image.new("RGB", img.size, (255, 255, 255)) - background.paste(img, mask=img.split()[3]) # 3 is the alpha channel - img = background - assert img.mode == 'RGB' - img = transform(img) - img = img.unsqueeze(dim=0) - return img \ No newline at end of file diff --git a/spaces/RichardMB1217/blip/data/vqa_dataset.py b/spaces/RichardMB1217/blip/data/vqa_dataset.py deleted file mode 100644 index 92ec1df429b3910316ddd554bfea01c6e7922cae..0000000000000000000000000000000000000000 --- a/spaces/RichardMB1217/blip/data/vqa_dataset.py +++ /dev/null @@ -1,88 +0,0 @@ -import os -import json -import random -from PIL import Image - -import torch -from torch.utils.data import Dataset -from data.utils import pre_question - -from torchvision.datasets.utils import download_url - -class vqa_dataset(Dataset): - def __init__(self, transform, ann_root, vqa_root, vg_root, train_files=[], split="train"): - self.split = split - - self.transform = transform - self.vqa_root = vqa_root - self.vg_root = vg_root - - if split=='train': - urls = {'vqa_train':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_train.json', - 'vqa_val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_val.json', - 'vg_qa':'https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_qa.json'} - - self.annotation = [] - for f in train_files: - download_url(urls[f],ann_root) - self.annotation += json.load(open(os.path.join(ann_root,'%s.json'%f),'r')) - else: - download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_test.json',ann_root) - self.annotation = json.load(open(os.path.join(ann_root,'vqa_test.json'),'r')) - - download_url('https://storage.googleapis.com/sfr-vision-language-research/datasets/answer_list.json',ann_root) - self.answer_list = json.load(open(os.path.join(ann_root,'answer_list.json'),'r')) - - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - if ann['dataset']=='vqa': - image_path = os.path.join(self.vqa_root,ann['image']) - elif ann['dataset']=='vg': - image_path = os.path.join(self.vg_root,ann['image']) - - image = Image.open(image_path).convert('RGB') - image = self.transform(image) - - if self.split == 'test': - question = pre_question(ann['question']) - question_id = ann['question_id'] - return image, question, question_id - - - elif self.split=='train': - - question = pre_question(ann['question']) - - if ann['dataset']=='vqa': - answer_weight = {} - for answer in ann['answer']: - if answer in answer_weight.keys(): - answer_weight[answer] += 1/len(ann['answer']) - else: - answer_weight[answer] = 1/len(ann['answer']) - - answers = list(answer_weight.keys()) - weights = list(answer_weight.values()) - - elif ann['dataset']=='vg': - answers = [ann['answer']] - weights = [0.2] - - return image, question, answers, weights - - -def vqa_collate_fn(batch): - image_list, question_list, answer_list, weight_list, n = [], [], [], [], [] - for image, question, answer, weights in batch: - image_list.append(image) - question_list.append(question) - weight_list += weights - answer_list += answer - n.append(len(answer)) - return torch.stack(image_list,dim=0), question_list, answer_list, torch.Tensor(weight_list), n \ No newline at end of file diff --git a/spaces/RobLi/ControlNet-v1-1/app_scribble.py b/spaces/RobLi/ControlNet-v1-1/app_scribble.py deleted file mode 100644 index 17a8565cb741e12a65b46e3e7a66b20e7efb301c..0000000000000000000000000000000000000000 --- a/spaces/RobLi/ControlNet-v1-1/app_scribble.py +++ /dev/null @@ -1,105 +0,0 @@ -#!/usr/bin/env python - -import gradio as gr - -from utils import randomize_seed_fn - - -def create_demo(process, max_images=12, default_num_images=3): - with gr.Blocks() as demo: - with gr.Row(): - with gr.Column(): - image = gr.Image() - prompt = gr.Textbox(label='Prompt') - run_button = gr.Button('Run') - with gr.Accordion('Advanced options', open=False): - preprocessor_name = gr.Radio( - label='Preprocessor', - choices=['HED', 'PidiNet', 'None'], - type='value', - value='HED') - num_samples = gr.Slider(label='Number of images', - minimum=1, - maximum=max_images, - value=default_num_images, - step=1) - image_resolution = gr.Slider(label='Image resolution', - minimum=256, - maximum=512, - value=512, - step=256) - preprocess_resolution = gr.Slider( - label='Preprocess resolution', - minimum=128, - maximum=512, - value=512, - step=1) - num_steps = gr.Slider(label='Number of steps', - minimum=1, - maximum=100, - value=20, - step=1) - guidance_scale = gr.Slider(label='Guidance scale', - minimum=0.1, - maximum=30.0, - value=9.0, - step=0.1) - seed = gr.Slider(label='Seed', - minimum=0, - maximum=1000000, - step=1, - value=0, - randomize=True) - randomize_seed = gr.Checkbox(label='Randomize seed', - value=True) - a_prompt = gr.Textbox( - label='Additional prompt', - value='best quality, extremely detailed') - n_prompt = gr.Textbox( - label='Negative prompt', - value= - 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' - ) - with gr.Column(): - result = gr.Gallery(label='Output', show_label=False).style( - columns=2, object_fit='scale-down') - inputs = [ - image, - prompt, - a_prompt, - n_prompt, - num_samples, - image_resolution, - preprocess_resolution, - num_steps, - guidance_scale, - seed, - preprocessor_name, - ] - prompt.submit( - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - ).then( - fn=process, - inputs=inputs, - outputs=result, - ) - run_button.click( - fn=randomize_seed_fn, - inputs=[seed, randomize_seed], - outputs=seed, - ).then( - fn=process, - inputs=inputs, - outputs=result, - api_name='scribble', - ) - return demo - - -if __name__ == '__main__': - from model import Model - model = Model(task_name='scribble') - demo = create_demo(model.process_scribble) - demo.queue().launch() diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/upernet_global_small/run.sh b/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/upernet_global_small/run.sh deleted file mode 100644 index 9fb22edfa7a32624ea08a63fe7d720c40db3b696..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/exp/upernet_global_small/run.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env bash - -work_path=$(dirname $0) -PYTHONPATH="$(dirname $0)/../../":$PYTHONPATH \ -python -m torch.distributed.launch --nproc_per_node=8 \ - tools/train.py ${work_path}/config.py \ - --launcher pytorch \ - --options model.backbone.pretrained_path='your_model_path/uniformer_small_in1k.pth' \ - --work-dir ${work_path}/ckpt \ - 2>&1 | tee -a ${work_path}/log.txt diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/bbox/samplers/base_sampler.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/bbox/samplers/base_sampler.py deleted file mode 100644 index 9ea35def115b49dfdad8a1f7c040ef3cd983b0d1..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/core/bbox/samplers/base_sampler.py +++ /dev/null @@ -1,101 +0,0 @@ -from abc import ABCMeta, abstractmethod - -import torch - -from .sampling_result import SamplingResult - - -class BaseSampler(metaclass=ABCMeta): - """Base class of samplers.""" - - def __init__(self, - num, - pos_fraction, - neg_pos_ub=-1, - add_gt_as_proposals=True, - **kwargs): - self.num = num - self.pos_fraction = pos_fraction - self.neg_pos_ub = neg_pos_ub - self.add_gt_as_proposals = add_gt_as_proposals - self.pos_sampler = self - self.neg_sampler = self - - @abstractmethod - def _sample_pos(self, assign_result, num_expected, **kwargs): - """Sample positive samples.""" - pass - - @abstractmethod - def _sample_neg(self, assign_result, num_expected, **kwargs): - """Sample negative samples.""" - pass - - def sample(self, - assign_result, - bboxes, - gt_bboxes, - gt_labels=None, - **kwargs): - """Sample positive and negative bboxes. - - This is a simple implementation of bbox sampling given candidates, - assigning results and ground truth bboxes. - - Args: - assign_result (:obj:`AssignResult`): Bbox assigning results. - bboxes (Tensor): Boxes to be sampled from. - gt_bboxes (Tensor): Ground truth bboxes. - gt_labels (Tensor, optional): Class labels of ground truth bboxes. - - Returns: - :obj:`SamplingResult`: Sampling result. - - Example: - >>> from mmdet.core.bbox import RandomSampler - >>> from mmdet.core.bbox import AssignResult - >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes - >>> rng = ensure_rng(None) - >>> assign_result = AssignResult.random(rng=rng) - >>> bboxes = random_boxes(assign_result.num_preds, rng=rng) - >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng) - >>> gt_labels = None - >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1, - >>> add_gt_as_proposals=False) - >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels) - """ - if len(bboxes.shape) < 2: - bboxes = bboxes[None, :] - - bboxes = bboxes[:, :4] - - gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8) - if self.add_gt_as_proposals and len(gt_bboxes) > 0: - if gt_labels is None: - raise ValueError( - 'gt_labels must be given when add_gt_as_proposals is True') - bboxes = torch.cat([gt_bboxes, bboxes], dim=0) - assign_result.add_gt_(gt_labels) - gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) - gt_flags = torch.cat([gt_ones, gt_flags]) - - num_expected_pos = int(self.num * self.pos_fraction) - pos_inds = self.pos_sampler._sample_pos( - assign_result, num_expected_pos, bboxes=bboxes, **kwargs) - # We found that sampled indices have duplicated items occasionally. - # (may be a bug of PyTorch) - pos_inds = pos_inds.unique() - num_sampled_pos = pos_inds.numel() - num_expected_neg = self.num - num_sampled_pos - if self.neg_pos_ub >= 0: - _pos = max(1, num_sampled_pos) - neg_upper_bound = int(self.neg_pos_ub * _pos) - if num_expected_neg > neg_upper_bound: - num_expected_neg = neg_upper_bound - neg_inds = self.neg_sampler._sample_neg( - assign_result, num_expected_neg, bboxes=bboxes, **kwargs) - neg_inds = neg_inds.unique() - - sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, - assign_result, gt_flags) - return sampling_result diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/logger/dvclive.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/logger/dvclive.py deleted file mode 100644 index 687cdc58c0336c92b1e4f9a410ba67ebaab2bc7a..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/hooks/logger/dvclive.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from ...dist_utils import master_only -from ..hook import HOOKS -from .base import LoggerHook - - -@HOOKS.register_module() -class DvcliveLoggerHook(LoggerHook): - """Class to log metrics with dvclive. - - It requires `dvclive`_ to be installed. - - Args: - path (str): Directory where dvclive will write TSV log files. - interval (int): Logging interval (every k iterations). - Default 10. - ignore_last (bool): Ignore the log of last iterations in each epoch - if less than `interval`. - Default: True. - reset_flag (bool): Whether to clear the output buffer after logging. - Default: True. - by_epoch (bool): Whether EpochBasedRunner is used. - Default: True. - - .. _dvclive: - https://dvc.org/doc/dvclive - """ - - def __init__(self, - path, - interval=10, - ignore_last=True, - reset_flag=True, - by_epoch=True): - - super(DvcliveLoggerHook, self).__init__(interval, ignore_last, - reset_flag, by_epoch) - self.path = path - self.import_dvclive() - - def import_dvclive(self): - try: - import dvclive - except ImportError: - raise ImportError( - 'Please run "pip install dvclive" to install dvclive') - self.dvclive = dvclive - - @master_only - def before_run(self, runner): - self.dvclive.init(self.path) - - @master_only - def log(self, runner): - tags = self.get_loggable_tags(runner) - if tags: - for k, v in tags.items(): - self.dvclive.log(k, v, step=self.get_iter(runner)) diff --git a/spaces/Rongjiehuang/GenerSpeech/data_gen/tts/txt_processors/__init__.py b/spaces/Rongjiehuang/GenerSpeech/data_gen/tts/txt_processors/__init__.py deleted file mode 100644 index 7bff3e9af7d634363116c6605f22a52aad614dea..0000000000000000000000000000000000000000 --- a/spaces/Rongjiehuang/GenerSpeech/data_gen/tts/txt_processors/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import en \ No newline at end of file diff --git a/spaces/Sapphire-356/Video2MC/common/loss.py b/spaces/Sapphire-356/Video2MC/common/loss.py deleted file mode 100644 index 12e5f437f89a137eff7580f12e37677c7caf797d..0000000000000000000000000000000000000000 --- a/spaces/Sapphire-356/Video2MC/common/loss.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright (c) 2018-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -import numpy as np -import torch - - -def mpjpe(predicted, target): - """ - Mean per-joint position error (i.e. mean Euclidean distance), - often referred to as "Protocol #1" in many papers. - """ - assert predicted.shape == target.shape - return torch.mean(torch.norm(predicted - target, dim=len(target.shape) - 1)) - - -def weighted_mpjpe(predicted, target, w): - """ - Weighted mean per-joint position error (i.e. mean Euclidean distance) - """ - assert predicted.shape == target.shape - assert w.shape[0] == predicted.shape[0] - return torch.mean(w * torch.norm(predicted - target, dim=len(target.shape) - 1)) - - -def p_mpjpe(predicted, target): - """ - Pose error: MPJPE after rigid alignment (scale, rotation, and translation), - often referred to as "Protocol #2" in many papers. - """ - assert predicted.shape == target.shape - - muX = np.mean(target, axis=1, keepdims=True) - muY = np.mean(predicted, axis=1, keepdims=True) - - X0 = target - muX - Y0 = predicted - muY - - normX = np.sqrt(np.sum(X0 ** 2, axis=(1, 2), keepdims=True)) - normY = np.sqrt(np.sum(Y0 ** 2, axis=(1, 2), keepdims=True)) - - X0 /= normX - Y0 /= normY - - H = np.matmul(X0.transpose(0, 2, 1), Y0) - U, s, Vt = np.linalg.svd(H) - V = Vt.transpose(0, 2, 1) - R = np.matmul(V, U.transpose(0, 2, 1)) - - # Avoid improper rotations (reflections), i.e. rotations with det(R) = -1 - sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1)) - V[:, :, -1] *= sign_detR - s[:, -1] *= sign_detR.flatten() - R = np.matmul(V, U.transpose(0, 2, 1)) # Rotation - - tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2) - - a = tr * normX / normY # Scale - t = muX - a * np.matmul(muY, R) # Translation - - # Perform rigid transformation on the input - predicted_aligned = a * np.matmul(predicted, R) + t - - # Return MPJPE - return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape) - 1)) - - -def n_mpjpe(predicted, target): - """ - Normalized MPJPE (scale only), adapted from: - https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py - """ - assert predicted.shape == target.shape - - norm_predicted = torch.mean(torch.sum(predicted ** 2, dim=3, keepdim=True), dim=2, keepdim=True) - norm_target = torch.mean(torch.sum(target * predicted, dim=3, keepdim=True), dim=2, keepdim=True) - scale = norm_target / norm_predicted - return mpjpe(scale * predicted, target) - - -def mean_velocity_error(predicted, target): - """ - Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative) - """ - assert predicted.shape == target.shape - - velocity_predicted = np.diff(predicted, axis=0) - velocity_target = np.diff(target, axis=0) - - return np.mean(np.linalg.norm(velocity_predicted - velocity_target, axis=len(target.shape) - 1)) diff --git a/spaces/SeViLA/SeViLA/lavis/models/albef_models/albef_feature_extractor.py b/spaces/SeViLA/SeViLA/lavis/models/albef_models/albef_feature_extractor.py deleted file mode 100644 index df7632c6d8e7eac7e6ae019379e53febd3f7ef0c..0000000000000000000000000000000000000000 --- a/spaces/SeViLA/SeViLA/lavis/models/albef_models/albef_feature_extractor.py +++ /dev/null @@ -1,204 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -import warnings - -import torch -import torch.nn.functional as F -from lavis.common.registry import registry -from lavis.common.utils import get_abs_path -from lavis.models.albef_models import AlbefBase -from lavis.models.albef_models.albef_outputs import AlbefOutputFeatures -from lavis.models.med import BertForMaskedLM -from lavis.models.vit import VisionTransformerEncoder -from torch import nn -from transformers import BertConfig - - -@registry.register_model("albef_feature_extractor") -class AlbefFeatureExtractor(AlbefBase): - PRETRAINED_MODEL_CONFIG_DICT = { - "base": "configs/models/albef_feature_extractor.yaml", - } - - def __init__(self, image_encoder, text_encoder, embed_dim=256, max_txt_len=30): - super().__init__() - - self.tokenizer = self.init_tokenizer() - - self.visual_encoder = image_encoder - self.text_encoder = text_encoder - - text_width = text_encoder.config.hidden_size - vision_width = image_encoder.vision_width - - self.embed_dim = embed_dim - - self.vision_proj = nn.Linear(vision_width, embed_dim) - self.text_proj = nn.Linear(text_width, embed_dim) - - self.max_txt_len = max_txt_len - - self.temp = nn.Parameter(0.07 * torch.ones([])) - - @torch.no_grad() - def extract_features(self, samples, mode="multimodal"): - """ - Extract features for multimodal or unimodal samples. - - Args: - samples (dict): A dictionary of samples, containing the following keys: - - image (torch.Tensor): A tensor of shape (B, C, H, W) containing the image. - Raw images should be preprocessed before being passed to feature extractor. - - text_input (list): A list of strings containing the text, length B. - mode (str): The mode of feature extraction. Can be either "multimodal", "text" or "image". - If "multimodal", return image features and multimodal features; - if "text", return text features; - if "image", return image features. - Default: "multimodal". - - Returns: - An AlbefOutputFeatures object, see lavis/models/albef_models/albef_outputs.py for details. - - Examples: - ```python - >>> from PIL import Image - >>> from lavis.models import load_model_and_preprocess - >>> raw_image = Image.open("docs/data/merlion.png").convert("RGB") - >>> caption = "a large fountain spewing water into the air" - >>> model, vis_processors, txt_processors = load_model_and_preprocess("albef_feature_extractor", is_eval=True) - >>> image = vis_processors["eval"](raw_image).unsqueeze(0) - >>> text_input = txt_processors["eval"](caption) - - >>> sample = {"image": image, "text_input": [text_input]} - - >>> features_multimodal = model.extract_features(sample) - >>> features_multimodal.keys() - odict_keys(['image_embeds', 'multimodal_embeds']) - >>> features_multimodal.image_embeds.shape - torch.Size([1, 197, 768]) - >>> features_multimodal.multimodal_embeds.shape - torch.Size([1, 12, 768]) - - >>> features_text = model.extract_features(sample, mode="text") - >>> features_text.keys() - odict_keys(['text_embeds', 'text_features']) - >>> features_text.text_embeds.shape - torch.Size([1, 12, 768]) - >>> features_text.text_features.shape - torch.Size([1, 12, 256]) - - >>> features_image = model.extract_features(sample, mode="image") - >>> features_image.keys() - odict_keys(['image_embeds', 'image_features']) - >>> features_image.image_embeds.shape - torch.Size([1, 197, 768]) - >>> features_image.image_features.shape - torch.Size([1, 197, 256]) - ``` - """ - image = samples["image"] - caption = samples["text_input"] - - if isinstance(mode, str): - mode = [mode] - - for m in mode: - assert m in [ - "multimodal", - "image", - "text", - ], "mode must be one of [multimodal, image, text], but got {}".format(m) - - # initalize output - image_embeds, text_embeds, multimodal_embeds = None, None, None - image_features, text_features = None, None - - if "image" in mode or "multimodal" in mode: - assert ( - image is not None - ), "image must be provided if mode is 'image' or 'multimodal'" - - image_embeds = self.visual_encoder.forward_features(image) - image_features = F.normalize(self.vision_proj(image_embeds), dim=-1) - - if "text" in mode or "multimodal" in mode: - assert ( - caption is not None - ), "text must be provided if mode is 'text' or 'multimodal'" - - text = self.tokenizer( - caption, - padding=True, - return_tensors="pt", - ).to(self.device) - - text_output = self.text_encoder.bert( - text.input_ids, - attention_mask=text.attention_mask, - return_dict=True, - mode="text", - ) - text_embeds = text_output.last_hidden_state - text_features = F.normalize(self.text_proj(text_embeds), dim=-1) - - if "multimodal" in mode: - image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to( - self.device - ) - - # forward the positve image-text pair - output = self.text_encoder.bert( - encoder_embeds=text_embeds, - attention_mask=text.attention_mask, - encoder_hidden_states=image_embeds, - encoder_attention_mask=image_atts, - return_dict=True, - mode="fusion", - ) - - multimodal_embeds = output.last_hidden_state - - return AlbefOutputFeatures( - image_embeds=image_embeds, - image_embeds_proj=image_features, - text_embeds=text_embeds, - text_embeds_proj=text_features, - multimodal_embeds=multimodal_embeds, - ) - - @classmethod - def from_config(cls, cfg=None): - image_encoder = VisionTransformerEncoder.from_config(cfg, from_pretrained=True) - config_text_encoder = BertConfig.from_json_file( - get_abs_path(cfg["med_config_path"]) - ) - config_text_encoder.fusion_layer = 6 - text_encoder = BertForMaskedLM.from_pretrained( - "bert-base-uncased", config=config_text_encoder - ) - - embed_dim = cfg.get("embed_dim", 256) - max_txt_len = cfg.get("max_txt_len", 30) - - model = cls( - image_encoder=image_encoder, - text_encoder=text_encoder, - embed_dim=embed_dim, - max_txt_len=max_txt_len, - ) - - # load pre-trained weights - pretrain_path = cfg.get("pretrained", None) - if pretrain_path is not None: - msg = model.load_from_pretrained( - url_or_filename=pretrain_path, rename_text_keys=False - ) - else: - warnings.warn("No pretrained weights are loaded.") - - return model diff --git a/spaces/ServerX/PorcoDiaz/infer/modules/vc/__init__.py b/spaces/ServerX/PorcoDiaz/infer/modules/vc/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/SeyedAli/Multilingual-Text-Similarity/app.py b/spaces/SeyedAli/Multilingual-Text-Similarity/app.py deleted file mode 100644 index c945f79f4d3d06e5925d13c1add87d1091fe881b..0000000000000000000000000000000000000000 --- a/spaces/SeyedAli/Multilingual-Text-Similarity/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1").launch() \ No newline at end of file diff --git a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/latex/attention/introduction.tex b/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/latex/attention/introduction.tex deleted file mode 100644 index 1baa8915f4cf7aec2520894a87470fc9436d954b..0000000000000000000000000000000000000000 --- a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/latex/attention/introduction.tex +++ /dev/null @@ -1,18 +0,0 @@ -Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. - -Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. -%\marginpar{not sure if the memory constraints are understandable here} -Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains. - -%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away} - -Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network. - -%\marginpar{not sure if "cross-positional communication" is understandable without explanation} -%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?} - -In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. -%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.} - -% Just a standard paragraph with citations, rewrite. -%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do. \ No newline at end of file diff --git a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/python/dqn/dqn.py b/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/python/dqn/dqn.py deleted file mode 100644 index 6cea64d39baa7ff4c1e549869aaa4b0ae17779a9..0000000000000000000000000000000000000000 --- a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/python/dqn/dqn.py +++ /dev/null @@ -1,245 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Type, Union - -import gym -import numpy as np -import torch as th -from torch.nn import functional as F - -from stable_baselines3.common import logger -from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm -from stable_baselines3.common.preprocessing import maybe_transpose -from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule -from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update -from stable_baselines3.dqn.policies import DQNPolicy - - -class DQN(OffPolicyAlgorithm): - """ - Deep Q-Network (DQN) - - Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236 - Default hyperparameters are taken from the nature paper, - except for the optimizer and learning rate that were taken from Stable Baselines defaults. - - :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...) - :param env: The environment to learn from (if registered in Gym, can be str) - :param learning_rate: The learning rate, it can be a function - of the current progress remaining (from 1 to 0) - :param buffer_size: size of the replay buffer - :param learning_starts: how many steps of the model to collect transitions for before learning starts - :param batch_size: Minibatch size for each gradient update - :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update - :param gamma: the discount factor - :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit - like ``(5, "step")`` or ``(2, "episode")``. - :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``) - Set to ``-1`` means to do as many gradient steps as steps done in the environment - during the rollout. - :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer - at a cost of more complexity. - See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195 - :param target_update_interval: update the target network every ``target_update_interval`` - environment steps. - :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced - :param exploration_initial_eps: initial value of random action probability - :param exploration_final_eps: final value of random action probability - :param max_grad_norm: The maximum value for the gradient clipping - :param tensorboard_log: the log location for tensorboard (if None, no logging) - :param create_eval_env: Whether to create a second environment that will be - used for evaluating the agent periodically. (Only available when passing string for the environment) - :param policy_kwargs: additional arguments to be passed to the policy on creation - :param verbose: the verbosity level: 0 no output, 1 info, 2 debug - :param seed: Seed for the pseudo random generators - :param device: Device (cpu, cuda, ...) on which the code should be run. - Setting it to auto, the code will be run on the GPU if possible. - :param _init_setup_model: Whether or not to build the network at the creation of the instance - """ - - def __init__( - self, - policy: Union[str, Type[DQNPolicy]], - env: Union[GymEnv, str], - learning_rate: Union[float, Schedule] = 1e-4, - buffer_size: int = 1000000, - learning_starts: int = 50000, - batch_size: Optional[int] = 32, - tau: float = 1.0, - gamma: float = 0.99, - train_freq: Union[int, Tuple[int, str]] = 4, - gradient_steps: int = 1, - optimize_memory_usage: bool = False, - target_update_interval: int = 10000, - exploration_fraction: float = 0.1, - exploration_initial_eps: float = 1.0, - exploration_final_eps: float = 0.05, - max_grad_norm: float = 10, - tensorboard_log: Optional[str] = None, - create_eval_env: bool = False, - policy_kwargs: Optional[Dict[str, Any]] = None, - verbose: int = 0, - seed: Optional[int] = None, - device: Union[th.device, str] = "auto", - _init_setup_model: bool = True, - ): - - super(DQN, self).__init__( - policy, - env, - DQNPolicy, - learning_rate, - buffer_size, - learning_starts, - batch_size, - tau, - gamma, - train_freq, - gradient_steps, - action_noise=None, # No action noise - policy_kwargs=policy_kwargs, - tensorboard_log=tensorboard_log, - verbose=verbose, - device=device, - create_eval_env=create_eval_env, - seed=seed, - sde_support=False, - optimize_memory_usage=optimize_memory_usage, - supported_action_spaces=(gym.spaces.Discrete,), - ) - - self.exploration_initial_eps = exploration_initial_eps - self.exploration_final_eps = exploration_final_eps - self.exploration_fraction = exploration_fraction - self.target_update_interval = target_update_interval - self.max_grad_norm = max_grad_norm - # "epsilon" for the epsilon-greedy exploration - self.exploration_rate = 0.0 - # Linear schedule will be defined in `_setup_model()` - self.exploration_schedule = None - self.q_net, self.q_net_target = None, None - - if _init_setup_model: - self._setup_model() - - def _setup_model(self) -> None: - super(DQN, self)._setup_model() - self._create_aliases() - self.exploration_schedule = get_linear_fn( - self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction - ) - - def _create_aliases(self) -> None: - self.q_net = self.policy.q_net - self.q_net_target = self.policy.q_net_target - - def _on_step(self) -> None: - """ - Update the exploration rate and target network if needed. - This method is called in ``collect_rollouts()`` after each step in the environment. - """ - if self.num_timesteps % self.target_update_interval == 0: - polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau) - - self.exploration_rate = self.exploration_schedule(self._current_progress_remaining) - logger.record("rollout/exploration rate", self.exploration_rate) - - def train(self, gradient_steps: int, batch_size: int = 100) -> None: - # Update learning rate according to schedule - self._update_learning_rate(self.policy.optimizer) - - losses = [] - for _ in range(gradient_steps): - # Sample replay buffer - replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) - - with th.no_grad(): - # Compute the next Q-values using the target network - next_q_values = self.q_net_target(replay_data.next_observations) - # Follow greedy policy: use the one with the highest value - next_q_values, _ = next_q_values.max(dim=1) - # Avoid potential broadcast issue - next_q_values = next_q_values.reshape(-1, 1) - # 1-step TD target - target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values - - # Get current Q-values estimates - current_q_values = self.q_net(replay_data.observations) - - # Retrieve the q-values for the actions from the replay buffer - current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long()) - - # Compute Huber loss (less sensitive to outliers) - loss = F.smooth_l1_loss(current_q_values, target_q_values) - losses.append(loss.item()) - - # Optimize the policy - self.policy.optimizer.zero_grad() - loss.backward() - # Clip gradient norm - th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm) - self.policy.optimizer.step() - - # Increase update counter - self._n_updates += gradient_steps - - logger.record("train/n_updates", self._n_updates, exclude="tensorboard") - logger.record("train/loss", np.mean(losses)) - - def predict( - self, - observation: np.ndarray, - state: Optional[np.ndarray] = None, - mask: Optional[np.ndarray] = None, - deterministic: bool = False, - ) -> Tuple[np.ndarray, Optional[np.ndarray]]: - """ - Overrides the base_class predict function to include epsilon-greedy exploration. - - :param observation: the input observation - :param state: The last states (can be None, used in recurrent policies) - :param mask: The last masks (can be None, used in recurrent policies) - :param deterministic: Whether or not to return deterministic actions. - :return: the model's action and the next state - (used in recurrent policies) - """ - if not deterministic and np.random.rand() < self.exploration_rate: - if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space): - n_batch = observation.shape[0] - action = np.array([self.action_space.sample() for _ in range(n_batch)]) - else: - action = np.array(self.action_space.sample()) - else: - action, state = self.policy.predict(observation, state, mask, deterministic) - return action, state - - def learn( - self, - total_timesteps: int, - callback: MaybeCallback = None, - log_interval: int = 4, - eval_env: Optional[GymEnv] = None, - eval_freq: int = -1, - n_eval_episodes: int = 5, - tb_log_name: str = "DQN", - eval_log_path: Optional[str] = None, - reset_num_timesteps: bool = True, - ) -> OffPolicyAlgorithm: - - return super(DQN, self).learn( - total_timesteps=total_timesteps, - callback=callback, - log_interval=log_interval, - eval_env=eval_env, - eval_freq=eval_freq, - n_eval_episodes=n_eval_episodes, - tb_log_name=tb_log_name, - eval_log_path=eval_log_path, - reset_num_timesteps=reset_num_timesteps, - ) - - def _excluded_save_params(self) -> List[str]: - return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"] - - def _get_torch_save_params(self) -> Tuple[List[str], List[str]]: - state_dicts = ["policy", "policy.optimizer"] - - return state_dicts, [] diff --git a/spaces/Sriharsha6902/Chat-Analyser/setup.sh b/spaces/Sriharsha6902/Chat-Analyser/setup.sh deleted file mode 100644 index f0ab2585fe12edf5a8ea8eb3a8614ba23ed52e7f..0000000000000000000000000000000000000000 --- a/spaces/Sriharsha6902/Chat-Analyser/setup.sh +++ /dev/null @@ -1,8 +0,0 @@ -mkdir -p ~/.streamlit/ -echo "\ -[server]\n\ -headless = true\n\ -port = $PORT\n\ -enableCORS = false\n\ -\n\ -" > ~/.streamlit/config.toml \ No newline at end of file diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/utils/__init__.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/utils/__init__.py deleted file mode 100644 index 75e25a0212f98e4a18d97c86c6cda225636a3215..0000000000000000000000000000000000000000 --- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/utils/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -"""Utilities.""" diff --git a/spaces/SumanthKarnati/SumanthKarnati-Image2Ingredients2/README.md b/spaces/SumanthKarnati/SumanthKarnati-Image2Ingredients2/README.md deleted file mode 100644 index e4f3fb4d2deda2840b96484da6d4e12a183b8da1..0000000000000000000000000000000000000000 --- a/spaces/SumanthKarnati/SumanthKarnati-Image2Ingredients2/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: SumanthKarnati Image2Ingredients2 -emoji: ⚡ -colorFrom: indigo -colorTo: indigo -sdk: gradio -sdk_version: 3.35.2 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/shellapp.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/shellapp.py deleted file mode 100644 index 29325a0ad2b427aea25f3088fa5648b520c6ca4b..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/shellapp.py +++ /dev/null @@ -1,451 +0,0 @@ -# encoding: utf-8 -""" -A mixin for :class:`~IPython.core.application.Application` classes that -launch InteractiveShell instances, load extensions, etc. -""" - -# Copyright (c) IPython Development Team. -# Distributed under the terms of the Modified BSD License. - -import glob -from itertools import chain -import os -import sys - -from traitlets.config.application import boolean_flag -from traitlets.config.configurable import Configurable -from traitlets.config.loader import Config -from IPython.core.application import SYSTEM_CONFIG_DIRS, ENV_CONFIG_DIRS -from IPython.core import pylabtools -from IPython.utils.contexts import preserve_keys -from IPython.utils.path import filefind -from traitlets import ( - Unicode, Instance, List, Bool, CaselessStrEnum, observe, - DottedObjectName, -) -from IPython.terminal import pt_inputhooks - -#----------------------------------------------------------------------------- -# Aliases and Flags -#----------------------------------------------------------------------------- - -gui_keys = tuple(sorted(pt_inputhooks.backends) + sorted(pt_inputhooks.aliases)) - -backend_keys = sorted(pylabtools.backends.keys()) -backend_keys.insert(0, 'auto') - -shell_flags = {} - -addflag = lambda *args: shell_flags.update(boolean_flag(*args)) -addflag('autoindent', 'InteractiveShell.autoindent', - 'Turn on autoindenting.', 'Turn off autoindenting.' -) -addflag('automagic', 'InteractiveShell.automagic', - """Turn on the auto calling of magic commands. Type %%magic at the - IPython prompt for more information.""", - 'Turn off the auto calling of magic commands.' -) -addflag('pdb', 'InteractiveShell.pdb', - "Enable auto calling the pdb debugger after every exception.", - "Disable auto calling the pdb debugger after every exception." -) -addflag('pprint', 'PlainTextFormatter.pprint', - "Enable auto pretty printing of results.", - "Disable auto pretty printing of results." -) -addflag('color-info', 'InteractiveShell.color_info', - """IPython can display information about objects via a set of functions, - and optionally can use colors for this, syntax highlighting - source code and various other elements. This is on by default, but can cause - problems with some pagers. If you see such problems, you can disable the - colours.""", - "Disable using colors for info related things." -) -addflag('ignore-cwd', 'InteractiveShellApp.ignore_cwd', - "Exclude the current working directory from sys.path", - "Include the current working directory in sys.path", -) -nosep_config = Config() -nosep_config.InteractiveShell.separate_in = '' -nosep_config.InteractiveShell.separate_out = '' -nosep_config.InteractiveShell.separate_out2 = '' - -shell_flags['nosep']=(nosep_config, "Eliminate all spacing between prompts.") -shell_flags['pylab'] = ( - {'InteractiveShellApp' : {'pylab' : 'auto'}}, - """Pre-load matplotlib and numpy for interactive use with - the default matplotlib backend.""" -) -shell_flags['matplotlib'] = ( - {'InteractiveShellApp' : {'matplotlib' : 'auto'}}, - """Configure matplotlib for interactive use with - the default matplotlib backend.""" -) - -# it's possible we don't want short aliases for *all* of these: -shell_aliases = dict( - autocall='InteractiveShell.autocall', - colors='InteractiveShell.colors', - logfile='InteractiveShell.logfile', - logappend='InteractiveShell.logappend', - c='InteractiveShellApp.code_to_run', - m='InteractiveShellApp.module_to_run', - ext="InteractiveShellApp.extra_extensions", - gui='InteractiveShellApp.gui', - pylab='InteractiveShellApp.pylab', - matplotlib='InteractiveShellApp.matplotlib', -) -shell_aliases['cache-size'] = 'InteractiveShell.cache_size' - -#----------------------------------------------------------------------------- -# Main classes and functions -#----------------------------------------------------------------------------- - -class InteractiveShellApp(Configurable): - """A Mixin for applications that start InteractiveShell instances. - - Provides configurables for loading extensions and executing files - as part of configuring a Shell environment. - - The following methods should be called by the :meth:`initialize` method - of the subclass: - - - :meth:`init_path` - - :meth:`init_shell` (to be implemented by the subclass) - - :meth:`init_gui_pylab` - - :meth:`init_extensions` - - :meth:`init_code` - """ - extensions = List(Unicode(), - help="A list of dotted module names of IPython extensions to load." - ).tag(config=True) - - extra_extensions = List( - DottedObjectName(), - help=""" - Dotted module name(s) of one or more IPython extensions to load. - - For specifying extra extensions to load on the command-line. - - .. versionadded:: 7.10 - """, - ).tag(config=True) - - reraise_ipython_extension_failures = Bool(False, - help="Reraise exceptions encountered loading IPython extensions?", - ).tag(config=True) - - # Extensions that are always loaded (not configurable) - default_extensions = List(Unicode(), [u'storemagic']).tag(config=False) - - hide_initial_ns = Bool(True, - help="""Should variables loaded at startup (by startup files, exec_lines, etc.) - be hidden from tools like %who?""" - ).tag(config=True) - - exec_files = List(Unicode(), - help="""List of files to run at IPython startup.""" - ).tag(config=True) - exec_PYTHONSTARTUP = Bool(True, - help="""Run the file referenced by the PYTHONSTARTUP environment - variable at IPython startup.""" - ).tag(config=True) - file_to_run = Unicode('', - help="""A file to be run""").tag(config=True) - - exec_lines = List(Unicode(), - help="""lines of code to run at IPython startup.""" - ).tag(config=True) - code_to_run = Unicode('', - help="Execute the given command string." - ).tag(config=True) - module_to_run = Unicode('', - help="Run the module as a script." - ).tag(config=True) - gui = CaselessStrEnum(gui_keys, allow_none=True, - help="Enable GUI event loop integration with any of {0}.".format(gui_keys) - ).tag(config=True) - matplotlib = CaselessStrEnum(backend_keys, allow_none=True, - help="""Configure matplotlib for interactive use with - the default matplotlib backend.""" - ).tag(config=True) - pylab = CaselessStrEnum(backend_keys, allow_none=True, - help="""Pre-load matplotlib and numpy for interactive use, - selecting a particular matplotlib backend and loop integration. - """ - ).tag(config=True) - pylab_import_all = Bool(True, - help="""If true, IPython will populate the user namespace with numpy, pylab, etc. - and an ``import *`` is done from numpy and pylab, when using pylab mode. - - When False, pylab mode should not import any names into the user namespace. - """ - ).tag(config=True) - ignore_cwd = Bool( - False, - help="""If True, IPython will not add the current working directory to sys.path. - When False, the current working directory is added to sys.path, allowing imports - of modules defined in the current directory.""" - ).tag(config=True) - shell = Instance('IPython.core.interactiveshell.InteractiveShellABC', - allow_none=True) - # whether interact-loop should start - interact = Bool(True) - - user_ns = Instance(dict, args=None, allow_none=True) - @observe('user_ns') - def _user_ns_changed(self, change): - if self.shell is not None: - self.shell.user_ns = change['new'] - self.shell.init_user_ns() - - def init_path(self): - """Add current working directory, '', to sys.path - - Unlike Python's default, we insert before the first `site-packages` - or `dist-packages` directory, - so that it is after the standard library. - - .. versionchanged:: 7.2 - Try to insert after the standard library, instead of first. - .. versionchanged:: 8.0 - Allow optionally not including the current directory in sys.path - """ - if '' in sys.path or self.ignore_cwd: - return - for idx, path in enumerate(sys.path): - parent, last_part = os.path.split(path) - if last_part in {'site-packages', 'dist-packages'}: - break - else: - # no site-packages or dist-packages found (?!) - # back to original behavior of inserting at the front - idx = 0 - sys.path.insert(idx, '') - - def init_shell(self): - raise NotImplementedError("Override in subclasses") - - def init_gui_pylab(self): - """Enable GUI event loop integration, taking pylab into account.""" - enable = False - shell = self.shell - if self.pylab: - enable = lambda key: shell.enable_pylab(key, import_all=self.pylab_import_all) - key = self.pylab - elif self.matplotlib: - enable = shell.enable_matplotlib - key = self.matplotlib - elif self.gui: - enable = shell.enable_gui - key = self.gui - - if not enable: - return - - try: - r = enable(key) - except ImportError: - self.log.warning("Eventloop or matplotlib integration failed. Is matplotlib installed?") - self.shell.showtraceback() - return - except Exception: - self.log.warning("GUI event loop or pylab initialization failed") - self.shell.showtraceback() - return - - if isinstance(r, tuple): - gui, backend = r[:2] - self.log.info("Enabling GUI event loop integration, " - "eventloop=%s, matplotlib=%s", gui, backend) - if key == "auto": - print("Using matplotlib backend: %s" % backend) - else: - gui = r - self.log.info("Enabling GUI event loop integration, " - "eventloop=%s", gui) - - def init_extensions(self): - """Load all IPython extensions in IPythonApp.extensions. - - This uses the :meth:`ExtensionManager.load_extensions` to load all - the extensions listed in ``self.extensions``. - """ - try: - self.log.debug("Loading IPython extensions...") - extensions = ( - self.default_extensions + self.extensions + self.extra_extensions - ) - for ext in extensions: - try: - self.log.info("Loading IPython extension: %s", ext) - self.shell.extension_manager.load_extension(ext) - except: - if self.reraise_ipython_extension_failures: - raise - msg = ("Error in loading extension: {ext}\n" - "Check your config files in {location}".format( - ext=ext, - location=self.profile_dir.location - )) - self.log.warning(msg, exc_info=True) - except: - if self.reraise_ipython_extension_failures: - raise - self.log.warning("Unknown error in loading extensions:", exc_info=True) - - def init_code(self): - """run the pre-flight code, specified via exec_lines""" - self._run_startup_files() - self._run_exec_lines() - self._run_exec_files() - - # Hide variables defined here from %who etc. - if self.hide_initial_ns: - self.shell.user_ns_hidden.update(self.shell.user_ns) - - # command-line execution (ipython -i script.py, ipython -m module) - # should *not* be excluded from %whos - self._run_cmd_line_code() - self._run_module() - - # flush output, so itwon't be attached to the first cell - sys.stdout.flush() - sys.stderr.flush() - self.shell._sys_modules_keys = set(sys.modules.keys()) - - def _run_exec_lines(self): - """Run lines of code in IPythonApp.exec_lines in the user's namespace.""" - if not self.exec_lines: - return - try: - self.log.debug("Running code from IPythonApp.exec_lines...") - for line in self.exec_lines: - try: - self.log.info("Running code in user namespace: %s" % - line) - self.shell.run_cell(line, store_history=False) - except: - self.log.warning("Error in executing line in user " - "namespace: %s" % line) - self.shell.showtraceback() - except: - self.log.warning("Unknown error in handling IPythonApp.exec_lines:") - self.shell.showtraceback() - - def _exec_file(self, fname, shell_futures=False): - try: - full_filename = filefind(fname, [u'.', self.ipython_dir]) - except IOError: - self.log.warning("File not found: %r"%fname) - return - # Make sure that the running script gets a proper sys.argv as if it - # were run from a system shell. - save_argv = sys.argv - sys.argv = [full_filename] + self.extra_args[1:] - try: - if os.path.isfile(full_filename): - self.log.info("Running file in user namespace: %s" % - full_filename) - # Ensure that __file__ is always defined to match Python - # behavior. - with preserve_keys(self.shell.user_ns, '__file__'): - self.shell.user_ns['__file__'] = fname - if full_filename.endswith('.ipy') or full_filename.endswith('.ipynb'): - self.shell.safe_execfile_ipy(full_filename, - shell_futures=shell_futures) - else: - # default to python, even without extension - self.shell.safe_execfile(full_filename, - self.shell.user_ns, - shell_futures=shell_futures, - raise_exceptions=True) - finally: - sys.argv = save_argv - - def _run_startup_files(self): - """Run files from profile startup directory""" - startup_dirs = [self.profile_dir.startup_dir] + [ - os.path.join(p, 'startup') for p in chain(ENV_CONFIG_DIRS, SYSTEM_CONFIG_DIRS) - ] - startup_files = [] - - if self.exec_PYTHONSTARTUP and os.environ.get('PYTHONSTARTUP', False) and \ - not (self.file_to_run or self.code_to_run or self.module_to_run): - python_startup = os.environ['PYTHONSTARTUP'] - self.log.debug("Running PYTHONSTARTUP file %s...", python_startup) - try: - self._exec_file(python_startup) - except: - self.log.warning("Unknown error in handling PYTHONSTARTUP file %s:", python_startup) - self.shell.showtraceback() - for startup_dir in startup_dirs[::-1]: - startup_files += glob.glob(os.path.join(startup_dir, '*.py')) - startup_files += glob.glob(os.path.join(startup_dir, '*.ipy')) - if not startup_files: - return - - self.log.debug("Running startup files from %s...", startup_dir) - try: - for fname in sorted(startup_files): - self._exec_file(fname) - except: - self.log.warning("Unknown error in handling startup files:") - self.shell.showtraceback() - - def _run_exec_files(self): - """Run files from IPythonApp.exec_files""" - if not self.exec_files: - return - - self.log.debug("Running files in IPythonApp.exec_files...") - try: - for fname in self.exec_files: - self._exec_file(fname) - except: - self.log.warning("Unknown error in handling IPythonApp.exec_files:") - self.shell.showtraceback() - - def _run_cmd_line_code(self): - """Run code or file specified at the command-line""" - if self.code_to_run: - line = self.code_to_run - try: - self.log.info("Running code given at command line (c=): %s" % - line) - self.shell.run_cell(line, store_history=False) - except: - self.log.warning("Error in executing line in user namespace: %s" % - line) - self.shell.showtraceback() - if not self.interact: - self.exit(1) - - # Like Python itself, ignore the second if the first of these is present - elif self.file_to_run: - fname = self.file_to_run - if os.path.isdir(fname): - fname = os.path.join(fname, "__main__.py") - if not os.path.exists(fname): - self.log.warning("File '%s' doesn't exist", fname) - if not self.interact: - self.exit(2) - try: - self._exec_file(fname, shell_futures=True) - except: - self.shell.showtraceback(tb_offset=4) - if not self.interact: - self.exit(1) - - def _run_module(self): - """Run module specified at the command-line.""" - if self.module_to_run: - # Make sure that the module gets a proper sys.argv as if it were - # run using `python -m`. - save_argv = sys.argv - sys.argv = [sys.executable] + self.extra_args - try: - self.shell.safe_run_module(self.module_to_run, - self.shell.user_ns) - finally: - sys.argv = save_argv diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/tests/test_shortcuts.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/tests/test_shortcuts.py deleted file mode 100644 index 45bb327988b918151fffc6314d855ac1d788d77b..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/tests/test_shortcuts.py +++ /dev/null @@ -1,468 +0,0 @@ -import pytest -from IPython.terminal.shortcuts.auto_suggest import ( - accept, - accept_or_jump_to_end, - accept_token, - accept_character, - accept_word, - accept_and_keep_cursor, - discard, - NavigableAutoSuggestFromHistory, - swap_autosuggestion_up, - swap_autosuggestion_down, -) -from IPython.terminal.shortcuts.auto_match import skip_over -from IPython.terminal.shortcuts import create_ipython_shortcuts - -from prompt_toolkit.history import InMemoryHistory -from prompt_toolkit.buffer import Buffer -from prompt_toolkit.document import Document -from prompt_toolkit.auto_suggest import AutoSuggestFromHistory - -from unittest.mock import patch, Mock - - -def test_deprected(): - import IPython.terminal.shortcuts.auto_suggest as iptsa - - with pytest.warns(DeprecationWarning, match=r"8\.12.+accept_or_jump_to_end"): - iptsa.accept_in_vi_insert_mode - - -def make_event(text, cursor, suggestion): - event = Mock() - event.current_buffer = Mock() - event.current_buffer.suggestion = Mock() - event.current_buffer.text = text - event.current_buffer.cursor_position = cursor - event.current_buffer.suggestion.text = suggestion - event.current_buffer.document = Document(text=text, cursor_position=cursor) - return event - - -@pytest.mark.parametrize( - "text, suggestion, expected", - [ - ("", "def out(tag: str, n=50):", "def out(tag: str, n=50):"), - ("def ", "out(tag: str, n=50):", "out(tag: str, n=50):"), - ], -) -def test_accept(text, suggestion, expected): - event = make_event(text, len(text), suggestion) - buffer = event.current_buffer - buffer.insert_text = Mock() - accept(event) - assert buffer.insert_text.called - assert buffer.insert_text.call_args[0] == (expected,) - - -@pytest.mark.parametrize( - "text, suggestion", - [ - ("", "def out(tag: str, n=50):"), - ("def ", "out(tag: str, n=50):"), - ], -) -def test_discard(text, suggestion): - event = make_event(text, len(text), suggestion) - buffer = event.current_buffer - buffer.insert_text = Mock() - discard(event) - assert not buffer.insert_text.called - assert buffer.suggestion is None - - -@pytest.mark.parametrize( - "text, cursor, suggestion, called", - [ - ("123456", 6, "123456789", True), - ("123456", 3, "123456789", False), - ("123456 \n789", 6, "123456789", True), - ], -) -def test_autosuggest_at_EOL(text, cursor, suggestion, called): - """ - test that autosuggest is only applied at end of line. - """ - - event = make_event(text, cursor, suggestion) - event.current_buffer.insert_text = Mock() - accept_or_jump_to_end(event) - if called: - event.current_buffer.insert_text.assert_called() - else: - event.current_buffer.insert_text.assert_not_called() - # event.current_buffer.document.get_end_of_line_position.assert_called() - - -@pytest.mark.parametrize( - "text, suggestion, expected", - [ - ("", "def out(tag: str, n=50):", "def "), - ("d", "ef out(tag: str, n=50):", "ef "), - ("de ", "f out(tag: str, n=50):", "f "), - ("def", " out(tag: str, n=50):", " "), - ("def ", "out(tag: str, n=50):", "out("), - ("def o", "ut(tag: str, n=50):", "ut("), - ("def ou", "t(tag: str, n=50):", "t("), - ("def out", "(tag: str, n=50):", "("), - ("def out(", "tag: str, n=50):", "tag: "), - ("def out(t", "ag: str, n=50):", "ag: "), - ("def out(ta", "g: str, n=50):", "g: "), - ("def out(tag", ": str, n=50):", ": "), - ("def out(tag:", " str, n=50):", " "), - ("def out(tag: ", "str, n=50):", "str, "), - ("def out(tag: s", "tr, n=50):", "tr, "), - ("def out(tag: st", "r, n=50):", "r, "), - ("def out(tag: str", ", n=50):", ", n"), - ("def out(tag: str,", " n=50):", " n"), - ("def out(tag: str, ", "n=50):", "n="), - ("def out(tag: str, n", "=50):", "="), - ("def out(tag: str, n=", "50):", "50)"), - ("def out(tag: str, n=5", "0):", "0)"), - ("def out(tag: str, n=50", "):", "):"), - ("def out(tag: str, n=50)", ":", ":"), - ], -) -def test_autosuggest_token(text, suggestion, expected): - event = make_event(text, len(text), suggestion) - event.current_buffer.insert_text = Mock() - accept_token(event) - assert event.current_buffer.insert_text.called - assert event.current_buffer.insert_text.call_args[0] == (expected,) - - -@pytest.mark.parametrize( - "text, suggestion, expected", - [ - ("", "def out(tag: str, n=50):", "d"), - ("d", "ef out(tag: str, n=50):", "e"), - ("de ", "f out(tag: str, n=50):", "f"), - ("def", " out(tag: str, n=50):", " "), - ], -) -def test_accept_character(text, suggestion, expected): - event = make_event(text, len(text), suggestion) - event.current_buffer.insert_text = Mock() - accept_character(event) - assert event.current_buffer.insert_text.called - assert event.current_buffer.insert_text.call_args[0] == (expected,) - - -@pytest.mark.parametrize( - "text, suggestion, expected", - [ - ("", "def out(tag: str, n=50):", "def "), - ("d", "ef out(tag: str, n=50):", "ef "), - ("de", "f out(tag: str, n=50):", "f "), - ("def", " out(tag: str, n=50):", " "), - # (this is why we also have accept_token) - ("def ", "out(tag: str, n=50):", "out(tag: "), - ], -) -def test_accept_word(text, suggestion, expected): - event = make_event(text, len(text), suggestion) - event.current_buffer.insert_text = Mock() - accept_word(event) - assert event.current_buffer.insert_text.called - assert event.current_buffer.insert_text.call_args[0] == (expected,) - - -@pytest.mark.parametrize( - "text, suggestion, expected, cursor", - [ - ("", "def out(tag: str, n=50):", "def out(tag: str, n=50):", 0), - ("def ", "out(tag: str, n=50):", "out(tag: str, n=50):", 4), - ], -) -def test_accept_and_keep_cursor(text, suggestion, expected, cursor): - event = make_event(text, cursor, suggestion) - buffer = event.current_buffer - buffer.insert_text = Mock() - accept_and_keep_cursor(event) - assert buffer.insert_text.called - assert buffer.insert_text.call_args[0] == (expected,) - assert buffer.cursor_position == cursor - - -def test_autosuggest_token_empty(): - full = "def out(tag: str, n=50):" - event = make_event(full, len(full), "") - event.current_buffer.insert_text = Mock() - - with patch( - "prompt_toolkit.key_binding.bindings.named_commands.forward_word" - ) as forward_word: - accept_token(event) - assert not event.current_buffer.insert_text.called - assert forward_word.called - - -def test_other_providers(): - """Ensure that swapping autosuggestions does not break with other providers""" - provider = AutoSuggestFromHistory() - ip = get_ipython() - ip.auto_suggest = provider - event = Mock() - event.current_buffer = Buffer() - assert swap_autosuggestion_up(event) is None - assert swap_autosuggestion_down(event) is None - - -async def test_navigable_provider(): - provider = NavigableAutoSuggestFromHistory() - history = InMemoryHistory(history_strings=["very_a", "very", "very_b", "very_c"]) - buffer = Buffer(history=history) - ip = get_ipython() - ip.auto_suggest = provider - - async for _ in history.load(): - pass - - buffer.cursor_position = 5 - buffer.text = "very" - - up = swap_autosuggestion_up - down = swap_autosuggestion_down - - event = Mock() - event.current_buffer = buffer - - def get_suggestion(): - suggestion = provider.get_suggestion(buffer, buffer.document) - buffer.suggestion = suggestion - return suggestion - - assert get_suggestion().text == "_c" - - # should go up - up(event) - assert get_suggestion().text == "_b" - - # should skip over 'very' which is identical to buffer content - up(event) - assert get_suggestion().text == "_a" - - # should cycle back to beginning - up(event) - assert get_suggestion().text == "_c" - - # should cycle back through end boundary - down(event) - assert get_suggestion().text == "_a" - - down(event) - assert get_suggestion().text == "_b" - - down(event) - assert get_suggestion().text == "_c" - - down(event) - assert get_suggestion().text == "_a" - - -async def test_navigable_provider_multiline_entries(): - provider = NavigableAutoSuggestFromHistory() - history = InMemoryHistory(history_strings=["very_a\nvery_b", "very_c"]) - buffer = Buffer(history=history) - ip = get_ipython() - ip.auto_suggest = provider - - async for _ in history.load(): - pass - - buffer.cursor_position = 5 - buffer.text = "very" - up = swap_autosuggestion_up - down = swap_autosuggestion_down - - event = Mock() - event.current_buffer = buffer - - def get_suggestion(): - suggestion = provider.get_suggestion(buffer, buffer.document) - buffer.suggestion = suggestion - return suggestion - - assert get_suggestion().text == "_c" - - up(event) - assert get_suggestion().text == "_b" - - up(event) - assert get_suggestion().text == "_a" - - down(event) - assert get_suggestion().text == "_b" - - down(event) - assert get_suggestion().text == "_c" - - -def create_session_mock(): - session = Mock() - session.default_buffer = Buffer() - return session - - -def test_navigable_provider_connection(): - provider = NavigableAutoSuggestFromHistory() - provider.skip_lines = 1 - - session_1 = create_session_mock() - provider.connect(session_1) - - assert provider.skip_lines == 1 - session_1.default_buffer.on_text_insert.fire() - assert provider.skip_lines == 0 - - session_2 = create_session_mock() - provider.connect(session_2) - provider.skip_lines = 2 - - assert provider.skip_lines == 2 - session_2.default_buffer.on_text_insert.fire() - assert provider.skip_lines == 0 - - provider.skip_lines = 3 - provider.disconnect() - session_1.default_buffer.on_text_insert.fire() - session_2.default_buffer.on_text_insert.fire() - assert provider.skip_lines == 3 - - -@pytest.fixture -def ipython_with_prompt(): - ip = get_ipython() - ip.pt_app = Mock() - ip.pt_app.key_bindings = create_ipython_shortcuts(ip) - try: - yield ip - finally: - ip.pt_app = None - - -def find_bindings_by_command(command): - ip = get_ipython() - return [ - binding - for binding in ip.pt_app.key_bindings.bindings - if binding.handler == command - ] - - -def test_modify_unique_shortcut(ipython_with_prompt): - original = find_bindings_by_command(accept_token) - assert len(original) == 1 - - ipython_with_prompt.shortcuts = [ - {"command": "IPython:auto_suggest.accept_token", "new_keys": ["a", "b", "c"]} - ] - matched = find_bindings_by_command(accept_token) - assert len(matched) == 1 - assert list(matched[0].keys) == ["a", "b", "c"] - assert list(matched[0].keys) != list(original[0].keys) - assert matched[0].filter == original[0].filter - - ipython_with_prompt.shortcuts = [ - {"command": "IPython:auto_suggest.accept_token", "new_filter": "always"} - ] - matched = find_bindings_by_command(accept_token) - assert len(matched) == 1 - assert list(matched[0].keys) != ["a", "b", "c"] - assert list(matched[0].keys) == list(original[0].keys) - assert matched[0].filter != original[0].filter - - -def test_disable_shortcut(ipython_with_prompt): - matched = find_bindings_by_command(accept_token) - assert len(matched) == 1 - - ipython_with_prompt.shortcuts = [ - {"command": "IPython:auto_suggest.accept_token", "new_keys": []} - ] - matched = find_bindings_by_command(accept_token) - assert len(matched) == 0 - - ipython_with_prompt.shortcuts = [] - matched = find_bindings_by_command(accept_token) - assert len(matched) == 1 - - -def test_modify_shortcut_with_filters(ipython_with_prompt): - matched = find_bindings_by_command(skip_over) - matched_keys = {m.keys[0] for m in matched} - assert matched_keys == {")", "]", "}", "'", '"'} - - with pytest.raises(ValueError, match="Multiple shortcuts matching"): - ipython_with_prompt.shortcuts = [ - {"command": "IPython:auto_match.skip_over", "new_keys": ["x"]} - ] - - ipython_with_prompt.shortcuts = [ - { - "command": "IPython:auto_match.skip_over", - "new_keys": ["x"], - "match_filter": "focused_insert & auto_match & followed_by_single_quote", - } - ] - matched = find_bindings_by_command(skip_over) - matched_keys = {m.keys[0] for m in matched} - assert matched_keys == {")", "]", "}", "x", '"'} - - -def example_command(): - pass - - -def test_add_shortcut_for_new_command(ipython_with_prompt): - matched = find_bindings_by_command(example_command) - assert len(matched) == 0 - - with pytest.raises(ValueError, match="example_command is not a known"): - ipython_with_prompt.shortcuts = [ - {"command": "example_command", "new_keys": ["x"]} - ] - matched = find_bindings_by_command(example_command) - assert len(matched) == 0 - - -def test_modify_shortcut_failure(ipython_with_prompt): - with pytest.raises(ValueError, match="No shortcuts matching"): - ipython_with_prompt.shortcuts = [ - { - "command": "IPython:auto_match.skip_over", - "match_keys": ["x"], - "new_keys": ["y"], - } - ] - - -def test_add_shortcut_for_existing_command(ipython_with_prompt): - matched = find_bindings_by_command(skip_over) - assert len(matched) == 5 - - with pytest.raises(ValueError, match="Cannot add a shortcut without keys"): - ipython_with_prompt.shortcuts = [ - {"command": "IPython:auto_match.skip_over", "new_keys": [], "create": True} - ] - - ipython_with_prompt.shortcuts = [ - {"command": "IPython:auto_match.skip_over", "new_keys": ["x"], "create": True} - ] - matched = find_bindings_by_command(skip_over) - assert len(matched) == 6 - - ipython_with_prompt.shortcuts = [] - matched = find_bindings_by_command(skip_over) - assert len(matched) == 5 - - -def test_setting_shortcuts_before_pt_app_init(): - ipython = get_ipython() - assert ipython.pt_app is None - shortcuts = [ - {"command": "IPython:auto_match.skip_over", "new_keys": ["x"], "create": True} - ] - ipython.shortcuts = shortcuts - assert ipython.shortcuts == shortcuts diff --git a/spaces/Suniilkumaar/SwapMukham/nsfw_checker/__init__.py b/spaces/Suniilkumaar/SwapMukham/nsfw_checker/__init__.py deleted file mode 100644 index a6dc0ae6d70e03fa601037db4846b40a91871ed4..0000000000000000000000000000000000000000 --- a/spaces/Suniilkumaar/SwapMukham/nsfw_checker/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . opennsfw import NSFWChecker \ No newline at end of file diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/upfirdn2d.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/upfirdn2d.py deleted file mode 100644 index c8bb2c3c949eed38a6465ed369fa881538dca010..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/upfirdn2d.py +++ /dev/null @@ -1,330 +0,0 @@ -# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 - -# Copyright (c) 2021, NVIDIA Corporation. All rights reserved. -# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator -# Augmentation (ADA) -# ======================================================================= - -# 1. Definitions - -# "Licensor" means any person or entity that distributes its Work. - -# "Software" means the original work of authorship made available under -# this License. - -# "Work" means the Software and any additions to or derivative works of -# the Software that are made available under this License. - -# The terms "reproduce," "reproduction," "derivative works," and -# "distribution" have the meaning as provided under U.S. copyright law; -# provided, however, that for the purposes of this License, derivative -# works shall not include works that remain separable from, or merely -# link (or bind by name) to the interfaces of, the Work. - -# Works, including the Software, are "made available" under this License -# by including in or with the Work either (a) a copyright notice -# referencing the applicability of this License to the Work, or (b) a -# copy of this License. - -# 2. License Grants - -# 2.1 Copyright Grant. Subject to the terms and conditions of this -# License, each Licensor grants to you a perpetual, worldwide, -# non-exclusive, royalty-free, copyright license to reproduce, -# prepare derivative works of, publicly display, publicly perform, -# sublicense and distribute its Work and any resulting derivative -# works in any form. - -# 3. Limitations - -# 3.1 Redistribution. You may reproduce or distribute the Work only -# if (a) you do so under this License, (b) you include a complete -# copy of this License with your distribution, and (c) you retain -# without modification any copyright, patent, trademark, or -# attribution notices that are present in the Work. - -# 3.2 Derivative Works. You may specify that additional or different -# terms apply to the use, reproduction, and distribution of your -# derivative works of the Work ("Your Terms") only if (a) Your Terms -# provide that the use limitation in Section 3.3 applies to your -# derivative works, and (b) you identify the specific derivative -# works that are subject to Your Terms. Notwithstanding Your Terms, -# this License (including the redistribution requirements in Section -# 3.1) will continue to apply to the Work itself. - -# 3.3 Use Limitation. The Work and any derivative works thereof only -# may be used or intended for use non-commercially. Notwithstanding -# the foregoing, NVIDIA and its affiliates may use the Work and any -# derivative works commercially. As used herein, "non-commercially" -# means for research or evaluation purposes only. - -# 3.4 Patent Claims. If you bring or threaten to bring a patent claim -# against any Licensor (including any claim, cross-claim or -# counterclaim in a lawsuit) to enforce any patents that you allege -# are infringed by any Work, then your rights under this License from -# such Licensor (including the grant in Section 2.1) will terminate -# immediately. - -# 3.5 Trademarks. This License does not grant any rights to use any -# Licensor’s or its affiliates’ names, logos, or trademarks, except -# as necessary to reproduce the notices described in this License. - -# 3.6 Termination. If you violate any term of this License, then your -# rights under this License (including the grant in Section 2.1) will -# terminate immediately. - -# 4. Disclaimer of Warranty. - -# THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF -# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR -# NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER -# THIS LICENSE. - -# 5. Limitation of Liability. - -# EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL -# THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE -# SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, -# INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF -# OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK -# (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, -# LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER -# COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGES. - -# ======================================================================= - -import torch -from torch.autograd import Function -from torch.nn import functional as F - -from annotator.uniformer.mmcv.utils import to_2tuple -from ..utils import ext_loader - -upfirdn2d_ext = ext_loader.load_ext('_ext', ['upfirdn2d']) - - -class UpFirDn2dBackward(Function): - - @staticmethod - def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, - in_size, out_size): - - up_x, up_y = up - down_x, down_y = down - g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad - - grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) - - grad_input = upfirdn2d_ext.upfirdn2d( - grad_output, - grad_kernel, - up_x=down_x, - up_y=down_y, - down_x=up_x, - down_y=up_y, - pad_x0=g_pad_x0, - pad_x1=g_pad_x1, - pad_y0=g_pad_y0, - pad_y1=g_pad_y1) - grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], - in_size[3]) - - ctx.save_for_backward(kernel) - - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - ctx.up_x = up_x - ctx.up_y = up_y - ctx.down_x = down_x - ctx.down_y = down_y - ctx.pad_x0 = pad_x0 - ctx.pad_x1 = pad_x1 - ctx.pad_y0 = pad_y0 - ctx.pad_y1 = pad_y1 - ctx.in_size = in_size - ctx.out_size = out_size - - return grad_input - - @staticmethod - def backward(ctx, gradgrad_input): - kernel, = ctx.saved_tensors - - gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], - ctx.in_size[3], 1) - - gradgrad_out = upfirdn2d_ext.upfirdn2d( - gradgrad_input, - kernel, - up_x=ctx.up_x, - up_y=ctx.up_y, - down_x=ctx.down_x, - down_y=ctx.down_y, - pad_x0=ctx.pad_x0, - pad_x1=ctx.pad_x1, - pad_y0=ctx.pad_y0, - pad_y1=ctx.pad_y1) - # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], - # ctx.out_size[1], ctx.in_size[3]) - gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], - ctx.out_size[0], ctx.out_size[1]) - - return gradgrad_out, None, None, None, None, None, None, None, None - - -class UpFirDn2d(Function): - - @staticmethod - def forward(ctx, input, kernel, up, down, pad): - up_x, up_y = up - down_x, down_y = down - pad_x0, pad_x1, pad_y0, pad_y1 = pad - - kernel_h, kernel_w = kernel.shape - batch, channel, in_h, in_w = input.shape - ctx.in_size = input.shape - - input = input.reshape(-1, in_h, in_w, 1) - - ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - ctx.out_size = (out_h, out_w) - - ctx.up = (up_x, up_y) - ctx.down = (down_x, down_y) - ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) - - g_pad_x0 = kernel_w - pad_x0 - 1 - g_pad_y0 = kernel_h - pad_y0 - 1 - g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 - g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 - - ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) - - out = upfirdn2d_ext.upfirdn2d( - input, - kernel, - up_x=up_x, - up_y=up_y, - down_x=down_x, - down_y=down_y, - pad_x0=pad_x0, - pad_x1=pad_x1, - pad_y0=pad_y0, - pad_y1=pad_y1) - # out = out.view(major, out_h, out_w, minor) - out = out.view(-1, channel, out_h, out_w) - - return out - - @staticmethod - def backward(ctx, grad_output): - kernel, grad_kernel = ctx.saved_tensors - - grad_input = UpFirDn2dBackward.apply( - grad_output, - kernel, - grad_kernel, - ctx.up, - ctx.down, - ctx.pad, - ctx.g_pad, - ctx.in_size, - ctx.out_size, - ) - - return grad_input, None, None, None, None - - -def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): - """UpFRIDn for 2d features. - - UpFIRDn is short for upsample, apply FIR filter and downsample. More - details can be found in: - https://www.mathworks.com/help/signal/ref/upfirdn.html - - Args: - input (Tensor): Tensor with shape of (n, c, h, w). - kernel (Tensor): Filter kernel. - up (int | tuple[int], optional): Upsampling factor. If given a number, - we will use this factor for the both height and width side. - Defaults to 1. - down (int | tuple[int], optional): Downsampling factor. If given a - number, we will use this factor for the both height and width side. - Defaults to 1. - pad (tuple[int], optional): Padding for tensors, (x_pad, y_pad) or - (x_pad_0, x_pad_1, y_pad_0, y_pad_1). Defaults to (0, 0). - - Returns: - Tensor: Tensor after UpFIRDn. - """ - if input.device.type == 'cpu': - if len(pad) == 2: - pad = (pad[0], pad[1], pad[0], pad[1]) - - up = to_2tuple(up) - - down = to_2tuple(down) - - out = upfirdn2d_native(input, kernel, up[0], up[1], down[0], down[1], - pad[0], pad[1], pad[2], pad[3]) - else: - _up = to_2tuple(up) - - _down = to_2tuple(down) - - if len(pad) == 4: - _pad = pad - elif len(pad) == 2: - _pad = (pad[0], pad[1], pad[0], pad[1]) - - out = UpFirDn2d.apply(input, kernel, _up, _down, _pad) - - return out - - -def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, - pad_y0, pad_y1): - _, channel, in_h, in_w = input.shape - input = input.reshape(-1, in_h, in_w, 1) - - _, in_h, in_w, minor = input.shape - kernel_h, kernel_w = kernel.shape - - out = input.view(-1, in_h, 1, in_w, 1, minor) - out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) - out = out.view(-1, in_h * up_y, in_w * up_x, minor) - - out = F.pad( - out, - [0, 0, - max(pad_x0, 0), - max(pad_x1, 0), - max(pad_y0, 0), - max(pad_y1, 0)]) - out = out[:, - max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ] - - out = out.permute(0, 3, 1, 2) - out = out.reshape( - [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) - w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) - out = F.conv2d(out, w) - out = out.reshape( - -1, - minor, - in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, - in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, - ) - out = out.permute(0, 2, 3, 1) - out = out[:, ::down_y, ::down_x, :] - - out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 - out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - - return out.view(-1, channel, out_h, out_w) diff --git a/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/monotonic_align/core.py b/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/monotonic_align/core.py deleted file mode 100644 index 7c962adea65543ef426034c4d53c4f0e615e8181..0000000000000000000000000000000000000000 --- a/spaces/TLME/Bert-VITS-Umamusume-Genshin-HonkaiSR/monotonic_align/core.py +++ /dev/null @@ -1,46 +0,0 @@ -import numba - - -@numba.jit( - numba.void( - numba.int32[:, :, ::1], - numba.float32[:, :, ::1], - numba.int32[::1], - numba.int32[::1], - ), - nopython=True, - nogil=True, -) -def maximum_path_jit(paths, values, t_ys, t_xs): - b = paths.shape[0] - max_neg_val = -1e9 - for i in range(int(b)): - path = paths[i] - value = values[i] - t_y = t_ys[i] - t_x = t_xs[i] - - v_prev = v_cur = 0.0 - index = t_x - 1 - - for y in range(t_y): - for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): - if x == y: - v_cur = max_neg_val - else: - v_cur = value[y - 1, x] - if x == 0: - if y == 0: - v_prev = 0.0 - else: - v_prev = max_neg_val - else: - v_prev = value[y - 1, x - 1] - value[y, x] += max(v_prev, v_cur) - - for y in range(t_y - 1, -1, -1): - path[y, index] = 1 - if index != 0 and ( - index == y or value[y - 1, index] < value[y - 1, index - 1] - ): - index = index - 1 diff --git a/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/__init__.py b/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/__init__.py deleted file mode 100644 index 3407398b08379f975aa59cb35e731b82d2a50360..0000000000000000000000000000000000000000 --- a/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from . import fast_gp, mlp, flexible_categorical, differentiable_prior, prior_bag - - - diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/__main__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/__main__.py deleted file mode 100644 index 2f7f8cbad05d3955be8fbe68ac8ba6c13ef974e6..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/__main__.py +++ /dev/null @@ -1,17 +0,0 @@ -""" - pygments.__main__ - ~~~~~~~~~~~~~~~~~ - - Main entry point for ``python -m pygments``. - - :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import sys -from pip._vendor.pygments.cmdline import main - -try: - sys.exit(main(sys.argv)) -except KeyboardInterrupt: - sys.exit(1) diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/scope.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/scope.py deleted file mode 100644 index c9d134cc3cedae929e5bef2b5547f7e33dc10a52..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/scope.py +++ /dev/null @@ -1,86 +0,0 @@ -from collections.abc import Mapping -from typing import TYPE_CHECKING, Any, Optional, Tuple - -from .highlighter import ReprHighlighter -from .panel import Panel -from .pretty import Pretty -from .table import Table -from .text import Text, TextType - -if TYPE_CHECKING: - from .console import ConsoleRenderable - - -def render_scope( - scope: "Mapping[str, Any]", - *, - title: Optional[TextType] = None, - sort_keys: bool = True, - indent_guides: bool = False, - max_length: Optional[int] = None, - max_string: Optional[int] = None, -) -> "ConsoleRenderable": - """Render python variables in a given scope. - - Args: - scope (Mapping): A mapping containing variable names and values. - title (str, optional): Optional title. Defaults to None. - sort_keys (bool, optional): Enable sorting of items. Defaults to True. - indent_guides (bool, optional): Enable indentation guides. Defaults to False. - max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation. - Defaults to None. - max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None. - - Returns: - ConsoleRenderable: A renderable object. - """ - highlighter = ReprHighlighter() - items_table = Table.grid(padding=(0, 1), expand=False) - items_table.add_column(justify="right") - - def sort_items(item: Tuple[str, Any]) -> Tuple[bool, str]: - """Sort special variables first, then alphabetically.""" - key, _ = item - return (not key.startswith("__"), key.lower()) - - items = sorted(scope.items(), key=sort_items) if sort_keys else scope.items() - for key, value in items: - key_text = Text.assemble( - (key, "scope.key.special" if key.startswith("__") else "scope.key"), - (" =", "scope.equals"), - ) - items_table.add_row( - key_text, - Pretty( - value, - highlighter=highlighter, - indent_guides=indent_guides, - max_length=max_length, - max_string=max_string, - ), - ) - return Panel.fit( - items_table, - title=title, - border_style="scope.border", - padding=(0, 1), - ) - - -if __name__ == "__main__": # pragma: no cover - from pip._vendor.rich import print - - print() - - def test(foo: float, bar: float) -> None: - list_of_things = [1, 2, 3, None, 4, True, False, "Hello World"] - dict_of_things = { - "version": "1.1", - "method": "confirmFruitPurchase", - "params": [["apple", "orange", "mangoes", "pomelo"], 1.123], - "id": "194521489", - } - print(render_scope(locals(), title="[i]locals", sort_keys=False)) - - test(20.3423, 3.1427) - print() diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/versionpredicate.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/versionpredicate.py deleted file mode 100644 index d6c0c007aad871b9348fea57c9188d0ffd5f10d2..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/versionpredicate.py +++ /dev/null @@ -1,175 +0,0 @@ -"""Module for parsing and testing package version predicate strings. -""" -import re -from . import version -import operator - - -re_validPackage = re.compile(r"(?i)^\s*([a-z_]\w*(?:\.[a-z_]\w*)*)(.*)", re.ASCII) -# (package) (rest) - -re_paren = re.compile(r"^\s*\((.*)\)\s*$") # (list) inside of parentheses -re_splitComparison = re.compile(r"^\s*(<=|>=|<|>|!=|==)\s*([^\s,]+)\s*$") -# (comp) (version) - - -def splitUp(pred): - """Parse a single version comparison. - - Return (comparison string, StrictVersion) - """ - res = re_splitComparison.match(pred) - if not res: - raise ValueError("bad package restriction syntax: %r" % pred) - comp, verStr = res.groups() - with version.suppress_known_deprecation(): - other = version.StrictVersion(verStr) - return (comp, other) - - -compmap = { - "<": operator.lt, - "<=": operator.le, - "==": operator.eq, - ">": operator.gt, - ">=": operator.ge, - "!=": operator.ne, -} - - -class VersionPredicate: - """Parse and test package version predicates. - - >>> v = VersionPredicate('pyepat.abc (>1.0, <3333.3a1, !=1555.1b3)') - - The `name` attribute provides the full dotted name that is given:: - - >>> v.name - 'pyepat.abc' - - The str() of a `VersionPredicate` provides a normalized - human-readable version of the expression:: - - >>> print(v) - pyepat.abc (> 1.0, < 3333.3a1, != 1555.1b3) - - The `satisfied_by()` method can be used to determine with a given - version number is included in the set described by the version - restrictions:: - - >>> v.satisfied_by('1.1') - True - >>> v.satisfied_by('1.4') - True - >>> v.satisfied_by('1.0') - False - >>> v.satisfied_by('4444.4') - False - >>> v.satisfied_by('1555.1b3') - False - - `VersionPredicate` is flexible in accepting extra whitespace:: - - >>> v = VersionPredicate(' pat( == 0.1 ) ') - >>> v.name - 'pat' - >>> v.satisfied_by('0.1') - True - >>> v.satisfied_by('0.2') - False - - If any version numbers passed in do not conform to the - restrictions of `StrictVersion`, a `ValueError` is raised:: - - >>> v = VersionPredicate('p1.p2.p3.p4(>=1.0, <=1.3a1, !=1.2zb3)') - Traceback (most recent call last): - ... - ValueError: invalid version number '1.2zb3' - - It the module or package name given does not conform to what's - allowed as a legal module or package name, `ValueError` is - raised:: - - >>> v = VersionPredicate('foo-bar') - Traceback (most recent call last): - ... - ValueError: expected parenthesized list: '-bar' - - >>> v = VersionPredicate('foo bar (12.21)') - Traceback (most recent call last): - ... - ValueError: expected parenthesized list: 'bar (12.21)' - - """ - - def __init__(self, versionPredicateStr): - """Parse a version predicate string.""" - # Fields: - # name: package name - # pred: list of (comparison string, StrictVersion) - - versionPredicateStr = versionPredicateStr.strip() - if not versionPredicateStr: - raise ValueError("empty package restriction") - match = re_validPackage.match(versionPredicateStr) - if not match: - raise ValueError("bad package name in %r" % versionPredicateStr) - self.name, paren = match.groups() - paren = paren.strip() - if paren: - match = re_paren.match(paren) - if not match: - raise ValueError("expected parenthesized list: %r" % paren) - str = match.groups()[0] - self.pred = [splitUp(aPred) for aPred in str.split(",")] - if not self.pred: - raise ValueError("empty parenthesized list in %r" % versionPredicateStr) - else: - self.pred = [] - - def __str__(self): - if self.pred: - seq = [cond + " " + str(ver) for cond, ver in self.pred] - return self.name + " (" + ", ".join(seq) + ")" - else: - return self.name - - def satisfied_by(self, version): - """True if version is compatible with all the predicates in self. - The parameter version must be acceptable to the StrictVersion - constructor. It may be either a string or StrictVersion. - """ - for cond, ver in self.pred: - if not compmap[cond](version, ver): - return False - return True - - -_provision_rx = None - - -def split_provision(value): - """Return the name and optional version number of a provision. - - The version number, if given, will be returned as a `StrictVersion` - instance, otherwise it will be `None`. - - >>> split_provision('mypkg') - ('mypkg', None) - >>> split_provision(' mypkg( 1.2 ) ') - ('mypkg', StrictVersion ('1.2')) - """ - global _provision_rx - if _provision_rx is None: - _provision_rx = re.compile( - r"([a-zA-Z_]\w*(?:\.[a-zA-Z_]\w*)*)(?:\s*\(\s*([^)\s]+)\s*\))?$", re.ASCII - ) - value = value.strip() - m = _provision_rx.match(value) - if not m: - raise ValueError("illegal provides specification: %r" % value) - ver = m.group(2) or None - if ver: - with version.suppress_known_deprecation(): - ver = version.StrictVersion(ver) - return m.group(1), ver diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/README.md b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/README.md deleted file mode 100644 index 9fcd33513fb81ef3aeb4d3c8d9732324dffa2646..0000000000000000000000000000000000000000 --- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/export/README.md +++ /dev/null @@ -1,13 +0,0 @@ - -This directory contains code to prepare a detectron2 model for deployment. -Currently it supports exporting a detectron2 model to Caffe2 format through ONNX. - -Please see [documentation](https://detectron2.readthedocs.io/tutorials/deployment.html) for its usage. - - -### Acknowledgements - -Thanks to Mobile Vision team at Facebook for developing the Caffe2 conversion tools. - -Thanks to Computing Platform Department - PAI team at Alibaba Group (@bddpqq, @chenbohua3) who -help export Detectron2 models to TorchScript. diff --git a/spaces/ThirdEyeData/Entity-Extraction/README.md b/spaces/ThirdEyeData/Entity-Extraction/README.md deleted file mode 100644 index eae771250da9ad9239ec46806ed45ec85d85cdfc..0000000000000000000000000000000000000000 --- a/spaces/ThirdEyeData/Entity-Extraction/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Entity Extraction -emoji: 🌖 -colorFrom: yellow -colorTo: pink -sdk: streamlit -sdk_version: 1.15.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/UserXTheUnknown/stablediffusion-infinity/js/toolbar.js b/spaces/UserXTheUnknown/stablediffusion-infinity/js/toolbar.js deleted file mode 100644 index 6c721bc84d3a41a0761ead58e6034ba4dfd4a6ef..0000000000000000000000000000000000000000 --- a/spaces/UserXTheUnknown/stablediffusion-infinity/js/toolbar.js +++ /dev/null @@ -1,581 +0,0 @@ -// import { w2ui,w2toolbar,w2field,query,w2alert, w2utils,w2confirm} from "https://rawgit.com/vitmalina/w2ui/master/dist/w2ui.es6.min.js" -// import { w2ui,w2toolbar,w2field,query,w2alert, w2utils,w2confirm} from "https://cdn.jsdelivr.net/gh/vitmalina/w2ui@master/dist/w2ui.es6.min.js" - -// https://stackoverflow.com/questions/36280818/how-to-convert-file-to-base64-in-javascript -function getBase64(file) { - var reader = new FileReader(); - reader.readAsDataURL(file); - reader.onload = function () { - add_image(reader.result); - // console.log(reader.result); - }; - reader.onerror = function (error) { - console.log("Error: ", error); - }; -} - -function getText(file) { - var reader = new FileReader(); - reader.readAsText(file); - reader.onload = function () { - window.postMessage(["load",reader.result],"*") - // console.log(reader.result); - }; - reader.onerror = function (error) { - console.log("Error: ", error); - }; -} - -document.querySelector("#upload_file").addEventListener("change", (event)=>{ - console.log(event); - let file = document.querySelector("#upload_file").files[0]; - getBase64(file); -}) - -document.querySelector("#upload_state").addEventListener("change", (event)=>{ - console.log(event); - let file = document.querySelector("#upload_state").files[0]; - getText(file); -}) - -open_setting = function() { - if (!w2ui.foo) { - new w2form({ - name: "foo", - style: "border: 0px; background-color: transparent;", - fields: [{ - field: "canvas_width", - type: "int", - required: true, - html: { - label: "Canvas Width" - } - }, - { - field: "canvas_height", - type: "int", - required: true, - html: { - label: "Canvas Height" - } - }, - ], - record: { - canvas_width: 1200, - canvas_height: 600, - }, - actions: { - Save() { - this.validate(); - let record = this.getCleanRecord(); - window.postMessage(["resize",record.canvas_width,record.canvas_height],"*"); - w2popup.close(); - }, - custom: { - text: "Cancel", - style: "text-transform: uppercase", - onClick(event) { - w2popup.close(); - } - } - } - }); - } - w2popup.open({ - title: "Form in a Popup", - body: "", - style: "padding: 15px 0px 0px 0px", - width: 500, - height: 280, - showMax: true, - async onToggle(event) { - await event.complete - w2ui.foo.resize(); - } - }) - .then((event) => { - w2ui.foo.render("#form") - }); -} - -var button_lst=["clear", "load", "save", "export", "upload", "selection", "canvas", "eraser", "outpaint", "accept", "cancel", "retry", "prev", "current", "next", "eraser_size_btn", "eraser_size", "resize_selection", "scale", "zoom_in", "zoom_out", "help"]; -var upload_button_lst=['clear', 'load', 'save', "upload", 'export', 'outpaint', 'resize_selection', 'help', "setting"]; -var resize_button_lst=['clear', 'load', 'save', "upload", 'export', "selection", "canvas", "eraser", 'outpaint', 'resize_selection',"zoom_in", "zoom_out", 'help', "setting"]; -var outpaint_button_lst=['clear', 'load', 'save', "canvas", "eraser", "upload", 'export', 'resize_selection', "zoom_in", "zoom_out",'help', "setting"]; -var outpaint_result_lst=["accept", "cancel", "retry", "prev", "current", "next"]; -var outpaint_result_func_lst=["accept", "retry", "prev", "current", "next"]; - -function check_button(id,text="",checked=true,tooltip="") -{ - return { type: "check", id: id, text: text, icon: checked?"fa-solid fa-square-check":"fa-regular fa-square", checked: checked, tooltip: tooltip }; -} - -var toolbar=new w2toolbar({ - box: "#toolbar", - name: "toolbar", - tooltip: "top", - items: [ - { type: "button", id: "clear", text: "Reset", tooltip: "Reset Canvas", icon: "fa-solid fa-rectangle-xmark" }, - { type: "break" }, - { type: "button", id: "load", tooltip: "Load Canvas", icon: "fa-solid fa-file-import" }, - { type: "button", id: "save", tooltip: "Save Canvas", icon: "fa-solid fa-file-export" }, - { type: "button", id: "export", tooltip: "Export Image", icon: "fa-solid fa-floppy-disk" }, - { type: "break" }, - { type: "button", id: "upload", text: "Upload Image", icon: "fa-solid fa-upload" }, - { type: "break" }, - { type: "radio", id: "selection", group: "1", tooltip: "Selection", icon: "fa-solid fa-arrows-up-down-left-right", checked: true }, - { type: "radio", id: "canvas", group: "1", tooltip: "Canvas", icon: "fa-solid fa-image" }, - { type: "radio", id: "eraser", group: "1", tooltip: "Eraser", icon: "fa-solid fa-eraser" }, - { type: "break" }, - { type: "button", id: "outpaint", text: "Outpaint", tooltip: "Run Outpainting", icon: "fa-solid fa-brush" }, - { type: "break" }, - { type: "button", id: "accept", text: "Accept", tooltip: "Accept current result", icon: "fa-solid fa-check", hidden: true, disable:true,}, - { type: "button", id: "cancel", text: "Cancel", tooltip: "Cancel current outpainting/error", icon: "fa-solid fa-ban", hidden: true}, - { type: "button", id: "retry", text: "Retry", tooltip: "Retry", icon: "fa-solid fa-rotate", hidden: true, disable:true,}, - { type: "button", id: "prev", tooltip: "Prev Result", icon: "fa-solid fa-caret-left", hidden: true, disable:true,}, - { type: "html", id: "current", hidden: true, disable:true, - async onRefresh(event) { - await event.complete - let fragment = query.html(` ---- - -- {(scene) ?-: null} -Action: {scene.action}
-Position: {scene.position}
-- -`) - query(this.box).find("#tb_toolbar_item_current").append(fragment) - } - }, - { type: "button", id: "next", tooltip: "Next Result", icon: "fa-solid fa-caret-right", hidden: true,disable:true,}, - { type: "button", id: "add_image", text: "Add Image", icon: "fa-solid fa-file-circle-plus", hidden: true,disable:true,}, - { type: "button", id: "delete_image", text: "Delete Image", icon: "fa-solid fa-trash-can", hidden: true,disable:true,}, - { type: "button", id: "confirm", text: "Confirm", icon: "fa-solid fa-check", hidden: true,disable:true,}, - { type: "button", id: "cancel_overlay", text: "Cancel", icon: "fa-solid fa-ban", hidden: true,disable:true,}, - { type: "break" }, - { type: "spacer" }, - { type: "break" }, - { type: "button", id: "eraser_size_btn", tooltip: "Eraser Size", text:"Size", icon: "fa-solid fa-eraser", hidden: true, count: 32}, - { type: "html", id: "eraser_size", hidden: true, - async onRefresh(event) { - await event.complete - // let fragment = query.html(` - // - // `) - let fragment = query.html(` - - `) - fragment.filter("input").on("change", event => { - this.eraser_size = event.target.value; - window.overlay.freeDrawingBrush.width=this.eraser_size; - this.setCount("eraser_size_btn", event.target.value); - window.postMessage(["eraser_size", event.target.value],"*") - this.refresh(); - }) - query(this.box).find("#tb_toolbar_item_eraser_size").append(fragment) - } - }, - // { type: "button", id: "resize_eraser", tooltip: "Resize Eraser", icon: "fa-solid fa-sliders" }, - { type: "button", id: "resize_selection", text: "Resize Selection", tooltip: "Resize Selection", icon: "fa-solid fa-expand" }, - { type: "break" }, - { type: "html", id: "scale", - async onRefresh(event) { - await event.complete - let fragment = query.html(` -- ${this.sel_value ?? "1/1"} --`) - query(this.box).find("#tb_toolbar_item_scale").append(fragment) - } - }, - { type: "button", id: "zoom_in", tooltip: "Zoom In", icon: "fa-solid fa-magnifying-glass-plus" }, - { type: "button", id: "zoom_out", tooltip: "Zoom Out", icon: "fa-solid fa-magnifying-glass-minus" }, - { type: "break" }, - { type: "button", id: "help", tooltip: "Help", icon: "fa-solid fa-circle-info" }, - { type: "new-line"}, - { type: "button", id: "setting", text: "Canvas Setting", tooltip: "Resize Canvas Here", icon: "fa-solid fa-sliders" }, - { type: "break" }, - check_button("enable_img2img","Enable Img2Img",false), - // check_button("use_correction","Photometric Correction",false), - check_button("resize_check","Resize Small Input",true), - check_button("enable_safety","Enable Safety Checker",true), - check_button("square_selection","Square Selection Only",false), - {type: "break"}, - check_button("use_seed","Use Seed:",false), - { type: "html", id: "seed_val", - async onRefresh(event) { - await event.complete - let fragment = query.html(` - `) - fragment.filter("input").on("change", event => { - this.config_obj.seed_val = event.target.value; - parent.config_obj=this.config_obj; - this.refresh(); - }) - query(this.box).find("#tb_toolbar_item_seed_val").append(fragment) - } - }, - { type: "button", id: "random_seed", tooltip: "Set a random seed", icon: "fa-solid fa-dice" }, - ], - onClick(event) { - switch(event.target){ - case "setting": - open_setting(); - break; - case "upload": - this.upload_mode=true - document.querySelector("#overlay_container").style.pointerEvents="auto"; - this.click("canvas"); - this.click("selection"); - this.show("confirm","cancel_overlay","add_image","delete_image"); - this.enable("confirm","cancel_overlay","add_image","delete_image"); - this.disable(...upload_button_lst); - query("#upload_file").click(); - if(this.upload_tip) - { - this.upload_tip=false; - w2utils.notify("Note that only visible images will be added to canvas",{timeout:10000,where:query("#container")}) - } - break; - case "resize_selection": - this.resize_mode=true; - this.disable(...resize_button_lst); - this.enable("confirm","cancel_overlay"); - this.show("confirm","cancel_overlay"); - window.postMessage(["resize_selection",""],"*"); - document.querySelector("#overlay_container").style.pointerEvents="auto"; - break; - case "confirm": - if(this.upload_mode) - { - export_image(); - } - else - { - let sel_box=this.selection_box; - window.postMessage(["resize_selection",sel_box.x,sel_box.y,sel_box.width,sel_box.height],"*"); - } - case "cancel_overlay": - end_overlay(); - this.hide("confirm","cancel_overlay","add_image","delete_image"); - if(this.upload_mode){ - this.enable(...upload_button_lst); - } - else - { - this.enable(...resize_button_lst); - window.postMessage(["resize_selection","",""],"*"); - if(event.target=="cancel_overlay") - { - this.selection_box=this.selection_box_bak; - } - } - if(this.selection_box) - { - this.setCount("resize_selection",`${Math.floor(this.selection_box.width/8)*8}x${Math.floor(this.selection_box.height/8)*8}`); - } - this.disable("confirm","cancel_overlay","add_image","delete_image"); - this.upload_mode=false; - this.resize_mode=false; - this.click("selection"); - break; - case "add_image": - query("#upload_file").click(); - break; - case "delete_image": - let active_obj = window.overlay.getActiveObject(); - if(active_obj) - { - window.overlay.remove(active_obj); - window.overlay.renderAll(); - } - else - { - w2utils.notify("You need to select an image first",{error:true,timeout:2000,where:query("#container")}) - } - break; - case "load": - query("#upload_state").click(); - this.selection_box=null; - this.setCount("resize_selection",""); - break; - case "next": - case "prev": - window.postMessage(["outpaint", "", event.target], "*"); - break; - case "outpaint": - this.click("selection"); - this.disable(...outpaint_button_lst); - this.show(...outpaint_result_lst); - if(this.outpaint_tip) - { - this.outpaint_tip=false; - w2utils.notify("The canvas stays locked until you accept/cancel current outpainting",{timeout:10000,where:query("#container")}) - } - document.querySelector("#container").style.pointerEvents="none"; - case "retry": - this.disable(...outpaint_result_func_lst); - window.postMessage(["transfer",""],"*") - break; - case "accept": - case "cancel": - this.hide(...outpaint_result_lst); - this.disable(...outpaint_result_func_lst); - this.enable(...outpaint_button_lst); - document.querySelector("#container").style.pointerEvents="auto"; - window.postMessage(["click", event.target],"*"); - let app=parent.document.querySelector("gradio-app"); - app=app.shadowRoot??app; - app.querySelector("#cancel").click(); - break; - case "eraser": - case "selection": - case "canvas": - if(event.target=="eraser") - { - this.show("eraser_size","eraser_size_btn"); - window.overlay.freeDrawingBrush.width=this.eraser_size; - window.overlay.isDrawingMode = true; - } - else - { - this.hide("eraser_size","eraser_size_btn"); - window.overlay.isDrawingMode = false; - } - if(this.upload_mode) - { - if(event.target=="canvas") - { - window.postMessage(["mode", event.target],"*") - document.querySelector("#overlay_container").style.pointerEvents="none"; - document.querySelector("#overlay_container").style.opacity = 0.5; - } - else - { - document.querySelector("#overlay_container").style.pointerEvents="auto"; - document.querySelector("#overlay_container").style.opacity = 1.0; - } - } - else - { - window.postMessage(["mode", event.target],"*") - } - break; - case "help": - w2popup.open({ - title: "Document", - body: "Usage: https://github.com/lkwq007/stablediffusion-infinity/blob/master/docs/usage.md" - }) - break; - case "clear": - w2confirm("Reset canvas?").yes(() => { - window.postMessage(["click", event.target],"*"); - }).no(() => {}) - break; - case "random_seed": - this.config_obj.seed_val=Math.floor(Math.random() * 3000000000); - parent.config_obj=this.config_obj; - this.refresh(); - break; - case "enable_img2img": - case "use_correction": - case "resize_check": - case "enable_safety": - case "use_seed": - case "square_selection": - let target=this.get(event.target); - target.icon=target.checked?"fa-regular fa-square":"fa-solid fa-square-check"; - this.config_obj[event.target]=!target.checked; - parent.config_obj=this.config_obj; - this.refresh(); - break; - case "save": - case "export": - ask_filename(event.target); - break; - default: - // clear, save, export, outpaint, retry - // break, save, export, accept, retry, outpaint - window.postMessage(["click", event.target],"*") - } - console.log("Target: "+ event.target, event) - } -}) -window.w2ui=w2ui; -w2ui.toolbar.config_obj={ - resize_check: true, - enable_safety: true, - use_correction: false, - enable_img2img: false, - use_seed: false, - seed_val: 0, - square_selection: false, -}; -w2ui.toolbar.outpaint_tip=true; -w2ui.toolbar.upload_tip=true; -window.update_count=function(cur,total){ - w2ui.toolbar.sel_value=`${cur}/${total}`; - w2ui.toolbar.refresh(); -} -window.update_eraser=function(val,max_val){ - w2ui.toolbar.eraser_size=`${val}`; - w2ui.toolbar.eraser_max=`${max_val}`; - w2ui.toolbar.setCount("eraser_size_btn", `${val}`); - w2ui.toolbar.refresh(); -} -window.update_scale=function(val){ - w2ui.toolbar.scale_value=`${val}`; - w2ui.toolbar.refresh(); -} -window.enable_result_lst=function(){ - w2ui.toolbar.enable(...outpaint_result_lst); -} -function onObjectScaled(e) -{ - let object = e.target; - if(object.isType("rect")) - { - let width=object.getScaledWidth(); - let height=object.getScaledHeight(); - object.scale(1); - width=Math.max(Math.min(width,window.overlay.width-object.left),256); - height=Math.max(Math.min(height,window.overlay.height-object.top),256); - let l=Math.max(Math.min(object.left,window.overlay.width-width-object.strokeWidth),0); - let t=Math.max(Math.min(object.top,window.overlay.height-height-object.strokeWidth),0); - if(window.w2ui.toolbar.config_obj.square_selection) - { - let max_val = Math.min(Math.max(width,height),window.overlay.width,window.overlay.height); - width=max_val; - height=max_val; - } - object.set({ width: width, height: height, left:l,top:t}) - window.w2ui.toolbar.selection_box={width: width, height: height, x:object.left, y:object.top}; - window.w2ui.toolbar.setCount("resize_selection",`${Math.floor(width/8)*8}x${Math.floor(height/8)*8}`); - window.w2ui.toolbar.refresh(); - } -} -function onObjectMoved(e) -{ - let object = e.target; - if(object.isType("rect")) - { - let l=Math.max(Math.min(object.left,window.overlay.width-object.width-object.strokeWidth),0); - let t=Math.max(Math.min(object.top,window.overlay.height-object.height-object.strokeWidth),0); - object.set({left:l,top:t}); - window.w2ui.toolbar.selection_box={width: object.width, height: object.height, x:object.left, y:object.top}; - } -} -window.setup_overlay=function(width,height) -{ - if(window.overlay) - { - window.overlay.setDimensions({width:width,height:height}); - let app=parent.document.querySelector("gradio-app"); - app=app.shadowRoot??app; - app.querySelector("#sdinfframe").style.height=80+Number(height)+"px"; - document.querySelector("#container").style.height= height+"px"; - document.querySelector("#container").style.width = width+"px"; - } - else - { - canvas=new fabric.Canvas("overlay_canvas"); - canvas.setDimensions({width:width,height:height}); - let app=parent.document.querySelector("gradio-app"); - app=app.shadowRoot??app; - app.querySelector("#sdinfframe").style.height=80+Number(height)+"px"; - canvas.freeDrawingBrush = new fabric.EraserBrush(canvas); - canvas.on("object:scaling", onObjectScaled); - canvas.on("object:moving", onObjectMoved); - window.overlay=canvas; - } - document.querySelector("#overlay_container").style.pointerEvents="none"; -} -window.update_overlay=function(width,height) -{ - window.overlay.setDimensions({width:width,height:height},{backstoreOnly:true}); - // document.querySelector("#overlay_container").style.pointerEvents="none"; -} -window.adjust_selection=function(x,y,width,height) -{ - var rect = new fabric.Rect({ - left: x, - top: y, - fill: "rgba(0,0,0,0)", - strokeWidth: 3, - stroke: "rgba(0,0,0,0.7)", - cornerColor: "red", - cornerStrokeColor: "red", - borderColor: "rgba(255, 0, 0, 1.0)", - width: width, - height: height, - lockRotation: true, - }); - rect.setControlsVisibility({ mtr: false }); - window.overlay.add(rect); - window.overlay.setActiveObject(window.overlay.item(0)); - window.w2ui.toolbar.selection_box={width: width, height: height, x:x, y:y}; - window.w2ui.toolbar.selection_box_bak={width: width, height: height, x:x, y:y}; -} -function add_image(url) -{ - fabric.Image.fromURL(url,function(img){ - window.overlay.add(img); - window.overlay.setActiveObject(img); - },{left:100,top:100}); -} -function export_image() -{ - data=window.overlay.toDataURL(); - document.querySelector("#upload_content").value=data; - window.postMessage(["upload",""],"*"); - end_overlay(); -} -function end_overlay() -{ - window.overlay.clear(); - document.querySelector("#overlay_container").style.opacity = 1.0; - document.querySelector("#overlay_container").style.pointerEvents="none"; -} -function ask_filename(target) -{ - w2prompt({ - label: "Enter filename", - value: `outpaint_${((new Date(Date.now() -(new Date()).getTimezoneOffset() * 60000))).toISOString().replace("T","_").replace(/[^0-9_]/g, "").substring(0,15)}`, - }) - .change((event) => { - console.log("change", event.detail.originalEvent.target.value); - }) - .ok((event) => { - console.log("value=", event.detail.value); - window.postMessage(["click",target,event.detail.value],"*"); - }) - .cancel((event) => { - console.log("cancel"); - }); -} - -document.querySelector("#container").addEventListener("wheel",(e)=>{e.preventDefault()}) -window.setup_shortcut=function(json) -{ - var config=JSON.parse(json); - var key_map={}; - Object.keys(config.shortcut).forEach(k=>{ - key_map[config.shortcut[k]]=k; - }) - document.addEventListener("keydown",(e)=>{ - if(e.target.tagName!="INPUT") - { - let key=e.key; - if(e.ctrlKey) - { - key="Ctrl+"+e.key; - if(key in key_map) - { - e.preventDefault(); - } - } - if(key in key_map) - { - w2ui.toolbar.click(key_map[key]); - } - } - }) -} \ No newline at end of file diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Bard.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Bard.py deleted file mode 100644 index 4c37c4b719430031fce41ce49946f0e6ac93d155..0000000000000000000000000000000000000000 --- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Bard.py +++ /dev/null @@ -1,74 +0,0 @@ -import os, requests, json, browser_cookie3, re, random -from ...typing import sha256, Dict, get_type_hints - -url = 'https://bard.google.com' -model = ['Palm2'] -supports_stream = False -needs_auth = True - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - psid = {cookie.name: cookie.value for cookie in browser_cookie3.chrome( - domain_name='.google.com')}['__Secure-1PSID'] - - formatted = '\n'.join([ - '%s: %s' % (message['role'], message['content']) for message in messages - ]) - prompt = f'{formatted}\nAssistant:' - - proxy = kwargs.get('proxy', False) - if proxy == False: - print('warning!, you did not give a proxy, a lot of countries are banned from Google Bard, so it may not work') - - snlm0e = None - conversation_id = None - response_id = None - choice_id = None - - client = requests.Session() - client.proxies = { - 'http': f'http://{proxy}', - 'https': f'http://{proxy}'} if proxy else None - - client.headers = { - 'authority': 'bard.google.com', - 'content-type': 'application/x-www-form-urlencoded;charset=UTF-8', - 'origin': 'https://bard.google.com', - 'referer': 'https://bard.google.com/', - 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36', - 'x-same-domain': '1', - 'cookie': f'__Secure-1PSID={psid}' - } - - snlm0e = re.search(r'SNlM0e\":\"(.*?)\"', - client.get('https://bard.google.com/').text).group(1) if not snlm0e else snlm0e - - params = { - 'bl': 'boq_assistant-bard-web-server_20230326.21_p0', - '_reqid': random.randint(1111, 9999), - 'rt': 'c' - } - - data = { - 'at': snlm0e, - 'f.req': json.dumps([None, json.dumps([[prompt], None, [conversation_id, response_id, choice_id]])])} - - intents = '.'.join([ - 'assistant', - 'lamda', - 'BardFrontendService' - ]) - - response = client.post(f'https://bard.google.com/_/BardChatUi/data/{intents}/StreamGenerate', - data=data, params=params) - - chat_data = json.loads(response.content.splitlines()[3])[0][2] - if chat_data: - json_chat_data = json.loads(chat_data) - - yield json_chat_data[0][0] - - else: - yield 'error' - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) \ No newline at end of file diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/activations.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/activations.py deleted file mode 100644 index 8bd6f2917a56d72db56555d0ff54b2311bc21778..0000000000000000000000000000000000000000 --- a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/activations.py +++ /dev/null @@ -1,96 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -from torch import Tensor -from typing import Union, Callable - - -class CustomGLU(nn.Module): - """Custom Gated Linear Unit activation. - Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half - of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation - function (i.e. sigmoid, swish, etc.). - - Args: - activation (nn.Module): The custom activation to apply in the Gated Linear Unit - dim (int): the dimension on which to split the input. Default: -1 - - Shape: - - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional - dimensions - - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2` - - Examples:: - >>> m = CustomGLU(nn.Sigmoid()) - >>> input = torch.randn(4, 2) - >>> output = m(input) - """ - def __init__(self, activation: nn.Module, dim: int = -1): - super(CustomGLU, self).__init__() - self.dim = dim - self.activation = activation - - def forward(self, x: Tensor): - assert x.shape[self.dim] % 2 == 0 # M = N / 2 - a, b = torch.chunk(x, 2, dim=self.dim) - return a * self.activation(b) - - -class SwiGLU(CustomGLU): - """SiLU Gated Linear Unit activation. - Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is - the first half of the input matrices, :math:`b` is the second half. - - Args: - dim (int): the dimension on which to split the input. Default: -1 - """ - def __init__(self, dim: int = -1): - super(SwiGLU, self).__init__(nn.SiLU(), dim) - - -class GeGLU(CustomGLU): - """GeLU Gated Linear Unit activation. - Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is - the first half of the input matrices, :math:`b` is the second half. - - Args: - dim (int): the dimension on which to split the input. Default: -1 - """ - def __init__(self, dim: int = -1): - super(GeGLU, self).__init__(nn.GELU(), dim) - - -class ReGLU(CustomGLU): - """ReLU Gated Linear Unit activation. - Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is - the first half of the input matrices, :math:`b` is the second half. - - Args: - dim (int): the dimension on which to split the input. Default: -1 - """ - def __init__(self, dim: int = -1): - super(ReGLU, self).__init__(nn.ReLU(), dim) - - -def get_activation_fn( - activation: Union[str, Callable[[Tensor], Tensor]] -) -> Union[str, Callable[[Tensor], Tensor]]: - """Helper function to map an activation string to the activation class. - If the supplied activation is not a string that is recognized, the activation is passed back. - - Args: - activation (Union[str, Callable[[Tensor], Tensor]]): Activation to check - """ - if isinstance(activation, str): - if activation == "reglu": - return ReGLU() - elif activation == "geglu": - return GeGLU() - elif activation == "swiglu": - return SwiGLU() - return activation diff --git a/spaces/YaYaB/text-to-magic/app.py b/spaces/YaYaB/text-to-magic/app.py deleted file mode 100644 index 5a443eb33a4bbd4f9685ec0f9adb447e3d2768fc..0000000000000000000000000000000000000000 --- a/spaces/YaYaB/text-to-magic/app.py +++ /dev/null @@ -1,196 +0,0 @@ -from contextlib import nullcontext -import gradio as gr -import torch -from torch import autocast -from diffusers import StableDiffusionPipeline - - -device = "cuda" if torch.cuda.is_available() else "cpu" -context = autocast if device == "cuda" else nullcontext -dtype = torch.float16 if device == "cuda" else torch.float32 - -pipe = StableDiffusionPipeline.from_pretrained("YaYaB/sd-magic-diffusers-test2", revision="5ed9cff0a416a6346aa17aee0d5ed57dfd59b809", torch_dtype=dtype) -pipe = pipe.to(device) - - -# Sometimes the nsfw checker is confused by the Pokémon images, you can disable -# it at your own risk here -disable_safety = True - -if disable_safety: - def null_safety(images, **kwargs): - return images, False - pipe.safety_checker = null_safety - - -def infer(prompt, n_samples, steps, scale): - - with context("cuda"): - images = pipe(n_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).images - - return images - -css = """ - a { - color: inherit; - text-decoration: underline; - } - .gradio-container { - font-family: 'IBM Plex Sans', sans-serif; - } - .gr-button { - color: white; - border-color: #9d66e5; - background: #9d66e5; - } - input[type='range'] { - accent-color: #9d66e5; - } - .dark input[type='range'] { - accent-color: #dfdfdf; - } - .container { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; - } - #gallery { - min-height: 22rem; - margin-bottom: 15px; - margin-left: auto; - margin-right: auto; - border-bottom-right-radius: .5rem !important; - border-bottom-left-radius: .5rem !important; - } - #gallery>div>.h-full { - min-height: 20rem; - } - .details:hover { - text-decoration: underline; - } - .gr-button { - white-space: nowrap; - } - .gr-button:focus { - border-color: rgb(147 197 253 / var(--tw-border-opacity)); - outline: none; - box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); - --tw-border-opacity: 1; - --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); - --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); - --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); - --tw-ring-opacity: .5; - } - #advanced-options { - margin-bottom: 20px; - } - .footer { - margin-bottom: 45px; - margin-top: 35px; - text-align: center; - border-bottom: 1px solid #e5e5e5; - } - .footer>p { - font-size: .8rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; - } - .dark .logo{ filter: invert(1); } - .dark .footer { - border-color: #303030; - } - .dark .footer>p { - background: #0b0f19; - } - .acknowledgments h4{ - margin: 1.25em 0 .25em 0; - font-weight: bold; - font-size: 115%; - } -""" - -block = gr.Blocks(css=css) - -examples = [ - [ - 'Yoda', - 2, - 7.5, - ], - [ - 'Abraham Lincoln', - 2, - 7.5, - ], - [ - 'George Washington', - 2, - 7, - ], -] - -with block: - gr.HTML( - """ -- ${this.scale_value ?? "100%"} --- """ - ) - with gr.Group(): - with gr.Box(): - with gr.Row().style(mobile_collapse=False, equal_height=True): - text = gr.Textbox( - label="Enter your prompt", - show_label=False, - max_lines=1, - placeholder="Enter your prompt", - ).style( - border=(True, False, True, True), - rounded=(True, False, False, True), - container=False, - ) - btn = gr.Button("Generate image").style( - margin=False, - rounded=(False, True, True, False), - ) - - gallery = gr.Gallery( - label="Generated images", show_label=False, elem_id="gallery" - ).style(grid=[2], height="auto") - - - with gr.Row(elem_id="advanced-options"): - samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1) - steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=25, step=5) - scale = gr.Slider( - label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1 - ) - - - ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False) - ex.dataset.headers = [""] - - - text.submit(infer, inputs=[text, samples, steps, scale], outputs=gallery) - btn.click(infer, inputs=[text, samples, steps, scale], outputs=gallery) - gr.HTML( - """ - - """ - ) - -block.launch() \ No newline at end of file diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py deleted file mode 100644 index d1a3d4c55e9199f448ccc820dc459131838ff299..0000000000000000000000000000000000000000 --- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py +++ /dev/null @@ -1,1118 +0,0 @@ -from typing import Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn as nn - -from ...configuration_utils import ConfigMixin, register_to_config -from ...modeling_utils import ModelMixin -from ...models.attention import DualTransformer2DModel, Transformer2DModel -from ...models.embeddings import TimestepEmbedding, Timesteps -from ...models.unet_2d_condition import UNet2DConditionOutput -from ...utils import logging - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -def get_down_block( - down_block_type, - num_layers, - in_channels, - out_channels, - temb_channels, - add_downsample, - resnet_eps, - resnet_act_fn, - attn_num_head_channels, - resnet_groups=None, - cross_attention_dim=None, - downsample_padding=None, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, -): - down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type - if down_block_type == "DownBlockFlat": - return DownBlockFlat( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - ) - elif down_block_type == "CrossAttnDownBlockFlat": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat") - return CrossAttnDownBlockFlat( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attn_num_head_channels, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - ) - raise ValueError(f"{down_block_type} is not supported.") - - -def get_up_block( - up_block_type, - num_layers, - in_channels, - out_channels, - prev_output_channel, - temb_channels, - add_upsample, - resnet_eps, - resnet_act_fn, - attn_num_head_channels, - resnet_groups=None, - cross_attention_dim=None, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, -): - up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type - if up_block_type == "UpBlockFlat": - return UpBlockFlat( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - ) - elif up_block_type == "CrossAttnUpBlockFlat": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat") - return CrossAttnUpBlockFlat( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attn_num_head_channels, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - ) - raise ValueError(f"{up_block_type} is not supported.") - - -# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat -class UNetFlatConditionModel(ModelMixin, ConfigMixin): - r""" - UNetFlatConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a - timestep and returns sample shaped output. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library - implements for all the models (such as downloading or saving, etc.) - - Parameters: - sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): - Height and width of input/output sample. - in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample. - out_channels (`int`, *optional*, defaults to 4): The number of channels in the output. - center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. - flip_sin_to_cos (`bool`, *optional*, defaults to `False`): - Whether to flip the sin to cos in the time embedding. - freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. - down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`): - The tuple of downsample blocks to use. - up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat",)`): - The tuple of upsample blocks to use. - block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): - The tuple of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. - downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. - mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. - norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. - cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features. - attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. - """ - - _supports_gradient_checkpointing = True - - @register_to_config - def __init__( - self, - sample_size: Optional[int] = None, - in_channels: int = 4, - out_channels: int = 4, - center_input_sample: bool = False, - flip_sin_to_cos: bool = True, - freq_shift: int = 0, - down_block_types: Tuple[str] = ( - "CrossAttnDownBlockFlat", - "CrossAttnDownBlockFlat", - "CrossAttnDownBlockFlat", - "DownBlockFlat", - ), - up_block_types: Tuple[str] = ( - "UpBlockFlat", - "CrossAttnUpBlockFlat", - "CrossAttnUpBlockFlat", - "CrossAttnUpBlockFlat", - ), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int] = (320, 640, 1280, 1280), - layers_per_block: int = 2, - downsample_padding: int = 1, - mid_block_scale_factor: float = 1, - act_fn: str = "silu", - norm_num_groups: int = 32, - norm_eps: float = 1e-5, - cross_attention_dim: int = 1280, - attention_head_dim: Union[int, Tuple[int]] = 8, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - num_class_embeds: Optional[int] = None, - ): - super().__init__() - - self.sample_size = sample_size - time_embed_dim = block_out_channels[0] * 4 - - # input - self.conv_in = LinearMultiDim(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) - - # time - self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) - timestep_input_dim = block_out_channels[0] - - self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) - - # class embedding - if num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - - self.down_blocks = nn.ModuleList([]) - self.mid_block = None - self.up_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block, - in_channels=input_channel, - out_channels=output_channel, - temb_channels=time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attention_head_dim[i], - downsample_padding=downsample_padding, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - ) - self.down_blocks.append(down_block) - - # mid - self.mid_block = UNetMidBlockFlatCrossAttn( - in_channels=block_out_channels[-1], - temb_channels=time_embed_dim, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - output_scale_factor=mid_block_scale_factor, - resnet_time_scale_shift="default", - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attention_head_dim[-1], - resnet_groups=norm_num_groups, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - ) - - # count how many layers upsample the images - self.num_upsamplers = 0 - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - reversed_attention_head_dim = list(reversed(attention_head_dim)) - only_cross_attention = list(reversed(only_cross_attention)) - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - is_final_block = i == len(block_out_channels) - 1 - - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] - - # add upsample block for all BUT final layer - if not is_final_block: - add_upsample = True - self.num_upsamplers += 1 - else: - add_upsample = False - - up_block = get_up_block( - up_block_type, - num_layers=layers_per_block + 1, - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=time_embed_dim, - add_upsample=add_upsample, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=reversed_attention_head_dim[i], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) - self.conv_act = nn.SiLU() - self.conv_out = LinearMultiDim(block_out_channels[0], out_channels, kernel_size=3, padding=1) - - def set_attention_slice(self, slice_size): - head_dims = self.config.attention_head_dim - head_dims = [head_dims] if isinstance(head_dims, int) else head_dims - if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims): - raise ValueError( - f"Make sure slice_size {slice_size} is a common divisor of " - f"the number of heads used in cross_attention: {head_dims}" - ) - if slice_size is not None and slice_size > min(head_dims): - raise ValueError( - f"slice_size {slice_size} has to be smaller or equal to " - f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}" - ) - - for block in self.down_blocks: - if hasattr(block, "attentions") and block.attentions is not None: - block.set_attention_slice(slice_size) - - self.mid_block.set_attention_slice(slice_size) - - for block in self.up_blocks: - if hasattr(block, "attentions") and block.attentions is not None: - block.set_attention_slice(slice_size) - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)): - module.gradient_checkpointing = value - - def forward( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - class_labels: Optional[torch.Tensor] = None, - return_dict: bool = True, - ) -> Union[UNet2DConditionOutput, Tuple]: - r""" - Args: - sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor - timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps - encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. - - Returns: - [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: - [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When - returning a tuple, the first element is the sample tensor. - """ - # By default samples have to be AT least a multiple of the overall upsampling factor. - # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). - # However, the upsampling interpolation output size can be forced to fit any upsampling size - # on the fly if necessary. - default_overall_up_factor = 2**self.num_upsamplers - - # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` - forward_upsample_size = False - upsample_size = None - - if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): - logger.info("Forward upsample size to force interpolation output size.") - forward_upsample_size = True - - # 0. center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # 1. time - timesteps = timestep - if not torch.is_tensor(timesteps): - # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - - # timesteps does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=self.dtype) - emb = self.time_embedding(t_emb) - - if self.config.num_class_embeds is not None: - if class_labels is None: - raise ValueError("class_labels should be provided when num_class_embeds > 0") - class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) - emb = emb + class_emb - - # 2. pre-process - sample = self.conv_in(sample) - - # 3. down - down_block_res_samples = (sample,) - for downsample_block in self.down_blocks: - if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None: - sample, res_samples = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - ) - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - - down_block_res_samples += res_samples - - # 4. mid - sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states) - - # 5. up - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] - - # if we have not reached the final block and need to forward the - # upsample size, we do it here - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if hasattr(upsample_block, "attentions") and upsample_block.attentions is not None: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states, - upsample_size=upsample_size, - ) - else: - sample = upsample_block( - hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size - ) - # 6. post-process - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if not return_dict: - return (sample,) - - return UNet2DConditionOutput(sample=sample) - - -class LinearMultiDim(nn.Linear): - def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs): - in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features) - if out_features is None: - out_features = in_features - out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features) - self.in_features_multidim = in_features - self.out_features_multidim = out_features - super().__init__(np.array(in_features).prod(), np.array(out_features).prod()) - - def forward(self, input_tensor, *args, **kwargs): - shape = input_tensor.shape - n_dim = len(self.in_features_multidim) - input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_features) - output_tensor = super().forward(input_tensor) - output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_features_multidim) - return output_tensor - - -class ResnetBlockFlat(nn.Module): - def __init__( - self, - *, - in_channels, - out_channels=None, - dropout=0.0, - temb_channels=512, - groups=32, - groups_out=None, - pre_norm=True, - eps=1e-6, - time_embedding_norm="default", - use_in_shortcut=None, - second_dim=4, - **kwargs, - ): - super().__init__() - self.pre_norm = pre_norm - self.pre_norm = True - - in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels) - self.in_channels_prod = np.array(in_channels).prod() - self.channels_multidim = in_channels - - if out_channels is not None: - out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels) - out_channels_prod = np.array(out_channels).prod() - self.out_channels_multidim = out_channels - else: - out_channels_prod = self.in_channels_prod - self.out_channels_multidim = self.channels_multidim - self.time_embedding_norm = time_embedding_norm - - if groups_out is None: - groups_out = groups - - self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, eps=eps, affine=True) - self.conv1 = torch.nn.Conv2d(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0) - - if temb_channels is not None: - self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels_prod) - else: - self.time_emb_proj = None - - self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, eps=eps, affine=True) - self.dropout = torch.nn.Dropout(dropout) - self.conv2 = torch.nn.Conv2d(out_channels_prod, out_channels_prod, kernel_size=1, padding=0) - - self.nonlinearity = nn.SiLU() - - self.use_in_shortcut = ( - self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut - ) - - self.conv_shortcut = None - if self.use_in_shortcut: - self.conv_shortcut = torch.nn.Conv2d( - self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0 - ) - - def forward(self, input_tensor, temb): - shape = input_tensor.shape - n_dim = len(self.channels_multidim) - input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_channels_prod, 1, 1) - input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1) - - hidden_states = input_tensor - - hidden_states = self.norm1(hidden_states) - hidden_states = self.nonlinearity(hidden_states) - hidden_states = self.conv1(hidden_states) - - if temb is not None: - temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None] - hidden_states = hidden_states + temb - - hidden_states = self.norm2(hidden_states) - hidden_states = self.nonlinearity(hidden_states) - - hidden_states = self.dropout(hidden_states) - hidden_states = self.conv2(hidden_states) - - if self.conv_shortcut is not None: - input_tensor = self.conv_shortcut(input_tensor) - - output_tensor = input_tensor + hidden_states - - output_tensor = output_tensor.view(*shape[0:-n_dim], -1) - output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_channels_multidim) - - return output_tensor - - -# Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim -class DownBlockFlat(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor=1.0, - add_downsample=True, - downsample_padding=1, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlockFlat( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - LinearMultiDim( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward(self, hidden_states, temb=None): - output_states = () - - for resnet in self.resnets: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - else: - hidden_states = resnet(hidden_states, temb) - - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -# Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim -class CrossAttnDownBlockFlat(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - cross_attention_dim=1280, - attention_type="default", - output_scale_factor=1.0, - downsample_padding=1, - add_downsample=True, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - ): - super().__init__() - resnets = [] - attentions = [] - - self.attention_type = attention_type - self.attn_num_head_channels = attn_num_head_channels - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlockFlat( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - if not dual_cross_attention: - attentions.append( - Transformer2DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - ) - ) - else: - attentions.append( - DualTransformer2DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - ) - ) - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - LinearMultiDim( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def set_attention_slice(self, slice_size): - head_dims = self.attn_num_head_channels - head_dims = [head_dims] if isinstance(head_dims, int) else head_dims - if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims): - raise ValueError( - f"Make sure slice_size {slice_size} is a common divisor of " - f"the number of heads used in cross_attention: {head_dims}" - ) - if slice_size is not None and slice_size > min(head_dims): - raise ValueError( - f"slice_size {slice_size} has to be smaller or equal to " - f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}" - ) - - for attn in self.attentions: - attn._set_attention_slice(slice_size) - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None): - output_states = () - - for resnet, attn in zip(self.resnets, self.attentions): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -# Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim -class UpBlockFlat(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor=1.0, - add_upsample=True, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlockFlat( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - - def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): - for resnet in self.resnets: - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - else: - hidden_states = resnet(hidden_states, temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - -# Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim -class CrossAttnUpBlockFlat(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - cross_attention_dim=1280, - attention_type="default", - output_scale_factor=1.0, - add_upsample=True, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - ): - super().__init__() - resnets = [] - attentions = [] - - self.attention_type = attention_type - self.attn_num_head_channels = attn_num_head_channels - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlockFlat( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - if not dual_cross_attention: - attentions.append( - Transformer2DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - ) - ) - else: - attentions.append( - DualTransformer2DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - ) - ) - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - - def set_attention_slice(self, slice_size): - head_dims = self.attn_num_head_channels - head_dims = [head_dims] if isinstance(head_dims, int) else head_dims - if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims): - raise ValueError( - f"Make sure slice_size {slice_size} is a common divisor of " - f"the number of heads used in cross_attention: {head_dims}" - ) - if slice_size is not None and slice_size > min(head_dims): - raise ValueError( - f"slice_size {slice_size} has to be smaller or equal to " - f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}" - ) - - for attn in self.attentions: - attn._set_attention_slice(slice_size) - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - res_hidden_states_tuple, - temb=None, - encoder_hidden_states=None, - upsample_size=None, - ): - for resnet, attn in zip(self.resnets, self.attentions): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - -# Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat -class UNetMidBlockFlatCrossAttn(nn.Module): - def __init__( - self, - in_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - attention_type="default", - output_scale_factor=1.0, - cross_attention_dim=1280, - dual_cross_attention=False, - use_linear_projection=False, - ): - super().__init__() - - self.attention_type = attention_type - self.attn_num_head_channels = attn_num_head_channels - resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - - # there is always at least one resnet - resnets = [ - ResnetBlockFlat( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ] - attentions = [] - - for _ in range(num_layers): - if not dual_cross_attention: - attentions.append( - Transformer2DModel( - attn_num_head_channels, - in_channels // attn_num_head_channels, - in_channels=in_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - ) - ) - else: - attentions.append( - DualTransformer2DModel( - attn_num_head_channels, - in_channels // attn_num_head_channels, - in_channels=in_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - ) - ) - resnets.append( - ResnetBlockFlat( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - def set_attention_slice(self, slice_size): - head_dims = self.attn_num_head_channels - head_dims = [head_dims] if isinstance(head_dims, int) else head_dims - if slice_size is not None and any(dim % slice_size != 0 for dim in head_dims): - raise ValueError( - f"Make sure slice_size {slice_size} is a common divisor of " - f"the number of heads used in cross_attention: {head_dims}" - ) - if slice_size is not None and slice_size > min(head_dims): - raise ValueError( - f"slice_size {slice_size} has to be smaller or equal to " - f"the lowest number of heads used in cross_attention: min({head_dims}) = {min(head_dims)}" - ) - - for attn in self.attentions: - attn._set_attention_slice(slice_size) - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None): - hidden_states = self.resnets[0](hidden_states, temb) - for attn, resnet in zip(self.attentions, self.resnets[1:]): - hidden_states = attn(hidden_states, encoder_hidden_states).sample - hidden_states = resnet(hidden_states, temb) - - return hidden_states diff --git a/spaces/ZJunTvT/ZJunChat/assets/Kelpy-Codos.js b/spaces/ZJunTvT/ZJunChat/assets/Kelpy-Codos.js deleted file mode 100644 index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000 --- a/spaces/ZJunTvT/ZJunChat/assets/Kelpy-Codos.js +++ /dev/null @@ -1,76 +0,0 @@ -// ==UserScript== -// @name Kelpy Codos -// @namespace https://github.com/Keldos-Li/Kelpy-Codos -// @version 1.0.5 -// @author Keldos; https://keldos.me/ -// @description Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. -// Based on Chuanhu ChatGPT version: ac04408 (2023-3-22) -// @license GPL-3.0 -// @grant none -// ==/UserScript== - -(function () { - 'use strict'; - - function addCopyButton(pre) { - var code = pre.querySelector('code'); - if (!code) { - return; // 如果没有找到--- Magic text to image -
-- Generate a new Magic card from a text description, - created by YaYaB. -
-元素，则不添加按钮 - } - var firstChild = code.firstChild; - if (!firstChild) { - return; // 如果元素没有子节点，则不添加按钮 - } - var button = document.createElement('button'); - button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本 - button.style.position = 'relative'; - button.style.float = 'right'; - button.style.fontSize = '1em'; // 可选：调整按钮大小 - button.style.background = 'none'; // 可选：去掉背景颜色 - button.style.border = 'none'; // 可选：去掉边框 - button.style.cursor = 'pointer'; // 可选：显示指针样式 - button.addEventListener('click', function () { - var range = document.createRange(); - range.selectNodeContents(code); - range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前 - var selection = window.getSelection(); - selection.removeAllRanges(); - selection.addRange(range); - - try { - var success = document.execCommand('copy'); - if (success) { - button.textContent = '\u2714'; - setTimeout(function () { - button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制” - }, 2000); - } else { - button.textContent = '\u2716'; - } - } catch (e) { - console.error(e); - button.textContent = '\u2716'; - } - - selection.removeAllRanges(); - }); - code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前 - } - - function handleNewElements(mutationsList, observer) { - for (var mutation of mutationsList) { - if (mutation.type === 'childList') { - for (var node of mutation.addedNodes) { - if (node.nodeName === 'PRE') { - addCopyButton(node); - } - } - } - } - } - - var observer = new MutationObserver(handleNewElements); - observer.observe(document.documentElement, { childList: true, subtree: true }); - - document.querySelectorAll('pre').forEach(addCopyButton); -})(); diff --git a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Arabic_poem_classifier/app.py b/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Arabic_poem_classifier/app.py deleted file mode 100644 index bbf72b782320453cd5d9fb4e7e1ebd99fc972af8..0000000000000000000000000000000000000000 --- a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Arabic_poem_classifier/app.py +++ /dev/null @@ -1,36 +0,0 @@ -import gradio as gr - -description = "التعرف على خاصيات البيت الشعري" -title = """هذا البرنامج يقوم بالتعرف على مختلف خاصيات البيت من الشعر. -يمكنكم إختيار الخاصية من بين: -- التعرف على البحر -- التعرف على الروي -التعرف على الموضوع-""" - -examples = [["سَلو قَلبي غَداةَ سَلا وَثابا لَعَلَّ عَلى الجَمالِ لَهُ عِتابا"], ["قفا نبك من ذِكرى حبيب ومنزلِ بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ"]] - - -meter = gr.Interface.load("huggingface/Yah216/Arabic_poem_meter_3", - description="من فضلك، أدخل البيت الشعري الذي تود التعرف عليه", - examples=examples, title = "التعرف على البحر", - inputs = gr.inputs.Textbox(lines = 3, label = "البيت") - -) -rawiy = gr.Interface.load("huggingface/Yah216/Poem_Qafiyah_Detection", - title ="التعرف على الروي", - examples=examples, - description="من فضلك، أدخل البيت الشعري الذي تود التعرف عليه", - inputs = gr.inputs.Textbox(lines = 3, label = "البيت") - -) -subject = gr.Interface.load( - "huggingface/zenkri/autotrain-Arabic_Poetry_by_Subject-920730230", - title="التعرف على الموضوع", - examples=examples, - description="من فضلك، أدخل البيت الشعري الذي تود التعرف عليه", - inputs = gr.inputs.Textbox(lines = 3, label = "البيت") - -) -demo = gr.TabbedInterface([meter, rawiy, subject], ["التعرف على البحر","التعرف على الروي","التعرف على الموضوع"]) -demo.launch() - diff --git a/spaces/abhijitguha/chatbot_gpt3/app.py b/spaces/abhijitguha/chatbot_gpt3/app.py deleted file mode 100644 index ced97e751f804ec57bd65ffebdddf68d8c14711a..0000000000000000000000000000000000000000 --- a/spaces/abhijitguha/chatbot_gpt3/app.py +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# In[ ]: - - -import os -import openai -import gradio as gr - -#openai.api_key = "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" -openai.api_key = "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" -start_sequence = "\nAI:" -restart_sequence = "\nHuman: " - -def predict(input,initial_prompt, history=[]): - - s = list(sum(history, ())) - s.append(input) -# initial_prompt="The following is a conversation with an AI movie recommendation assistant. The assistant is helpful, creative, clever, and very friendly.Along with movie recommendation it also talks about general topics" -# \n\nHuman: Hello, who are you?\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman: " - response = openai.Completion.create( - model="text-davinci-003", - prompt= initial_prompt + "\n" + str(s), - temperature=0.9, - max_tokens=150, - top_p=1, - frequency_penalty=0, - presence_penalty=0.6, - stop=[" Human:", " AI:"]) - # tokenize the new input sentence - response2 = response["choices"][0]["text"] - history.append((input, response2)) - - return history, history - - -gr.Interface(fn=predict, - inputs=["text","text",'state'], - - outputs=["chatbot",'state']).launch() - diff --git a/spaces/abhishek/sketch-to-image/annotator/hed/__init__.py b/spaces/abhishek/sketch-to-image/annotator/hed/__init__.py deleted file mode 100644 index edfc2927b50cdfb42f7cbfdc78300238a67bf9df..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/hed/__init__.py +++ /dev/null @@ -1,107 +0,0 @@ -''' - * Copyright (c) 2023 Salesforce, Inc. - * All rights reserved. - * SPDX-License-Identifier: Apache License 2.0 - * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/ - * By Can Qin - * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet - * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala -''' - -# This is an improved version and model of HED edge detection without GPL contamination -# Please use this implementation in your products -# This implementation may produce slightly different results from Saining Xie's official implementations, -# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations. -# Different from official models and other implementations, this is an RGB-input model (rather than BGR) -# and in this way it works better for gradio's RGB protocol - -import os -import cv2 -import torch -import numpy as np - -from einops import rearrange -from annotator.util import annotator_ckpts_path - - -class DoubleConvBlock(torch.nn.Module): - def __init__(self, input_channel, output_channel, layer_number): - super().__init__() - self.convs = torch.nn.Sequential() - self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) - for i in range(1, layer_number): - self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) - self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0) - - def __call__(self, x, down_sampling=False): - h = x - if down_sampling: - h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2)) - for conv in self.convs: - h = conv(h) - h = torch.nn.functional.relu(h) - return h, self.projection(h) - - -class ControlNetHED_Apache2(torch.nn.Module): - def __init__(self): - super().__init__() - self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1))) - self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2) - self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2) - self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3) - self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3) - self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3) - - def __call__(self, x): - h = x - self.norm - h, projection1 = self.block1(h) - h, projection2 = self.block2(h, down_sampling=True) - h, projection3 = self.block3(h, down_sampling=True) - h, projection4 = self.block4(h, down_sampling=True) - h, projection5 = self.block5(h, down_sampling=True) - return projection1, projection2, projection3, projection4, projection5 - - -class HEDdetector: - def __init__(self): - remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth" - modelpath = remote_model_path - modelpath = os.path.join(annotator_ckpts_path, "ControlNetHED.pth") - if not os.path.exists(modelpath): - from basicsr.utils.download_util import load_file_from_url - load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path) - self.netNetwork = ControlNetHED_Apache2().float().cuda().eval() - self.netNetwork.load_state_dict(torch.load(modelpath)) - - def __call__(self, input_image): - assert input_image.ndim == 3 - H, W, C = input_image.shape - with torch.no_grad(): - image_hed = torch.from_numpy(input_image.copy()).float().cuda() - image_hed = rearrange(image_hed, 'h w c -> 1 c h w') - edges = self.netNetwork(image_hed) - edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] - edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges] - edges = np.stack(edges, axis=2) - edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) - edge = (edge * 255.0).clip(0, 255).astype(np.uint8) - return edge - - -def nms(x, t, s): - x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) - - f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) - f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) - f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) - f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) - - y = np.zeros_like(x) - - for f in [f1, f2, f3, f4]: - np.putmask(y, cv2.dilate(x, kernel=f) == x, x) - - z = np.zeros_like(y, dtype=np.uint8) - z[y > t] = 255 - return z diff --git a/spaces/adirik/stylemc-demo/encoder4editing/utils/train_utils.py b/spaces/adirik/stylemc-demo/encoder4editing/utils/train_utils.py deleted file mode 100644 index 0c55177f7442010bc1fcc64de3d142585c22adc0..0000000000000000000000000000000000000000 --- a/spaces/adirik/stylemc-demo/encoder4editing/utils/train_utils.py +++ /dev/null @@ -1,13 +0,0 @@ - -def aggregate_loss_dict(agg_loss_dict): - mean_vals = {} - for output in agg_loss_dict: - for key in output: - mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]] - for key in mean_vals: - if len(mean_vals[key]) > 0: - mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key]) - else: - print('{} has no value'.format(key)) - mean_vals[key] = 0 - return mean_vals diff --git a/spaces/ahmedghani/Editing-Tools/README.md b/spaces/ahmedghani/Editing-Tools/README.md deleted file mode 100644 index 6288fff80057bc9bb6addf04040dd1a51f9ab034..0000000000000000000000000000000000000000 --- a/spaces/ahmedghani/Editing-Tools/README.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Editing Tools -emoji: 📽️📷🎥📹🎦🖼️🎨🖌️ -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.22.1 -app_file: app.py -pinned: false ---- - -```bash -conda create -n editing-tools python=3.9 -y -conda activate editing-tools -conda install -c "nvidia/label/cuda-11.7.0" cuda-toolkit cuda -pip install -r requirements.txt -python app.py -``` - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/akhaliq/Keypoint_Communities/README.md b/spaces/akhaliq/Keypoint_Communities/README.md deleted file mode 100644 index 1217eeb57b73fd355e773f5c039b4bcd0fe0164e..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/Keypoint_Communities/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Keypoint_Communities -emoji: 👁 -colorFrom: pink -colorTo: purple -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/akhaliq/stylegan3_clip/avg_spectra.py b/spaces/akhaliq/stylegan3_clip/avg_spectra.py deleted file mode 100644 index afaef87de54e49df230b432b52fda92667d17667..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/stylegan3_clip/avg_spectra.py +++ /dev/null @@ -1,276 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Compare average power spectra between real and generated images, -or between multiple generators.""" - -import os -import numpy as np -import torch -import torch.fft -import scipy.ndimage -import matplotlib.pyplot as plt -import click -import tqdm -import dnnlib - -import legacy -from training import dataset - -#---------------------------------------------------------------------------- -# Setup an iterator for streaming images, in uint8 NCHW format, based on the -# respective command line options. - -def stream_source_images(source, num, seed, device, data_loader_kwargs=None): # => num_images, image_size, image_iter - ext = source.split('.')[-1].lower() - if data_loader_kwargs is None: - data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2) - - if ext == 'pkl': - if num is None: - raise click.ClickException('--num is required when --source points to network pickle') - with dnnlib.util.open_url(source) as f: - G = legacy.load_network_pkl(f)['G_ema'].to(device) - def generate_image(seed): - rnd = np.random.RandomState(seed) - z = torch.from_numpy(rnd.randn(1, G.z_dim)).to(device) - c = torch.zeros([1, G.c_dim], device=device) - if G.c_dim > 0: - c[:, rnd.randint(G.c_dim)] = 1 - return (G(z=z, c=c) * 127.5 + 128).clamp(0, 255).to(torch.uint8) - _ = generate_image(seed) # warm up - image_iter = (generate_image(seed + idx) for idx in range(num)) - return num, G.img_resolution, image_iter - - elif ext == 'zip' or os.path.isdir(source): - dataset_obj = dataset.ImageFolderDataset(path=source, max_size=num, random_seed=seed) - if num is not None and num != len(dataset_obj): - raise click.ClickException(f'--source contains fewer than {num} images') - data_loader = torch.utils.data.DataLoader(dataset_obj, batch_size=1, **data_loader_kwargs) - image_iter = (image.to(device) for image, _label in data_loader) - return len(dataset_obj), dataset_obj.resolution, image_iter - - else: - raise click.ClickException('--source must point to network pickle, dataset zip, or directory') - -#---------------------------------------------------------------------------- -# Load average power spectrum from the specified .npz file and construct -# the corresponding heatmap for visualization. - -def construct_heatmap(npz_file, smooth): - npz_data = np.load(npz_file) - spectrum = npz_data['spectrum'] - image_size = npz_data['image_size'] - hmap = np.log10(spectrum) * 10 # dB - hmap = np.fft.fftshift(hmap) - hmap = np.concatenate([hmap, hmap[:1, :]], axis=0) - hmap = np.concatenate([hmap, hmap[:, :1]], axis=1) - if smooth > 0: - sigma = spectrum.shape[0] / image_size * smooth - hmap = scipy.ndimage.gaussian_filter(hmap, sigma=sigma, mode='nearest') - return hmap, image_size - -#---------------------------------------------------------------------------- - -@click.group() -def main(): - """Compare average power spectra between real and generated images, - or between multiple generators. - - Example: - - \b - # Calculate dataset mean and std, needed in subsequent steps. - python avg_spectra.py stats --source=~/datasets/ffhq-1024x1024.zip - - \b - # Calculate average spectrum for the training data. - python avg_spectra.py calc --source=~/datasets/ffhq-1024x1024.zip \\ - --dest=tmp/training-data.npz --mean=112.684 --std=69.509 - - \b - # Calculate average spectrum for a pre-trained generator. - python avg_spectra.py calc \\ - --source=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhq-1024x1024.pkl \\ - --dest=tmp/stylegan3-r.npz --mean=112.684 --std=69.509 --num=70000 - - \b - # Display results. - python avg_spectra.py heatmap tmp/training-data.npz - python avg_spectra.py heatmap tmp/stylegan3-r.npz - python avg_spectra.py slices tmp/training-data.npz tmp/stylegan3-r.npz - - \b - # Save as PNG. - python avg_spectra.py heatmap tmp/training-data.npz --save=tmp/training-data.png --dpi=300 - python avg_spectra.py heatmap tmp/stylegan3-r.npz --save=tmp/stylegan3-r.png --dpi=300 - python avg_spectra.py slices tmp/training-data.npz tmp/stylegan3-r.npz --save=tmp/slices.png --dpi=300 - """ - -#---------------------------------------------------------------------------- - -@main.command() -@click.option('--source', help='Network pkl, dataset zip, or directory', metavar='[PKL|ZIP|DIR]', required=True) -@click.option('--num', help='Number of images to process [default: all]', metavar='INT', type=click.IntRange(min=1)) -@click.option('--seed', help='Random seed for selecting the images', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True) -def stats(source, num, seed, device=torch.device('cuda')): - """Calculate dataset mean and standard deviation needed by 'calc'.""" - torch.multiprocessing.set_start_method('spawn') - num_images, _image_size, image_iter = stream_source_images(source=source, num=num, seed=seed, device=device) - - # Accumulate moments. - moments = torch.zeros([3], dtype=torch.float64, device=device) - for image in tqdm.tqdm(image_iter, total=num_images): - image = image.to(torch.float64) - moments += torch.stack([torch.ones_like(image).sum(), image.sum(), image.square().sum()]) - moments = moments / moments[0] - - # Compute mean and standard deviation. - mean = moments[1] - std = (moments[2] - moments[1].square()).sqrt() - print(f'--mean={mean:g} --std={std:g}') - -#---------------------------------------------------------------------------- - -@main.command() -@click.option('--source', help='Network pkl, dataset zip, or directory', metavar='[PKL|ZIP|DIR]', required=True) -@click.option('--dest', help='Where to store the result', metavar='NPZ', required=True) -@click.option('--mean', help='Dataset mean for whitening', metavar='FLOAT', type=float, required=True) -@click.option('--std', help='Dataset standard deviation for whitening', metavar='FLOAT', type=click.FloatRange(min=0), required=True) -@click.option('--num', help='Number of images to process [default: all]', metavar='INT', type=click.IntRange(min=1)) -@click.option('--seed', help='Random seed for selecting the images', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True) -@click.option('--beta', help='Shape parameter for the Kaiser window', metavar='FLOAT', type=click.FloatRange(min=0), default=8, show_default=True) -@click.option('--interp', help='Frequency-domain interpolation factor', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True) -def calc(source, dest, mean, std, num, seed, beta, interp, device=torch.device('cuda')): - """Calculate average power spectrum and store it in .npz file.""" - torch.multiprocessing.set_start_method('spawn') - num_images, image_size, image_iter = stream_source_images(source=source, num=num, seed=seed, device=device) - spectrum_size = image_size * interp - padding = spectrum_size - image_size - - # Setup window function. - window = torch.kaiser_window(image_size, periodic=False, beta=beta, device=device) - window *= window.square().sum().rsqrt() - window = window.ger(window).unsqueeze(0).unsqueeze(1) - - # Accumulate power spectrum. - spectrum = torch.zeros([spectrum_size, spectrum_size], dtype=torch.float64, device=device) - for image in tqdm.tqdm(image_iter, total=num_images): - image = (image.to(torch.float64) - mean) / std - image = torch.nn.functional.pad(image * window, [0, padding, 0, padding]) - spectrum += torch.fft.fftn(image, dim=[2,3]).abs().square().mean(dim=[0,1]) - spectrum /= num_images - - # Save result. - if os.path.dirname(dest): - os.makedirs(os.path.dirname(dest), exist_ok=True) - np.savez(dest, spectrum=spectrum.cpu().numpy(), image_size=image_size) - -#---------------------------------------------------------------------------- - -@main.command() -@click.argument('npz-file', nargs=1) -@click.option('--save', help='Save the plot and exit', metavar='[PNG|PDF|...]') -@click.option('--dpi', help='Figure resolution', metavar='FLOAT', type=click.FloatRange(min=1), default=100, show_default=True) -@click.option('--smooth', help='Amount of smoothing', metavar='FLOAT', type=click.FloatRange(min=0), default=1.25, show_default=True) -def heatmap(npz_file, save, smooth, dpi): - """Visualize 2D heatmap based on the given .npz file.""" - hmap, image_size = construct_heatmap(npz_file=npz_file, smooth=smooth) - - # Setup plot. - plt.figure(figsize=[6, 4.8], dpi=dpi, tight_layout=True) - freqs = np.linspace(-0.5, 0.5, num=hmap.shape[0], endpoint=True) * image_size - ticks = np.linspace(freqs[0], freqs[-1], num=5, endpoint=True) - levels = np.linspace(-40, 20, num=13, endpoint=True) - - # Draw heatmap. - plt.xlim(ticks[0], ticks[-1]) - plt.ylim(ticks[0], ticks[-1]) - plt.xticks(ticks) - plt.yticks(ticks) - plt.contourf(freqs, freqs, hmap, levels=levels, extend='both', cmap='Blues') - plt.gca().set_aspect('equal') - plt.colorbar(ticks=levels) - plt.contour(freqs, freqs, hmap, levels=levels, extend='both', linestyles='solid', linewidths=1, colors='midnightblue', alpha=0.2) - - # Display or save. - if save is None: - plt.show() - else: - if os.path.dirname(save): - os.makedirs(os.path.dirname(save), exist_ok=True) - plt.savefig(save) - -#---------------------------------------------------------------------------- - -@main.command() -@click.argument('npz-files', nargs=-1, required=True) -@click.option('--save', help='Save the plot and exit', metavar='[PNG|PDF|...]') -@click.option('--dpi', help='Figure resolution', metavar='FLOAT', type=click.FloatRange(min=1), default=100, show_default=True) -@click.option('--smooth', help='Amount of smoothing', metavar='FLOAT', type=click.FloatRange(min=0), default=0, show_default=True) -def slices(npz_files, save, dpi, smooth): - """Visualize 1D slices based on the given .npz files.""" - cases = [dnnlib.EasyDict(npz_file=npz_file) for npz_file in npz_files] - for c in cases: - c.hmap, c.image_size = construct_heatmap(npz_file=c.npz_file, smooth=smooth) - c.label = os.path.splitext(os.path.basename(c.npz_file))[0] - - # Check consistency. - image_size = cases[0].image_size - hmap_size = cases[0].hmap.shape[0] - if any(c.image_size != image_size or c.hmap.shape[0] != hmap_size for c in cases): - raise click.ClickException('All .npz must have the same resolution') - - # Setup plot. - plt.figure(figsize=[12, 4.6], dpi=dpi, tight_layout=True) - hmap_center = hmap_size // 2 - hmap_range = np.arange(hmap_center, hmap_size) - freqs0 = np.linspace(0, image_size / 2, num=(hmap_size // 2 + 1), endpoint=True) - freqs45 = np.linspace(0, image_size / np.sqrt(2), num=(hmap_size // 2 + 1), endpoint=True) - xticks0 = np.linspace(freqs0[0], freqs0[-1], num=9, endpoint=True) - xticks45 = np.round(np.linspace(freqs45[0], freqs45[-1], num=9, endpoint=True)) - yticks = np.linspace(-50, 30, num=9, endpoint=True) - - # Draw 0 degree slice. - plt.subplot(1, 2, 1) - plt.title('0\u00b0 slice') - plt.xlim(xticks0[0], xticks0[-1]) - plt.ylim(yticks[0], yticks[-1]) - plt.xticks(xticks0) - plt.yticks(yticks) - for c in cases: - plt.plot(freqs0, c.hmap[hmap_center, hmap_range], label=c.label) - plt.grid() - plt.legend(loc='upper right') - - # Draw 45 degree slice. - plt.subplot(1, 2, 2) - plt.title('45\u00b0 slice') - plt.xlim(xticks45[0], xticks45[-1]) - plt.ylim(yticks[0], yticks[-1]) - plt.xticks(xticks45) - plt.yticks(yticks) - for c in cases: - plt.plot(freqs45, c.hmap[hmap_range, hmap_range], label=c.label) - plt.grid() - plt.legend(loc='upper right') - - # Display or save. - if save is None: - plt.show() - else: - if os.path.dirname(save): - os.makedirs(os.path.dirname(save), exist_ok=True) - plt.savefig(save) - -#---------------------------------------------------------------------------- - -if __name__ == "__main__": - main() # pylint: disable=no-value-for-parameter - -#---------------------------------------------------------------------------- diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/unix.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/unix.py deleted file mode 100644 index 2fbd4d4f367863ff0cf635fddc5f6e44383e7d94..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/platformdirs/unix.py +++ /dev/null @@ -1,181 +0,0 @@ -from __future__ import annotations - -import os -import sys -from configparser import ConfigParser -from pathlib import Path - -from .api import PlatformDirsABC - -if sys.platform.startswith("linux"): # pragma: no branch # no op check, only to please the type checker - from os import getuid -else: - - def getuid() -> int: - raise RuntimeError("should only be used on Linux") - - -class Unix(PlatformDirsABC): - """ - On Unix/Linux, we follow the - `XDG Basedir Spec`, - `version `, - `multipath `, - `opinion `. - """ - - @property - def user_data_dir(self) -> str: - """ - :return: data directory tied to the user, e.g. ``~/.local/share/$appname/$version`` or - ``$XDG_DATA_HOME/$appname/$version`` - """ - path = os.environ.get("XDG_DATA_HOME", "") - if not path.strip(): - path = os.path.expanduser("~/.local/share") - return self._append_app_name_and_version(path) - - @property - def site_data_dir(self) -> str: - """ - :return: data directories shared by users (if `multipath ` is - enabled and ``XDG_DATA_DIR`` is set and a multi path the response is also a multi path separated by the OS - path separator), e.g. ``/usr/local/share/$appname/$version`` or ``/usr/share/$appname/$version`` - """ - # XDG default for $XDG_DATA_DIRS; only first, if multipath is False - path = os.environ.get("XDG_DATA_DIRS", "") - if not path.strip(): - path = f"/usr/local/share{os.pathsep}/usr/share" - return self._with_multi_path(path) - - def _with_multi_path(self, path: str) -> str: - path_list = path.split(os.pathsep) - if not self.multipath: - path_list = path_list[0:1] - path_list = [self._append_app_name_and_version(os.path.expanduser(p)) for p in path_list] - return os.pathsep.join(path_list) - - @property - def user_config_dir(self) -> str: - """ - :return: config directory tied to the user, e.g. ``~/.config/$appname/$version`` or - ``$XDG_CONFIG_HOME/$appname/$version`` - """ - path = os.environ.get("XDG_CONFIG_HOME", "") - if not path.strip(): - path = os.path.expanduser("~/.config") - return self._append_app_name_and_version(path) - - @property - def site_config_dir(self) -> str: - """ - :return: config directories shared by users (if `multipath ` - is enabled and ``XDG_DATA_DIR`` is set and a multi path the response is also a multi path separated by the OS - path separator), e.g. ``/etc/xdg/$appname/$version`` - """ - # XDG default for $XDG_CONFIG_DIRS only first, if multipath is False - path = os.environ.get("XDG_CONFIG_DIRS", "") - if not path.strip(): - path = "/etc/xdg" - return self._with_multi_path(path) - - @property - def user_cache_dir(self) -> str: - """ - :return: cache directory tied to the user, e.g. ``~/.cache/$appname/$version`` or - ``~/$XDG_CACHE_HOME/$appname/$version`` - """ - path = os.environ.get("XDG_CACHE_HOME", "") - if not path.strip(): - path = os.path.expanduser("~/.cache") - return self._append_app_name_and_version(path) - - @property - def user_state_dir(self) -> str: - """ - :return: state directory tied to the user, e.g. ``~/.local/state/$appname/$version`` or - ``$XDG_STATE_HOME/$appname/$version`` - """ - path = os.environ.get("XDG_STATE_HOME", "") - if not path.strip(): - path = os.path.expanduser("~/.local/state") - return self._append_app_name_and_version(path) - - @property - def user_log_dir(self) -> str: - """ - :return: log directory tied to the user, same as `user_data_dir` if not opinionated else ``log`` in it - """ - path = self.user_cache_dir - if self.opinion: - path = os.path.join(path, "log") - return path - - @property - def user_documents_dir(self) -> str: - """ - :return: documents directory tied to the user, e.g. ``~/Documents`` - """ - documents_dir = _get_user_dirs_folder("XDG_DOCUMENTS_DIR") - if documents_dir is None: - documents_dir = os.environ.get("XDG_DOCUMENTS_DIR", "").strip() - if not documents_dir: - documents_dir = os.path.expanduser("~/Documents") - - return documents_dir - - @property - def user_runtime_dir(self) -> str: - """ - :return: runtime directory tied to the user, e.g. ``/run/user/$(id -u)/$appname/$version`` or - ``$XDG_RUNTIME_DIR/$appname/$version`` - """ - path = os.environ.get("XDG_RUNTIME_DIR", "") - if not path.strip(): - path = f"/run/user/{getuid()}" - return self._append_app_name_and_version(path) - - @property - def site_data_path(self) -> Path: - """:return: data path shared by users. Only return first item, even if ``multipath`` is set to ``True``""" - return self._first_item_as_path_if_multipath(self.site_data_dir) - - @property - def site_config_path(self) -> Path: - """:return: config path shared by the users. Only return first item, even if ``multipath`` is set to ``True``""" - return self._first_item_as_path_if_multipath(self.site_config_dir) - - def _first_item_as_path_if_multipath(self, directory: str) -> Path: - if self.multipath: - # If multipath is True, the first path is returned. - directory = directory.split(os.pathsep)[0] - return Path(directory) - - -def _get_user_dirs_folder(key: str) -> str | None: - """Return directory from user-dirs.dirs config file. See https://freedesktop.org/wiki/Software/xdg-user-dirs/""" - user_dirs_config_path = os.path.join(Unix().user_config_dir, "user-dirs.dirs") - if os.path.exists(user_dirs_config_path): - parser = ConfigParser() - - with open(user_dirs_config_path) as stream: - # Add fake section header, so ConfigParser doesn't complain - parser.read_string(f"[top]\n{stream.read()}") - - if key not in parser["top"]: - return None - - path = parser["top"][key].strip('"') - # Handle relative home paths - path = path.replace("$HOME", os.path.expanduser("~")) - return path - - return None - - -__all__ = [ - "Unix", -] diff --git a/spaces/alistairmcleay/cambridge-masters-project/scripts/UBAR_code/__init__.py b/spaces/alistairmcleay/cambridge-masters-project/scripts/UBAR_code/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/alistairmcleay/cambridge-masters-project/scripts/UBAR_code/interaction/__init__.py b/spaces/alistairmcleay/cambridge-masters-project/scripts/UBAR_code/interaction/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/allknowingroger/Image-Models-Test119/README.md b/spaces/allknowingroger/Image-Models-Test119/README.md deleted file mode 100644 index 77af92a0c2ef86e2bfc609479cf59bb741dd3132..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test119/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: More Image Models -emoji: 😻 -colorFrom: red -colorTo: gray -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -duplicated_from: allknowingroger/Image-Models-Test118 ---- - - \ No newline at end of file diff --git a/spaces/alphahg/academic-paper-translate-summary/app.py b/spaces/alphahg/academic-paper-translate-summary/app.py deleted file mode 100644 index b1f7444bd5940242664b4a3e34b0fcaaa4522619..0000000000000000000000000000000000000000 --- a/spaces/alphahg/academic-paper-translate-summary/app.py +++ /dev/null @@ -1,167 +0,0 @@ -# %% -import gradio as gr -from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline -from nltk.tokenize import sent_tokenize -import gc - -import nltk -nltk.download('punkt') - -# from PyKakao import KoGPT -# kogpt_api = KoGPT(service_key = "") -import openai -openai.api_key = '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' -gpt2_tokenizer = AutoTokenizer.from_pretrained('gpt2') - -#en2ko = 'alphahg/m2m100_418M-finetuned-en-to-ko-4770260'#'alphahg/mbart-large-50-finetuned-en-to-ko-8603428-finetuned-en-to-ko-9914408' -en2ko = 'alphahg/mbart-large-50-finetuned-en-to-ko-8603428-finetuned-en-to-ko-9914408' -ko2en = 'alphahg/opus-mt-ko-en-finetuned-ko-to-en-2780616' -ensum = 'allenai/led-large-16384-arxiv' -kosum = 'alphahg/pko-t5-small-finetuned-paper-4564652' #'lcw99/t5-base-korean-text-summary' - -#en_pipe = pipeline('translation', model=en2ko, tokenizer=en2ko, src_lang = "en", tgt_lang = "ko", device_map="auto") -en2ko_model = AutoModelForSeq2SeqLM.from_pretrained(en2ko) - -en_pipe = pipeline('translation', model=en2ko_model, tokenizer=en2ko, src_lang = "en_XX", tgt_lang = "ko_KR") -ko_pipe = pipeline('translation', model=ko2en, tokenizer=ko2en) -style_pipe = pipeline('translation', model=en2ko_model, tokenizer=en2ko, src_lang = "ko_KR", tgt_lang = "ko_KR") - -en_sum = pipeline('summarization', model=ensum, tokenizer=ensum) -ko_sum = pipeline('summarization', model=kosum, tokenizer=kosum) - -def len_tokens(text, pipe): - return len(pipe.tokenizer(text)['input_ids']) - -def split_sent(sentences, pipe, max_len=256): - if not sentences: - return [] - - paragraphs = [] - example = sentences[0] - for i in range(1, len(sentences)): - if len_tokens(example + ' ' + sentences[i], pipe) > max_len: - paragraphs.append(example) - example = sentences[i] - else: - example += ' ' + sentences[i] - - paragraphs.append(example) - - return paragraphs - -# chatbot = Chatbot({ -# #"session_token": "eyJhbGciOiJkaXIiLCJlbmMiOiJBMjU2R0NNIn0..hV_ujfbYLwBgI-g6.zQW0evUrpYfli2cujTFp1ie5PhthUZayoSY2Chb1Eb8Ow3t6l2-NUwGJcYyxVKQS0aITN3-ph-KzPysnu7dCF9KrC-22DZzs1zMFm3PHEkjb4jD69qndcFEGGH8y4SejfYwvdj4wfKmVnGo3XTNZ31qPSDA2PBoaOMpxWABSqWULMJbS-_Y--wd0YhsqMFlkCQpXyfxSf9yxXlPYt0HR_NgupoBXP-WVYbODSUYFVqa3IsScbSPS-mUY0YrAb8AJUkvej7HiSCN9onyThgTtZgZklwqpB4FesJaC6R3nSXSg5cKDDupGVBUQlPRiemacCV6tXnSC-bCtN9a-l9RRqtX_FJNP8T7Kb75ktuedKKrXXTmk3x7hz_RhYhZ4wXFkbqXexZQXTfQoI2vKlLN73EHBlJOqsDLnOP7zT4Vr2RpBbk1HK5D_uHh5x1X3aBHslHfEQpjjZKiMs0to9DwKzSNHXlNmCeGjT9ZzKVsWYCiseO20IlKxQ63Q_nIbi-6y8e6LWw9O82ESKkkRe8kN-CzGxakKJegmHKQGRPZu9ZEIqwWYqlnahVyWRFOtjfMNN3ncGbQGi54VMyfSqmSvPXecaxsVNzl00gHvmCFBFJBDXM2GTsvEzQsJi1MLyopXtSuiU1anL_kC1eMvew61vd3TtC97ZwlQLaWc6dT14p5NdJpN9ihFpgtxMP1rcQhNTc5fo9BBoKrO4yGOuy0wixJs6ORVdY2o3c653X8PFmrso1XaV3KpBaSmcvszIhL1anJTA8SpnPNRmksEFONcX8AfpQ-4WbckCS737TZYCDulVtvukyVAbtq9cEQP5kXXAGOWKg5lX4nRFynM83f8P-XGIg-XUGE99NRIOCBo28cr04fWFOaJOyHf9eP6Rx5zjNv1qwp4FxhVVP9jlmSTfu97CZSR91L-k8V6jVgbj8F6YUZ6iiu51kaOAqf5de4EUncSFyGLuJfCGTJTPSYYl1lnR6bSfTVHKwP28YzzcU2myMM5B0ZXDwydD900TYXZOCxxLPUbu5-G3roR2KZnuWLXFOiafAvDx-LHYUHSWQZ9ouWcDaQBNsXmfTZtIWHQ8aTZwlNEnN4-uFdlk2Lm35qp1v-8Fp_3aXGQ3CrTy-ryMV0rUPTSMCEA8gVA_mD40zV6Wcb4asc3zsYAuomQ3Iu4iB5wyWGxUIJVzl1C9QaPpAx7vp5u7w-0_rtocVVXFRTZ8aSxNS3QAd62TbVyToIOrsvp4kOWDcqhNp5QBAsJtES9pbO9fiy_SJS83SFMliSFd-jhXfKu0kUYIUb9yaN5QC6eEpgJ7KzhwTcNDtoqyBKMyVTSdUXA9P2Yv2e4r-BVnxlW0RxknQdesK-wZrwuAZt_bnLaHSqFzyWz5AE7pukTBQ2QdVoity_tVURzhTcINh6rvPywm2IVl1gC3FjfhQVfTvHWFtUzNqLN4yUfI0Tc1mGHQlYuxZ_yux2B8HeYb_cyb1rR_mwDiOs3PKOnhfNRdXqXf6RWr7KdjNc0k-CMm13DAYQggmFCmEZW20FiwalKqVq3nFTFDhfp5mxtr0sLCVxGA3eTqC6_i2TAVGqDLjxzfz5WiK7J3FAN2_kmEZLBVXHabwa9kKyCzcgCx6FrxaFidskO6t4dWu3wok95hXMae0Z4ZFs7HVNisM1pkRm7LE3XdvnslKHAJkPr57HsFdlQITJRSx3Tg0EN1LUt80hKx8VGXPv7zBeXP5lni2ixpglMQmiKLiszowGoqu2oJPwougueu5Bj4BLhmoqK8DCtdxl3MYAyxLWWStXQqcEJQw7koYmPNwr4BzI9cQVk81LbPwrXBbJfR7G14e5qV0lULfuU5qVfNt7DU6FbwXmzv6qFI-jOClLzSTKpFzp51wQQ5fh2REs6CPJlL-kiyomJPXcqSeezDCLLwWjI_vIyODFkzt91l-dmFriu3HMkMC1v29AJlfPA_avSiJzJDEI7rb6AbEyT6piqp1TYlWMkI_rJCsCZXIb10Rjd2y9sR-Dz3_FZzRvJUA7BfRlP7Bf04HnYsyMRoJilbuyQ5fB0B2L2nxjYY2zoHJ_x6HTS6tcrAijOO4FSSQngWD9iTKCm6pjW3aZjFyXyjmP82S3VnhEyON390aIL7j9Y0wGnHzOkn54OfyxxGeo2mFAIv9kthL_Fi8d9G_rvvQOBUM2a7kjF5-n8wby0YDujoRl0ETg379HyMVf7F2BHWQ8nAbICRxWZ7EzPLwzrVjPiQVPZklkrVYgEmGxZDrgEG8IeNi7FMgGruaQ1tENczRMXzaApK8k6-FXKhfFIV7dN95tP4k6tnnxRFoMAUWcXwQCzRH8YhID36TAUFdBQ-c52MTogPo1Rki1N49j_e7Mph1OABQX2Fw9-CukT6reQkp3nGrwi0IKnKoyGhHOBHK3kzQwINfjOBbNpOjP-6MX_9kiRTstN2GLte8w0QJQVl84o8ACTjV8N4rhI6xyLKIvqoyZ6jNO3SYs8fEutmZO8-qB0iksIiQHupxQgcmgbAyM.KxAbJWqMvwm0PYtq7MuR6A" - -# }, conversation_id=None, parent_id=None) # You can start a custom conversation -# %% -def translate(text, lang, gpt_fix=False): - from_en = False if lang == '한영' else True - sentences = sent_tokenize(text) - #print(sentences) - if not sentences: - return '' - - paragraphs = split_sent(sentences, en_pipe, max_len=180) if from_en else split_sent(sentences, ko_pipe) - #print(paragraphs) - - ret = [] - for text in paragraphs: - result = en_pipe(text) if from_en else ko_pipe(text) - ret.append(result[0]['translation_text']) - - translated = ' '.join(ret) - gc.collect() - - if gpt_fix: - if lang == '한영': - prompt = 'Improve given formal article without adding:' - elif lang == '영한': - prompt = "추가적인 내용없이 주어진 글을 개선해:" - - def fix_sent(sent): - number_of_tokens = len(gpt2_tokenizer(sent)['input_ids']) - - response = openai.Completion.create( - model="text-davinci-003", - prompt=prompt+'\n'+sent, - temperature=0, - max_tokens=number_of_tokens+128, - top_p=1.0, - frequency_penalty=0.0, - presence_penalty=0.0 - ) - - return response.choices[0].text.strip() - - # def fix_sent(sent): - # generated = kogpt_api.generate(prompt+'\n'+sent, max_tokens=256) - # return generated['generations'][0]['text'] - - translated = fix_sent(translated) - - return translated - -#%% -def translate_with_sum(text, lang, gpt_fix=False): - from_en = False if lang == '한영' else True - - if lang == '영한': - summary = en_sum(text, max_length=int(len_tokens(text, en_sum)/2)+32) - text = summary[0]['summary_text'] - - sentences = sent_tokenize(text) - #print(sentences) - if not sentences: - return '' - - paragraphs = split_sent(sentences, en_pipe if from_en else ko_pipe) - #print(paragraphs) - - ret = [] - for text in paragraphs: - result = en_pipe(text) if from_en else ko_pipe(text) - ret.append(result[0]['translation_text']) - - summarized = ' '.join(ret) - if lang == '한영': - summary = en_sum(summarized, max_length=int(len_tokens(summarized, en_sum)/2)+32) - return summary[0]['summary_text'] - - gc.collect() - return summarized - -def summarize(text, lang): - if lang == 'Korean': - summarizer = ko_sum - elif lang == 'English': - summarizer = en_sum - - summary = summarizer(text, max_length=int(len_tokens(text, summarizer) * 0.7))[0]['summary_text'] - return summary - -def translate_styleonly(text): - sentences = sent_tokenize(text) - paragraphs = split_sent(sentences, style_pipe, max_len=180) - #print(paragraphs) - - ret = [] - for text in paragraphs: - result = style_pipe(text) - ret.append(result[0]['translation_text']) - - translated = ' '.join(ret) - gc.collect() - - return translated - -# %% -interface1 = gr.Interface(fn=translate, inputs=["text", gr.Radio(["영한", "한영"], value='영한'), 'checkbox'], outputs="text") -interface2 = gr.Interface(fn=translate_with_sum, inputs=["text", gr.Radio(["영한", "한영"], value='영한')], outputs="text") -parallel_interface = gr.Parallel(interface1, interface2) - -summarize_interface = gr.Interface(fn=summarize, inputs=["text", gr.Radio(["Korean", "English"], value='Korean')], outputs="text") -style_interface = gr.Interface(fn=translate_styleonly, inputs=["text"], outputs="text") - -demo = gr.TabbedInterface([parallel_interface, summarize_interface, style_interface], ['번역 및 요약', '요약', '스타일 번역'], css="footer {visibility: hidden}") # '요약' -demo.launch() # Share the demo -# %% \ No newline at end of file diff --git a/spaces/amasad/sahil2801-replit-code-instruct-glaive/app.py b/spaces/amasad/sahil2801-replit-code-instruct-glaive/app.py deleted file mode 100644 index 976f6a74229ccc0badfdaca594a78558f0afbab4..0000000000000000000000000000000000000000 --- a/spaces/amasad/sahil2801-replit-code-instruct-glaive/app.py +++ /dev/null @@ -1,75 +0,0 @@ -import os -import gradio as gr -import torch - -from transformers import AutoTokenizer, AutoModelForCausalLM - -REPO = "sahil2801/replit-code-instruct-glaive" - -description = """# Code Generation by Instruction with sahil2801/replit-code-instruct-glaive
- This model is trained on a large amount of code and fine tuned on code-instruct datasets. You can type an instruction in the ### Input: section and received code generation.""" - -device = "cuda" if torch.cuda.is_available() else "cpu" - -tokenizer = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True) -model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype=torch.bfloat16, trust_remote_code=True) -model.to(device) - -model.eval() - -custom_css = """ -.gradio-container { - background-color: #0D1525; - color:white -} -#orange-button { - background: #F26207 !important; - color: white; -} -.cm-gutters{ - border: none !important; -} -""" - -def post_processing(prompt, completion): - return prompt + completion - -def code_generation(prompt, max_new_tokens=1024, temperature=0.2, top_p=0.9, eos_token_id=tokenizer.eos_token_id): - input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) - generated_ids = model.generate(input_ids, max_new_tokens=max_new_tokens, do_sample=True, use_cache=True, temperature=temperature, top_p=top_p, eos_token_id=eos_token_id) - completion = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_spaces=False) - return post_processing(prompt, completion) - -demo = gr.Blocks( - css=custom_css -) - -with demo: - gr.Markdown(value=description) - with gr.Row(): - input_col , settings_col = gr.Column(scale=6), gr.Column(scale=6), - with input_col: - code = gr.Code(lines=28,label='Input', value="Below is an instruction that describes a task, paired with an input that provides further context.\n Write a response that appropriately completes the request.\n\n ### Instruction:\nWrite a program to perform the given task.\n\n###Input: \n\n### Response:") - with settings_col: - with gr.Accordion("Generation Settings", open=True): - max_new_tokens= gr.Slider( - minimum=8, - maximum=1024, - step=1, - value=48, - label="Max Tokens", - ) - temperature = gr.Slider( - minimum=0.1, - maximum=2.5, - step=0.1, - value=0.2, - label="Temperature", - ) - - with gr.Row(): - run = gr.Button(elem_id="orange-button", value="Generate Response") - - event = run.click(code_generation, [code, max_new_tokens, temperature], code, api_name="predict") - -demo.queue(max_size=40).launch() \ No newline at end of file diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/server/backend.py b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/server/backend.py deleted file mode 100644 index fd45b94d916512059e4d1f7850b63de6f9da6320..0000000000000000000000000000000000000000 --- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/server/backend.py +++ /dev/null @@ -1,176 +0,0 @@ -import re -from datetime import datetime -from g4f import ChatCompletion -from flask import request, Response, stream_with_context -from requests import get -from server.config import special_instructions - - -class Backend_Api: - def __init__(self, bp, config: dict) -> None: - """ - Initialize the Backend_Api class. - :param app: Flask application instance - :param config: Configuration dictionary - """ - self.bp = bp - self.routes = { - '/backend-api/v2/conversation': { - 'function': self._conversation, - 'methods': ['POST'] - } - } - - def _conversation(self): - """ - Handles the conversation route. - - :return: Response object containing the generated conversation stream - """ - conversation_id = request.json['conversation_id'] - - try: - jailbreak = request.json['jailbreak'] - model = request.json['model'] - messages = build_messages(jailbreak) - - # Generate response - response = ChatCompletion.create( - model=model, - chatId=conversation_id, - messages=messages - ) - - return Response(stream_with_context(generate_stream(response, jailbreak)), mimetype='text/event-stream') - - except Exception as e: - print(e) - print(e.__traceback__.tb_next) - - return { - '_action': '_ask', - 'success': False, - "error": f"an error occurred {str(e)}" - }, 400 - - -def build_messages(jailbreak): - """ - Build the messages for the conversation. - - :param jailbreak: Jailbreak instruction string - :return: List of messages for the conversation - """ - _conversation = request.json['meta']['content']['conversation'] - internet_access = request.json['meta']['content']['internet_access'] - prompt = request.json['meta']['content']['parts'][0] - - # Add the existing conversation - conversation = _conversation - - # Add web results if enabled - if internet_access: - current_date = datetime.now().strftime("%Y-%m-%d") - query = f'Current date: {current_date}. ' + prompt["content"] - search_results = fetch_search_results(query) - conversation.extend(search_results) - - # Add jailbreak instructions if enabled - if jailbreak_instructions := getJailbreak(jailbreak): - conversation.extend(jailbreak_instructions) - - # Add the prompt - conversation.append(prompt) - - # Reduce conversation size to avoid API Token quantity error - if len(conversation) > 3: - conversation = conversation[-4:] - - return conversation - - -def fetch_search_results(query): - """ - Fetch search results for a given query. - - :param query: Search query string - :return: List of search results - """ - search = get('https://ddg-api.herokuapp.com/search', - params={ - 'query': query, - 'limit': 3, - }) - - snippets = "" - for index, result in enumerate(search.json()): - snippet = f'[{index + 1}] "{result["snippet"]}" URL:{result["link"]}.' - snippets += snippet - - response = "Here are some updated web searches. Use this to improve user response:" - response += snippets - - return [{'role': 'system', 'content': response}] - - -def generate_stream(response, jailbreak): - """ - Generate the conversation stream. - - :param response: Response object from ChatCompletion.create - :param jailbreak: Jailbreak instruction string - :return: Generator object yielding messages in the conversation - """ - if getJailbreak(jailbreak): - response_jailbreak = '' - jailbroken_checked = False - for message in response: - response_jailbreak += message - if jailbroken_checked: - yield message - else: - if response_jailbroken_success(response_jailbreak): - jailbroken_checked = True - if response_jailbroken_failed(response_jailbreak): - yield response_jailbreak - jailbroken_checked = True - else: - yield from response - - -def response_jailbroken_success(response: str) -> bool: - """Check if the response has been jailbroken. - - :param response: Response string - :return: Boolean indicating if the response has been jailbroken - """ - act_match = re.search(r'ACT:', response, flags=re.DOTALL) - return bool(act_match) - - -def response_jailbroken_failed(response): - """ - Check if the response has not been jailbroken. - - :param response: Response string - :return: Boolean indicating if the response has not been jailbroken - """ - return False if len(response) < 4 else not (response.startswith("GPT:") or response.startswith("ACT:")) - - -def getJailbreak(jailbreak): - """ - Check if jailbreak instructions are provided. - - :param jailbreak: Jailbreak instruction string - :return: Jailbreak instructions if provided, otherwise None - """ - if jailbreak != "default": - special_instructions[jailbreak][0]['content'] += special_instructions['two_responses_instruction'] - if jailbreak in special_instructions: - special_instructions[jailbreak] - return special_instructions[jailbreak] - else: - return None - else: - return None diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/webui_sd_pipeline.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/webui_sd_pipeline.py deleted file mode 100644 index 21b46152c3167038954f9f170a65647929c2e929..0000000000000000000000000000000000000000 --- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/webui_sd_pipeline.py +++ /dev/null @@ -1,49 +0,0 @@ -from modules.processing import StableDiffusionProcessingImg2Img -from modules.shared import opts, sd_model -import os - -def get_webui_sd_pipeline(args, root, frame): - import re - assert args.prompt is not None - - # Setup the pipeline - p = StableDiffusionProcessingImg2Img( - sd_model=sd_model, - outpath_samples = opts.outdir_samples or opts.outdir_img2img_samples, - #we'll setup the rest later - ) - - os.makedirs(args.outdir, exist_ok=True) - p.width, p.height = map(lambda x: x - x % 64, (args.W, args.H)) - p.steps = args.steps - p.seed = args.seed - p.sampler_name = args.sampler - p.batch_size = args.n_batch - p.tiling = args.tiling - p.restore_faces = args.restore_faces - p.subseed = args.subseed - p.subseed_strength = args.subseed_strength - p.seed_resize_from_w = args.seed_resize_from_w - p.seed_resize_from_h = args.seed_resize_from_h - p.fill = args.fill - p.ddim_eta = args.ddim_eta - p.batch_size = args.n_samples - p.width = args.W - p.height = args.H - p.seed = args.seed - p.do_not_save_samples = not args.save_sample_per_step - p.sampler_name = args.sampler - p.mask_blur = args.mask_overlay_blur - p.extra_generation_params["Mask blur"] = args.mask_overlay_blur - p.n_iter = 1 - p.steps = args.steps - if opts.img2img_fix_steps: - p.denoising_strength = 1 / (1 - args.strength + 1.0/args.steps) #see https://github.com/deforum-art/deforum-for-automatic1111-webui/issues/3 - else: - p.denoising_strength = 1 - args.strength - p.cfg_scale = args.scale - p.image_cfg_scale = args.pix2pix_img_cfg_scale - p.outpath_samples = root.outpath_samples - - - return p \ No newline at end of file diff --git a/spaces/aodianyun/stable-diffusion-webui/test/basic_features/extras_test.py b/spaces/aodianyun/stable-diffusion-webui/test/basic_features/extras_test.py deleted file mode 100644 index 0170c511fe54cc6bcf49ec7f75ca7c747de41db5..0000000000000000000000000000000000000000 --- a/spaces/aodianyun/stable-diffusion-webui/test/basic_features/extras_test.py +++ /dev/null @@ -1,54 +0,0 @@ -import unittest -import requests -from gradio.processing_utils import encode_pil_to_base64 -from PIL import Image - -class TestExtrasWorking(unittest.TestCase): - def setUp(self): - self.url_extras_single = "http://localhost:7860/sdapi/v1/extra-single-image" - self.extras_single = { - "resize_mode": 0, - "show_extras_results": True, - "gfpgan_visibility": 0, - "codeformer_visibility": 0, - "codeformer_weight": 0, - "upscaling_resize": 2, - "upscaling_resize_w": 128, - "upscaling_resize_h": 128, - "upscaling_crop": True, - "upscaler_1": "None", - "upscaler_2": "None", - "extras_upscaler_2_visibility": 0, - "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")) - } - - def test_simple_upscaling_performed(self): - self.extras_single["upscaler_1"] = "Lanczos" - self.assertEqual(requests.post(self.url_extras_single, json=self.extras_single).status_code, 200) - - -class TestPngInfoWorking(unittest.TestCase): - def setUp(self): - self.url_png_info = "http://localhost:7860/sdapi/v1/extra-single-image" - self.png_info = { - "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")) - } - - def test_png_info_performed(self): - self.assertEqual(requests.post(self.url_png_info, json=self.png_info).status_code, 200) - - -class TestInterrogateWorking(unittest.TestCase): - def setUp(self): - self.url_interrogate = "http://localhost:7860/sdapi/v1/extra-single-image" - self.interrogate = { - "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")), - "model": "clip" - } - - def test_interrogate_performed(self): - self.assertEqual(requests.post(self.url_interrogate, json=self.interrogate).status_code, 200) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/Blowfish.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/Blowfish.py deleted file mode 100644 index 6005ffe2b90694ae241c87404862f5f66db8f271..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Cipher/Blowfish.py +++ /dev/null @@ -1,159 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Cipher/Blowfish.py : Blowfish -# -# =================================================================== -# The contents of this file are dedicated to the public domain. To -# the extent that dedication to the public domain is not available, -# everyone is granted a worldwide, perpetual, royalty-free, -# non-exclusive license to exercise all rights associated with the -# contents of this file for any purpose whatsoever. -# No rights are reserved. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, -# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF -# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND -# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS -# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN -# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. -# =================================================================== -""" -Module's constants for the modes of operation supported with Blowfish: - -:var MODE_ECB: :ref:`Electronic Code Book (ECB)` -:var MODE_CBC: :ref:`Cipher-Block Chaining (CBC) ` -:var MODE_CFB: :ref:`Cipher FeedBack (CFB) ` -:var MODE_OFB: :ref:`Output FeedBack (OFB) ` -:var MODE_CTR: :ref:`CounTer Mode (CTR) ` -:var MODE_OPENPGP: :ref:`OpenPGP Mode ` -:var MODE_EAX: :ref:`EAX Mode ` -""" - -import sys - -from Crypto.Cipher import _create_cipher -from Crypto.Util._raw_api import (load_pycryptodome_raw_lib, - VoidPointer, SmartPointer, c_size_t, - c_uint8_ptr) - -_raw_blowfish_lib = load_pycryptodome_raw_lib( - "Crypto.Cipher._raw_blowfish", - """ - int Blowfish_start_operation(const uint8_t key[], - size_t key_len, - void **pResult); - int Blowfish_encrypt(const void *state, - const uint8_t *in, - uint8_t *out, - size_t data_len); - int Blowfish_decrypt(const void *state, - const uint8_t *in, - uint8_t *out, - size_t data_len); - int Blowfish_stop_operation(void *state); - """ - ) - - -def _create_base_cipher(dict_parameters): - """This method instantiates and returns a smart pointer to - a low-level base cipher. It will absorb named parameters in - the process.""" - - try: - key = dict_parameters.pop("key") - except KeyError: - raise TypeError("Missing 'key' parameter") - - if len(key) not in key_size: - raise ValueError("Incorrect Blowfish key length (%d bytes)" % len(key)) - - start_operation = _raw_blowfish_lib.Blowfish_start_operation - stop_operation = _raw_blowfish_lib.Blowfish_stop_operation - - void_p = VoidPointer() - result = start_operation(c_uint8_ptr(key), - c_size_t(len(key)), - void_p.address_of()) - if result: - raise ValueError("Error %X while instantiating the Blowfish cipher" - % result) - return SmartPointer(void_p.get(), stop_operation) - - -def new(key, mode, *args, **kwargs): - """Create a new Blowfish cipher - - :param key: - The secret key to use in the symmetric cipher. - Its length can vary from 5 to 56 bytes. - :type key: bytes, bytearray, memoryview - - :param mode: - The chaining mode to use for encryption or decryption. - :type mode: One of the supported ``MODE_*`` constants - - :Keyword Arguments: - * **iv** (*bytes*, *bytearray*, *memoryview*) -- - (Only applicable for ``MODE_CBC``, ``MODE_CFB``, ``MODE_OFB``, - and ``MODE_OPENPGP`` modes). - - The initialization vector to use for encryption or decryption. - - For ``MODE_CBC``, ``MODE_CFB``, and ``MODE_OFB`` it must be 8 bytes long. - - For ``MODE_OPENPGP`` mode only, - it must be 8 bytes long for encryption - and 10 bytes for decryption (in the latter case, it is - actually the *encrypted* IV which was prefixed to the ciphertext). - - If not provided, a random byte string is generated (you must then - read its value with the :attr:`iv` attribute). - - * **nonce** (*bytes*, *bytearray*, *memoryview*) -- - (Only applicable for ``MODE_EAX`` and ``MODE_CTR``). - - A value that must never be reused for any other encryption done - with this key. - - For ``MODE_EAX`` there are no - restrictions on its length (recommended: **16** bytes). - - For ``MODE_CTR``, its length must be in the range **[0..7]**. - - If not provided for ``MODE_EAX``, a random byte string is generated (you - can read it back via the ``nonce`` attribute). - - * **segment_size** (*integer*) -- - (Only ``MODE_CFB``).The number of **bits** the plaintext and ciphertext - are segmented in. It must be a multiple of 8. - If not specified, it will be assumed to be 8. - - * **mac_len** : (*integer*) -- - (Only ``MODE_EAX``) - Length of the authentication tag, in bytes. - It must be no longer than 8 (default). - - * **initial_value** : (*integer*) -- - (Only ``MODE_CTR``). The initial value for the counter within - the counter block. By default it is **0**. - - :Return: a Blowfish object, of the applicable mode. - """ - - return _create_cipher(sys.modules[__name__], key, mode, *args, **kwargs) - -MODE_ECB = 1 -MODE_CBC = 2 -MODE_CFB = 3 -MODE_OFB = 5 -MODE_CTR = 6 -MODE_OPENPGP = 7 -MODE_EAX = 9 - -# Size of a data block (in bytes) -block_size = 8 -# Size of a key (in bytes) -key_size = range(4, 56 + 1) diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_Salsa20.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_Salsa20.py deleted file mode 100644 index a710462ed68cf64ee3b5fc76d200e6061d648672..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Cipher/test_Salsa20.py +++ /dev/null @@ -1,367 +0,0 @@ -# -*- coding: utf-8 -*- -# -# SelfTest/Cipher/Salsa20.py: Self-test for the Salsa20 stream cipher -# -# Written in 2013 by Fabrizio Tarizzo - -//////////////////// IncludeCppStringH.proto //////////////////// - -#include - -//////////////////// InitStrings.proto //////////////////// - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ - -//////////////////// InitStrings //////////////////// - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else /* Python 3+ has unicode identifiers */ - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - // initialise cached hash value - if (PyObject_Hash(*t->p) == -1) - return -1; - ++t; - } - return 0; -} - -//////////////////// BytesContains.proto //////////////////// - -static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character); /*proto*/ - -//////////////////// BytesContains //////////////////// -//@requires: IncludeStringH - -static CYTHON_INLINE int __Pyx_BytesContains(PyObject* bytes, char character) { - const Py_ssize_t length = PyBytes_GET_SIZE(bytes); - char* char_start = PyBytes_AS_STRING(bytes); - return memchr(char_start, (unsigned char)character, (size_t)length) != NULL; -} - - -//////////////////// PyUCS4InUnicode.proto //////////////////// - -static CYTHON_INLINE int __Pyx_UnicodeContainsUCS4(PyObject* unicode, Py_UCS4 character); /*proto*/ - -//////////////////// PyUCS4InUnicode //////////////////// - -#if PY_VERSION_HEX < 0x03090000 || (defined(PyUnicode_WCHAR_KIND) && defined(PyUnicode_AS_UNICODE)) - -#if PY_VERSION_HEX < 0x03090000 -#define __Pyx_PyUnicode_AS_UNICODE(op) PyUnicode_AS_UNICODE(op) -#define __Pyx_PyUnicode_GET_SIZE(op) PyUnicode_GET_SIZE(op) -#else -// Avoid calling deprecated C-API functions in Py3.9+ that PEP-623 schedules for removal in Py3.12. -// https://www.python.org/dev/peps/pep-0623/ -#define __Pyx_PyUnicode_AS_UNICODE(op) (((PyASCIIObject *)(op))->wstr) -#define __Pyx_PyUnicode_GET_SIZE(op) ((PyCompactUnicodeObject *)(op))->wstr_length -#endif - -#if !defined(Py_UNICODE_SIZE) || Py_UNICODE_SIZE == 2 -static int __Pyx_PyUnicodeBufferContainsUCS4_SP(Py_UNICODE* buffer, Py_ssize_t length, Py_UCS4 character) { - /* handle surrogate pairs for Py_UNICODE buffers in 16bit Unicode builds */ - Py_UNICODE high_val, low_val; - Py_UNICODE* pos; - high_val = (Py_UNICODE) (0xD800 | (((character - 0x10000) >> 10) & ((1<<10)-1))); - low_val = (Py_UNICODE) (0xDC00 | ( (character - 0x10000) & ((1<<10)-1))); - for (pos=buffer; pos < buffer+length-1; pos++) { - if (unlikely((high_val == pos[0]) & (low_val == pos[1]))) return 1; - } - return 0; -} -#endif - -static int __Pyx_PyUnicodeBufferContainsUCS4_BMP(Py_UNICODE* buffer, Py_ssize_t length, Py_UCS4 character) { - Py_UNICODE uchar; - Py_UNICODE* pos; - uchar = (Py_UNICODE) character; - for (pos=buffer; pos < buffer+length; pos++) { - if (unlikely(uchar == pos[0])) return 1; - } - return 0; -} -#endif - -static CYTHON_INLINE int __Pyx_UnicodeContainsUCS4(PyObject* unicode, Py_UCS4 character) { -#if CYTHON_PEP393_ENABLED - const int kind = PyUnicode_KIND(unicode); - #ifdef PyUnicode_WCHAR_KIND - if (likely(kind != PyUnicode_WCHAR_KIND)) - #endif - { - Py_ssize_t i; - const void* udata = PyUnicode_DATA(unicode); - const Py_ssize_t length = PyUnicode_GET_LENGTH(unicode); - for (i=0; i < length; i++) { - if (unlikely(character == PyUnicode_READ(kind, udata, i))) return 1; - } - return 0; - } -#elif PY_VERSION_HEX >= 0x03090000 - #error Cannot use "UChar in Unicode" in Python 3.9 without PEP-393 unicode strings. -#elif !defined(PyUnicode_AS_UNICODE) - #error Cannot use "UChar in Unicode" in Python < 3.9 without Py_UNICODE support. -#endif - -#if PY_VERSION_HEX < 0x03090000 || (defined(PyUnicode_WCHAR_KIND) && defined(PyUnicode_AS_UNICODE)) -#if !defined(Py_UNICODE_SIZE) || Py_UNICODE_SIZE == 2 - if ((sizeof(Py_UNICODE) == 2) && unlikely(character > 65535)) { - return __Pyx_PyUnicodeBufferContainsUCS4_SP( - __Pyx_PyUnicode_AS_UNICODE(unicode), - __Pyx_PyUnicode_GET_SIZE(unicode), - character); - } else -#endif - { - return __Pyx_PyUnicodeBufferContainsUCS4_BMP( - __Pyx_PyUnicode_AS_UNICODE(unicode), - __Pyx_PyUnicode_GET_SIZE(unicode), - character); - - } -#endif -} - - -//////////////////// PyUnicodeContains.proto //////////////////// - -static CYTHON_INLINE int __Pyx_PyUnicode_ContainsTF(PyObject* substring, PyObject* text, int eq) { - int result = PyUnicode_Contains(text, substring); - return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); -} - - -//////////////////// CStringEquals.proto //////////////////// - -static CYTHON_INLINE int __Pyx_StrEq(const char *, const char *); /*proto*/ - -//////////////////// CStringEquals //////////////////// - -static CYTHON_INLINE int __Pyx_StrEq(const char *s1, const char *s2) { - while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; } - return *s1 == *s2; -} - - -//////////////////// StrEquals.proto //////////////////// -//@requires: BytesEquals -//@requires: UnicodeEquals - -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals -#else -#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals -#endif - - -//////////////////// UnicodeEquals.proto //////////////////// - -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/ - -//////////////////// UnicodeEquals //////////////////// -//@requires: BytesEquals - -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY - return PyObject_RichCompareBool(s1, s2, equals); -#else -#if PY_MAJOR_VERSION < 3 - PyObject* owned_ref = NULL; -#endif - int s1_is_unicode, s2_is_unicode; - if (s1 == s2) { - /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */ - goto return_eq; - } - s1_is_unicode = PyUnicode_CheckExact(s1); - s2_is_unicode = PyUnicode_CheckExact(s2); -#if PY_MAJOR_VERSION < 3 - if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { - owned_ref = PyUnicode_FromObject(s2); - if (unlikely(!owned_ref)) - return -1; - s2 = owned_ref; - s2_is_unicode = 1; - } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { - owned_ref = PyUnicode_FromObject(s1); - if (unlikely(!owned_ref)) - return -1; - s1 = owned_ref; - s1_is_unicode = 1; - } else if (((!s2_is_unicode) & (!s1_is_unicode))) { - return __Pyx_PyBytes_Equals(s1, s2, equals); - } -#endif - if (s1_is_unicode & s2_is_unicode) { - Py_ssize_t length; - int kind; - void *data1, *data2; - if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) - return -1; - length = __Pyx_PyUnicode_GET_LENGTH(s1); - if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { - goto return_ne; - } -#if CYTHON_USE_UNICODE_INTERNALS - { - Py_hash_t hash1, hash2; - #if CYTHON_PEP393_ENABLED - hash1 = ((PyASCIIObject*)s1)->hash; - hash2 = ((PyASCIIObject*)s2)->hash; - #else - hash1 = ((PyUnicodeObject*)s1)->hash; - hash2 = ((PyUnicodeObject*)s2)->hash; - #endif - if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { - goto return_ne; - } - } -#endif - // len(s1) == len(s2) >= 1 (empty string is interned, and "s1 is not s2") - kind = __Pyx_PyUnicode_KIND(s1); - if (kind != __Pyx_PyUnicode_KIND(s2)) { - goto return_ne; - } - data1 = __Pyx_PyUnicode_DATA(s1); - data2 = __Pyx_PyUnicode_DATA(s2); - if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { - goto return_ne; - } else if (length == 1) { - goto return_eq; - } else { - int result = memcmp(data1, data2, (size_t)(length * kind)); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & s2_is_unicode) { - goto return_ne; - } else if ((s2 == Py_None) & s1_is_unicode) { - goto return_ne; - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -return_eq: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ); -return_ne: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_NE); -#endif -} - - -//////////////////// BytesEquals.proto //////////////////// - -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/ - -//////////////////// BytesEquals //////////////////// -//@requires: IncludeStringH - -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY - return PyObject_RichCompareBool(s1, s2, equals); -#else - if (s1 == s2) { - /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */ - return (equals == Py_EQ); - } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { - const char *ps1, *ps2; - Py_ssize_t length = PyBytes_GET_SIZE(s1); - if (length != PyBytes_GET_SIZE(s2)) - return (equals == Py_NE); - // len(s1) == len(s2) >= 1 (empty string is interned, and "s1 is not s2") - ps1 = PyBytes_AS_STRING(s1); - ps2 = PyBytes_AS_STRING(s2); - if (ps1[0] != ps2[0]) { - return (equals == Py_NE); - } else if (length == 1) { - return (equals == Py_EQ); - } else { - int result; -#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000) - Py_hash_t hash1, hash2; - hash1 = ((PyBytesObject*)s1)->ob_shash; - hash2 = ((PyBytesObject*)s2)->ob_shash; - if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { - return (equals == Py_NE); - } -#endif - result = memcmp(ps1, ps2, (size_t)length); - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { - return (equals == Py_NE); - } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { - return (equals == Py_NE); - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -#endif -} - -//////////////////// GetItemIntByteArray.proto //////////////////// - -#define __Pyx_GetItemInt_ByteArray(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ - __Pyx_GetItemInt_ByteArray_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \ - (PyErr_SetString(PyExc_IndexError, "bytearray index out of range"), -1)) - -static CYTHON_INLINE int __Pyx_GetItemInt_ByteArray_Fast(PyObject* string, Py_ssize_t i, - int wraparound, int boundscheck); - -//////////////////// GetItemIntByteArray //////////////////// - -static CYTHON_INLINE int __Pyx_GetItemInt_ByteArray_Fast(PyObject* string, Py_ssize_t i, - int wraparound, int boundscheck) { - Py_ssize_t length; - if (wraparound | boundscheck) { - length = PyByteArray_GET_SIZE(string); - if (wraparound & unlikely(i < 0)) i += length; - if ((!boundscheck) || likely(__Pyx_is_valid_index(i, length))) { - return (unsigned char) (PyByteArray_AS_STRING(string)[i]); - } else { - PyErr_SetString(PyExc_IndexError, "bytearray index out of range"); - return -1; - } - } else { - return (unsigned char) (PyByteArray_AS_STRING(string)[i]); - } -} - - -//////////////////// SetItemIntByteArray.proto //////////////////// - -#define __Pyx_SetItemInt_ByteArray(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ - __Pyx_SetItemInt_ByteArray_Fast(o, (Py_ssize_t)i, v, wraparound, boundscheck) : \ - (PyErr_SetString(PyExc_IndexError, "bytearray index out of range"), -1)) - -static CYTHON_INLINE int __Pyx_SetItemInt_ByteArray_Fast(PyObject* string, Py_ssize_t i, unsigned char v, - int wraparound, int boundscheck); - -//////////////////// SetItemIntByteArray //////////////////// - -static CYTHON_INLINE int __Pyx_SetItemInt_ByteArray_Fast(PyObject* string, Py_ssize_t i, unsigned char v, - int wraparound, int boundscheck) { - Py_ssize_t length; - if (wraparound | boundscheck) { - length = PyByteArray_GET_SIZE(string); - if (wraparound & unlikely(i < 0)) i += length; - if ((!boundscheck) || likely(__Pyx_is_valid_index(i, length))) { - PyByteArray_AS_STRING(string)[i] = (char) v; - return 0; - } else { - PyErr_SetString(PyExc_IndexError, "bytearray index out of range"); - return -1; - } - } else { - PyByteArray_AS_STRING(string)[i] = (char) v; - return 0; - } -} - - -//////////////////// GetItemIntUnicode.proto //////////////////// - -#define __Pyx_GetItemInt_Unicode(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ - __Pyx_GetItemInt_Unicode_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \ - (PyErr_SetString(PyExc_IndexError, "string index out of range"), (Py_UCS4)-1)) - -static CYTHON_INLINE Py_UCS4 __Pyx_GetItemInt_Unicode_Fast(PyObject* ustring, Py_ssize_t i, - int wraparound, int boundscheck); - -//////////////////// GetItemIntUnicode //////////////////// - -static CYTHON_INLINE Py_UCS4 __Pyx_GetItemInt_Unicode_Fast(PyObject* ustring, Py_ssize_t i, - int wraparound, int boundscheck) { - Py_ssize_t length; - if (unlikely(__Pyx_PyUnicode_READY(ustring) < 0)) return (Py_UCS4)-1; - if (wraparound | boundscheck) { - length = __Pyx_PyUnicode_GET_LENGTH(ustring); - if (wraparound & unlikely(i < 0)) i += length; - if ((!boundscheck) || likely(__Pyx_is_valid_index(i, length))) { - return __Pyx_PyUnicode_READ_CHAR(ustring, i); - } else { - PyErr_SetString(PyExc_IndexError, "string index out of range"); - return (Py_UCS4)-1; - } - } else { - return __Pyx_PyUnicode_READ_CHAR(ustring, i); - } -} - - -/////////////// decode_c_string_utf16.proto /////////////// - -static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { - int byteorder = 0; - return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); -} -static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { - int byteorder = -1; - return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); -} -static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { - int byteorder = 1; - return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); -} - -/////////////// decode_cpp_string.proto /////////////// -//@requires: IncludeCppStringH -//@requires: decode_c_bytes - -static CYTHON_INLINE PyObject* __Pyx_decode_cpp_string( - std::string cppstring, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - return __Pyx_decode_c_bytes( - cppstring.data(), cppstring.size(), start, stop, encoding, errors, decode_func); -} - -/////////////// decode_c_string.proto /////////////// - -static CYTHON_INLINE PyObject* __Pyx_decode_c_string( - const char* cstring, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); - -/////////////// decode_c_string /////////////// -//@requires: IncludeStringH -//@requires: decode_c_string_utf16 -//@substitute: naming - -/* duplicate code to avoid calling strlen() if start >= 0 and stop >= 0 */ -static CYTHON_INLINE PyObject* __Pyx_decode_c_string( - const char* cstring, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - Py_ssize_t length; - if (unlikely((start < 0) | (stop < 0))) { - size_t slen = strlen(cstring); - if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { - PyErr_SetString(PyExc_OverflowError, - "c-string too long to convert to Python"); - return NULL; - } - length = (Py_ssize_t) slen; - if (start < 0) { - start += length; - if (start < 0) - start = 0; - } - if (stop < 0) - stop += length; - } - if (unlikely(stop <= start)) - return __Pyx_NewRef($empty_unicode); - length = stop - start; - cstring += start; - if (decode_func) { - return decode_func(cstring, length, errors); - } else { - return PyUnicode_Decode(cstring, length, encoding, errors); - } -} - -/////////////// decode_c_bytes.proto /////////////// - -static CYTHON_INLINE PyObject* __Pyx_decode_c_bytes( - const char* cstring, Py_ssize_t length, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); - -/////////////// decode_c_bytes /////////////// -//@requires: decode_c_string_utf16 -//@substitute: naming - -static CYTHON_INLINE PyObject* __Pyx_decode_c_bytes( - const char* cstring, Py_ssize_t length, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - if (unlikely((start < 0) | (stop < 0))) { - if (start < 0) { - start += length; - if (start < 0) - start = 0; - } - if (stop < 0) - stop += length; - } - if (stop > length) - stop = length; - if (unlikely(stop <= start)) - return __Pyx_NewRef($empty_unicode); - length = stop - start; - cstring += start; - if (decode_func) { - return decode_func(cstring, length, errors); - } else { - return PyUnicode_Decode(cstring, length, encoding, errors); - } -} - -/////////////// decode_bytes.proto /////////////// -//@requires: decode_c_bytes - -static CYTHON_INLINE PyObject* __Pyx_decode_bytes( - PyObject* string, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - return __Pyx_decode_c_bytes( - PyBytes_AS_STRING(string), PyBytes_GET_SIZE(string), - start, stop, encoding, errors, decode_func); -} - -/////////////// decode_bytearray.proto /////////////// -//@requires: decode_c_bytes - -static CYTHON_INLINE PyObject* __Pyx_decode_bytearray( - PyObject* string, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - return __Pyx_decode_c_bytes( - PyByteArray_AS_STRING(string), PyByteArray_GET_SIZE(string), - start, stop, encoding, errors, decode_func); -} - -/////////////// PyUnicode_Substring.proto /////////////// - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_Substring( - PyObject* text, Py_ssize_t start, Py_ssize_t stop); - -/////////////// PyUnicode_Substring /////////////// -//@substitute: naming - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_Substring( - PyObject* text, Py_ssize_t start, Py_ssize_t stop) { - Py_ssize_t length; - if (unlikely(__Pyx_PyUnicode_READY(text) == -1)) return NULL; - length = __Pyx_PyUnicode_GET_LENGTH(text); - if (start < 0) { - start += length; - if (start < 0) - start = 0; - } - if (stop < 0) - stop += length; - else if (stop > length) - stop = length; - if (stop <= start) - return __Pyx_NewRef($empty_unicode); -#if CYTHON_PEP393_ENABLED - return PyUnicode_FromKindAndData(PyUnicode_KIND(text), - PyUnicode_1BYTE_DATA(text) + start*PyUnicode_KIND(text), stop-start); -#else - return PyUnicode_FromUnicode(PyUnicode_AS_UNICODE(text)+start, stop-start); -#endif -} - - -/////////////// py_unicode_istitle.proto /////////////// - -// Py_UNICODE_ISTITLE() doesn't match unicode.istitle() as the latter -// additionally allows character that comply with Py_UNICODE_ISUPPER() - -#if PY_VERSION_HEX < 0x030200A2 -static CYTHON_INLINE int __Pyx_Py_UNICODE_ISTITLE(Py_UNICODE uchar) -#else -static CYTHON_INLINE int __Pyx_Py_UNICODE_ISTITLE(Py_UCS4 uchar) -#endif -{ - return Py_UNICODE_ISTITLE(uchar) || Py_UNICODE_ISUPPER(uchar); -} - - -/////////////// unicode_tailmatch.proto /////////////// - -static int __Pyx_PyUnicode_Tailmatch( - PyObject* s, PyObject* substr, Py_ssize_t start, Py_ssize_t end, int direction); /*proto*/ - -/////////////// unicode_tailmatch /////////////// - -// Python's unicode.startswith() and unicode.endswith() support a -// tuple of prefixes/suffixes, whereas it's much more common to -// test for a single unicode string. - -static int __Pyx_PyUnicode_TailmatchTuple(PyObject* s, PyObject* substrings, - Py_ssize_t start, Py_ssize_t end, int direction) { - Py_ssize_t i, count = PyTuple_GET_SIZE(substrings); - for (i = 0; i < count; i++) { - Py_ssize_t result; -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - result = PyUnicode_Tailmatch(s, PyTuple_GET_ITEM(substrings, i), - start, end, direction); -#else - PyObject* sub = PySequence_ITEM(substrings, i); - if (unlikely(!sub)) return -1; - result = PyUnicode_Tailmatch(s, sub, start, end, direction); - Py_DECREF(sub); -#endif - if (result) { - return (int) result; - } - } - return 0; -} - -static int __Pyx_PyUnicode_Tailmatch(PyObject* s, PyObject* substr, - Py_ssize_t start, Py_ssize_t end, int direction) { - if (unlikely(PyTuple_Check(substr))) { - return __Pyx_PyUnicode_TailmatchTuple(s, substr, start, end, direction); - } - return (int) PyUnicode_Tailmatch(s, substr, start, end, direction); -} - - -/////////////// bytes_tailmatch.proto /////////////// - -static int __Pyx_PyBytes_SingleTailmatch(PyObject* self, PyObject* arg, - Py_ssize_t start, Py_ssize_t end, int direction); /*proto*/ -static int __Pyx_PyBytes_Tailmatch(PyObject* self, PyObject* substr, - Py_ssize_t start, Py_ssize_t end, int direction); /*proto*/ - -/////////////// bytes_tailmatch /////////////// - -static int __Pyx_PyBytes_SingleTailmatch(PyObject* self, PyObject* arg, - Py_ssize_t start, Py_ssize_t end, int direction) { - const char* self_ptr = PyBytes_AS_STRING(self); - Py_ssize_t self_len = PyBytes_GET_SIZE(self); - const char* sub_ptr; - Py_ssize_t sub_len; - int retval; - - Py_buffer view; - view.obj = NULL; - - if ( PyBytes_Check(arg) ) { - sub_ptr = PyBytes_AS_STRING(arg); - sub_len = PyBytes_GET_SIZE(arg); - } -#if PY_MAJOR_VERSION < 3 - // Python 2.x allows mixing unicode and str - else if ( PyUnicode_Check(arg) ) { - return (int) PyUnicode_Tailmatch(self, arg, start, end, direction); - } -#endif - else { - if (unlikely(PyObject_GetBuffer(self, &view, PyBUF_SIMPLE) == -1)) - return -1; - sub_ptr = (const char*) view.buf; - sub_len = view.len; - } - - if (end > self_len) - end = self_len; - else if (end < 0) - end += self_len; - if (end < 0) - end = 0; - if (start < 0) - start += self_len; - if (start < 0) - start = 0; - - if (direction > 0) { - /* endswith */ - if (end-sub_len > start) - start = end - sub_len; - } - - if (start + sub_len <= end) - retval = !memcmp(self_ptr+start, sub_ptr, (size_t)sub_len); - else - retval = 0; - - if (view.obj) - PyBuffer_Release(&view); - - return retval; -} - -static int __Pyx_PyBytes_TailmatchTuple(PyObject* self, PyObject* substrings, - Py_ssize_t start, Py_ssize_t end, int direction) { - Py_ssize_t i, count = PyTuple_GET_SIZE(substrings); - for (i = 0; i < count; i++) { - int result; -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - result = __Pyx_PyBytes_SingleTailmatch(self, PyTuple_GET_ITEM(substrings, i), - start, end, direction); -#else - PyObject* sub = PySequence_ITEM(substrings, i); - if (unlikely(!sub)) return -1; - result = __Pyx_PyBytes_SingleTailmatch(self, sub, start, end, direction); - Py_DECREF(sub); -#endif - if (result) { - return result; - } - } - return 0; -} - -static int __Pyx_PyBytes_Tailmatch(PyObject* self, PyObject* substr, - Py_ssize_t start, Py_ssize_t end, int direction) { - if (unlikely(PyTuple_Check(substr))) { - return __Pyx_PyBytes_TailmatchTuple(self, substr, start, end, direction); - } - - return __Pyx_PyBytes_SingleTailmatch(self, substr, start, end, direction); -} - - -/////////////// str_tailmatch.proto /////////////// - -static CYTHON_INLINE int __Pyx_PyStr_Tailmatch(PyObject* self, PyObject* arg, Py_ssize_t start, - Py_ssize_t end, int direction); /*proto*/ - -/////////////// str_tailmatch /////////////// -//@requires: bytes_tailmatch -//@requires: unicode_tailmatch - -static CYTHON_INLINE int __Pyx_PyStr_Tailmatch(PyObject* self, PyObject* arg, Py_ssize_t start, - Py_ssize_t end, int direction) -{ - // We do not use a C compiler macro here to avoid "unused function" - // warnings for the *_Tailmatch() function that is not being used in - // the specific CPython version. The C compiler will generate the same - // code anyway, and will usually just remove the unused function. - if (PY_MAJOR_VERSION < 3) - return __Pyx_PyBytes_Tailmatch(self, arg, start, end, direction); - else - return __Pyx_PyUnicode_Tailmatch(self, arg, start, end, direction); -} - - -/////////////// bytes_index.proto /////////////// - -static CYTHON_INLINE char __Pyx_PyBytes_GetItemInt(PyObject* bytes, Py_ssize_t index, int check_bounds); /*proto*/ - -/////////////// bytes_index /////////////// - -static CYTHON_INLINE char __Pyx_PyBytes_GetItemInt(PyObject* bytes, Py_ssize_t index, int check_bounds) { - if (index < 0) - index += PyBytes_GET_SIZE(bytes); - if (check_bounds) { - Py_ssize_t size = PyBytes_GET_SIZE(bytes); - if (unlikely(!__Pyx_is_valid_index(index, size))) { - PyErr_SetString(PyExc_IndexError, "string index out of range"); - return (char) -1; - } - } - return PyBytes_AS_STRING(bytes)[index]; -} - - -//////////////////// StringJoin.proto //////////////////// - -#if PY_MAJOR_VERSION < 3 -#define __Pyx_PyString_Join __Pyx_PyBytes_Join -#define __Pyx_PyBaseString_Join(s, v) (PyUnicode_CheckExact(s) ? PyUnicode_Join(s, v) : __Pyx_PyBytes_Join(s, v)) -#else -#define __Pyx_PyString_Join PyUnicode_Join -#define __Pyx_PyBaseString_Join PyUnicode_Join -#endif - -#if CYTHON_COMPILING_IN_CPYTHON - #if PY_MAJOR_VERSION < 3 - #define __Pyx_PyBytes_Join _PyString_Join - #else - #define __Pyx_PyBytes_Join _PyBytes_Join - #endif -#else -static CYTHON_INLINE PyObject* __Pyx_PyBytes_Join(PyObject* sep, PyObject* values); /*proto*/ -#endif - - -//////////////////// StringJoin //////////////////// - -#if !CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyBytes_Join(PyObject* sep, PyObject* values) { - return PyObject_CallMethodObjArgs(sep, PYIDENT("join"), values, NULL); -} -#endif - - -/////////////// JoinPyUnicode.proto /////////////// - -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - Py_UCS4 max_char); - -/////////////// JoinPyUnicode /////////////// -//@requires: IncludeStringH -//@substitute: naming - -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - CYTHON_UNUSED Py_UCS4 max_char) { -#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - PyObject *result_uval; - int result_ukind; - Py_ssize_t i, char_pos; - void *result_udata; -#if CYTHON_PEP393_ENABLED - // Py 3.3+ (post PEP-393) - result_uval = PyUnicode_New(result_ulength, max_char); - if (unlikely(!result_uval)) return NULL; - result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; - result_udata = PyUnicode_DATA(result_uval); -#else - // Py 2.x/3.2 (pre PEP-393) - result_uval = PyUnicode_FromUnicode(NULL, result_ulength); - if (unlikely(!result_uval)) return NULL; - result_ukind = sizeof(Py_UNICODE); - result_udata = PyUnicode_AS_UNICODE(result_uval); -#endif - - char_pos = 0; - for (i=0; i < value_count; i++) { - int ukind; - Py_ssize_t ulength; - void *udata; - PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); - if (unlikely(__Pyx_PyUnicode_READY(uval))) - goto bad; - ulength = __Pyx_PyUnicode_GET_LENGTH(uval); - if (unlikely(!ulength)) - continue; - if (unlikely(char_pos + ulength < 0)) - goto overflow; - ukind = __Pyx_PyUnicode_KIND(uval); - udata = __Pyx_PyUnicode_DATA(uval); - if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { - memcpy((char *)result_udata + char_pos * result_ukind, udata, (size_t) (ulength * result_ukind)); - } else { - #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) - _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); - #else - Py_ssize_t j; - for (j=0; j < ulength; j++) { - Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); - __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); - } - #endif - } - char_pos += ulength; - } - return result_uval; -overflow: - PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); -bad: - Py_DECREF(result_uval); - return NULL; -#else - // non-CPython fallback - result_ulength++; - value_count++; - return PyUnicode_Join($empty_unicode, value_tuple); -#endif -} - - -/////////////// BuildPyUnicode.proto /////////////// - -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char); - -/////////////// BuildPyUnicode /////////////// - -// Create a PyUnicode object from an ASCII char*, e.g. a formatted number. - -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char) { - PyObject *uval; - Py_ssize_t uoffset = ulength - clength; -#if CYTHON_USE_UNICODE_INTERNALS - Py_ssize_t i; -#if CYTHON_PEP393_ENABLED - // Py 3.3+ (post PEP-393) - void *udata; - uval = PyUnicode_New(ulength, 127); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_DATA(uval); -#else - // Py 2.x/3.2 (pre PEP-393) - Py_UNICODE *udata; - uval = PyUnicode_FromUnicode(NULL, ulength); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_AS_UNICODE(uval); -#endif - if (uoffset > 0) { - i = 0; - if (prepend_sign) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); - i++; - } - for (; i < uoffset; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); - } - } - for (i=0; i < clength; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); - } - -#else - // non-CPython - { - PyObject *sign = NULL, *padding = NULL; - uval = NULL; - if (uoffset > 0) { - prepend_sign = !!prepend_sign; - if (uoffset > prepend_sign) { - padding = PyUnicode_FromOrdinal(padding_char); - if (likely(padding) && uoffset > prepend_sign + 1) { - PyObject *tmp; - PyObject *repeat = PyInt_FromSize_t(uoffset - prepend_sign); - if (unlikely(!repeat)) goto done_or_error; - tmp = PyNumber_Multiply(padding, repeat); - Py_DECREF(repeat); - Py_DECREF(padding); - padding = tmp; - } - if (unlikely(!padding)) goto done_or_error; - } - if (prepend_sign) { - sign = PyUnicode_FromOrdinal('-'); - if (unlikely(!sign)) goto done_or_error; - } - } - - uval = PyUnicode_DecodeASCII(chars, clength, NULL); - if (likely(uval) && padding) { - PyObject *tmp = PyNumber_Add(padding, uval); - Py_DECREF(uval); - uval = tmp; - } - if (likely(uval) && sign) { - PyObject *tmp = PyNumber_Add(sign, uval); - Py_DECREF(uval); - uval = tmp; - } -done_or_error: - Py_XDECREF(padding); - Py_XDECREF(sign); - } -#endif - - return uval; -} - - -//////////////////// ByteArrayAppendObject.proto //////////////////// - -static CYTHON_INLINE int __Pyx_PyByteArray_AppendObject(PyObject* bytearray, PyObject* value); - -//////////////////// ByteArrayAppendObject //////////////////// -//@requires: ByteArrayAppend - -static CYTHON_INLINE int __Pyx_PyByteArray_AppendObject(PyObject* bytearray, PyObject* value) { - Py_ssize_t ival; -#if PY_MAJOR_VERSION < 3 - if (unlikely(PyString_Check(value))) { - if (unlikely(PyString_GET_SIZE(value) != 1)) { - PyErr_SetString(PyExc_ValueError, "string must be of size 1"); - return -1; - } - ival = (unsigned char) (PyString_AS_STRING(value)[0]); - } else -#endif -#if CYTHON_USE_PYLONG_INTERNALS - if (likely(PyLong_CheckExact(value)) && likely(Py_SIZE(value) == 1 || Py_SIZE(value) == 0)) { - if (Py_SIZE(value) == 0) { - ival = 0; - } else { - ival = ((PyLongObject*)value)->ob_digit[0]; - if (unlikely(ival > 255)) goto bad_range; - } - } else -#endif - { - // CPython calls PyNumber_Index() internally - ival = __Pyx_PyIndex_AsSsize_t(value); - if (unlikely(!__Pyx_is_valid_index(ival, 256))) { - if (ival == -1 && PyErr_Occurred()) - return -1; - goto bad_range; - } - } - return __Pyx_PyByteArray_Append(bytearray, ival); -bad_range: - PyErr_SetString(PyExc_ValueError, "byte must be in range(0, 256)"); - return -1; -} - -//////////////////// ByteArrayAppend.proto //////////////////// - -static CYTHON_INLINE int __Pyx_PyByteArray_Append(PyObject* bytearray, int value); - -//////////////////// ByteArrayAppend //////////////////// -//@requires: ObjectHandling.c::PyObjectCallMethod1 - -static CYTHON_INLINE int __Pyx_PyByteArray_Append(PyObject* bytearray, int value) { - PyObject *pyval, *retval; -#if CYTHON_COMPILING_IN_CPYTHON - if (likely(__Pyx_is_valid_index(value, 256))) { - Py_ssize_t n = Py_SIZE(bytearray); - if (likely(n != PY_SSIZE_T_MAX)) { - if (unlikely(PyByteArray_Resize(bytearray, n + 1) < 0)) - return -1; - PyByteArray_AS_STRING(bytearray)[n] = value; - return 0; - } - } else { - PyErr_SetString(PyExc_ValueError, "byte must be in range(0, 256)"); - return -1; - } -#endif - pyval = PyInt_FromLong(value); - if (unlikely(!pyval)) - return -1; - retval = __Pyx_PyObject_CallMethod1(bytearray, PYIDENT("append"), pyval); - Py_DECREF(pyval); - if (unlikely(!retval)) - return -1; - Py_DECREF(retval); - return 0; -} - - -//////////////////// PyObjectFormat.proto //////////////////// - -#if CYTHON_USE_UNICODE_WRITER -static PyObject* __Pyx_PyObject_Format(PyObject* s, PyObject* f); -#else -#define __Pyx_PyObject_Format(s, f) PyObject_Format(s, f) -#endif - -//////////////////// PyObjectFormat //////////////////// - -#if CYTHON_USE_UNICODE_WRITER -static PyObject* __Pyx_PyObject_Format(PyObject* obj, PyObject* format_spec) { - int ret; - _PyUnicodeWriter writer; - - if (likely(PyFloat_CheckExact(obj))) { - // copied from CPython 3.5 "float__format__()" in floatobject.c -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x03040000 - _PyUnicodeWriter_Init(&writer, 0); -#else - _PyUnicodeWriter_Init(&writer); -#endif - ret = _PyFloat_FormatAdvancedWriter( - &writer, - obj, - format_spec, 0, PyUnicode_GET_LENGTH(format_spec)); - } else if (likely(PyLong_CheckExact(obj))) { - // copied from CPython 3.5 "long__format__()" in longobject.c -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x03040000 - _PyUnicodeWriter_Init(&writer, 0); -#else - _PyUnicodeWriter_Init(&writer); -#endif - ret = _PyLong_FormatAdvancedWriter( - &writer, - obj, - format_spec, 0, PyUnicode_GET_LENGTH(format_spec)); 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(Py_INCREF(s), s) : \ - PyObject_Format(s, f)) -#elif PY_MAJOR_VERSION < 3 - // str is common in Py2, but formatting must return a Unicode string - #define __Pyx_PyObject_FormatSimple(s, f) ( \ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) : \ - likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") : \ - PyObject_Format(s, f)) -#elif CYTHON_USE_TYPE_SLOTS - // Py3 nicely returns unicode strings from str() which makes this quite efficient for builtin types - #define __Pyx_PyObject_FormatSimple(s, f) ( \ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) : \ - likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_str(s) : \ - likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_str(s) : \ - PyObject_Format(s, f)) -#else - #define __Pyx_PyObject_FormatSimple(s, f) ( \ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) : \ - PyObject_Format(s, f)) -#endif - - -//////////////////// PyObjectFormatAndDecref.proto //////////////////// - -static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatSimpleAndDecref(PyObject* s, PyObject* f); -static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatAndDecref(PyObject* s, PyObject* f); - -//////////////////// PyObjectFormatAndDecref //////////////////// - -static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatSimpleAndDecref(PyObject* s, PyObject* f) { - if (unlikely(!s)) return NULL; - if (likely(PyUnicode_CheckExact(s))) return s; - #if PY_MAJOR_VERSION < 3 - // str is common in Py2, but formatting must return a Unicode string - if (likely(PyString_CheckExact(s))) { - PyObject *result = PyUnicode_FromEncodedObject(s, NULL, "strict"); - Py_DECREF(s); - return result; - } - #endif - return __Pyx_PyObject_FormatAndDecref(s, f); -} - -static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatAndDecref(PyObject* s, PyObject* f) { - PyObject *result = PyObject_Format(s, f); - Py_DECREF(s); - return result; -} - - -//////////////////// PyUnicode_Unicode.proto //////////////////// - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_Unicode(PyObject *obj);/*proto*/ - -//////////////////// PyUnicode_Unicode //////////////////// - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_Unicode(PyObject *obj) { - if (unlikely(obj == Py_None)) - obj = PYUNICODE("None"); - return __Pyx_NewRef(obj); -} - - -//////////////////// PyObject_Unicode.proto //////////////////// - -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyObject_Unicode(obj) \ - (likely(PyUnicode_CheckExact(obj)) ? __Pyx_NewRef(obj) : PyObject_Str(obj)) -#else -#define __Pyx_PyObject_Unicode(obj) \ - (likely(PyUnicode_CheckExact(obj)) ? __Pyx_NewRef(obj) : PyObject_Unicode(obj)) -#endif diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/bar_chart_with_highlighted_segment.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/bar_chart_with_highlighted_segment.py deleted file mode 100644 index 3326ac500cbc3fb309c714b06db37eefd7ae0cdb..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/bar_chart_with_highlighted_segment.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Bar Chart with Highlighted Segment ----------------------------------- -This example shows a bar chart that highlights values beyond a threshold. -""" -import altair as alt -import pandas as pd -from vega_datasets import data - -source = data.wheat() -threshold = pd.DataFrame([{"threshold": 90}]) - -bars = alt.Chart(source).mark_bar().encode( - x="year:O", - y="wheat:Q", -) - -highlight = alt.Chart(source).mark_bar(color="#e45755").encode( - x='year:O', - y='baseline:Q', - y2='wheat:Q' -).transform_filter( - alt.datum.wheat > 90 -).transform_calculate("baseline", "90") - -rule = alt.Chart(threshold).mark_rule().encode( - y='threshold:Q' -) - -(bars + highlight + rule).properties(width=600) diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/tree/ParseTreeMatch.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/tree/ParseTreeMatch.py deleted file mode 100644 index bbda73e8f29f2636d9cad1351c3ac26d18f46d1c..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/tree/ParseTreeMatch.py +++ /dev/null @@ -1,118 +0,0 @@ -# -# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. -# Use of this file is governed by the BSD 3-clause license that -# can be found in the LICENSE.txt file in the project root. -# - - -# -# Represents the result of matching a {@link ParseTree} against a tree pattern. -# -from io import StringIO -from antlr4.tree.ParseTreePattern import ParseTreePattern -from antlr4.tree.Tree import ParseTree - - -class ParseTreeMatch(object): - - # - # Constructs a new instance of {@link ParseTreeMatch} from the specified - # parse tree and pattern. - # - # @param tree The parse tree to match against the pattern. - # @param pattern The parse tree pattern. - # @param labels A mapping from label names to collections of - # {@link ParseTree} objects located by the tree pattern matching process. - # @param mismatchedNode The first node which failed to match the tree - # pattern during the matching process. - # - # @exception IllegalArgumentException if {@code tree} is {@code null} - # @exception IllegalArgumentException if {@code pattern} is {@code null} - # @exception IllegalArgumentException if {@code labels} is {@code null} - # - def __init__(self, tree:ParseTree, pattern:ParseTreePattern, labels:dict, mismatchedNode:ParseTree): - if tree is None: - raise Exception("tree cannot be null") - if pattern is None: - raise Exception("pattern cannot be null") - if labels is None: - raise Exception("labels cannot be null") - self.tree = tree - self.pattern = pattern - self.labels = labels - self.mismatchedNode = mismatchedNode - - # - # Get the last node associated with a specific {@code label}. - # - # For example, for pattern {@code
- # - #Pattern tags like {@code
- # - # @param label The label to check. - # - # @return The last {@link ParseTree} to match a tag with the specified - # label, or {@code null} if no parse tree matched a tag with the label. - # - def get(self, label:str): - parseTrees = self.labels.get(label, None) - if parseTrees is None or len(parseTrees)==0: - return None - else: - return parseTrees[len(parseTrees)-1] - - # - # Return all nodes matching a rule or token tag with the specified label. - # - #} and {@code } without labels are - # considered to be labeled with {@code ID} and {@code expr}, respectively. If the {@code label} is the name of a parser rule or token in the - # grammar, the resulting list will contain both the parse trees matching - # rule or tags explicitly labeled with the label and the complete set of - # parse trees matching the labeled and unlabeled tags in the pattern for - # the parser rule or token. For example, if {@code label} is {@code "foo"}, - # the result will contain all of the following.
- # - #-
- #
- Parse tree nodes matching tags of the form {@code
- Parse tree nodes matching tags of the form {@code
- Parse tree nodes matching tags of the form {@code
}.
- #
') - text = '```'.join(ts) - return text - - -def is_valid_key(enforce_h2ogpt_api_key, h2ogpt_api_keys, h2ogpt_key1, requests_state1=None): - valid_key = False - if not enforce_h2ogpt_api_key: - # no token barrier - valid_key = 'not enforced' - else: - if isinstance(h2ogpt_api_keys, list) and h2ogpt_key1 in h2ogpt_api_keys: - # passed token barrier - valid_key = True - elif isinstance(h2ogpt_api_keys, str) and os.path.isfile(h2ogpt_api_keys): - with filelock.FileLock(h2ogpt_api_keys + '.lock'): - with open(h2ogpt_api_keys, 'rt') as f: - h2ogpt_api_keys = json.load(f) - if h2ogpt_key1 in h2ogpt_api_keys: - valid_key = True - if isinstance(requests_state1, dict) and 'username' in requests_state1 and requests_state1['username']: - # no UI limit currently - valid_key = True - return valid_key - - -def go_gradio(**kwargs): - allow_api = kwargs['allow_api'] - is_public = kwargs['is_public'] - is_hf = kwargs['is_hf'] - memory_restriction_level = kwargs['memory_restriction_level'] - n_gpus = kwargs['n_gpus'] - admin_pass = kwargs['admin_pass'] - model_states = kwargs['model_states'] - dbs = kwargs['dbs'] - db_type = kwargs['db_type'] - visible_langchain_actions = kwargs['visible_langchain_actions'] - visible_langchain_agents = kwargs['visible_langchain_agents'] - allow_upload_to_user_data = kwargs['allow_upload_to_user_data'] - allow_upload_to_my_data = kwargs['allow_upload_to_my_data'] - enable_sources_list = kwargs['enable_sources_list'] - enable_url_upload = kwargs['enable_url_upload'] - enable_text_upload = kwargs['enable_text_upload'] - use_openai_embedding = kwargs['use_openai_embedding'] - hf_embedding_model = kwargs['hf_embedding_model'] - load_db_if_exists = kwargs['load_db_if_exists'] - migrate_embedding_model = kwargs['migrate_embedding_model'] - auto_migrate_db = kwargs['auto_migrate_db'] - captions_model = kwargs['captions_model'] - caption_loader = kwargs['caption_loader'] - doctr_loader = kwargs['doctr_loader'] - - n_jobs = kwargs['n_jobs'] - verbose = kwargs['verbose'] - - # for dynamic state per user session in gradio - model_state0 = kwargs['model_state0'] - score_model_state0 = kwargs['score_model_state0'] - my_db_state0 = kwargs['my_db_state0'] - selection_docs_state0 = kwargs['selection_docs_state0'] - visible_models_state0 = kwargs['visible_models_state0'] - # For Heap analytics - is_heap_analytics_enabled = kwargs['enable_heap_analytics'] - heap_app_id = kwargs['heap_app_id'] - - # easy update of kwargs needed for evaluate() etc. - queue = True - allow_upload = allow_upload_to_user_data or allow_upload_to_my_data - allow_upload_api = allow_api and allow_upload - - kwargs.update(locals()) - - # import control - if kwargs['langchain_mode'] != 'Disabled': - from gpt_langchain import file_types, have_arxiv - else: - have_arxiv = False - file_types = [] - - if 'mbart-' in kwargs['model_lower']: - instruction_label_nochat = "Text to translate" - else: - instruction_label_nochat = "Instruction (Shift-Enter or push Submit to send message," \ - " use Enter for multiple input lines)" - - title = 'h2oGPT' - if kwargs['visible_h2ogpt_header']: - description = """h2oGPT LLM Leaderboard LLM Studio
CodeLlama
🤗 Models""" - else: - description = None - description_bottom = "If this host is busy, try
[Multi-Model](https://gpt.h2o.ai)
[CodeLlama](https://codellama.h2o.ai)
[Llama2 70B](https://llama.h2o.ai)
[Falcon 40B](https://falcon.h2o.ai)
[HF Spaces1](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot)
[HF Spaces2](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)
" - if is_hf: - description_bottom += '''''' - task_info_md = '' - css_code = get_css(kwargs) - - if kwargs['gradio_offline_level'] >= 0: - # avoid GoogleFont that pulls from internet - if kwargs['gradio_offline_level'] == 1: - # front end would still have to download fonts or have cached it at some point - base_font = 'Source Sans Pro' - else: - base_font = 'Helvetica' - theme_kwargs = dict(font=(base_font, 'ui-sans-serif', 'system-ui', 'sans-serif'), - font_mono=('IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace')) - else: - theme_kwargs = dict() - if kwargs['gradio_size'] == 'xsmall': - theme_kwargs.update(dict(spacing_size=spacing_xsm, text_size=text_xsm, radius_size=radius_xsm)) - elif kwargs['gradio_size'] in [None, 'small']: - theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_sm, text_size=gr.themes.sizes.text_sm, - radius_size=gr.themes.sizes.spacing_sm)) - elif kwargs['gradio_size'] == 'large': - theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_lg, text_size=gr.themes.sizes.text_lg), - radius_size=gr.themes.sizes.spacing_lg) - elif kwargs['gradio_size'] == 'medium': - theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_md, text_size=gr.themes.sizes.text_md, - radius_size=gr.themes.sizes.spacing_md)) - - theme = H2oTheme(**theme_kwargs) if kwargs['h2ocolors'] else SoftTheme(**theme_kwargs) - demo = gr.Blocks(theme=theme, css=css_code, title="h2oGPT", analytics_enabled=False) - callback = gr.CSVLogger() - - model_options0 = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options'] - if kwargs['base_model'].strip() not in model_options0: - model_options0 = [kwargs['base_model'].strip()] + model_options0 - lora_options = kwargs['extra_lora_options'] - if kwargs['lora_weights'].strip() not in lora_options: - lora_options = [kwargs['lora_weights'].strip()] + lora_options - server_options = kwargs['extra_server_options'] - if kwargs['inference_server'].strip() not in server_options: - server_options = [kwargs['inference_server'].strip()] + server_options - if os.getenv('OPENAI_API_KEY'): - if 'openai_chat' not in server_options: - server_options += ['openai_chat'] - if 'openai' not in server_options: - server_options += ['openai'] - - # always add in no lora case - # add fake space so doesn't go away in gradio dropdown - model_options0 = [no_model_str] + sorted(model_options0) - lora_options = [no_lora_str] + sorted(lora_options) - server_options = [no_server_str] + sorted(server_options) - # always add in no model case so can free memory - # add fake space so doesn't go away in gradio dropdown - - # transcribe, will be detranscribed before use by evaluate() - if not kwargs['base_model'].strip(): - kwargs['base_model'] = no_model_str - - if not kwargs['lora_weights'].strip(): - kwargs['lora_weights'] = no_lora_str - - if not kwargs['inference_server'].strip(): - kwargs['inference_server'] = no_server_str - - # transcribe for gradio - kwargs['gpu_id'] = str(kwargs['gpu_id']) - - no_model_msg = 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]' - output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get( - 'base_model') else no_model_msg - output_label0_model2 = no_model_msg - - def update_prompt(prompt_type1, prompt_dict1, model_state1, which_model=0): - if not prompt_type1 or which_model != 0: - # keep prompt_type and prompt_dict in sync if possible - prompt_type1 = kwargs.get('prompt_type', prompt_type1) - prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1) - # prefer model specific prompt type instead of global one - if not prompt_type1 or which_model != 0: - prompt_type1 = model_state1.get('prompt_type', prompt_type1) - prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1) - - if not prompt_dict1 or which_model != 0: - # if still not defined, try to get - prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1) - if not prompt_dict1 or which_model != 0: - prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1) - return prompt_type1, prompt_dict1 - - def visible_models_to_model_choice(visible_models1): - if isinstance(visible_models1, list): - assert len( - visible_models1) >= 1, "Invalid visible_models1=%s, can only be single entry" % visible_models1 - # just take first - model_active_choice1 = visible_models1[0] - elif isinstance(visible_models1, (str, int)): - model_active_choice1 = visible_models1 - else: - assert isinstance(visible_models1, type(None)), "Invalid visible_models1=%s" % visible_models1 - model_active_choice1 = visible_models1 - if model_active_choice1 is not None: - if isinstance(model_active_choice1, str): - base_model_list = [x['base_model'] for x in model_states] - if model_active_choice1 in base_model_list: - # if dups, will just be first one - model_active_choice1 = base_model_list.index(model_active_choice1) - else: - # NOTE: Could raise, but sometimes raising in certain places fails too hard and requires UI restart - model_active_choice1 = 0 - else: - model_active_choice1 = 0 - return model_active_choice1 - - default_kwargs = {k: kwargs[k] for k in eval_func_param_names_defaults} - # ensure prompt_type consistent with prep_bot(), so nochat API works same way - default_kwargs['prompt_type'], default_kwargs['prompt_dict'] = \ - update_prompt(default_kwargs['prompt_type'], default_kwargs['prompt_dict'], - model_state1=model_state0, - which_model=visible_models_to_model_choice(kwargs['visible_models'])) - for k in no_default_param_names: - default_kwargs[k] = '' - - def dummy_fun(x): - # need dummy function to block new input from being sent until output is done, - # else gets input_list at time of submit that is old, and shows up as truncated in chatbot - return x - - def update_auth_selection(auth_user, selection_docs_state1, save=False): - # in-place update of both - if 'selection_docs_state' not in auth_user: - auth_user['selection_docs_state'] = selection_docs_state0 - for k, v in auth_user['selection_docs_state'].items(): - if isinstance(selection_docs_state1[k], dict): - if save: - auth_user['selection_docs_state'][k].clear() - auth_user['selection_docs_state'][k].update(selection_docs_state1[k]) - else: - selection_docs_state1[k].clear() - selection_docs_state1[k].update(auth_user['selection_docs_state'][k]) - elif isinstance(selection_docs_state1[k], list): - if save: - auth_user['selection_docs_state'][k].clear() - auth_user['selection_docs_state'][k].extend(selection_docs_state1[k]) - else: - selection_docs_state1[k].clear() - selection_docs_state1[k].extend(auth_user['selection_docs_state'][k]) - else: - raise RuntimeError("Bad type: %s" % selection_docs_state1[k]) - - # BEGIN AUTH THINGS - def auth_func(username1, password1, auth_pairs=None, auth_filename=None, - auth_access=None, - auth_freeze=None, - guest_name=None, - selection_docs_state1=None, - selection_docs_state00=None, - **kwargs): - assert auth_freeze is not None - if selection_docs_state1 is None: - selection_docs_state1 = selection_docs_state00 - assert selection_docs_state1 is not None - assert auth_filename and isinstance(auth_filename, str), "Auth file must be a non-empty string, got: %s" % str( - auth_filename) - if auth_access == 'open' and username1 == guest_name: - return True - if username1 == '': - # some issue with login - return False - with filelock.FileLock(auth_filename + '.lock'): - auth_dict = {} - if os.path.isfile(auth_filename): - try: - with open(auth_filename, 'rt') as f: - auth_dict = json.load(f) - except json.decoder.JSONDecodeError as e: - print("Auth exception: %s" % str(e), flush=True) - shutil.move(auth_filename, auth_filename + '.bak' + str(uuid.uuid4())) - auth_dict = {} - if username1 in auth_dict and username1 in auth_pairs: - if password1 == auth_dict[username1]['password'] and password1 == auth_pairs[username1]: - auth_user = auth_dict[username1] - update_auth_selection(auth_user, selection_docs_state1) - save_auth_dict(auth_dict, auth_filename) - return True - else: - return False - elif username1 in auth_dict: - if password1 == auth_dict[username1]['password']: - auth_user = auth_dict[username1] - update_auth_selection(auth_user, selection_docs_state1) - save_auth_dict(auth_dict, auth_filename) - return True - else: - return False - elif username1 in auth_pairs: - # copy over CLI auth to file so only one state to manage - auth_dict[username1] = dict(password=auth_pairs[username1], userid=str(uuid.uuid4())) - auth_user = auth_dict[username1] - update_auth_selection(auth_user, selection_docs_state1) - save_auth_dict(auth_dict, auth_filename) - return True - else: - if auth_access == 'closed': - return False - # open access - auth_dict[username1] = dict(password=password1, userid=str(uuid.uuid4())) - auth_user = auth_dict[username1] - update_auth_selection(auth_user, selection_docs_state1) - save_auth_dict(auth_dict, auth_filename) - if auth_access == 'open': - return True - else: - raise RuntimeError("Invalid auth_access: %s" % auth_access) - - def auth_func_open(*args, **kwargs): - return True - - def get_username(requests_state1): - username1 = None - if 'username' in requests_state1: - username1 = requests_state1['username'] - return username1 - - def get_userid_auth_func(requests_state1, auth_filename=None, auth_access=None, guest_name=None, **kwargs): - if auth_filename and isinstance(auth_filename, str): - username1 = get_username(requests_state1) - if username1: - if username1 == guest_name: - return str(uuid.uuid4()) - with filelock.FileLock(auth_filename + '.lock'): - if os.path.isfile(auth_filename): - with open(auth_filename, 'rt') as f: - auth_dict = json.load(f) - if username1 in auth_dict: - return auth_dict[username1]['userid'] - # if here, then not persistently associated with username1, - # but should only be one-time asked if going to persist within a single session! - return str(uuid.uuid4()) - - get_userid_auth = functools.partial(get_userid_auth_func, - auth_filename=kwargs['auth_filename'], - auth_access=kwargs['auth_access'], - guest_name=kwargs['guest_name'], - ) - if kwargs['auth_access'] == 'closed': - auth_message1 = "Closed access" - else: - auth_message1 = "WELCOME! Open access" \ - " (%s/%s or any unique user/pass)" % (kwargs['guest_name'], kwargs['guest_name']) - - if kwargs['auth_message'] is not None: - auth_message = kwargs['auth_message'] - else: - auth_message = auth_message1 - - # always use same callable - auth_pairs0 = {} - if isinstance(kwargs['auth'], list): - for k, v in kwargs['auth']: - auth_pairs0[k] = v - authf = functools.partial(auth_func, - auth_pairs=auth_pairs0, - auth_filename=kwargs['auth_filename'], - auth_access=kwargs['auth_access'], - auth_freeze=kwargs['auth_freeze'], - guest_name=kwargs['guest_name'], - selection_docs_state00=copy.deepcopy(selection_docs_state0)) - - def get_request_state(requests_state1, request, db1s): - # if need to get state, do it now - if not requests_state1: - requests_state1 = requests_state0.copy() - if requests: - if not requests_state1.get('headers', '') and hasattr(request, 'headers'): - requests_state1.update(request.headers) - if not requests_state1.get('host', '') and hasattr(request, 'host'): - requests_state1.update(dict(host=request.host)) - if not requests_state1.get('host2', '') and hasattr(request, 'client') and hasattr(request.client, 'host'): - requests_state1.update(dict(host2=request.client.host)) - if not requests_state1.get('username', '') and hasattr(request, 'username'): - # use already-defined username instead of keep changing to new uuid - # should be same as in requests_state1 - db_username = get_username_direct(db1s) - requests_state1.update(dict(username=request.username or db_username or str(uuid.uuid4()))) - requests_state1 = {str(k): str(v) for k, v in requests_state1.items()} - return requests_state1 - - def user_state_setup(db1s, requests_state1, request: gr.Request, *args): - requests_state1 = get_request_state(requests_state1, request, db1s) - set_userid(db1s, requests_state1, get_userid_auth) - args_list = [db1s, requests_state1] + list(args) - return tuple(args_list) - - # END AUTH THINGS - - def allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1): - allow = False - allow |= langchain_action1 not in LangChainAction.QUERY.value - allow |= document_subset1 in DocumentSubset.TopKSources.name - if langchain_mode1 in [LangChainMode.LLM.value]: - allow = False - return allow - - image_loaders_options0, image_loaders_options, \ - pdf_loaders_options0, pdf_loaders_options, \ - url_loaders_options0, url_loaders_options = lg_to_gr(**kwargs) - jq_schema0 = '.[]' - - with demo: - # avoid actual model/tokenizer here or anything that would be bad to deepcopy - # https://github.com/gradio-app/gradio/issues/3558 - model_state = gr.State( - dict(model='model', tokenizer='tokenizer', device=kwargs['device'], - base_model=kwargs['base_model'], - tokenizer_base_model=kwargs['tokenizer_base_model'], - lora_weights=kwargs['lora_weights'], - inference_server=kwargs['inference_server'], - prompt_type=kwargs['prompt_type'], - prompt_dict=kwargs['prompt_dict'], - visible_models=kwargs['visible_models'], - h2ogpt_key=kwargs['h2ogpt_key'], - ) - ) - - def update_langchain_mode_paths(selection_docs_state1): - dup = selection_docs_state1['langchain_mode_paths'].copy() - for k, v in dup.items(): - if k not in selection_docs_state1['langchain_modes']: - selection_docs_state1['langchain_mode_paths'].pop(k) - for k in selection_docs_state1['langchain_modes']: - if k not in selection_docs_state1['langchain_mode_types']: - # if didn't specify shared, then assume scratch if didn't login or personal if logged in - selection_docs_state1['langchain_mode_types'][k] = LangChainTypes.PERSONAL.value - return selection_docs_state1 - - # Setup some gradio states for per-user dynamic state - model_state2 = gr.State(kwargs['model_state_none'].copy()) - model_options_state = gr.State([model_options0]) - lora_options_state = gr.State([lora_options]) - server_options_state = gr.State([server_options]) - my_db_state = gr.State(my_db_state0) - chat_state = gr.State({}) - docs_state00 = kwargs['document_choice'] + [DocumentChoice.ALL.value] - docs_state0 = [] - [docs_state0.append(x) for x in docs_state00 if x not in docs_state0] - docs_state = gr.State(docs_state0) - viewable_docs_state0 = [] - viewable_docs_state = gr.State(viewable_docs_state0) - selection_docs_state0 = update_langchain_mode_paths(selection_docs_state0) - selection_docs_state = gr.State(selection_docs_state0) - requests_state0 = dict(headers='', host='', username='') - requests_state = gr.State(requests_state0) - - if description is not None: - gr.Markdown(f""" - {get_h2o_title(title, description) if kwargs['h2ocolors'] else get_simple_title(title, description)} - """) - - # go button visible if - base_wanted = kwargs['base_model'] != no_model_str and kwargs['login_mode_if_model0'] - go_btn = gr.Button(value="ENTER", visible=base_wanted, variant="primary") - - nas = ' '.join(['NA'] * len(kwargs['model_states'])) - res_value = "Response Score: NA" if not kwargs[ - 'model_lock'] else "Response Scores: %s" % nas - - user_can_do_sum = kwargs['langchain_mode'] != LangChainMode.DISABLED.value and \ - (kwargs['visible_side_bar'] or kwargs['visible_system_tab']) - if user_can_do_sum: - extra_prompt_form = ". For summarization, no query required, just click submit" - else: - extra_prompt_form = "" - if kwargs['input_lines'] > 1: - instruction_label = "Shift-Enter to Submit, Enter for more lines%s" % extra_prompt_form - else: - instruction_label = "Enter to Submit, Shift-Enter for more lines%s" % extra_prompt_form - - def get_langchain_choices(selection_docs_state1): - langchain_modes = selection_docs_state1['langchain_modes'] - - if is_hf: - # don't show 'wiki' since only usually useful for internal testing at moment - no_show_modes = ['Disabled', 'wiki'] - else: - no_show_modes = ['Disabled'] - allowed_modes = langchain_modes.copy() - # allowed_modes = [x for x in allowed_modes if x in dbs] - allowed_modes += ['LLM'] - if allow_upload_to_my_data and 'MyData' not in allowed_modes: - allowed_modes += ['MyData'] - if allow_upload_to_user_data and 'UserData' not in allowed_modes: - allowed_modes += ['UserData'] - choices = [x for x in langchain_modes if x in allowed_modes and x not in no_show_modes] - return choices - - def get_df_langchain_mode_paths(selection_docs_state1, db1s, dbs1=None): - langchain_choices1 = get_langchain_choices(selection_docs_state1) - langchain_mode_paths = selection_docs_state1['langchain_mode_paths'] - langchain_mode_paths = {k: v for k, v in langchain_mode_paths.items() if k in langchain_choices1} - if langchain_mode_paths: - langchain_mode_paths = langchain_mode_paths.copy() - for langchain_mode1 in langchain_modes_non_db: - langchain_mode_paths.pop(langchain_mode1, None) - df1 = pd.DataFrame.from_dict(langchain_mode_paths.items(), orient='columns') - df1.columns = ['Collection', 'Path'] - df1 = df1.set_index('Collection') - else: - df1 = pd.DataFrame(None) - langchain_mode_types = selection_docs_state1['langchain_mode_types'] - langchain_mode_types = {k: v for k, v in langchain_mode_types.items() if k in langchain_choices1} - if langchain_mode_types: - langchain_mode_types = langchain_mode_types.copy() - for langchain_mode1 in langchain_modes_non_db: - langchain_mode_types.pop(langchain_mode1, None) - - df2 = pd.DataFrame.from_dict(langchain_mode_types.items(), orient='columns') - df2.columns = ['Collection', 'Type'] - df2 = df2.set_index('Collection') - - from src.gpt_langchain import get_persist_directory, load_embed - persist_directory_dict = {} - embed_dict = {} - chroma_version_dict = {} - for langchain_mode3 in langchain_mode_types: - langchain_type3 = langchain_mode_types.get(langchain_mode3, LangChainTypes.EITHER.value) - persist_directory3, langchain_type3 = get_persist_directory(langchain_mode3, - langchain_type=langchain_type3, - db1s=db1s, dbs=dbs1) - got_embedding3, use_openai_embedding3, hf_embedding_model3 = load_embed( - persist_directory=persist_directory3) - persist_directory_dict[langchain_mode3] = persist_directory3 - embed_dict[langchain_mode3] = 'OpenAI' if not hf_embedding_model3 else hf_embedding_model3 - - if os.path.isfile(os.path.join(persist_directory3, 'chroma.sqlite3')): - chroma_version_dict[langchain_mode3] = 'ChromaDB>=0.4' - elif os.path.isdir(os.path.join(persist_directory3, 'index')): - chroma_version_dict[langchain_mode3] = 'ChromaDB<0.4' - elif not os.listdir(persist_directory3): - if db_type == 'chroma': - chroma_version_dict[langchain_mode3] = 'ChromaDB>=0.4' # will be - elif db_type == 'chroma_old': - chroma_version_dict[langchain_mode3] = 'ChromaDB<0.4' # will be - else: - chroma_version_dict[langchain_mode3] = 'Weaviate' # will be - if isinstance(hf_embedding_model, dict): - hf_embedding_model3 = hf_embedding_model['name'] - else: - hf_embedding_model3 = hf_embedding_model - assert isinstance(hf_embedding_model3, str) - embed_dict[langchain_mode3] = hf_embedding_model3 # will be - else: - chroma_version_dict[langchain_mode3] = 'Weaviate' - - df3 = pd.DataFrame.from_dict(persist_directory_dict.items(), orient='columns') - df3.columns = ['Collection', 'Directory'] - df3 = df3.set_index('Collection') - - df4 = pd.DataFrame.from_dict(embed_dict.items(), orient='columns') - df4.columns = ['Collection', 'Embedding'] - df4 = df4.set_index('Collection') - - df5 = pd.DataFrame.from_dict(chroma_version_dict.items(), orient='columns') - df5.columns = ['Collection', 'DB'] - df5 = df5.set_index('Collection') - else: - df2 = pd.DataFrame(None) - df3 = pd.DataFrame(None) - df4 = pd.DataFrame(None) - df5 = pd.DataFrame(None) - df_list = [df2, df1, df3, df4, df5] - df_list = [x for x in df_list if x.shape[1] > 0] - if len(df_list) > 1: - df = df_list[0].join(df_list[1:]).replace(np.nan, '').reset_index() - elif len(df_list) == 0: - df = df_list[0].replace(np.nan, '').reset_index() - else: - df = pd.DataFrame(None) - return df - - normal_block = gr.Row(visible=not base_wanted, equal_height=False, elem_id="col_container") - with normal_block: - side_bar = gr.Column(elem_id="sidebar", scale=1, min_width=100, visible=kwargs['visible_side_bar']) - with side_bar: - with gr.Accordion("Chats", open=False, visible=True): - radio_chats = gr.Radio(value=None, label="Saved Chats", show_label=False, - visible=True, interactive=True, - type='value') - upload_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload - with gr.Accordion("Upload", open=False, visible=upload_visible): - with gr.Column(): - with gr.Row(equal_height=False): - fileup_output = gr.File(show_label=False, - file_types=['.' + x for x in file_types], - # file_types=['*', '*.*'], # for iPhone etc. needs to be unconstrained else doesn't work with extension-based restrictions - file_count="multiple", - scale=1, - min_width=0, - elem_id="warning", elem_classes="feedback", - ) - fileup_output_text = gr.Textbox(visible=False) - max_quality = gr.Checkbox(label="Maximum Ingest Quality", value=kwargs['max_quality'], - visible=not is_public) - url_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload - url_label = 'URL/ArXiv' if have_arxiv else 'URL' - url_text = gr.Textbox(label=url_label, - # placeholder="Enter Submits", - max_lines=1, - interactive=True) - text_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload - user_text_text = gr.Textbox(label='Paste Text', - # placeholder="Enter Submits", - interactive=True, - visible=text_visible) - github_textbox = gr.Textbox(label="Github URL", visible=False) # FIXME WIP - database_visible = kwargs['langchain_mode'] != 'Disabled' - with gr.Accordion("Resources", open=False, visible=database_visible): - langchain_choices0 = get_langchain_choices(selection_docs_state0) - langchain_mode = gr.Radio( - langchain_choices0, - value=kwargs['langchain_mode'], - label="Collections", - show_label=True, - visible=kwargs['langchain_mode'] != 'Disabled', - min_width=100) - add_chat_history_to_context = gr.Checkbox(label="Chat History", - value=kwargs['add_chat_history_to_context']) - add_search_to_context = gr.Checkbox(label="Web Search", - value=kwargs['add_search_to_context'], - visible=os.environ.get('SERPAPI_API_KEY') is not None \ - and have_serpapi) - document_subset = gr.Radio([x.name for x in DocumentSubset], - label="Subset", - value=DocumentSubset.Relevant.name, - interactive=True, - ) - allowed_actions = [x for x in langchain_actions if x in visible_langchain_actions] - langchain_action = gr.Radio( - allowed_actions, - value=allowed_actions[0] if len(allowed_actions) > 0 else None, - label="Action", - visible=True) - allowed_agents = [x for x in langchain_agents_list if x in visible_langchain_agents] - if os.getenv('OPENAI_API_KEY') is None and LangChainAgent.JSON.value in allowed_agents: - allowed_agents.remove(LangChainAgent.JSON.value) - if os.getenv('OPENAI_API_KEY') is None and LangChainAgent.PYTHON.value in allowed_agents: - allowed_agents.remove(LangChainAgent.PYTHON.value) - if LangChainAgent.PANDAS.value in allowed_agents: - allowed_agents.remove(LangChainAgent.PANDAS.value) - langchain_agents = gr.Dropdown( - allowed_agents, - value=None, - label="Agents", - multiselect=True, - interactive=True, - visible=True, - elem_id="langchain_agents", - filterable=False) - visible_doc_track = upload_visible and kwargs['visible_doc_track'] and not kwargs[ - 'large_file_count_mode'] - row_doc_track = gr.Row(visible=visible_doc_track) - with row_doc_track: - if kwargs['langchain_mode'] in langchain_modes_non_db: - doc_counts_str = "Pure LLM Mode" - else: - doc_counts_str = "Name: %s\nDocs: Unset\nChunks: Unset" % kwargs['langchain_mode'] - text_doc_count = gr.Textbox(lines=3, label="Doc Counts", value=doc_counts_str, - visible=visible_doc_track) - text_file_last = gr.Textbox(lines=1, label="Newest Doc", value=None, visible=visible_doc_track) - text_viewable_doc_count = gr.Textbox(lines=2, label=None, visible=False) - col_tabs = gr.Column(elem_id="col-tabs", scale=10) - with col_tabs, gr.Tabs(): - if kwargs['chat_tables']: - chat_tab = gr.Row(visible=True) - else: - chat_tab = gr.TabItem("Chat") \ - if kwargs['visible_chat_tab'] else gr.Row(visible=False) - with chat_tab: - if kwargs['langchain_mode'] == 'Disabled': - text_output_nochat = gr.Textbox(lines=5, label=output_label0, show_copy_button=True, - visible=not kwargs['chat']) - else: - # text looks a bit worse, but HTML links work - text_output_nochat = gr.HTML(label=output_label0, visible=not kwargs['chat']) - with gr.Row(): - # NOCHAT - instruction_nochat = gr.Textbox( - lines=kwargs['input_lines'], - label=instruction_label_nochat, - placeholder=kwargs['placeholder_instruction'], - visible=not kwargs['chat'], - ) - iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction", - placeholder=kwargs['placeholder_input'], - value=kwargs['iinput'], - visible=not kwargs['chat']) - submit_nochat = gr.Button("Submit", size='sm', visible=not kwargs['chat']) - flag_btn_nochat = gr.Button("Flag", size='sm', visible=not kwargs['chat']) - score_text_nochat = gr.Textbox("Response Score: NA", show_label=False, - visible=not kwargs['chat']) - submit_nochat_api = gr.Button("Submit nochat API", visible=False) - submit_nochat_api_plain = gr.Button("Submit nochat API Plain", visible=False) - inputs_dict_str = gr.Textbox(label='API input for nochat', show_label=False, visible=False) - text_output_nochat_api = gr.Textbox(lines=5, label='API nochat output', visible=False, - show_copy_button=True) - - visible_upload = (allow_upload_to_user_data or - allow_upload_to_my_data) and \ - kwargs['langchain_mode'] != 'Disabled' - # CHAT - col_chat = gr.Column(visible=kwargs['chat']) - with col_chat: - with gr.Row(): - with gr.Column(scale=50): - with gr.Row(elem_id="prompt-form-row"): - label_instruction = 'Ask anything' - instruction = gr.Textbox( - lines=kwargs['input_lines'], - label=label_instruction, - placeholder=instruction_label, - info=None, - elem_id='prompt-form', - container=True, - ) - attach_button = gr.UploadButton( - elem_id="attach-button" if visible_upload else None, - value="", - label="Upload File(s)", - size="sm", - min_width=24, - file_types=['.' + x for x in file_types], - file_count="multiple", - visible=visible_upload) - - submit_buttons = gr.Row(equal_height=False, visible=kwargs['visible_submit_buttons']) - with submit_buttons: - mw1 = 50 - mw2 = 50 - with gr.Column(min_width=mw1): - submit = gr.Button(value='Submit', variant='primary', size='sm', - min_width=mw1) - stop_btn = gr.Button(value="Stop", variant='secondary', size='sm', - min_width=mw1) - save_chat_btn = gr.Button("Save", size='sm', min_width=mw1) - with gr.Column(min_width=mw2): - retry_btn = gr.Button("Redo", size='sm', min_width=mw2) - undo = gr.Button("Undo", size='sm', min_width=mw2) - clear_chat_btn = gr.Button(value="Clear", size='sm', min_width=mw2) - - visible_model_choice = bool(kwargs['model_lock']) and \ - len(model_states) > 1 and \ - kwargs['visible_visible_models'] - with gr.Row(visible=visible_model_choice): - visible_models = gr.Dropdown(kwargs['all_models'], - label="Visible Models", - value=visible_models_state0, - interactive=True, - multiselect=True, - visible=visible_model_choice, - elem_id="visible-models", - filterable=False, - ) - - text_output, text_output2, text_outputs = make_chatbots(output_label0, output_label0_model2, - **kwargs) - - with gr.Row(): - with gr.Column(visible=kwargs['score_model']): - score_text = gr.Textbox(res_value, - show_label=False, - visible=True) - score_text2 = gr.Textbox("Response Score2: NA", show_label=False, - visible=False and not kwargs['model_lock']) - - doc_selection_tab = gr.TabItem("Document Selection") \ - if kwargs['visible_doc_selection_tab'] else gr.Row(visible=False) - with doc_selection_tab: - if kwargs['langchain_mode'] in langchain_modes_non_db: - dlabel1 = 'Choose Resources->Collections and Pick Collection' - active_collection = gr.Markdown(value="#### Not Chatting with Any Collection\n%s" % dlabel1) - else: - dlabel1 = 'Select Subset of Document(s) for Chat with Collection: %s' % kwargs['langchain_mode'] - active_collection = gr.Markdown( - value="#### Chatting with Collection: %s" % kwargs['langchain_mode']) - document_choice = gr.Dropdown(docs_state0, - label=dlabel1, - value=[DocumentChoice.ALL.value], - interactive=True, - multiselect=True, - visible=kwargs['langchain_mode'] != 'Disabled', - ) - sources_visible = kwargs['langchain_mode'] != 'Disabled' and enable_sources_list - with gr.Row(): - with gr.Column(scale=1): - get_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0, size='sm', - visible=sources_visible and kwargs['large_file_count_mode']) - # handle API get sources - get_sources_api_btn = gr.Button(visible=False) - get_sources_api_text = gr.Textbox(visible=False) - - get_document_api_btn = gr.Button(visible=False) - get_document_api_text = gr.Textbox(visible=False) - - show_sources_btn = gr.Button(value="Show Sources from DB", scale=0, size='sm', - visible=sources_visible and kwargs['large_file_count_mode']) - delete_sources_btn = gr.Button(value="Delete Selected Sources from DB", scale=0, size='sm', - visible=sources_visible) - refresh_sources_btn = gr.Button(value="Update DB with new/changed files on disk", scale=0, - size='sm', - visible=sources_visible and allow_upload_to_user_data) - with gr.Column(scale=4): - pass - visible_add_remove_collection = visible_upload - with gr.Row(): - with gr.Column(scale=1): - add_placeholder = "e.g. UserData2, shared, user_path2" \ - if not is_public else "e.g. MyData2, personal (optional)" - remove_placeholder = "e.g. UserData2" if not is_public else "e.g. MyData2" - new_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection, - label='Add Collection', - placeholder=add_placeholder, - interactive=True) - remove_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection, - label='Remove Collection from UI', - placeholder=remove_placeholder, - interactive=True) - purge_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection, - label='Purge Collection (UI, DB, & source files)', - placeholder=remove_placeholder, - interactive=True) - sync_sources_btn = gr.Button( - value="Synchronize DB and UI [only required if did not login and have shared docs]", - scale=0, size='sm', - visible=sources_visible and allow_upload_to_user_data and not kwargs[ - 'large_file_count_mode']) - load_langchain = gr.Button( - value="Load Collections State [only required if logged in another user ", scale=0, - size='sm', - visible=False and allow_upload_to_user_data and - kwargs['langchain_mode'] != 'Disabled') - with gr.Column(scale=5): - if kwargs['langchain_mode'] != 'Disabled' and visible_add_remove_collection: - df0 = get_df_langchain_mode_paths(selection_docs_state0, None, dbs1=dbs) - else: - df0 = pd.DataFrame(None) - langchain_mode_path_text = gr.Dataframe(value=df0, - visible=visible_add_remove_collection, - label='LangChain Mode-Path', - show_label=False, - interactive=False) - - sources_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list, - equal_height=False) - with sources_row: - with gr.Column(scale=1): - file_source = gr.File(interactive=False, - label="Download File w/Sources") - with gr.Column(scale=2): - sources_text = gr.HTML(label='Sources Added', interactive=False) - - doc_exception_text = gr.Textbox(value="", label='Document Exceptions', - interactive=False, - visible=kwargs['langchain_mode'] != 'Disabled') - file_types_str = ' '.join(file_types) + ' URL ArXiv TEXT' - gr.Textbox(value=file_types_str, label='Document Types Supported', - lines=2, - interactive=False, - visible=kwargs['langchain_mode'] != 'Disabled') - - doc_view_tab = gr.TabItem("Document Viewer") \ - if kwargs['visible_doc_view_tab'] else gr.Row(visible=False) - with doc_view_tab: - with gr.Row(visible=kwargs['langchain_mode'] != 'Disabled'): - with gr.Column(scale=2): - get_viewable_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0, - size='sm', - visible=sources_visible and kwargs[ - 'large_file_count_mode']) - view_document_choice = gr.Dropdown(viewable_docs_state0, - label="Select Single Document to View", - value=None, - interactive=True, - multiselect=False, - visible=True, - ) - info_view_raw = "Raw text shown if render of original doc fails" - if is_public: - info_view_raw += " (Up to %s chunks in public portal)" % kwargs['max_raw_chunks'] - view_raw_text_checkbox = gr.Checkbox(label="View Database Text", value=False, - info=info_view_raw, - visible=kwargs['db_type'] in ['chroma', 'chroma_old']) - with gr.Column(scale=4): - pass - doc_view = gr.HTML(visible=False) - doc_view2 = gr.Dataframe(visible=False) - doc_view3 = gr.JSON(visible=False) - doc_view4 = gr.Markdown(visible=False) - doc_view5 = gr.HTML(visible=False) - - chat_tab = gr.TabItem("Chat History") \ - if kwargs['visible_chat_history_tab'] else gr.Row(visible=False) - with chat_tab: - with gr.Row(): - with gr.Column(scale=1): - remove_chat_btn = gr.Button(value="Remove Selected Saved Chats", visible=True, size='sm') - flag_btn = gr.Button("Flag Current Chat", size='sm') - export_chats_btn = gr.Button(value="Export Chats to Download", size='sm') - with gr.Column(scale=4): - pass - with gr.Row(): - chats_file = gr.File(interactive=False, label="Download Exported Chats") - chatsup_output = gr.File(label="Upload Chat File(s)", - file_types=['.json'], - file_count='multiple', - elem_id="warning", elem_classes="feedback") - with gr.Row(): - if 'mbart-' in kwargs['model_lower']: - src_lang = gr.Dropdown(list(languages_covered().keys()), - value=kwargs['src_lang'], - label="Input Language") - tgt_lang = gr.Dropdown(list(languages_covered().keys()), - value=kwargs['tgt_lang'], - label="Output Language") - - chat_exception_text = gr.Textbox(value="", visible=True, label='Chat Exceptions', - interactive=False) - expert_tab = gr.TabItem("Expert") \ - if kwargs['visible_expert_tab'] else gr.Row(visible=False) - with expert_tab: - with gr.Row(): - with gr.Column(): - prompt_type = gr.Dropdown(prompt_types_strings, - value=kwargs['prompt_type'], label="Prompt Type", - visible=not kwargs['model_lock'], - interactive=not is_public, - ) - prompt_type2 = gr.Dropdown(prompt_types_strings, - value=kwargs['prompt_type'], label="Prompt Type Model 2", - visible=False and not kwargs['model_lock'], - interactive=not is_public) - system_prompt = gr.Textbox(label="System Prompt", - info="If 'auto', then uses model's system prompt," - " else use this message." - " If empty, no system message is used", - value=kwargs['system_prompt']) - context = gr.Textbox(lines=2, label="System Pre-Context", - info="Directly pre-appended without prompt processing (before Pre-Conversation)", - value=kwargs['context']) - chat_conversation = gr.Textbox(lines=2, label="Pre-Conversation", - info="Pre-append conversation for instruct/chat models as List of tuple of (human, bot)", - value=kwargs['chat_conversation']) - text_context_list = gr.Textbox(lines=2, label="Text Doc Q/A", - info="List of strings, for document Q/A, for bypassing database (i.e. also works in LLM Mode)", - value=kwargs['chat_conversation'], - visible=not is_public, # primarily meant for API - ) - iinput = gr.Textbox(lines=2, label="Input for Instruct prompt types", - info="If given for document query, added after query", - value=kwargs['iinput'], - placeholder=kwargs['placeholder_input'], - interactive=not is_public) - with gr.Column(): - pre_prompt_query = gr.Textbox(label="Query Pre-Prompt", - info="Added before documents", - value=kwargs['pre_prompt_query'] or '') - prompt_query = gr.Textbox(label="Query Prompt", - info="Added after documents", - value=kwargs['prompt_query'] or '') - pre_prompt_summary = gr.Textbox(label="Summary Pre-Prompt", - info="Added before documents", - value=kwargs['pre_prompt_summary'] or '') - prompt_summary = gr.Textbox(label="Summary Prompt", - info="Added after documents (if query given, 'Focusing on {query}, ' is pre-appended)", - value=kwargs['prompt_summary'] or '') - with gr.Row(visible=not is_public): - image_loaders = gr.CheckboxGroup(image_loaders_options, - label="Force Image Reader", - value=image_loaders_options0) - pdf_loaders = gr.CheckboxGroup(pdf_loaders_options, - label="Force PDF Reader", - value=pdf_loaders_options0) - url_loaders = gr.CheckboxGroup(url_loaders_options, - label="Force URL Reader", value=url_loaders_options0) - jq_schema = gr.Textbox(label="JSON jq_schema", value=jq_schema0) - - min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public) - top_k_docs = gr.Slider(minimum=min_top_k_docs, maximum=max_top_k_docs, step=1, - value=kwargs['top_k_docs'], - label=label_top_k_docs, - # info="For LangChain", - visible=kwargs['langchain_mode'] != 'Disabled', - interactive=not is_public) - chunk_size = gr.Number(value=kwargs['chunk_size'], - label="Chunk size for document chunking", - info="For LangChain (ignored if chunk=False)", - minimum=128, - maximum=2048, - visible=kwargs['langchain_mode'] != 'Disabled', - interactive=not is_public, - precision=0) - docs_ordering_type = gr.Radio( - docs_ordering_types, - value=kwargs['docs_ordering_type'], - label="Document Sorting in LLM Context", - visible=True) - chunk = gr.components.Checkbox(value=kwargs['chunk'], - label="Whether to chunk documents", - info="For LangChain", - visible=kwargs['langchain_mode'] != 'Disabled', - interactive=not is_public) - embed = gr.components.Checkbox(value=True, - label="Whether to embed text", - info="For LangChain", - visible=False) - with gr.Row(): - stream_output = gr.components.Checkbox(label="Stream output", - value=kwargs['stream_output']) - do_sample = gr.Checkbox(label="Sample", - info="Enable sampler (required for use of temperature, top_p, top_k)", - value=kwargs['do_sample']) - max_time = gr.Slider(minimum=0, maximum=kwargs['max_max_time'], step=1, - value=min(kwargs['max_max_time'], - kwargs['max_time']), label="Max. time", - info="Max. time to search optimal output.") - temperature = gr.Slider(minimum=0.01, maximum=2, - value=kwargs['temperature'], - label="Temperature", - info="Lower is deterministic, higher more creative") - top_p = gr.Slider(minimum=1e-3, maximum=1.0 - 1e-3, - value=kwargs['top_p'], label="Top p", - info="Cumulative probability of tokens to sample from") - top_k = gr.Slider( - minimum=1, maximum=100, step=1, - value=kwargs['top_k'], label="Top k", - info='Num. tokens to sample from' - ) - # FIXME: https://github.com/h2oai/h2ogpt/issues/106 - if os.getenv('TESTINGFAIL'): - max_beams = 8 if not (memory_restriction_level or is_public) else 1 - else: - max_beams = 1 - num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1, - value=min(max_beams, kwargs['num_beams']), label="Beams", - info="Number of searches for optimal overall probability. " - "Uses more GPU memory/compute", - interactive=False, visible=max_beams > 1) - max_max_new_tokens = get_max_max_new_tokens(model_state0, **kwargs) - max_new_tokens = gr.Slider( - minimum=1, maximum=max_max_new_tokens, step=1, - value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length", - ) - min_new_tokens = gr.Slider( - minimum=0, maximum=max_max_new_tokens, step=1, - value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length", - ) - max_new_tokens2 = gr.Slider( - minimum=1, maximum=max_max_new_tokens, step=1, - value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length 2", - visible=False and not kwargs['model_lock'], - ) - min_new_tokens2 = gr.Slider( - minimum=0, maximum=max_max_new_tokens, step=1, - value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length 2", - visible=False and not kwargs['model_lock'], - ) - min_max_new_tokens = gr.Slider( - minimum=1, maximum=max_max_new_tokens, step=1, - value=min(max_max_new_tokens, kwargs['min_max_new_tokens']), - label="Min. of Max output length", - ) - early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search", - value=kwargs['early_stopping'], visible=max_beams > 1) - repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0, - value=kwargs['repetition_penalty'], - label="Repetition Penalty") - num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1, - value=kwargs['num_return_sequences'], - label="Number Returns", info="Must be <= num_beams", - interactive=not is_public, visible=max_beams > 1) - chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'], - visible=False, # no longer support nochat in UI - interactive=not is_public, - ) - with gr.Row(): - count_chat_tokens_btn = gr.Button(value="Count Chat Tokens", - visible=not is_public and not kwargs['model_lock'], - interactive=not is_public, size='sm') - chat_token_count = gr.Textbox(label="Chat Token Count Result", value=None, - visible=not is_public and not kwargs['model_lock'], - interactive=False) - - models_tab = gr.TabItem("Models") \ - if kwargs['visible_models_tab'] and not bool(kwargs['model_lock']) else gr.Row(visible=False) - with models_tab: - load_msg = "Download/Load Model" if not is_public \ - else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO" - if kwargs['base_model'] not in ['', None, no_model_str]: - load_msg += ' [WARNING: Avoid --base_model on CLI for memory efficient Load-Unload]' - load_msg2 = load_msg + "(Model 2)" - variant_load_msg = 'primary' if not is_public else 'secondary' - with gr.Row(): - n_gpus_list = [str(x) for x in list(range(-1, n_gpus))] - with gr.Column(): - with gr.Row(): - with gr.Column(scale=20, visible=not kwargs['model_lock']): - load_model_button = gr.Button(load_msg, variant=variant_load_msg, scale=0, - size='sm', interactive=not is_public) - model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Base Model", - value=kwargs['base_model']) - lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA", - value=kwargs['lora_weights'], visible=kwargs['show_lora']) - server_choice = gr.Dropdown(server_options_state.value[0], label="Choose Server", - value=kwargs['inference_server'], visible=not is_public) - max_seq_len = gr.Number(value=kwargs['max_seq_len'] or 2048, - minimum=128, - maximum=2 ** 18, - info="If standard LLaMa-2, choose up to 4096", - label="max_seq_len") - rope_scaling = gr.Textbox(value=str(kwargs['rope_scaling'] or {}), - label="rope_scaling") - row_llama = gr.Row(visible=kwargs['show_llama'] and kwargs['base_model'] == 'llama') - with row_llama: - model_path_llama = gr.Textbox(value=kwargs['llamacpp_dict']['model_path_llama'], - lines=4, - label="Choose LLaMa.cpp Model Path/URL (for Base Model: llama)", - visible=kwargs['show_llama']) - n_gpu_layers = gr.Number(value=kwargs['llamacpp_dict']['n_gpu_layers'], - minimum=0, maximum=100, - label="LLaMa.cpp Num. GPU Layers Offloaded", - visible=kwargs['show_llama']) - n_batch = gr.Number(value=kwargs['llamacpp_dict']['n_batch'], - minimum=0, maximum=2048, - label="LLaMa.cpp Batch Size", - visible=kwargs['show_llama']) - n_gqa = gr.Number(value=kwargs['llamacpp_dict']['n_gqa'], - minimum=0, maximum=32, - label="LLaMa.cpp Num. Group Query Attention (8 for 70B LLaMa2)", - visible=kwargs['show_llama']) - llamacpp_dict_more = gr.Textbox(value="{}", - lines=4, - label="Dict for other LLaMa.cpp/GPT4All options", - visible=kwargs['show_llama']) - row_gpt4all = gr.Row( - visible=kwargs['show_gpt4all'] and kwargs['base_model'] in ['gptj', - 'gpt4all_llama']) - with row_gpt4all: - model_name_gptj = gr.Textbox(value=kwargs['llamacpp_dict']['model_name_gptj'], - label="Choose GPT4All GPTJ Model Path/URL (for Base Model: gptj)", - visible=kwargs['show_gpt4all']) - model_name_gpt4all_llama = gr.Textbox( - value=kwargs['llamacpp_dict']['model_name_gpt4all_llama'], - label="Choose GPT4All LLaMa Model Path/URL (for Base Model: gpt4all_llama)", - visible=kwargs['show_gpt4all']) - with gr.Column(scale=1, visible=not kwargs['model_lock']): - model_load8bit_checkbox = gr.components.Checkbox( - label="Load 8-bit [requires support]", - value=kwargs['load_8bit'], interactive=not is_public) - model_load4bit_checkbox = gr.components.Checkbox( - label="Load 4-bit [requires support]", - value=kwargs['load_4bit'], interactive=not is_public) - model_low_bit_mode = gr.Slider(value=kwargs['low_bit_mode'], - minimum=0, maximum=4, step=1, - label="low_bit_mode") - model_load_gptq = gr.Textbox(label="gptq", value=kwargs['load_gptq'], - interactive=not is_public) - model_load_exllama_checkbox = gr.components.Checkbox( - label="Load load_exllama [requires support]", - value=kwargs['load_exllama'], interactive=not is_public) - model_safetensors_checkbox = gr.components.Checkbox( - label="Safetensors [requires support]", - value=kwargs['use_safetensors'], interactive=not is_public) - model_revision = gr.Textbox(label="revision", value=kwargs['revision'], - interactive=not is_public) - model_use_gpu_id_checkbox = gr.components.Checkbox( - label="Choose Devices [If not Checked, use all GPUs]", - value=kwargs['use_gpu_id'], interactive=not is_public, - visible=n_gpus != 0) - model_gpu = gr.Dropdown(n_gpus_list, - label="GPU ID [-1 = all GPUs, if Choose is enabled]", - value=kwargs['gpu_id'], interactive=not is_public, - visible=n_gpus != 0) - model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'], - interactive=False) - lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'], - visible=kwargs['show_lora'], interactive=False) - server_used = gr.Textbox(label="Current Server", - value=kwargs['inference_server'], - visible=bool(kwargs['inference_server']) and not is_public, - interactive=False) - prompt_dict = gr.Textbox(label="Prompt (or Custom)", - value=pprint.pformat(kwargs['prompt_dict'], indent=4), - interactive=not is_public, lines=4) - col_model2 = gr.Column(visible=False) - with col_model2: - with gr.Row(): - with gr.Column(scale=20, visible=not kwargs['model_lock']): - load_model_button2 = gr.Button(load_msg2, variant=variant_load_msg, scale=0, - size='sm', interactive=not is_public) - model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2", - value=no_model_str) - lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2", - value=no_lora_str, - visible=kwargs['show_lora']) - server_choice2 = gr.Dropdown(server_options_state.value[0], label="Choose Server 2", - value=no_server_str, - visible=not is_public) - max_seq_len2 = gr.Number(value=kwargs['max_seq_len'] or 2048, - minimum=128, - maximum=2 ** 18, - info="If standard LLaMa-2, choose up to 4096", - label="max_seq_len Model 2") - rope_scaling2 = gr.Textbox(value=str(kwargs['rope_scaling'] or {}), - label="rope_scaling Model 2") - - row_llama2 = gr.Row( - visible=kwargs['show_llama'] and kwargs['base_model'] == 'llama') - with row_llama2: - model_path_llama2 = gr.Textbox( - value=kwargs['llamacpp_dict']['model_path_llama'], - label="Choose LLaMa.cpp Model 2 Path/URL (for Base Model: llama)", - lines=4, - visible=kwargs['show_llama']) - n_gpu_layers2 = gr.Number(value=kwargs['llamacpp_dict']['n_gpu_layers'], - minimum=0, maximum=100, - label="LLaMa.cpp Num. GPU 2 Layers Offloaded", - visible=kwargs['show_llama']) - n_batch2 = gr.Number(value=kwargs['llamacpp_dict']['n_batch'], - minimum=0, maximum=2048, - label="LLaMa.cpp Model 2 Batch Size", - visible=kwargs['show_llama']) - n_gqa2 = gr.Number(value=kwargs['llamacpp_dict']['n_gqa'], - minimum=0, maximum=32, - label="LLaMa.cpp Model 2 Num. Group Query Attention (8 for 70B LLaMa2)", - visible=kwargs['show_llama']) - llamacpp_dict_more2 = gr.Textbox(value="{}", - lines=4, - label="Model 2 Dict for other LLaMa.cpp/GPT4All options", - visible=kwargs['show_llama']) - row_gpt4all2 = gr.Row( - visible=kwargs['show_gpt4all'] and kwargs['base_model'] in ['gptj', - 'gpt4all_llama']) - with row_gpt4all2: - model_name_gptj2 = gr.Textbox(value=kwargs['llamacpp_dict']['model_name_gptj'], - label="Choose GPT4All GPTJ Model 2 Path/URL (for Base Model: gptj)", - visible=kwargs['show_gpt4all']) - model_name_gpt4all_llama2 = gr.Textbox( - value=kwargs['llamacpp_dict']['model_name_gpt4all_llama'], - label="Choose GPT4All LLaMa Model 2 Path/URL (for Base Model: gpt4all_llama)", - visible=kwargs['show_gpt4all']) - - with gr.Column(scale=1, visible=not kwargs['model_lock']): - model_load8bit_checkbox2 = gr.components.Checkbox( - label="Load 8-bit (Model 2) [requires support]", - value=kwargs['load_8bit'], interactive=not is_public) - model_load4bit_checkbox2 = gr.components.Checkbox( - label="Load 4-bit (Model 2) [requires support]", - value=kwargs['load_4bit'], interactive=not is_public) - model_low_bit_mode2 = gr.Slider(value=kwargs['low_bit_mode'], - # ok that same as Model 1 - minimum=0, maximum=4, step=1, - label="low_bit_mode (Model 2)") - model_load_gptq2 = gr.Textbox(label="gptq (Model 2)", value='', - interactive=not is_public) - model_load_exllama_checkbox2 = gr.components.Checkbox( - label="Load load_exllama (Model 2) [requires support]", - value=False, interactive=not is_public) - model_safetensors_checkbox2 = gr.components.Checkbox( - label="Safetensors (Model 2) [requires support]", - value=False, interactive=not is_public) - model_revision2 = gr.Textbox(label="revision (Model 2)", value='', - interactive=not is_public) - model_use_gpu_id_checkbox2 = gr.components.Checkbox( - label="Choose Devices (Model 2) [If not Checked, use all GPUs]", - value=kwargs[ - 'use_gpu_id'], interactive=not is_public) - model_gpu2 = gr.Dropdown(n_gpus_list, - label="GPU ID (Model 2) [-1 = all GPUs, if choose is enabled]", - value=kwargs['gpu_id'], interactive=not is_public) - # no model/lora loaded ever in model2 by default - model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str, - interactive=False) - lora_used2 = gr.Textbox(label="Current LORA (Model 2)", value=no_lora_str, - visible=kwargs['show_lora'], interactive=False) - server_used2 = gr.Textbox(label="Current Server (Model 2)", value=no_server_str, - interactive=False, - visible=not is_public) - prompt_dict2 = gr.Textbox(label="Prompt (or Custom) (Model 2)", - value=pprint.pformat(kwargs['prompt_dict'], indent=4), - interactive=not is_public, lines=4) - compare_checkbox = gr.components.Checkbox(label="Compare Two Models", - value=kwargs['model_lock'], - visible=not is_public and not kwargs['model_lock']) - with gr.Row(visible=not kwargs['model_lock']): - with gr.Column(scale=50): - new_model = gr.Textbox(label="New Model name/path/URL", interactive=not is_public) - with gr.Column(scale=50): - new_lora = gr.Textbox(label="New LORA name/path/URL", visible=kwargs['show_lora'], - interactive=not is_public) - with gr.Column(scale=50): - new_server = gr.Textbox(label="New Server url:port", interactive=not is_public) - with gr.Row(): - add_model_lora_server_button = gr.Button("Add new Model, Lora, Server url:port", scale=0, - variant=variant_load_msg, - size='sm', interactive=not is_public) - system_tab = gr.TabItem("System") \ - if kwargs['visible_system_tab'] else gr.Row(visible=False) - with system_tab: - with gr.Row(): - with gr.Column(scale=1): - side_bar_text = gr.Textbox('on' if kwargs['visible_side_bar'] else 'off', - visible=False, interactive=False) - doc_count_text = gr.Textbox('on' if kwargs['visible_doc_track'] else 'off', - visible=False, interactive=False) - submit_buttons_text = gr.Textbox('on' if kwargs['visible_submit_buttons'] else 'off', - visible=False, interactive=False) - visible_models_text = gr.Textbox('on' if kwargs['visible_visible_models'] else 'off', - visible=False, interactive=False) - - side_bar_btn = gr.Button("Toggle SideBar", variant="secondary", size="sm") - doc_count_btn = gr.Button("Toggle SideBar Document Count/Show Newest", variant="secondary", - size="sm") - submit_buttons_btn = gr.Button("Toggle Submit Buttons", variant="secondary", size="sm") - visible_model_btn = gr.Button("Toggle Visible Models", variant="secondary", size="sm") - col_tabs_scale = gr.Slider(minimum=1, maximum=20, value=10, step=1, label='Window Size') - text_outputs_height = gr.Slider(minimum=100, maximum=2000, value=kwargs['height'] or 400, - step=50, label='Chat Height') - dark_mode_btn = gr.Button("Dark Mode", variant="secondary", size="sm") - with gr.Column(scale=4): - pass - system_visible0 = not is_public and not admin_pass - admin_row = gr.Row() - with admin_row: - with gr.Column(scale=1): - admin_pass_textbox = gr.Textbox(label="Admin Password", - type='password', - visible=not system_visible0) - with gr.Column(scale=4): - pass - system_row = gr.Row(visible=system_visible0) - with system_row: - with gr.Column(): - with gr.Row(): - system_btn = gr.Button(value='Get System Info', size='sm') - system_text = gr.Textbox(label='System Info', interactive=False, show_copy_button=True) - with gr.Row(): - system_input = gr.Textbox(label='System Info Dict Password', interactive=True, - visible=not is_public) - system_btn2 = gr.Button(value='Get System Info Dict', visible=not is_public, size='sm') - system_text2 = gr.Textbox(label='System Info Dict', interactive=False, - visible=not is_public, show_copy_button=True) - with gr.Row(): - system_btn3 = gr.Button(value='Get Hash', visible=not is_public, size='sm') - system_text3 = gr.Textbox(label='Hash', interactive=False, - visible=not is_public, show_copy_button=True) - system_btn4 = gr.Button(value='Get Model Names', visible=not is_public, size='sm') - system_text4 = gr.Textbox(label='Model Names', interactive=False, - visible=not is_public, show_copy_button=True) - - with gr.Row(): - zip_btn = gr.Button("Zip", size='sm') - zip_text = gr.Textbox(label="Zip file name", interactive=False) - file_output = gr.File(interactive=False, label="Zip file to Download") - with gr.Row(): - s3up_btn = gr.Button("S3UP", size='sm') - s3up_text = gr.Textbox(label='S3UP result', interactive=False) - - tos_tab = gr.TabItem("Terms of Service") \ - if kwargs['visible_tos_tab'] else gr.Row(visible=False) - with tos_tab: - description = "" - description += """
DISCLAIMERS:
- The model was trained on The Pile and other data, which may contain objectionable content. Use at own risk.
"""
- if kwargs['load_8bit']:
- description += """ - Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version. """ - description += """
- Conversations may be used to improve h2oGPT. Do not share sensitive information. """ - if 'h2ogpt-research' in kwargs['base_model']: - description += """
- Research demonstration only, not used for commercial purposes. """ - description += """
- By using h2oGPT, you accept our Terms of Service
' + fix_text_for_gradio(res_dict['response']) - if stream_output1: - # yield as it goes, else need to wait since predict only returns first yield - yield ret - finally: - clear_torch_cache() - clear_embeddings(user_kwargs['langchain_mode'], my_db_state1) - save_generate_output(**save_dict) - if not stream_output1: - # return back last ret - yield ret - - kwargs_evaluate_nochat = kwargs_evaluate.copy() - # nominally never want sources appended for API calls, which is what nochat used for primarily - kwargs_evaluate_nochat.update(dict(append_sources_to_answer=False)) - fun = partial(evaluate_nochat, - default_kwargs1=default_kwargs, - str_api=False, - **kwargs_evaluate_nochat) - fun_with_dict_str = partial(evaluate_nochat, - default_kwargs1=default_kwargs, - str_api=True, - **kwargs_evaluate_nochat - ) - - fun_with_dict_str_plain = partial(evaluate_nochat, - default_kwargs1=default_kwargs, - str_api=True, - plain_api=True, - **kwargs_evaluate_nochat - ) - - dark_mode_btn.click( - None, - None, - None, - _js=wrap_js_to_lambda(0, get_dark_js()), - api_name="dark" if allow_api else None, - queue=False, - ) - - # Handle uploads from API - upload_api_btn = gr.UploadButton("Upload File Results", visible=False) - file_upload_api = gr.File(visible=False) - file_upload_text = gr.Textbox(visible=False) - - def upload_file(files): - if isinstance(files, list): - file_paths = [file.name for file in files] - else: - file_paths = files.name - return file_paths, file_paths - - upload_api_btn.upload(fn=upload_file, - inputs=upload_api_btn, - outputs=[file_upload_api, file_upload_text], - api_name='upload_api' if allow_upload_api else None) - - def visible_toggle(x): - x = 'off' if x == 'on' else 'on' - return x, gr.Column.update(visible=True if x == 'on' else False) - - side_bar_btn.click(fn=visible_toggle, - inputs=side_bar_text, - outputs=[side_bar_text, side_bar], - queue=False) - - doc_count_btn.click(fn=visible_toggle, - inputs=doc_count_text, - outputs=[doc_count_text, row_doc_track], - queue=False) - - submit_buttons_btn.click(fn=visible_toggle, - inputs=submit_buttons_text, - outputs=[submit_buttons_text, submit_buttons], - queue=False) - - visible_model_btn.click(fn=visible_toggle, - inputs=visible_models_text, - outputs=[visible_models_text, visible_models], - queue=False) - - # examples after submit or any other buttons for chat or no chat - if kwargs['examples'] is not None and kwargs['show_examples']: - gr.Examples(examples=kwargs['examples'], inputs=inputs_list) - - # Score - def score_last_response(*args, nochat=False, num_model_lock=0): - try: - if num_model_lock > 0: - # then lock way - args_list = list(args).copy() - outputs = args_list[-num_model_lock:] - score_texts1 = [] - for output in outputs: - # same input, put into form good for _score_last_response() - args_list[-1] = output - score_texts1.append( - _score_last_response(*tuple(args_list), nochat=nochat, - num_model_lock=num_model_lock, prefix='')) - if len(score_texts1) > 1: - return "Response Scores: %s" % ' '.join(score_texts1) - else: - return "Response Scores: %s" % score_texts1[0] - else: - return _score_last_response(*args, nochat=nochat, num_model_lock=num_model_lock) - finally: - clear_torch_cache() - - def _score_last_response(*args, nochat=False, num_model_lock=0, prefix='Response Score: '): - """ Similar to user() """ - args_list = list(args) - smodel = score_model_state0['model'] - stokenizer = score_model_state0['tokenizer'] - sdevice = score_model_state0['device'] - - if memory_restriction_level > 0: - max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256 - elif hasattr(stokenizer, 'model_max_length'): - max_length_tokenize = stokenizer.model_max_length - else: - # limit to 1024, not worth OOMing on reward score - max_length_tokenize = 2048 - 1024 - cutoff_len = max_length_tokenize * 4 # restrict deberta related to max for LLM - - if not nochat: - history = args_list[-1] - if history is None: - history = [] - if smodel is not None and \ - stokenizer is not None and \ - sdevice is not None and \ - history is not None and len(history) > 0 and \ - history[-1] is not None and \ - len(history[-1]) >= 2: - os.environ['TOKENIZERS_PARALLELISM'] = 'false' - - question = history[-1][0] - - answer = history[-1][1] - else: - return '%sNA' % prefix - else: - answer = args_list[-1] - instruction_nochat_arg_id = eval_func_param_names.index('instruction_nochat') - question = args_list[instruction_nochat_arg_id] - - if question is None: - return '%sBad Question' % prefix - if answer is None: - return '%sBad Answer' % prefix - try: - score = score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len) - finally: - clear_torch_cache() - if isinstance(score, str): - return '%sNA' % prefix - return '{}{:.1%}'.format(prefix, score) - - def noop_score_last_response(*args, **kwargs): - return "Response Score: Disabled" - - if kwargs['score_model']: - score_fun = score_last_response - else: - score_fun = noop_score_last_response - - score_args = dict(fn=score_fun, - inputs=inputs_list + [text_output], - outputs=[score_text], - ) - score_args2 = dict(fn=partial(score_fun), - inputs=inputs_list2 + [text_output2], - outputs=[score_text2], - ) - score_fun_func = functools.partial(score_fun, num_model_lock=len(text_outputs)) - all_score_args = dict(fn=score_fun_func, - inputs=inputs_list + text_outputs, - outputs=score_text, - ) - - score_args_nochat = dict(fn=partial(score_fun, nochat=True), - inputs=inputs_list + [text_output_nochat], - outputs=[score_text_nochat], - ) - - def update_history(*args, undo=False, retry=False, sanitize_user_prompt=False): - """ - User that fills history for bot - :param args: - :param undo: - :param retry: - :param sanitize_user_prompt: - :return: - """ - args_list = list(args) - user_message = args_list[eval_func_param_names.index('instruction')] # chat only - input1 = args_list[eval_func_param_names.index('iinput')] # chat only - prompt_type1 = args_list[eval_func_param_names.index('prompt_type')] - langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')] - langchain_action1 = args_list[eval_func_param_names.index('langchain_action')] - langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')] - document_subset1 = args_list[eval_func_param_names.index('document_subset')] - document_choice1 = args_list[eval_func_param_names.index('document_choice')] - if not prompt_type1: - # shouldn't have to specify if CLI launched model - prompt_type1 = kwargs['prompt_type'] - # apply back - args_list[eval_func_param_names.index('prompt_type')] = prompt_type1 - if input1 and not user_message.endswith(':'): - user_message1 = user_message + ":" + input1 - elif input1: - user_message1 = user_message + input1 - else: - user_message1 = user_message - if sanitize_user_prompt: - pass - # requirements.txt has comment that need to re-enable the below 2 lines - # from better_profanity import profanity - # user_message1 = profanity.censor(user_message1) - - history = args_list[-1] - if history is None: - # bad history - history = [] - history = history.copy() - - if undo: - if len(history) > 0: - history.pop() - return history - if retry: - if history: - history[-1][1] = None - return history - if user_message1 in ['', None, '\n']: - if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1): - # reject non-retry submit/enter - return history - user_message1 = fix_text_for_gradio(user_message1) - return history + [[user_message1, None]] - - def user(*args, undo=False, retry=False, sanitize_user_prompt=False): - return update_history(*args, undo=undo, retry=retry, sanitize_user_prompt=sanitize_user_prompt) - - def all_user(*args, undo=False, retry=False, sanitize_user_prompt=False, num_model_lock=0, all_models=None): - args_list = list(args) - - visible_models1 = args_list[eval_func_param_names.index('visible_models')] - assert isinstance(all_models, list) - visible_list = get_model_lock_visible_list(visible_models1, all_models) - - history_list = args_list[-num_model_lock:] - assert len(all_models) == len(history_list) - assert len(history_list) > 0, "Bad history list: %s" % history_list - for hi, history in enumerate(history_list): - if not visible_list[hi]: - continue - if num_model_lock > 0: - hargs = args_list[:-num_model_lock].copy() - else: - hargs = args_list.copy() - hargs += [history] - history_list[hi] = update_history(*hargs, undo=undo, retry=retry, - sanitize_user_prompt=sanitize_user_prompt) - if len(history_list) > 1: - return tuple(history_list) - else: - return history_list[0] - - def get_model_max_length(model_state1): - if model_state1 and not isinstance(model_state1["tokenizer"], str): - tokenizer = model_state1["tokenizer"] - elif model_state0 and not isinstance(model_state0["tokenizer"], str): - tokenizer = model_state0["tokenizer"] - else: - tokenizer = None - if tokenizer is not None: - return tokenizer.model_max_length - else: - return 2000 - - def prep_bot(*args, retry=False, which_model=0): - """ - - :param args: - :param retry: - :param which_model: identifies which model if doing model_lock - API only called for which_model=0, default for inputs_list, but rest should ignore inputs_list - :return: last element is True if should run bot, False if should just yield history - """ - isize = len(input_args_list) + 1 # states + chat history - # don't deepcopy, can contain model itself - args_list = list(args).copy() - model_state1 = args_list[-isize] - my_db_state1 = args_list[-isize + 1] - selection_docs_state1 = args_list[-isize + 2] - requests_state1 = args_list[-isize + 3] - history = args_list[-1] - if not history: - history = [] - prompt_type1 = args_list[eval_func_param_names.index('prompt_type')] - prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')] - langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')] - langchain_action1 = args_list[eval_func_param_names.index('langchain_action')] - document_subset1 = args_list[eval_func_param_names.index('document_subset')] - h2ogpt_key1 = args_list[eval_func_param_names.index('h2ogpt_key')] - chat_conversation1 = args_list[eval_func_param_names.index('chat_conversation')] - valid_key = is_valid_key(kwargs['enforce_h2ogpt_api_key'], kwargs['h2ogpt_api_keys'], h2ogpt_key1, - requests_state1=requests_state1) - - dummy_return = history, None, langchain_mode1, my_db_state1, requests_state1, valid_key, h2ogpt_key1 - - if model_state1['model'] is None or model_state1['model'] == no_model_str: - return dummy_return - - args_list = args_list[:-isize] # only keep rest needed for evaluate() - if not history: - print("No history", flush=True) - return dummy_return - instruction1 = history[-1][0] - if retry and history: - # if retry, pop history and move onto bot stuff - instruction1 = history[-1][0] - history[-1][1] = None - elif not instruction1: - if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1): - # if not retrying, then reject empty query - return dummy_return - elif len(history) > 0 and history[-1][1] not in [None, '']: - # reject submit button if already filled and not retrying - # None when not filling with '' to keep client happy - return dummy_return - - evaluate_local = evaluate if valid_key else evaluate_fake - - # shouldn't have to specify in API prompt_type if CLI launched model, so prefer global CLI one if have it - prompt_type1, prompt_dict1 = update_prompt(prompt_type1, prompt_dict1, model_state1, - which_model=which_model) - # apply back to args_list for evaluate() - args_list[eval_func_param_names.index('prompt_type')] = prompt_type1 - args_list[eval_func_param_names.index('prompt_dict')] = prompt_dict1 - context1 = args_list[eval_func_param_names.index('context')] - - chat_conversation1 = merge_chat_conversation_history(chat_conversation1, history) - args_list[eval_func_param_names.index('chat_conversation')] = chat_conversation1 - - if 'visible_models' in model_state1 and model_state1['visible_models'] is not None: - assert isinstance(model_state1['visible_models'], int) - args_list[eval_func_param_names.index('visible_models')] = model_state1['visible_models'] - if 'h2ogpt_key' in model_state1 and model_state1['h2ogpt_key'] is not None: - # i.e. may be '' and used to override overall local key - assert isinstance(model_state1['h2ogpt_key'], str) - args_list[eval_func_param_names.index('h2ogpt_key')] = model_state1['h2ogpt_key'] - - args_list[0] = instruction1 # override original instruction with history from user - args_list[2] = context1 - - fun1 = partial(evaluate_local, - model_state1, - my_db_state1, - selection_docs_state1, - requests_state1, - *tuple(args_list), - **kwargs_evaluate) - - return history, fun1, langchain_mode1, my_db_state1, requests_state1, valid_key, h2ogpt_key1 - - def gen1_fake(fun1, history): - error = '' - extra = '' - save_dict = dict() - yield history, error, extra, save_dict - return - - def get_response(fun1, history): - """ - bot that consumes history for user input - instruction (from input_list) itself is not consumed by bot - :return: - """ - error = '' - extra = '' - save_dict = dict() - if not fun1: - yield history, error, extra, save_dict - return - try: - for output_fun in fun1(): - output = output_fun['response'] - extra = output_fun['sources'] # FIXME: can show sources in separate text box etc. - save_dict = output_fun.get('save_dict', {}) - # ensure good visually, else markdown ignores multiple \n - bot_message = fix_text_for_gradio(output) - history[-1][1] = bot_message - yield history, error, extra, save_dict - except StopIteration: - yield history, error, extra, save_dict - except RuntimeError as e: - if "generator raised StopIteration" in str(e): - # assume last entry was bad, undo - history.pop() - yield history, error, extra, save_dict - else: - if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None: - history[-1][1] = '' - yield history, str(e), extra, save_dict - raise - except Exception as e: - # put error into user input - ex = "Exception: %s" % str(e) - if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None: - history[-1][1] = '' - yield history, ex, extra, save_dict - raise - finally: - # clear_torch_cache() - # don't clear torch cache here, too early and stalls generation if used for all_bot() - pass - return - - def clear_embeddings(langchain_mode1, db1s): - # clear any use of embedding that sits on GPU, else keeps accumulating GPU usage even if clear torch cache - if db_type in ['chroma', 'chroma_old'] and langchain_mode1 not in ['LLM', 'Disabled', None, '']: - from gpt_langchain import clear_embedding, length_db1 - db = dbs.get('langchain_mode1') - if db is not None and not isinstance(db, str): - clear_embedding(db) - if db1s is not None and langchain_mode1 in db1s: - db1 = db1s[langchain_mode1] - if len(db1) == length_db1(): - clear_embedding(db1[0]) - - def bot(*args, retry=False): - history, fun1, langchain_mode1, db1, requests_state1, valid_key, h2ogpt_key1 = prep_bot(*args, retry=retry) - save_dict = dict() - error = '' - extra = '' - try: - for res in get_response(fun1, history): - history, error, extra, save_dict = res - # pass back to gradio only these, rest are consumed in this function - yield history, error - finally: - clear_torch_cache() - clear_embeddings(langchain_mode1, db1) - if 'extra_dict' not in save_dict: - save_dict['extra_dict'] = {} - save_dict['valid_key'] = valid_key - save_dict['h2ogpt_key'] = h2ogpt_key1 - if requests_state1: - save_dict['extra_dict'].update(requests_state1) - else: - save_dict['extra_dict'].update(dict(username='NO_REQUEST')) - save_dict['error'] = error - save_dict['extra'] = extra - save_dict['which_api'] = 'bot' - save_generate_output(**save_dict) - - def all_bot(*args, retry=False, model_states1=None, all_models=None): - args_list = list(args).copy() - chatbots = args_list[-len(model_states1):] - args_list0 = args_list[:-len(model_states1)] # same for all models - exceptions = [] - stream_output1 = args_list[eval_func_param_names.index('stream_output')] - max_time1 = args_list[eval_func_param_names.index('max_time')] - langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')] - - visible_models1 = args_list[eval_func_param_names.index('visible_models')] - assert isinstance(all_models, list) - assert len(all_models) == len(model_states1) - visible_list = get_model_lock_visible_list(visible_models1, all_models) - - isize = len(input_args_list) + 1 # states + chat history - db1s = None - requests_state1 = None - valid_key = False - h2ogpt_key1 = '' - extras = [] - exceptions = [] - save_dicts = [] - try: - gen_list = [] - for chatboti, (chatbot1, model_state1) in enumerate(zip(chatbots, model_states1)): - args_list1 = args_list0.copy() - args_list1.insert(-isize + 2, - model_state1) # insert at -2 so is at -3, and after chatbot1 added, at -4 - # if at start, have None in response still, replace with '' so client etc. acts like normal - # assumes other parts of code treat '' and None as if no response yet from bot - # can't do this later in bot code as racy with threaded generators - if len(chatbot1) > 0 and len(chatbot1[-1]) == 2 and chatbot1[-1][1] is None: - chatbot1[-1][1] = '' - args_list1.append(chatbot1) - # so consistent with prep_bot() - # with model_state1 at -3, my_db_state1 at -2, and history(chatbot) at -1 - # langchain_mode1 and my_db_state1 and requests_state1 should be same for every bot - history, fun1, langchain_mode1, db1s, requests_state1, valid_key, h2ogpt_key1, = \ - prep_bot(*tuple(args_list1), retry=retry, - which_model=chatboti) - if visible_list[chatboti]: - gen1 = get_response(fun1, history) - if stream_output1: - gen1 = TimeoutIterator(gen1, timeout=0.01, sentinel=None, raise_on_exception=False) - # else timeout will truncate output for non-streaming case - else: - gen1 = gen1_fake(fun1, history) - gen_list.append(gen1) - - bots_old = chatbots.copy() - exceptions_old = [''] * len(bots_old) - extras_old = [''] * len(bots_old) - save_dicts_old = [{}] * len(bots_old) - tgen0 = time.time() - for res1 in itertools.zip_longest(*gen_list): - if time.time() - tgen0 > max_time1: - print("Took too long: %s" % max_time1, flush=True) - break - - bots = [x[0] if x is not None and not isinstance(x, BaseException) else y - for x, y in zip(res1, bots_old)] - bots_old = bots.copy() - - def larger_str(x, y): - return x if len(x) > len(y) else y - - exceptions = [x[1] if x is not None and not isinstance(x, BaseException) else larger_str(str(x), y) - for x, y in zip(res1, exceptions_old)] - exceptions_old = exceptions.copy() - - extras = [x[2] if x is not None and not isinstance(x, BaseException) else y - for x, y in zip(res1, extras_old)] - extras_old = extras.copy() - - save_dicts = [x[3] if x is not None and not isinstance(x, BaseException) else y - for x, y in zip(res1, save_dicts_old)] - save_dicts_old = save_dicts.copy() - - def choose_exc(x): - # don't expose ports etc. to exceptions window - if is_public: - return "Endpoint unavailable or failed" - else: - return x - - exceptions_str = '\n'.join( - ['Model %s: %s' % (iix, choose_exc(x)) for iix, x in enumerate(exceptions) if - x not in [None, '', 'None']]) - # yield back to gradio only is bots + exceptions, rest are consumed locally - if len(bots) > 1: - yield tuple(bots + [exceptions_str]) - else: - yield bots[0], exceptions_str - if exceptions: - exceptions_reduced = [x for x in exceptions if x not in ['', None, 'None']] - if exceptions_reduced: - print("Generate exceptions: %s" % exceptions_reduced, flush=True) - finally: - clear_torch_cache() - clear_embeddings(langchain_mode1, db1s) - for extra, error, save_dict, model_name in zip(extras, exceptions, save_dicts, all_models): - if 'extra_dict' not in save_dict: - save_dict['extra_dict'] = {} - if requests_state1: - save_dict['extra_dict'].update(requests_state1) - else: - save_dict['extra_dict'].update(dict(username='NO_REQUEST')) - save_dict['error'] = error - save_dict['extra'] = extra - save_dict['which_api'] = 'all_bot_%s' % model_name - save_dict['valid_key'] = valid_key - save_dict['h2ogpt_key'] = h2ogpt_key1 - save_generate_output(**save_dict) - - # NORMAL MODEL - user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']), - inputs=inputs_list + [text_output], - outputs=text_output, - ) - bot_args = dict(fn=bot, - inputs=inputs_list + [model_state, my_db_state, selection_docs_state, requests_state] + [ - text_output], - outputs=[text_output, chat_exception_text], - ) - retry_bot_args = dict(fn=functools.partial(bot, retry=True), - inputs=inputs_list + [model_state, my_db_state, selection_docs_state, requests_state] + [ - text_output], - outputs=[text_output, chat_exception_text], - ) - retry_user_args = dict(fn=functools.partial(user, retry=True), - inputs=inputs_list + [text_output], - outputs=text_output, - ) - undo_user_args = dict(fn=functools.partial(user, undo=True), - inputs=inputs_list + [text_output], - outputs=text_output, - ) - - # MODEL2 - user_args2 = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']), - inputs=inputs_list2 + [text_output2], - outputs=text_output2, - ) - bot_args2 = dict(fn=bot, - inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state, requests_state] + [ - text_output2], - outputs=[text_output2, chat_exception_text], - ) - retry_bot_args2 = dict(fn=functools.partial(bot, retry=True), - inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state, - requests_state] + [text_output2], - outputs=[text_output2, chat_exception_text], - ) - retry_user_args2 = dict(fn=functools.partial(user, retry=True), - inputs=inputs_list2 + [text_output2], - outputs=text_output2, - ) - undo_user_args2 = dict(fn=functools.partial(user, undo=True), - inputs=inputs_list2 + [text_output2], - outputs=text_output2, - ) - - # MODEL N - all_user_args = dict(fn=functools.partial(all_user, - sanitize_user_prompt=kwargs['sanitize_user_prompt'], - num_model_lock=len(text_outputs), - all_models=kwargs['all_models'] - ), - inputs=inputs_list + text_outputs, - outputs=text_outputs, - ) - all_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states, - all_models=kwargs['all_models']), - inputs=inputs_list + [my_db_state, selection_docs_state, requests_state] + - text_outputs, - outputs=text_outputs + [chat_exception_text], - ) - all_retry_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states, - all_models=kwargs['all_models'], - retry=True), - inputs=inputs_list + [my_db_state, selection_docs_state, requests_state] + - text_outputs, - outputs=text_outputs + [chat_exception_text], - ) - all_retry_user_args = dict(fn=functools.partial(all_user, retry=True, - sanitize_user_prompt=kwargs['sanitize_user_prompt'], - num_model_lock=len(text_outputs), - all_models=kwargs['all_models'] - ), - inputs=inputs_list + text_outputs, - outputs=text_outputs, - ) - all_undo_user_args = dict(fn=functools.partial(all_user, undo=True, - sanitize_user_prompt=kwargs['sanitize_user_prompt'], - num_model_lock=len(text_outputs), - all_models=kwargs['all_models'] - ), - inputs=inputs_list + text_outputs, - outputs=text_outputs, - ) - - def clear_instruct(): - return gr.Textbox.update(value='') - - def deselect_radio_chats(): - return gr.update(value=None) - - def clear_all(): - return gr.Textbox.update(value=''), gr.Textbox.update(value=''), gr.update(value=None), \ - gr.Textbox.update(value=''), gr.Textbox.update(value='') - - if kwargs['model_states']: - submits1 = submits2 = submits3 = [] - submits4 = [] - - triggers = [instruction, submit, retry_btn] - fun_source = [instruction.submit, submit.click, retry_btn.click] - fun_name = ['instruction', 'submit', 'retry'] - user_args = [all_user_args, all_user_args, all_retry_user_args] - bot_args = [all_bot_args, all_bot_args, all_retry_bot_args] - for userargs1, botarg1, funn1, funs1, trigger1, in zip(user_args, bot_args, fun_name, fun_source, triggers): - submit_event11 = funs1(fn=user_state_setup, - inputs=[my_db_state, requests_state, trigger1, trigger1], - outputs=[my_db_state, requests_state, trigger1], - queue=queue) - submit_event1a = submit_event11.then(**userargs1, queue=queue, - api_name='%s' % funn1 if allow_api else None) - # if hit enter on new instruction for submitting new query, no longer the saved chat - submit_event1b = submit_event1a.then(clear_all, inputs=None, - outputs=[instruction, iinput, radio_chats, score_text, - score_text2], - queue=queue) - submit_event1c = submit_event1b.then(**botarg1, - api_name='%s_bot' % funn1 if allow_api else None, - queue=queue) - submit_event1d = submit_event1c.then(**all_score_args, - api_name='%s_bot_score' % funn1 if allow_api else None, - queue=queue) - - submits1.extend([submit_event1a, submit_event1b, submit_event1c, submit_event1d]) - - # if undo, no longer the saved chat - submit_event4 = undo.click(fn=user_state_setup, - inputs=[my_db_state, requests_state, undo, undo], - outputs=[my_db_state, requests_state, undo], - queue=queue) \ - .then(**all_undo_user_args, api_name='undo' if allow_api else None) \ - .then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text, - score_text2], queue=queue) \ - .then(**all_score_args, api_name='undo_score' if allow_api else None) - submits4 = [submit_event4] - - else: - # in case 2nd model, consume instruction first, so can clear quickly - # bot doesn't consume instruction itself, just history from user, so why works - submit_event11 = instruction.submit(fn=user_state_setup, - inputs=[my_db_state, requests_state, instruction, instruction], - outputs=[my_db_state, requests_state, instruction], - queue=queue) - submit_event1a = submit_event11.then(**user_args, queue=queue, - api_name='instruction' if allow_api else None) - # if hit enter on new instruction for submitting new query, no longer the saved chat - submit_event1a2 = submit_event1a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue) - submit_event1b = submit_event1a2.then(**user_args2, api_name='instruction2' if allow_api else None) - submit_event1c = submit_event1b.then(clear_instruct, None, instruction) \ - .then(clear_instruct, None, iinput) - submit_event1d = submit_event1c.then(**bot_args, api_name='instruction_bot' if allow_api else None, - queue=queue) - submit_event1e = submit_event1d.then(**score_args, - api_name='instruction_bot_score' if allow_api else None, - queue=queue) - submit_event1f = submit_event1e.then(**bot_args2, api_name='instruction_bot2' if allow_api else None, - queue=queue) - submit_event1g = submit_event1f.then(**score_args2, - api_name='instruction_bot_score2' if allow_api else None, queue=queue) - - submits1 = [submit_event1a, submit_event1a2, submit_event1b, submit_event1c, submit_event1d, - submit_event1e, - submit_event1f, submit_event1g] - - submit_event21 = submit.click(fn=user_state_setup, - inputs=[my_db_state, requests_state, submit, submit], - outputs=[my_db_state, requests_state, submit], - queue=queue) - submit_event2a = submit_event21.then(**user_args, api_name='submit' if allow_api else None) - # if submit new query, no longer the saved chat - submit_event2a2 = submit_event2a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue) - submit_event2b = submit_event2a2.then(**user_args2, api_name='submit2' if allow_api else None) - submit_event2c = submit_event2b.then(clear_all, inputs=None, - outputs=[instruction, iinput, radio_chats, score_text, score_text2], - queue=queue) - submit_event2d = submit_event2c.then(**bot_args, api_name='submit_bot' if allow_api else None, queue=queue) - submit_event2e = submit_event2d.then(**score_args, - api_name='submit_bot_score' if allow_api else None, - queue=queue) - submit_event2f = submit_event2e.then(**bot_args2, api_name='submit_bot2' if allow_api else None, - queue=queue) - submit_event2g = submit_event2f.then(**score_args2, - api_name='submit_bot_score2' if allow_api else None, - queue=queue) - - submits2 = [submit_event2a, submit_event2a2, submit_event2b, submit_event2c, submit_event2d, - submit_event2e, - submit_event2f, submit_event2g] - - submit_event31 = retry_btn.click(fn=user_state_setup, - inputs=[my_db_state, requests_state, retry_btn, retry_btn], - outputs=[my_db_state, requests_state, retry_btn], - queue=queue) - submit_event3a = submit_event31.then(**user_args, api_name='retry' if allow_api else None) - # if retry, no longer the saved chat - submit_event3a2 = submit_event3a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue) - submit_event3b = submit_event3a2.then(**user_args2, api_name='retry2' if allow_api else None) - submit_event3c = submit_event3b.then(clear_instruct, None, instruction) \ - .then(clear_instruct, None, iinput) - submit_event3d = submit_event3c.then(**retry_bot_args, api_name='retry_bot' if allow_api else None, - queue=queue) - submit_event3e = submit_event3d.then(**score_args, - api_name='retry_bot_score' if allow_api else None, - queue=queue) - submit_event3f = submit_event3e.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None, - queue=queue) - submit_event3g = submit_event3f.then(**score_args2, - api_name='retry_bot_score2' if allow_api else None, - queue=queue) - - submits3 = [submit_event3a, submit_event3a2, submit_event3b, submit_event3c, submit_event3d, - submit_event3e, - submit_event3f, submit_event3g] - - # if undo, no longer the saved chat - submit_event4 = undo.click(fn=user_state_setup, - inputs=[my_db_state, requests_state, undo, undo], - outputs=[my_db_state, requests_state, undo], - queue=queue) \ - .then(**undo_user_args, api_name='undo' if allow_api else None) \ - .then(**undo_user_args2, api_name='undo2' if allow_api else None) \ - .then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text, - score_text2], queue=queue) \ - .then(**score_args, api_name='undo_score' if allow_api else None) \ - .then(**score_args2, api_name='undo_score2' if allow_api else None) - submits4 = [submit_event4] - - # MANAGE CHATS - def dedup(short_chat, short_chats): - if short_chat not in short_chats: - return short_chat - for i in range(1, 1000): - short_chat_try = short_chat + "_" + str(i) - if short_chat_try not in short_chats: - return short_chat_try - # fallback and hope for best - short_chat = short_chat + "_" + str(random.random()) - return short_chat - - def get_short_chat(x, short_chats, short_len=20, words=4): - if x and len(x[0]) == 2 and x[0][0] is not None: - short_chat = ' '.join(x[0][0][:short_len].split(' ')[:words]).strip() - if not short_chat: - # e.g.summarization, try using answer - short_chat = ' '.join(x[0][1][:short_len].split(' ')[:words]).strip() - if not short_chat: - short_chat = 'Unk' - short_chat = dedup(short_chat, short_chats) - else: - short_chat = None - return short_chat - - def is_chat_same(x, y): - #
etc. added in chat, try to remove some of that to help avoid dup entries when hit new conversation - is_same = True - # length of conversation has to be same - if len(x) != len(y): - return False - if len(x) != len(y): - return False - for stepx, stepy in zip(x, y): - if len(stepx) != len(stepy): - # something off with a conversation - return False - for stepxx, stepyy in zip(stepx, stepy): - if len(stepxx) != len(stepyy): - # something off with a conversation - return False - if len(stepxx) != 2: - # something off - return False - if len(stepyy) != 2: - # something off - return False - questionx = stepxx[0].replace('
', '').replace('
', '') if stepxx[0] is not None else None - answerx = stepxx[1].replace('', '').replace('
', '') if stepxx[1] is not None else None - - questiony = stepyy[0].replace('', '').replace('
', '') if stepyy[0] is not None else None - answery = stepyy[1].replace('', '').replace('
', '') if stepyy[1] is not None else None - - if questionx != questiony or answerx != answery: - return False - return is_same - - def save_chat(*args, chat_is_list=False, auth_filename=None, auth_freeze=None): - args_list = list(args) - db1s = args_list[0] - requests_state1 = args_list[1] - args_list = args_list[2:] - if not chat_is_list: - # list of chatbot histories, - # can't pass in list with list of chatbot histories and state due to gradio limits - chat_list = args_list[:-1] - else: - assert len(args_list) == 2 - chat_list = args_list[0] - # if old chat file with single chatbot, get into shape - if isinstance(chat_list, list) and len(chat_list) > 0 and isinstance(chat_list[0], list) and len( - chat_list[0]) == 2 and isinstance(chat_list[0][0], str) and isinstance(chat_list[0][1], str): - chat_list = [chat_list] - # remove None histories - chat_list_not_none = [x for x in chat_list if x and len(x) > 0 and len(x[0]) == 2 and x[0][1] is not None] - chat_list_none = [x for x in chat_list if x not in chat_list_not_none] - if len(chat_list_none) > 0 and len(chat_list_not_none) == 0: - raise ValueError("Invalid chat file") - # dict with keys of short chat names, values of list of list of chatbot histories - chat_state1 = args_list[-1] - short_chats = list(chat_state1.keys()) - if len(chat_list_not_none) > 0: - # make short_chat key from only first history, based upon question that is same anyways - chat_first = chat_list_not_none[0] - short_chat = get_short_chat(chat_first, short_chats) - if short_chat: - old_chat_lists = list(chat_state1.values()) - already_exists = any([is_chat_same(chat_list, x) for x in old_chat_lists]) - if not already_exists: - chat_state1[short_chat] = chat_list.copy() - - # reverse so newest at top - choices = list(chat_state1.keys()).copy() - choices.reverse() - - # save saved chats and chatbots to auth file - text_output1 = chat_list[0] - text_output21 = chat_list[1] - text_outputs1 = chat_list[2:] - save_auth(requests_state1, auth_filename, auth_freeze, chat_state1=chat_state1, - text_output1=text_output1, text_output21=text_output21, text_outputs1=text_outputs1) - - return chat_state1, gr.update(choices=choices, value=None) - - def switch_chat(chat_key, chat_state1, num_model_lock=0): - chosen_chat = chat_state1[chat_key] - # deal with possible different size of chat list vs. current list - ret_chat = [None] * (2 + num_model_lock) - for chati in range(0, 2 + num_model_lock): - ret_chat[chati % len(ret_chat)] = chosen_chat[chati % len(chosen_chat)] - return tuple(ret_chat) - - def clear_texts(*args): - return tuple([gr.Textbox.update(value='')] * len(args)) - - def clear_scores(): - return gr.Textbox.update(value=res_value), \ - gr.Textbox.update(value='Response Score: NA'), \ - gr.Textbox.update(value='Response Score: NA') - - switch_chat_fun = functools.partial(switch_chat, num_model_lock=len(text_outputs)) - radio_chats.input(switch_chat_fun, - inputs=[radio_chats, chat_state], - outputs=[text_output, text_output2] + text_outputs) \ - .then(clear_scores, outputs=[score_text, score_text2, score_text_nochat]) - - def remove_chat(chat_key, chat_state1): - if isinstance(chat_key, str): - chat_state1.pop(chat_key, None) - return gr.update(choices=list(chat_state1.keys()), value=None), chat_state1 - - remove_chat_event = remove_chat_btn.click(remove_chat, - inputs=[radio_chats, chat_state], - outputs=[radio_chats, chat_state], - queue=False, api_name='remove_chat') - - def get_chats1(chat_state1): - base = 'chats' - base = makedirs(base, exist_ok=True, tmp_ok=True, use_base=True) - filename = os.path.join(base, 'chats_%s.json' % str(uuid.uuid4())) - with open(filename, "wt") as f: - f.write(json.dumps(chat_state1, indent=2)) - return filename - - export_chat_event = export_chats_btn.click(get_chats1, inputs=chat_state, outputs=chats_file, queue=False, - api_name='export_chats' if allow_api else None) - - def add_chats_from_file(db1s, requests_state1, file, chat_state1, radio_chats1, chat_exception_text1, - auth_filename=None, auth_freeze=None): - if not file: - return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1 - if isinstance(file, str): - files = [file] - else: - files = file - if not files: - return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1 - chat_exception_list = [] - for file1 in files: - try: - if hasattr(file1, 'name'): - file1 = file1.name - with open(file1, "rt") as f: - new_chats = json.loads(f.read()) - for chat1_k, chat1_v in new_chats.items(): - # ignore chat1_k, regenerate and de-dup to avoid loss - chat_state1, _ = save_chat(db1s, requests_state1, chat1_v, chat_state1, chat_is_list=True) - except BaseException as e: - t, v, tb = sys.exc_info() - ex = ''.join(traceback.format_exception(t, v, tb)) - ex_str = "File %s exception: %s" % (file1, str(e)) - print(ex_str, flush=True) - chat_exception_list.append(ex_str) - chat_exception_text1 = '\n'.join(chat_exception_list) - # save chat to auth file - save_auth(requests_state1, auth_filename, auth_freeze, chat_state1=chat_state1) - return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1 - - # note for update_user_db_func output is ignored for db - chatup_change_eventa = chatsup_output.change(user_state_setup, - inputs=[my_db_state, requests_state, langchain_mode], - outputs=[my_db_state, requests_state, langchain_mode], - show_progress='minimal') - add_chats_from_file_func = functools.partial(add_chats_from_file, - auth_filename=kwargs['auth_filename'], - auth_freeze=kwargs['auth_freeze'], - ) - chatup_change_event = chatup_change_eventa.then(add_chats_from_file_func, - inputs=[my_db_state, requests_state] + - [chatsup_output, chat_state, radio_chats, - chat_exception_text], - outputs=[chatsup_output, chat_state, radio_chats, - chat_exception_text], - queue=False, - api_name='add_to_chats' if allow_api else None) - - clear_chat_event = clear_chat_btn.click(fn=clear_texts, - inputs=[text_output, text_output2] + text_outputs, - outputs=[text_output, text_output2] + text_outputs, - queue=False, api_name='clear' if allow_api else None) \ - .then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False) \ - .then(clear_scores, outputs=[score_text, score_text2, score_text_nochat]) - - clear_eventa = save_chat_btn.click(user_state_setup, - inputs=[my_db_state, requests_state, langchain_mode], - outputs=[my_db_state, requests_state, langchain_mode], - show_progress='minimal') - save_chat_func = functools.partial(save_chat, - auth_filename=kwargs['auth_filename'], - auth_freeze=kwargs['auth_freeze'], - ) - clear_event = clear_eventa.then(save_chat_func, - inputs=[my_db_state, requests_state] + - [text_output, text_output2] + text_outputs + - [chat_state], - outputs=[chat_state, radio_chats], - api_name='save_chat' if allow_api else None) - if kwargs['score_model']: - clear_event2 = clear_event.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat]) - - # NOTE: clear of instruction/iinput for nochat has to come after score, - # because score for nochat consumes actual textbox, while chat consumes chat history filled by user() - no_chat_args = dict(fn=fun, - inputs=[model_state, my_db_state, selection_docs_state, requests_state] + inputs_list, - outputs=text_output_nochat, - queue=queue, - ) - submit_event_nochat = submit_nochat.click(**no_chat_args, api_name='submit_nochat' if allow_api else None) \ - .then(clear_torch_cache) \ - .then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None, queue=queue) \ - .then(clear_instruct, None, instruction_nochat) \ - .then(clear_instruct, None, iinput_nochat) \ - .then(clear_torch_cache) - # copy of above with text box submission - submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \ - .then(clear_torch_cache) \ - .then(**score_args_nochat, queue=queue) \ - .then(clear_instruct, None, instruction_nochat) \ - .then(clear_instruct, None, iinput_nochat) \ - .then(clear_torch_cache) - - submit_event_nochat_api = submit_nochat_api.click(fun_with_dict_str, - inputs=[model_state, my_db_state, selection_docs_state, - requests_state, - inputs_dict_str], - outputs=text_output_nochat_api, - queue=True, # required for generator - api_name='submit_nochat_api' if allow_api else None) - - submit_event_nochat_api_plain = submit_nochat_api_plain.click(fun_with_dict_str_plain, - inputs=inputs_dict_str, - outputs=text_output_nochat_api, - queue=False, - api_name='submit_nochat_plain_api' if allow_api else None) - - def load_model(model_name, lora_weights, server_name, model_state_old, prompt_type_old, - load_8bit, load_4bit, low_bit_mode, - load_gptq, load_exllama, use_safetensors, revision, - use_gpu_id, gpu_id, max_seq_len1, rope_scaling1, - model_path_llama1, model_name_gptj1, model_name_gpt4all_llama1, - n_gpu_layers1, n_batch1, n_gqa1, llamacpp_dict_more1, - system_prompt1): - try: - llamacpp_dict = ast.literal_eval(llamacpp_dict_more1) - except: - print("Failed to use user input for llamacpp_dict_more1 dict", flush=True) - llamacpp_dict = {} - llamacpp_dict.update(dict(model_path_llama=model_path_llama1, - model_name_gptj=model_name_gptj1, - model_name_gpt4all_llama=model_name_gpt4all_llama1, - n_gpu_layers=n_gpu_layers1, - n_batch=n_batch1, - n_gqa=n_gqa1, - )) - - # ensure no API calls reach here - if is_public: - raise RuntimeError("Illegal access for %s" % model_name) - # ensure old model removed from GPU memory - if kwargs['debug']: - print("Pre-switch pre-del GPU memory: %s" % get_torch_allocated(), flush=True) - - model0 = model_state0['model'] - if isinstance(model_state_old['model'], str) and \ - model0 is not None and \ - hasattr(model0, 'cpu'): - # best can do, move model loaded at first to CPU - model0.cpu() - - if model_state_old['model'] is not None and \ - not isinstance(model_state_old['model'], str): - if hasattr(model_state_old['model'], 'cpu'): - try: - model_state_old['model'].cpu() - except Exception as e: - # sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data! - print("Unable to put model on CPU: %s" % str(e), flush=True) - del model_state_old['model'] - model_state_old['model'] = None - - if model_state_old['tokenizer'] is not None and not isinstance(model_state_old['tokenizer'], str): - del model_state_old['tokenizer'] - model_state_old['tokenizer'] = None - - clear_torch_cache() - if kwargs['debug']: - print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True) - if not model_name: - model_name = no_model_str - if model_name == no_model_str: - # no-op if no model, just free memory - # no detranscribe needed for model, never go into evaluate - lora_weights = no_lora_str - server_name = no_server_str - return kwargs['model_state_none'].copy(), \ - model_name, lora_weights, server_name, prompt_type_old, \ - gr.Slider.update(maximum=256), \ - gr.Slider.update(maximum=256) - - # don't deepcopy, can contain model itself - all_kwargs1 = all_kwargs.copy() - all_kwargs1['base_model'] = model_name.strip() - all_kwargs1['load_8bit'] = load_8bit - all_kwargs1['load_4bit'] = load_4bit - all_kwargs1['low_bit_mode'] = low_bit_mode - all_kwargs1['load_gptq'] = load_gptq - all_kwargs1['load_exllama'] = load_exllama - all_kwargs1['use_safetensors'] = use_safetensors - all_kwargs1['revision'] = None if not revision else revision # transcribe, don't pass '' - all_kwargs1['use_gpu_id'] = use_gpu_id - all_kwargs1['gpu_id'] = int(gpu_id) if gpu_id not in [None, 'None'] else None # detranscribe - all_kwargs1['llamacpp_dict'] = llamacpp_dict - all_kwargs1['max_seq_len'] = max_seq_len1 - try: - all_kwargs1['rope_scaling'] = str_to_dict(rope_scaling1) # transcribe - except: - print("Failed to use user input for rope_scaling dict", flush=True) - all_kwargs1['rope_scaling'] = {} - model_lower = model_name.strip().lower() - if model_lower in inv_prompt_type_to_model_lower: - prompt_type1 = inv_prompt_type_to_model_lower[model_lower] - else: - prompt_type1 = prompt_type_old - - # detranscribe - if lora_weights == no_lora_str: - lora_weights = '' - all_kwargs1['lora_weights'] = lora_weights.strip() - if server_name == no_server_str: - server_name = '' - all_kwargs1['inference_server'] = server_name.strip() - - model1, tokenizer1, device1 = get_model(reward_type=False, - **get_kwargs(get_model, exclude_names=['reward_type'], - **all_kwargs1)) - clear_torch_cache() - - tokenizer_base_model = model_name - prompt_dict1, error0 = get_prompt(prompt_type1, '', - chat=False, context='', reduced=False, making_context=False, - return_dict=True, system_prompt=system_prompt1) - model_state_new = dict(model=model1, tokenizer=tokenizer1, device=device1, - base_model=model_name, tokenizer_base_model=tokenizer_base_model, - lora_weights=lora_weights, inference_server=server_name, - prompt_type=prompt_type1, prompt_dict=prompt_dict1, - # FIXME: not typically required, unless want to expose adding h2ogpt endpoint in UI - visible_models=None, h2ogpt_key=None, - ) - - max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs) - - if kwargs['debug']: - print("Post-switch GPU memory: %s" % get_torch_allocated(), flush=True) - return model_state_new, model_name, lora_weights, server_name, prompt_type1, \ - gr.Slider.update(maximum=max_max_new_tokens1), \ - gr.Slider.update(maximum=max_max_new_tokens1) - - def get_prompt_str(prompt_type1, prompt_dict1, system_prompt1, which=0): - if prompt_type1 in ['', None]: - print("Got prompt_type %s: %s" % (which, prompt_type1), flush=True) - return str({}) - prompt_dict1, prompt_dict_error = get_prompt(prompt_type1, prompt_dict1, chat=False, context='', - reduced=False, making_context=False, return_dict=True, - system_prompt=system_prompt1) - if prompt_dict_error: - return str(prompt_dict_error) - else: - # return so user can manipulate if want and use as custom - return str(prompt_dict1) - - get_prompt_str_func1 = functools.partial(get_prompt_str, which=1) - get_prompt_str_func2 = functools.partial(get_prompt_str, which=2) - prompt_type.change(fn=get_prompt_str_func1, inputs=[prompt_type, prompt_dict, system_prompt], - outputs=prompt_dict, queue=False) - prompt_type2.change(fn=get_prompt_str_func2, inputs=[prompt_type2, prompt_dict2, system_prompt], - outputs=prompt_dict2, - queue=False) - - def dropdown_prompt_type_list(x): - return gr.Dropdown.update(value=x) - - def chatbot_list(x, model_used_in): - return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]') - - load_model_args = dict(fn=load_model, - inputs=[model_choice, lora_choice, server_choice, model_state, prompt_type, - model_load8bit_checkbox, model_load4bit_checkbox, model_low_bit_mode, - model_load_gptq, model_load_exllama_checkbox, - model_safetensors_checkbox, model_revision, - model_use_gpu_id_checkbox, model_gpu, - max_seq_len, rope_scaling, - model_path_llama, model_name_gptj, model_name_gpt4all_llama, - n_gpu_layers, n_batch, n_gqa, llamacpp_dict_more, - system_prompt], - outputs=[model_state, model_used, lora_used, server_used, - # if prompt_type changes, prompt_dict will change via change rule - prompt_type, max_new_tokens, min_new_tokens, - ]) - prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type) - chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output) - nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat) - load_model_event = load_model_button.click(**load_model_args, - api_name='load_model' if allow_api and not is_public else None) \ - .then(**prompt_update_args) \ - .then(**chatbot_update_args) \ - .then(**nochat_update_args) \ - .then(clear_torch_cache) - - load_model_args2 = dict(fn=load_model, - inputs=[model_choice2, lora_choice2, server_choice2, model_state2, prompt_type2, - model_load8bit_checkbox2, model_load4bit_checkbox2, model_low_bit_mode2, - model_load_gptq2, model_load_exllama_checkbox2, - model_safetensors_checkbox2, model_revision2, - model_use_gpu_id_checkbox2, model_gpu2, - max_seq_len2, rope_scaling2, - model_path_llama2, model_name_gptj2, model_name_gpt4all_llama2, - n_gpu_layers2, n_batch2, n_gqa2, llamacpp_dict_more2, - system_prompt], - outputs=[model_state2, model_used2, lora_used2, server_used2, - # if prompt_type2 changes, prompt_dict2 will change via change rule - prompt_type2, max_new_tokens2, min_new_tokens2 - ]) - prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2) - chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2) - load_model_event2 = load_model_button2.click(**load_model_args2, - api_name='load_model2' if allow_api and not is_public else None) \ - .then(**prompt_update_args2) \ - .then(**chatbot_update_args2) \ - .then(clear_torch_cache) - - def dropdown_model_lora_server_list(model_list0, model_x, - lora_list0, lora_x, - server_list0, server_x, - model_used1, lora_used1, server_used1, - model_used2, lora_used2, server_used2, - ): - model_new_state = [model_list0[0] + [model_x]] - model_new_options = [*model_new_state[0]] - if no_model_str in model_new_options: - model_new_options.remove(no_model_str) - model_new_options = [no_model_str] + sorted(model_new_options) - x1 = model_x if model_used1 == no_model_str else model_used1 - x2 = model_x if model_used2 == no_model_str else model_used2 - ret1 = [gr.Dropdown.update(value=x1, choices=model_new_options), - gr.Dropdown.update(value=x2, choices=model_new_options), - '', model_new_state] - - lora_new_state = [lora_list0[0] + [lora_x]] - lora_new_options = [*lora_new_state[0]] - if no_lora_str in lora_new_options: - lora_new_options.remove(no_lora_str) - lora_new_options = [no_lora_str] + sorted(lora_new_options) - # don't switch drop-down to added lora if already have model loaded - x1 = lora_x if model_used1 == no_model_str else lora_used1 - x2 = lora_x if model_used2 == no_model_str else lora_used2 - ret2 = [gr.Dropdown.update(value=x1, choices=lora_new_options), - gr.Dropdown.update(value=x2, choices=lora_new_options), - '', lora_new_state] - - server_new_state = [server_list0[0] + [server_x]] - server_new_options = [*server_new_state[0]] - if no_server_str in server_new_options: - server_new_options.remove(no_server_str) - server_new_options = [no_server_str] + sorted(server_new_options) - # don't switch drop-down to added server if already have model loaded - x1 = server_x if model_used1 == no_model_str else server_used1 - x2 = server_x if model_used2 == no_model_str else server_used2 - ret3 = [gr.Dropdown.update(value=x1, choices=server_new_options), - gr.Dropdown.update(value=x2, choices=server_new_options), - '', server_new_state] - - return tuple(ret1 + ret2 + ret3) - - add_model_lora_server_event = \ - add_model_lora_server_button.click(fn=dropdown_model_lora_server_list, - inputs=[model_options_state, new_model] + - [lora_options_state, new_lora] + - [server_options_state, new_server] + - [model_used, lora_used, server_used] + - [model_used2, lora_used2, server_used2], - outputs=[model_choice, model_choice2, new_model, model_options_state] + - [lora_choice, lora_choice2, new_lora, lora_options_state] + - [server_choice, server_choice2, new_server, - server_options_state], - queue=False) - - go_event = go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None, - queue=False) \ - .then(lambda: gr.update(visible=True), None, normal_block, queue=False) \ - .then(**load_model_args, queue=False).then(**prompt_update_args, queue=False) - - def compare_textbox_fun(x): - return gr.Textbox.update(visible=x) - - def compare_column_fun(x): - return gr.Column.update(visible=x) - - def compare_prompt_fun(x): - return gr.Dropdown.update(visible=x) - - def slider_fun(x): - return gr.Slider.update(visible=x) - - compare_checkbox.select(compare_textbox_fun, compare_checkbox, text_output2, - api_name="compare_checkbox" if allow_api else None) \ - .then(compare_column_fun, compare_checkbox, col_model2) \ - .then(compare_prompt_fun, compare_checkbox, prompt_type2) \ - .then(compare_textbox_fun, compare_checkbox, score_text2) \ - .then(slider_fun, compare_checkbox, max_new_tokens2) \ - .then(slider_fun, compare_checkbox, min_new_tokens2) - # FIXME: add score_res2 in condition, but do better - - # callback for logging flagged input/output - callback.setup(inputs_list + [text_output, text_output2] + text_outputs, "flagged_data_points") - flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output, text_output2] + text_outputs, - None, - preprocess=False, - api_name='flag' if allow_api else None, queue=False) - flag_btn_nochat.click(lambda *args: callback.flag(args), inputs_list + [text_output_nochat], None, - preprocess=False, - api_name='flag_nochat' if allow_api else None, queue=False) - - def get_system_info(): - if is_public: - time.sleep(10) # delay to avoid spam since queue=False - return gr.Textbox.update(value=system_info_print()) - - system_event = system_btn.click(get_system_info, outputs=system_text, - api_name='system_info' if allow_api else None, queue=False) - - def get_system_info_dict(system_input1, **kwargs1): - if system_input1 != os.getenv("ADMIN_PASS", ""): - return json.dumps({}) - exclude_list = ['admin_pass', 'examples'] - sys_dict = {k: v for k, v in kwargs1.items() if - isinstance(v, (str, int, bool, float)) and k not in exclude_list} - try: - sys_dict.update(system_info()) - except Exception as e: - # protection - print("Exception: %s" % str(e), flush=True) - return json.dumps(sys_dict) - - system_kwargs = all_kwargs.copy() - system_kwargs.update(dict(command=str(' '.join(sys.argv)))) - get_system_info_dict_func = functools.partial(get_system_info_dict, **all_kwargs) - - system_dict_event = system_btn2.click(get_system_info_dict_func, - inputs=system_input, - outputs=system_text2, - api_name='system_info_dict' if allow_api else None, - queue=False, # queue to avoid spam - ) - - def get_hash(): - return kwargs['git_hash'] - - system_event = system_btn3.click(get_hash, - outputs=system_text3, - api_name='system_hash' if allow_api else None, - queue=False, - ) - - def get_model_names(): - key_list = ['base_model', 'prompt_type', 'prompt_dict'] + list(kwargs['other_model_state_defaults'].keys()) - # don't want to expose backend inference server IP etc. - # key_list += ['inference_server'] - return [{k: x[k] for k in key_list if k in x} for x in model_states] - - models_list_event = system_btn4.click(get_model_names, - outputs=system_text4, - api_name='model_names' if allow_api else None, - queue=False, - ) - - def count_chat_tokens(model_state1, chat1, prompt_type1, prompt_dict1, - system_prompt1, chat_conversation1, - memory_restriction_level1=0, - keep_sources_in_context1=False, - ): - if model_state1 and not isinstance(model_state1['tokenizer'], str): - tokenizer = model_state1['tokenizer'] - elif model_state0 and not isinstance(model_state0['tokenizer'], str): - tokenizer = model_state0['tokenizer'] - else: - tokenizer = None - if tokenizer is not None: - langchain_mode1 = 'LLM' - add_chat_history_to_context1 = True - # fake user message to mimic bot() - chat1 = copy.deepcopy(chat1) - chat1 = chat1 + [['user_message1', None]] - model_max_length1 = tokenizer.model_max_length - context1 = history_to_context(chat1, - langchain_mode=langchain_mode1, - add_chat_history_to_context=add_chat_history_to_context1, - prompt_type=prompt_type1, - prompt_dict=prompt_dict1, - chat=True, - model_max_length=model_max_length1, - memory_restriction_level=memory_restriction_level1, - keep_sources_in_context=keep_sources_in_context1, - system_prompt=system_prompt1, - chat_conversation=chat_conversation1) - tokens = tokenizer(context1, return_tensors="pt")['input_ids'] - if len(tokens.shape) == 1: - return str(tokens.shape[0]) - elif len(tokens.shape) == 2: - return str(tokens.shape[1]) - else: - return "N/A" - else: - return "N/A" - - count_chat_tokens_func = functools.partial(count_chat_tokens, - memory_restriction_level1=memory_restriction_level, - keep_sources_in_context1=kwargs['keep_sources_in_context']) - count_tokens_event = count_chat_tokens_btn.click(fn=count_chat_tokens_func, - inputs=[model_state, text_output, prompt_type, prompt_dict, - system_prompt, chat_conversation], - outputs=chat_token_count, - api_name='count_tokens' if allow_api else None) - - # don't pass text_output, don't want to clear output, just stop it - # cancel only stops outer generation, not inner generation or non-generation - stop_btn.click(lambda: None, None, None, - cancels=submits1 + submits2 + submits3 + submits4 + - [submit_event_nochat, submit_event_nochat2] + - [eventdb1, eventdb2, eventdb3] + - [eventdb7a, eventdb7, eventdb8a, eventdb8, eventdb9a, eventdb9, eventdb12a, eventdb12] + - db_events + - [eventdbloadla, eventdbloadlb] + - [clear_event] + - [submit_event_nochat_api, submit_event_nochat] + - [load_model_event, load_model_event2] + - [count_tokens_event] - , - queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache, queue=False) - - if kwargs['auth'] is not None: - auth = authf - load_func = user_state_setup - load_inputs = [my_db_state, requests_state, login_btn, login_btn] - load_outputs = [my_db_state, requests_state, login_btn] - else: - auth = None - load_func, load_inputs, load_outputs = None, None, None - - app_js = wrap_js_to_lambda( - len(load_inputs) if load_inputs else 0, - get_dark_js() if kwargs['dark'] else None, - get_heap_js(heap_app_id) if is_heap_analytics_enabled else None) - - load_event = demo.load(fn=load_func, inputs=load_inputs, outputs=load_outputs, _js=app_js) - - if load_func: - load_event2 = load_event.then(load_login_func, - inputs=login_inputs, - outputs=login_outputs) - if not kwargs['large_file_count_mode']: - load_event3 = load_event2.then(**get_sources_kwargs) - load_event4 = load_event3.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice) - load_event5 = load_event4.then(**show_sources_kwargs) - load_event6 = load_event5.then(**get_viewable_sources_args) - load_event7 = load_event6.then(**viewable_kwargs) - - demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open']) - favicon_file = "h2o-logo.svg" - favicon_path = favicon_file - if not os.path.isfile(favicon_file): - print("favicon_path1=%s not found" % favicon_file, flush=True) - alt_path = os.path.dirname(os.path.abspath(__file__)) - favicon_path = os.path.join(alt_path, favicon_file) - if not os.path.isfile(favicon_path): - print("favicon_path2: %s not found in %s" % (favicon_file, alt_path), flush=True) - alt_path = os.path.dirname(alt_path) - favicon_path = os.path.join(alt_path, favicon_file) - if not os.path.isfile(favicon_path): - print("favicon_path3: %s not found in %s" % (favicon_file, alt_path), flush=True) - favicon_path = None - - if kwargs['prepare_offline_level'] > 0: - from src.prepare_offline import go_prepare_offline - go_prepare_offline(**locals()) - return - - scheduler = BackgroundScheduler() - scheduler.add_job(func=clear_torch_cache, trigger="interval", seconds=20) - if is_public and \ - kwargs['base_model'] not in non_hf_types: - # FIXME: disable for gptj, langchain or gpt4all modify print itself - # FIXME: and any multi-threaded/async print will enter model output! - scheduler.add_job(func=ping, trigger="interval", seconds=60) - if is_public or os.getenv('PING_GPU'): - scheduler.add_job(func=ping_gpu, trigger="interval", seconds=60 * 10) - scheduler.start() - - # import control - if kwargs['langchain_mode'] == 'Disabled' and \ - os.environ.get("TEST_LANGCHAIN_IMPORT") and \ - kwargs['base_model'] not in non_hf_types: - assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have" - assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have" - - # set port in case GRADIO_SERVER_PORT was already set in prior main() call, - # gradio does not listen if change after import - # Keep None if not set so can find an open port above used ports - server_port = os.getenv('GRADIO_SERVER_PORT') - if server_port is not None: - server_port = int(server_port) - - demo.launch(share=kwargs['share'], - server_name=kwargs['server_name'], - show_error=True, - server_port=server_port, - favicon_path=favicon_path, - prevent_thread_lock=True, - auth=auth, - auth_message=auth_message, - root_path=kwargs['root_path']) - if kwargs['verbose'] or not (kwargs['base_model'] in ['gptj', 'gpt4all_llama']): - print("Started Gradio Server and/or GUI: server_name: %s port: %s" % (kwargs['server_name'], server_port), - flush=True) - if kwargs['block_gradio_exit']: - demo.block_thread() - - -def show_doc(db1s, selection_docs_state1, requests_state1, - langchain_mode1, - single_document_choice1, - view_raw_text_checkbox1, - text_context_list1, - dbs1=None, - load_db_if_exists1=None, - db_type1=None, - use_openai_embedding1=None, - hf_embedding_model1=None, - migrate_embedding_model_or_db1=None, - auto_migrate_db1=None, - verbose1=False, - get_userid_auth1=None, - max_raw_chunks=1000000, - api=False, - n_jobs=-1): - file = single_document_choice1 - document_choice1 = [single_document_choice1] - content = None - db_documents = [] - db_metadatas = [] - if db_type1 in ['chroma', 'chroma_old']: - assert langchain_mode1 is not None - langchain_mode_paths = selection_docs_state1['langchain_mode_paths'] - langchain_mode_types = selection_docs_state1['langchain_mode_types'] - from src.gpt_langchain import set_userid, get_any_db, get_docs_and_meta - set_userid(db1s, requests_state1, get_userid_auth1) - top_k_docs = -1 - db = get_any_db(db1s, langchain_mode1, langchain_mode_paths, langchain_mode_types, - dbs=dbs1, - load_db_if_exists=load_db_if_exists1, - db_type=db_type1, - use_openai_embedding=use_openai_embedding1, - hf_embedding_model=hf_embedding_model1, - migrate_embedding_model=migrate_embedding_model_or_db1, - auto_migrate_db=auto_migrate_db1, - for_sources_list=True, - verbose=verbose1, - n_jobs=n_jobs, - ) - query_action = False # long chunks like would be used for summarize - # the below is as or filter, so will show doc or by chunk, unrestricted - from langchain.vectorstores import Chroma - if isinstance(db, Chroma): - # chroma >= 0.4 - if view_raw_text_checkbox1: - one_filter = \ - [{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x}, - "chunk_id": { - "$gte": -1}} - for x in document_choice1][0] - else: - one_filter = \ - [{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x}, - "chunk_id": { - "$eq": -1}} - for x in document_choice1][0] - filter_kwargs = dict(filter={"$and": [dict(source=one_filter['source']), - dict(chunk_id=one_filter['chunk_id'])]}) - else: - # migration for chroma < 0.4 - one_filter = \ - [{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x}, - "chunk_id": { - "$eq": -1}} - for x in document_choice1][0] - if view_raw_text_checkbox1: - # like or, full raw all chunk types - filter_kwargs = dict(filter=one_filter) - else: - filter_kwargs = dict(filter={"$and": [dict(source=one_filter['source']), - dict(chunk_id=one_filter['chunk_id'])]}) - db_documents, db_metadatas = get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs, - text_context_list=text_context_list1) - # order documents - from langchain.docstore.document import Document - docs_with_score = [(Document(page_content=result[0], metadata=result[1] or {}), 0) - for result in zip(db_documents, db_metadatas)] - doc_chunk_ids = [x.get('chunk_id', -1) for x in db_metadatas] - doc_page_ids = [x.get('page', 0) for x in db_metadatas] - doc_hashes = [x.get('doc_hash', 'None') for x in db_metadatas] - docs_with_score = [x for hx, px, cx, x in - sorted(zip(doc_hashes, doc_page_ids, doc_chunk_ids, docs_with_score), - key=lambda x: (x[0], x[1], x[2])) - # if cx == -1 - ] - db_metadatas = [x[0].metadata for x in docs_with_score][:max_raw_chunks] - db_documents = [x[0].page_content for x in docs_with_score][:max_raw_chunks] - # done reordering - if view_raw_text_checkbox1: - content = [dict_to_html(x) + '\n' + text_to_html(y) for x, y in zip(db_metadatas, db_documents)] - else: - content = [text_to_html(y) for x, y in zip(db_metadatas, db_documents)] - content = '\n'.join(content) - content = f""" - - -
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- diff --git a/spaces/bioriAsaeru/text-to-voice/Gta V Default Xex Download Everything You Need to Know About Xex Menu and GTA V Modding.md b/spaces/bioriAsaeru/text-to-voice/Gta V Default Xex Download Everything You Need to Know About Xex Menu and GTA V Modding.md deleted file mode 100644 index 460da825edeb77a930fab986167cfc4c00062179..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Gta V Default Xex Download Everything You Need to Know About Xex Menu and GTA V Modding.md +++ /dev/null @@ -1,19 +0,0 @@ - -
when i open a xex in ida a window pops up asking for the file format, processor type and a bunch of other stuff. i changed the processor to ppc and selected xbox360xexfile for the format. do i have to change any of the other settings or can i keep the defaults?
-as well for benefits xbdm allowed you to realtime on any xex(yea, any game, not even limited to just reach which aso includes default unmodded xex's you can realtime on and take pictures of your console at any time.
-Gta V Default Xex Download
Download ✅ https://urloso.com/2uyOn0
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working:
-TUs check and download
-covers donwload
not working
-push from unity to xbox
my little improvement:
-you can see all avaliable TUs for the game(it means, you can update games like GTA V). but, if TU`s mediaid differs from mediaid of installed game, TU`s displayed name would be something like "MID:0C48794E GTA V Title Update 26", where "MID:0C48794E" - TU`s mediaid.
how to install:
1. download my default.xex
2. replace the original default.xex in fsd folder with mine
3. restart fsd
*UPDATE 17.10.2016*
thanks to dizazter, everything is up and running again on the new hosting. you need to download updated version of default.xex
there are two versions now:
default.rar - shows TU for all MediaIDs
default_filter.rar - shows TU only for your MediaID
from the beginning i was using FSD 775. but i found:
1. It is not downloding custom game covers
2. Covers are not downloaded in HD
3. only front side of game cover is visible
4. most importantly FSD Freezes (Hang\stop responding)
it force me to switch to Aurora. at least from my experience i can say, Aurora never freezes.
But i love FSD, if your version is fixed from above issue than surely i am going to use FSD again.
Plz tell me
Thanks a lot for this Gualdimar. Been using it for a while now. I like the fact it doesn't filter all the non-matching Media ID TU's from view. It's especially handy for games that use disc 2 to boot (eg. Watch Dogs), where you have to grab one of the 'MID' labeled TU's. Weird thing is, about three weeks or so ago, cover downloading just seem to stop working for about a week, whereas the other modded version carried on working. Strange. I tried yours again today and it was working again, so I've gone back to your version. Cheers.
-Try to create account on JQE360 but I can't open the website. After some search, I got that FSD is not support anymore, people migrate to aurora. So I download aurora. For game covers I use WebUI in FSD and Aurora Title Editor manually game by game and it's nice. After that I decide to buy external harddisc with xbox games in it, about 1 Tb with around 279 games in it, and think it will be a load of works if I update it one by one.
-My internet on my mobile phone, I don't have router. So I using Laptop with windows 8.1, connect to mobile phone hotspot using laptop's wifi. Using Internet connection sharing to ethernet connect to Xbox. Both set manually IP address on laptop and xbox ethernets, since I don't have dhcp on my laptop. First try with aurora, it works, and I found some website says in FSD using unity account. So I give it a try on last friday. I works, FreeStyle start to download Covers, Background and Description along with screenshots. But after some time, FSD start crash, several restart didn't work, it keep crash. Open file manager and find out OnBoardMU is out of space since my freestyle located in OnBoardMU. Copy it using FTP to my laptop, and count the size about 1.5 Gb. GameDate is too much, so I decide to move it to internal HDD1. After that FSD3 stop crashing, but it no longer resume the download, Refresh workarts one by one still work though, but to much manual selection. Delete the scan paths and readd them do the trick, and it start download automatically. Finally done all the games covers update with total 293 games...
-Free style version : it comes up with this version when I buy this XBOX, so I don't know if the default.xex already updated with the above link. My Dashlauch setting liveblock enabled, livestrong disabled.
- -so my brother gave me his xbox 360 star wars edition, a couple of weeks ago he had someone modify it for him so it can play games from the hard drive, it had XeXMenu 1.2 and Freestyle dashboard, he also had a lot of games on it and every thing was working fine except for one game (GTA V) which gave a bad CD error or something which I ignored until I decided to try and solve it so I went to the game folder in XeXmenu hit Y and chose xex patch just to see what would happen then the error changed to "game error the game couldn't start try downloading the game again" and every other game no gives the same error even the ones that were playable before.
-Hey friend just remove xex menu and re-copy it from ur cd or external hd as u prefer i also mistakenly did hat once and only solution i found was to re-copy xex...just google about downloading xex menu it's free. And alwayd do as Swizzy says he's the champ trust me.
-All right i'm home now I deleted the existing xex menu 1.2 from settings >storage > HDD >demo and then plugged in the flash drive and copied the freshly downloaded xex menu 1.2 but I still get the same error when trying to start a game " game error: the game couldn't start. Try downloading the game again"
-ok will try as soon as I get home
but the thing is I don't have the ISO or sources for these games as I'm not the one who put them on the console in the first place, So what I'm planning to do is to download any XBOX 360 ISO off the internet and look for a tutorial how to run them on the console from a flash drive, in fact I'm currently downloading "Payday 2 [MULTI][XBOX360][Region Free][XDG2][COMPLEX] " this should work right??
should I remove the existing games?
but the thing is I don't have the ISO or sources for these games as I'm not the one who put them on the console in the first place, So what I'm planning to do is to download any XBOX 360 ISO off the internet and look for a tutorial how to run them on the console from a flash drive, in fact I'm currently downloading "Payday 2 [MULTI][XBOX360][Region Free][XDG2][COMPLEX] " this should work right??
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\ No newline at end of file diff --git a/spaces/breynolds1247/StarryNight_StyleTransfer/helper_functions.py b/spaces/breynolds1247/StarryNight_StyleTransfer/helper_functions.py deleted file mode 100644 index 0663918d6f311f7ab68b8754963c147bb3464a30..0000000000000000000000000000000000000000 --- a/spaces/breynolds1247/StarryNight_StyleTransfer/helper_functions.py +++ /dev/null @@ -1,49 +0,0 @@ -import tensorflow as tf -from tensorflow import keras - -def img_scaler(image, max_dim = 512): - - #Casts tensor to a new data type - original_shape = tf.cast(tf.shape(image)[:-1], tf.float32) - - #Creates scale constant for the image based on imput max_dim - scale_ratio = max_dim / max(original_shape) - - #Casts tensor to a new data type - new_shape = tf.cast(original_shape * scale_ratio, tf.int32) - - #Resizes image - return tf.image.resize(image, new_shape) - -def load_img(image_path, content=True, max_dim = 512): - - if content: - #content images come straight from the web app, so no opening or decoding - img = image_path - - #Convert image to dtype - img = tf.image.convert_image_dtype(img, tf.float32) - - #Scale the image using the created scaler function - img = img_scaler(img, max_dim) - - #Adds a fourth dimension to the Tensor because the model requires a 4-dimensional Tensor - return img[tf.newaxis, :] - - else: - #Read contents of the input filename - img = tf.io.read_file(image_path) - - #Detect whether an image is a BMP, GIF, JPEG, or PNG, - #performs the appropriate operation - #convert the input bytes string into a Tensor of type dtype - img = tf.image.decode_image(img, channels=3) - - #Convert image to dtype - img = tf.image.convert_image_dtype(img, tf.float32) - - #Scale the image using the created scaler function - img = img_scaler(img, max_dim) - - #Adds a fourth dimension to the Tensor because the model requires a 4-dimensional Tensor - return img[tf.newaxis, :] \ No newline at end of file diff --git a/spaces/bzd4576/sovits-sin/modules.py b/spaces/bzd4576/sovits-sin/modules.py deleted file mode 100644 index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000 --- a/spaces/bzd4576/sovits-sin/modules.py +++ /dev/null @@ -1,390 +0,0 @@ -import copy -import math -import numpy as np -import scipy -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding -from transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - self.hidden_channels =hidden_channels - self.kernel_size = kernel_size, - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, - dilation=dilation, padding=padding) - in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) - self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_derivatives = h[..., 2 * self.num_bins:] - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/structures/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/structures/__init__.py deleted file mode 100644 index ed32c5e9d6c4c1599ba960681d9e86889e2cdbd8..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/structures/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -from .chart import DensePoseChartPredictorOutput -from .chart_confidence import decorate_predictor_output_class_with_confidences -from .cse_confidence import decorate_cse_predictor_output_class_with_confidences -from .chart_result import ( - DensePoseChartResult, - DensePoseChartResultWithConfidences, - quantize_densepose_chart_result, - compress_quantized_densepose_chart_result, - decompress_compressed_densepose_chart_result, -) -from .cse import DensePoseEmbeddingPredictorOutput -from .data_relative import DensePoseDataRelative -from .list import DensePoseList -from .mesh import Mesh, create_mesh -from .transform_data import DensePoseTransformData, normalized_coords_transform diff --git a/spaces/caslabs/midi-autocompletion/musicautobot/utils/attention_mask.py b/spaces/caslabs/midi-autocompletion/musicautobot/utils/attention_mask.py deleted file mode 100644 index 7f570ed2ce747f2d11772f7a392d33c6bea576e0..0000000000000000000000000000000000000000 --- a/spaces/caslabs/midi-autocompletion/musicautobot/utils/attention_mask.py +++ /dev/null @@ -1,21 +0,0 @@ -import numpy as np -import torch - -def window_mask(x_len, device, m_len=0, size=(1,1)): - win_size,k = size - mem_mask = torch.zeros((x_len,m_len), device=device) - tri_mask = torch.triu(torch.ones((x_len//win_size+1,x_len//win_size+1), device=device),diagonal=k) - window_mask = tri_mask.repeat_interleave(win_size,dim=0).repeat_interleave(win_size,dim=1)[:x_len,:x_len] - if x_len: window_mask[...,0] = 0 # Always allowing first index to see. Otherwise you'll get NaN loss - mask = torch.cat((mem_mask, window_mask), dim=1)[None,None] - return mask.bool() if hasattr(mask, 'bool') else mask.byte() - -def rand_window_mask(x_len,m_len,device,max_size:int=None,p:float=0.2,is_eval:bool=False): - if is_eval or np.random.rand() >= p or max_size is None: - win_size,k = (1,1) - else: win_size,k = (np.random.randint(0,max_size)+1,0) - return window_mask(x_len, device, m_len, size=(win_size,k)) - -def lm_mask(x_len, device): - mask = torch.triu(torch.ones((x_len, x_len), device=device), diagonal=1)[None,None] - return mask.bool() if hasattr(mask, 'bool') else mask.byte() diff --git a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/utils.py b/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/utils.py deleted file mode 100644 index 9be920642581ae69f4a4c96795e8382c4f11b50b..0000000000000000000000000000000000000000 --- a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/utils.py +++ /dev/null @@ -1,400 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch -import regex as re - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - - - -zh_pattern = re.compile(r'[\u4e00-\u9fa5]') -en_pattern = re.compile(r'[a-zA-Z]') -jp_pattern = re.compile(r'[\u3040-\u30ff\u31f0-\u31ff]') -kr_pattern = re.compile(r'[\uac00-\ud7af\u1100-\u11ff\u3130-\u318f\ua960-\ua97f]') -num_pattern=re.compile(r'[0-9]') -comma=r"(?<=[.。!！?？；;，,、:：'\"‘“”’()（）《》「」~——])" #向前匹配但固定长度 -tags={'ZH':'[ZH]','EN':'[EN]','JP':'[JA]','KR':'[KR]'} - -def tag_cjke(text): - '''为中英日韩加tag,中日正则分不开，故先分句分离中日再识别，以应对大部分情况''' - sentences = re.split(r"([.。!！?？；;，,、:：'\"‘“”’()（）【】《》「」~——]+ *(?![0-9]))", text) #分句，排除小数点 - sentences.append("") - sentences = ["".join(i) for i in zip(sentences[0::2],sentences[1::2])] - # print(sentences) - prev_lang=None - tagged_text = "" - for s in sentences: - #全为符号跳过 - nu = re.sub(r'[\s\p{P}]+', '', s, flags=re.U).strip() - if len(nu)==0: - continue - s = re.sub(r'[()（）《》「」【】‘“”’]+', '', s) - jp=re.findall(jp_pattern, s) - #本句含日语字符判断为日语 - if len(jp)>0: - prev_lang,tagged_jke=tag_jke(s,prev_lang) - tagged_text +=tagged_jke - else: - prev_lang,tagged_cke=tag_cke(s,prev_lang) - tagged_text +=tagged_cke - return tagged_text - -def tag_jke(text,prev_sentence=None): - '''为英日韩加tag''' - # 初始化标记变量 - tagged_text = "" - prev_lang = None - tagged=0 - # 遍历文本 - for char in text: - # 判断当前字符属于哪种语言 - if jp_pattern.match(char): - lang = "JP" - elif zh_pattern.match(char): - lang = "JP" - elif kr_pattern.match(char): - lang = "KR" - elif en_pattern.match(char): - lang = "EN" - # elif num_pattern.match(char): - # lang = prev_sentence - else: - lang = None - tagged_text += char - continue - # 如果当前语言与上一个语言不同，就添加标记 - if lang != prev_lang: - tagged=1 - if prev_lang==None: # 开头 - tagged_text =tags[lang]+tagged_text - else: - tagged_text =tagged_text+tags[prev_lang]+tags[lang] - - # 重置标记变量 - prev_lang = lang - - # 添加当前字符到标记文本中 - tagged_text += char - - # 在最后一个语言的结尾添加对应的标记 - if prev_lang: - tagged_text += tags[prev_lang] - if not tagged: - prev_lang=prev_sentence - tagged_text =tags[prev_lang]+tagged_text+tags[prev_lang] - - return prev_lang,tagged_text - -def tag_cke(text,prev_sentence=None): - '''为中英韩加tag''' - # 初始化标记变量 - tagged_text = "" - prev_lang = None - # 是否全略过未标签 - tagged=0 - - # 遍历文本 - for char in text: - # 判断当前字符属于哪种语言 - if zh_pattern.match(char): - lang = "ZH" - elif kr_pattern.match(char): - lang = "KR" - elif en_pattern.match(char): - lang = "EN" - # elif num_pattern.match(char): - # lang = prev_sentence - else: - # 略过 - lang = None - tagged_text += char - continue - - # 如果当前语言与上一个语言不同，添加标记 - if lang != prev_lang: - tagged=1 - if prev_lang==None: # 开头 - tagged_text =tags[lang]+tagged_text - else: - tagged_text =tagged_text+tags[prev_lang]+tags[lang] - - # 重置标记变量 - prev_lang = lang - - # 添加当前字符到标记文本中 - tagged_text += char - - # 在最后一个语言的结尾添加对应的标记 - if prev_lang: - tagged_text += tags[prev_lang] - # 未标签则继承上一句标签 - if tagged==0: - prev_lang=prev_sentence - tagged_text =tags[prev_lang]+tagged_text+tags[prev_lang] - return prev_lang,tagged_text - - - -def load_checkpoint(checkpoint_path, model, optimizer=None, drop_speaker_emb=False): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - if k == 'emb_g.weight': - if drop_speaker_emb: - new_state_dict[k] = v - continue - v[:saved_state_dict[k].shape[0], :] = saved_state_dict[k] - new_state_dict[k] = v - else: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - logger.info("Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path)) - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save({'model': state_dict, - 'iteration': iteration, - 'optimizer': optimizer.state_dict() if optimizer is not None else None, - 'learning_rate': learning_rate}, checkpoint_path) - - -def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats='HWC') - for k, v in audios.items(): - writer.add_audio(k, v, global_step, audio_sampling_rate) - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - print(x) - return x - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/modified_finetune_speaker.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, default="pretrained_models", - help='Model name') - parser.add_argument('-n', '--max_epochs', type=int, default=50, - help='finetune epochs') - parser.add_argument('--drop_speaker_embed', type=bool, default=False, help='whether to drop existing characters') - - args = parser.parse_args() - model_dir = os.path.join("./", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - hparams.max_epochs = args.max_epochs - hparams.drop_speaker_embed = args.drop_speaker_embed - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() \ No newline at end of file diff --git a/spaces/ccds/vits_onnx/util/build_docker.sh b/spaces/ccds/vits_onnx/util/build_docker.sh deleted file mode 100644 index 3b657e7bc183a007ece05be70f7c828585bbce1e..0000000000000000000000000000000000000000 --- a/spaces/ccds/vits_onnx/util/build_docker.sh +++ /dev/null @@ -1,2 +0,0 @@ -export DOCKER_BUILDKIT=1 -docker build -f Dockerfile -t ccdesue/vits_demo . \ No newline at end of file diff --git a/spaces/ccolas/TastyPiano/src/music/pipeline/midi2processed.py b/spaces/ccolas/TastyPiano/src/music/pipeline/midi2processed.py deleted file mode 100644 index 69496fa31ef1efddd637bb58a5308d977e7b6f43..0000000000000000000000000000000000000000 --- a/spaces/ccolas/TastyPiano/src/music/pipeline/midi2processed.py +++ /dev/null @@ -1,152 +0,0 @@ -import time -import os -import sys -sys.path.append('../../') -import pretty_midi as pm -import numpy as np - -from src.music.utils import get_out_path -from src.music.config import MIN_LEN, MIN_NB_NOTES, MAX_GAP_IN_SONG, REMOVE_FIRST_AND_LAST - - -def sort_notes(notes): - starts = np.array([n.start for n in notes]) - index_sorted = np.argsort(starts) - return [notes[i] for i in index_sorted].copy() - - -def delete_notes_end_after_start(notes): - indexes_to_keep = [i for i, n in enumerate(notes) if n.start < n.end] - return [notes[i] for i in indexes_to_keep].copy() - -def compute_largest_gap(notes): - gaps = [] - latest_note_end_so_far = notes[0].end - for i in range(len(notes) - 1): - note_start = notes[i + 1].start - if latest_note_end_so_far < note_start: - gaps.append(note_start - latest_note_end_so_far) - latest_note_end_so_far = max(latest_note_end_so_far, notes[i+1].end) - if len(gaps) > 0: - largest_gap = np.max(gaps) - else: - largest_gap = 0 - return largest_gap - -def analyze_instrument(inst): - # test that piano plays throughout - init = time.time() - notes = inst.notes.copy() - nb_notes = len(notes) - start = notes[0].start - end = inst.get_end_time() - duration = end - start - largest_gap = compute_largest_gap(notes) - return nb_notes, start, end, duration, largest_gap - -def remove_beginning_and_end(midi, end_time): - notes = midi.instruments[0].notes.copy() - new_notes = [n for n in notes if n.start > REMOVE_FIRST_AND_LAST and n.end < end_time - REMOVE_FIRST_AND_LAST] - midi.instruments[0].notes = new_notes - return midi - -def remove_blanks_beginning_and_end(midi): - # remove blanks and the beginning and the end - shift = midi.instruments[0].notes[0].start - for n in midi.instruments[0].notes: - n.start = max(0, n.start - shift) - n.end = max(0, n.end - shift) - for ksc in midi.key_signature_changes: - ksc.time = max(0, ksc.time - shift) - for tsc in midi.time_signature_changes: - tsc.time = max(0, tsc.time - shift) - for pb in midi.instruments[0].pitch_bends: - pb.time = max(0, pb.time - shift) - for cc in midi.instruments[0].control_changes: - cc.time = max(0, cc.time - shift) - return midi - -def is_valid_inst(largest_gap, duration, nb_notes, gap_counts=True): - error_msg = '' - valid = True - if largest_gap > MAX_GAP_IN_SONG and gap_counts: - valid = False - error_msg += f'wide gap ({largest_gap:.2f} secs), ' - if duration < (MIN_LEN + 2 * REMOVE_FIRST_AND_LAST): - valid = False - error_msg += f'too short ({duration:.2f} secs), ' - if nb_notes < MIN_NB_NOTES * duration / 60: # nb of notes needs to be superior to the minimum number / min * the duration in minute - valid = False - error_msg += f'too few notes ({nb_notes}), ' - return valid, error_msg - -def midi2processed(midi_path, processed_path=None, apply_filtering=True, verbose=False, level=0): - assert midi_path.split('.')[-1] in ['mid', 'midi'] - if not processed_path: - processed_path, _, _ = get_out_path(in_path=midi_path, in_word='midi', out_word='processed', out_extension='.mid') - - if verbose: print(' ' * level + f'Processing {midi_path}.') - - if os.path.exists(processed_path): - if verbose: print(' ' * (level + 2) + 'Processed midi file already exists.') - return processed_path, '' - error_msg = 'Error in scrubbing. ' - #try: - inst_error_msg = '' - # load mid file - error_msg += 'Error in midi loading?' - midi = pm.PrettyMIDI(midi_path) - error_msg += ' Nope. Removing invalid notes?' - midi.remove_invalid_notes() # filter invalid notes - error_msg += ' Nope. Filtering instruments?' - # filter instruments - instruments = midi.instruments.copy() - new_instru = [] - instruments_data = [] - for i_inst, inst in enumerate(instruments): - if inst.program <= 7 and not inst.is_drum and len(inst.notes) > 5: - # inst is a piano - # check data - inst.notes = sort_notes(inst.notes) # sort notes - inst.notes = delete_notes_end_after_start(inst.notes) # delete invalid notes - nb_notes, start, end, duration, largest_gap = analyze_instrument(inst) - is_valid, err_msg = is_valid_inst(largest_gap=largest_gap, duration=duration, nb_notes=nb_notes, gap_counts='maestro' not in midi_path) - if is_valid or not apply_filtering: - new_instru.append(inst) - instruments_data.append([nb_notes, start, end, duration, largest_gap]) - else: - inst_error_msg += 'inst1: ' + err_msg + '\n' - instruments_data = np.array(instruments_data) - error_msg += ' Nope. Taking one instrument?' - - if len(new_instru) == 0: - error_msg = f'No piano instrument. {inst_error_msg}' - assert False - elif len(new_instru) > 1: - # take instrument playing the most notes - instrument = new_instru[np.argmax(instruments_data[:, 0])] - else: - instrument = new_instru[0] - instrument.program = 0 # set the instrument to Grand Piano. - midi.instruments = [instrument] # put instrument in midi file - error_msg += ' Nope. Removing blanks?' - # remove first and last REMOVE_FIRST_AND_LAST seconds (avoid clapping and jingles) - end_time = midi.get_end_time() - if apply_filtering: midi = remove_beginning_and_end(midi, end_time) - - # remove beginning and end - midi = remove_blanks_beginning_and_end(midi) - error_msg += ' Nope. Saving?' - - # save midi file - midi.write(processed_path) - error_msg += ' Nope.' - if verbose: - extra = f' Saved to {processed_path}' if midi_path else '' - print(' ' * (level + 2) + f'Success! {extra}') - return processed_path, '' - #except: - # if verbose: print(' ' * (level + 2) + 'Scrubbing failed.') - #if os.path.exists(processed_path): - # os.remove(processed_path) - #return None, error_msg + ' Yes.' diff --git a/spaces/ceckenrode/bigscience-bloom/README.md b/spaces/ceckenrode/bigscience-bloom/README.md deleted file mode 100644 index 7476185bdadf8a41dbfe71ea3f60c99b10c96929..0000000000000000000000000000000000000000 --- a/spaces/ceckenrode/bigscience-bloom/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Bigscience Bloom -emoji: 😻 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.21.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/cfwef/gpt/check_proxy.py b/spaces/cfwef/gpt/check_proxy.py deleted file mode 100644 index a6919dd37a559d0f3868fdc74b54c488779083d3..0000000000000000000000000000000000000000 --- a/spaces/cfwef/gpt/check_proxy.py +++ /dev/null @@ -1,27 +0,0 @@ - -def check_proxy(proxies): - import requests - proxies_https = proxies['https'] if proxies is not None else '无' - try: - response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4) - data = response.json() - print(f'查询代理的地理位置，返回的结果是{data}') - if 'country_name' in data: - country = data['country_name'] - result = f"代理配置 {proxies_https}, 代理所在地：{country}" - elif 'error' in data: - result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限" - print(result) - return result - except: - result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效" - print(result) - return result - - -if __name__ == '__main__': - import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染 - from toolbox import get_conf - proxies, = get_conf('proxies') - check_proxy(proxies) - \ No newline at end of file diff --git a/spaces/chrisjay/afro-speech/utils.py b/spaces/chrisjay/afro-speech/utils.py deleted file mode 100644 index f56708d24eff9522a7aee51996554a9f7a7cc215..0000000000000000000000000000000000000000 --- a/spaces/chrisjay/afro-speech/utils.py +++ /dev/null @@ -1,44 +0,0 @@ - -import json -import hashlib -import random -import string - - - -def get_unique_name(): - return ''.join([random.choice(string.ascii_letters - + string.digits) for n in range(32)]) - - -def read_json_lines(file): - with open(file,'r',encoding="utf8") as f: - lines = f.readlines() - data=[] - for l in lines: - data.append(json.loads(l)) - return data - - -def json_dump(thing): - return json.dumps(thing, - ensure_ascii=False, - sort_keys=True, - indent=None, - separators=(',', ':')) - -def get_hash(thing): # stable-hashing - return str(hashlib.md5(json_dump(thing).encode('utf-8')).hexdigest()) - - -def dump_json(thing,file): - with open(file,'w+',encoding="utf8") as f: - json.dump(thing,f) - -def read_json_lines(file): - with open(file,'r',encoding="utf8") as f: - lines = f.readlines() - data=[] - for l in lines: - data.append(json.loads(l)) - return data \ No newline at end of file diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/Markdown-3.4.3.dist-info/LICENSE.md b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/Markdown-3.4.3.dist-info/LICENSE.md deleted file mode 100644 index 2652d97ad1b4686e38b8f4122911bb80e8e16139..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/Markdown-3.4.3.dist-info/LICENSE.md +++ /dev/null @@ -1,29 +0,0 @@ -Copyright 2007, 2008 The Python Markdown Project (v. 1.7 and later) -Copyright 2004, 2005, 2006 Yuri Takhteyev (v. 0.2-1.6b) -Copyright 2004 Manfred Stienstra (the original version) - -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. -* Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. -* Neither the name of the Python Markdown Project nor the - names of its contributors may be used to endorse or promote products - derived from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE PYTHON MARKDOWN PROJECT ''AS IS'' AND ANY -EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL ANY CONTRIBUTORS TO THE PYTHON MARKDOWN PROJECT -BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE. diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/ImageFont.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/ImageFont.py deleted file mode 100644 index 05828a72fdf90dbe434cebf06f968ef7e91189b3..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/ImageFont.py +++ /dev/null @@ -1,997 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# PIL raster font management -# -# History: -# 1996-08-07 fl created (experimental) -# 1997-08-25 fl minor adjustments to handle fonts from pilfont 0.3 -# 1999-02-06 fl rewrote most font management stuff in C -# 1999-03-17 fl take pth files into account in load_path (from Richard Jones) -# 2001-02-17 fl added freetype support -# 2001-05-09 fl added TransposedFont wrapper class -# 2002-03-04 fl make sure we have a "L" or "1" font -# 2002-12-04 fl skip non-directory entries in the system path -# 2003-04-29 fl add embedded default font -# 2003-09-27 fl added support for truetype charmap encodings -# -# Todo: -# Adapt to PILFONT2 format (16-bit fonts, compressed, single file) -# -# Copyright (c) 1997-2003 by Secret Labs AB -# Copyright (c) 1996-2003 by Fredrik Lundh -# -# See the README file for information on usage and redistribution. -# - -import base64 -import os -import sys -import warnings -from enum import IntEnum -from io import BytesIO - -from . import Image -from ._util import is_directory, is_path - - -class Layout(IntEnum): - BASIC = 0 - RAQM = 1 - - -MAX_STRING_LENGTH = 1_000_000 - - -try: - from . import _imagingft as core -except ImportError as ex: - from ._util import DeferredError - - core = DeferredError(ex) - - -def _string_length_check(text): - if MAX_STRING_LENGTH is not None and len(text) > MAX_STRING_LENGTH: - msg = "too many characters in string" - raise ValueError(msg) - - -# FIXME: add support for pilfont2 format (see FontFile.py) - -# -------------------------------------------------------------------- -# Font metrics format: -# "PILfont" LF -# fontdescriptor LF -# (optional) key=value... LF -# "DATA" LF -# binary data: 256*10*2 bytes (dx, dy, dstbox, srcbox) -# -# To place a character, cut out srcbox and paste at dstbox, -# relative to the character position. Then move the character -# position according to dx, dy. -# -------------------------------------------------------------------- - - -class ImageFont: - """PIL font wrapper""" - - def _load_pilfont(self, filename): - with open(filename, "rb") as fp: - image = None - for ext in (".png", ".gif", ".pbm"): - if image: - image.close() - try: - fullname = os.path.splitext(filename)[0] + ext - image = Image.open(fullname) - except Exception: - pass - else: - if image and image.mode in ("1", "L"): - break - else: - if image: - image.close() - msg = "cannot find glyph data file" - raise OSError(msg) - - self.file = fullname - - self._load_pilfont_data(fp, image) - image.close() - - def _load_pilfont_data(self, file, image): - # read PILfont header - if file.readline() != b"PILfont\n": - msg = "Not a PILfont file" - raise SyntaxError(msg) - file.readline().split(b";") - self.info = [] # FIXME: should be a dictionary - while True: - s = file.readline() - if not s or s == b"DATA\n": - break - self.info.append(s) - - # read PILfont metrics - data = file.read(256 * 20) - - # check image - if image.mode not in ("1", "L"): - msg = "invalid font image mode" - raise TypeError(msg) - - image.load() - - self.font = Image.core.font(image.im, data) - - def getmask(self, text, mode="", *args, **kwargs): - """ - Create a bitmap for the text. - - If the font uses antialiasing, the bitmap should have mode ``L`` and use a - maximum value of 255. Otherwise, it should have mode ``1``. - - :param text: Text to render. - :param mode: Used by some graphics drivers to indicate what mode the - driver prefers; if empty, the renderer may return either - mode. Note that the mode is always a string, to simplify - C-level implementations. - - .. versionadded:: 1.1.5 - - :return: An internal PIL storage memory instance as defined by the - :py:mod:`PIL.Image.core` interface module. - """ - return self.font.getmask(text, mode) - - def getbbox(self, text, *args, **kwargs): - """ - Returns bounding box (in pixels) of given text. - - .. versionadded:: 9.2.0 - - :param text: Text to render. - :param mode: Used by some graphics drivers to indicate what mode the - driver prefers; if empty, the renderer may return either - mode. Note that the mode is always a string, to simplify - C-level implementations. - - :return: ``(left, top, right, bottom)`` bounding box - """ - _string_length_check(text) - width, height = self.font.getsize(text) - return 0, 0, width, height - - def getlength(self, text, *args, **kwargs): - """ - Returns length (in pixels) of given text. - This is the amount by which following text should be offset. - - .. versionadded:: 9.2.0 - """ - _string_length_check(text) - width, height = self.font.getsize(text) - return width - - -## -# Wrapper for FreeType fonts. Application code should use the -# truetype factory function to create font objects. - - -class FreeTypeFont: - """FreeType font wrapper (requires _imagingft service)""" - - def __init__(self, font=None, size=10, index=0, encoding="", layout_engine=None): - # FIXME: use service provider instead - - self.path = font - self.size = size - self.index = index - self.encoding = encoding - - if layout_engine not in (Layout.BASIC, Layout.RAQM): - layout_engine = Layout.BASIC - if core.HAVE_RAQM: - layout_engine = Layout.RAQM - elif layout_engine == Layout.RAQM and not core.HAVE_RAQM: - warnings.warn( - "Raqm layout was requested, but Raqm is not available. " - "Falling back to basic layout." - ) - layout_engine = Layout.BASIC - - self.layout_engine = layout_engine - - def load_from_bytes(f): - self.font_bytes = f.read() - self.font = core.getfont( - "", size, index, encoding, self.font_bytes, layout_engine - ) - - if is_path(font): - if sys.platform == "win32": - font_bytes_path = font if isinstance(font, bytes) else font.encode() - try: - font_bytes_path.decode("ascii") - except UnicodeDecodeError: - # FreeType cannot load fonts with non-ASCII characters on Windows - # So load it into memory first - with open(font, "rb") as f: - load_from_bytes(f) - return - self.font = core.getfont( - font, size, index, encoding, layout_engine=layout_engine - ) - else: - load_from_bytes(font) - - def __getstate__(self): - return [self.path, self.size, self.index, self.encoding, self.layout_engine] - - def __setstate__(self, state): - path, size, index, encoding, layout_engine = state - self.__init__(path, size, index, encoding, layout_engine) - - def getname(self): - """ - :return: A tuple of the font family (e.g. Helvetica) and the font style - (e.g. Bold) - """ - return self.font.family, self.font.style - - def getmetrics(self): - """ - :return: A tuple of the font ascent (the distance from the baseline to - the highest outline point) and descent (the distance from the - baseline to the lowest outline point, a negative value) - """ - return self.font.ascent, self.font.descent - - def getlength(self, text, mode="", direction=None, features=None, language=None): - """ - Returns length (in pixels with 1/64 precision) of given text when rendered - in font with provided direction, features, and language. - - This is the amount by which following text should be offset. - Text bounding box may extend past the length in some fonts, - e.g. when using italics or accents. - - The result is returned as a float; it is a whole number if using basic layout. - - Note that the sum of two lengths may not equal the length of a concatenated - string due to kerning. If you need to adjust for kerning, include the following - character and subtract its length. - - For example, instead of :: - - hello = font.getlength("Hello") - world = font.getlength("World") - hello_world = hello + world # not adjusted for kerning - assert hello_world == font.getlength("HelloWorld") # may fail - - use :: - - hello = font.getlength("HelloW") - font.getlength("W") # adjusted for kerning - world = font.getlength("World") - hello_world = hello + world # adjusted for kerning - assert hello_world == font.getlength("HelloWorld") # True - - or disable kerning with (requires libraqm) :: - - hello = draw.textlength("Hello", font, features=["-kern"]) - world = draw.textlength("World", font, features=["-kern"]) - hello_world = hello + world # kerning is disabled, no need to adjust - assert hello_world == draw.textlength("HelloWorld", font, features=["-kern"]) - - .. versionadded:: 8.0.0 - - :param text: Text to measure. - :param mode: Used by some graphics drivers to indicate what mode the - driver prefers; if empty, the renderer may return either - mode. Note that the mode is always a string, to simplify - C-level implementations. - - :param direction: Direction of the text. It can be 'rtl' (right to - left), 'ltr' (left to right) or 'ttb' (top to bottom). - Requires libraqm. - - :param features: A list of OpenType font features to be used during text - layout. This is usually used to turn on optional - font features that are not enabled by default, - for example 'dlig' or 'ss01', but can be also - used to turn off default font features for - example '-liga' to disable ligatures or '-kern' - to disable kerning. To get all supported - features, see - https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist - Requires libraqm. - - :param language: Language of the text. Different languages may use - different glyph shapes or ligatures. This parameter tells - the font which language the text is in, and to apply the - correct substitutions as appropriate, if available. - It should be a `BCP 47 language code -
-
When first published, this article received over eight hundred comments from students looking for direction and assistance with their high school art projects. Some of these comments have been published below. It is hoped that the answers provide valuable insight for others.
-Art Theme Project
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If you are looking for art themes to explore in GCSE or iGCSE lessons, the huge list below is a great starting point. Thank you to art teacher Annie Chapman for this amazing list. Some words link to art teaching resources on this website.
-Hi The Arty Teacher, I am teaching iGCSE Art and Design for the first time. Just wondering as to what you would consider as an ideal number of themes that can be introduced to a class over the course of two years. Is it several or is it a matter of concentrating on one theme only throughout the entire course? Much appreciated, thank you.
-Different teachers structure the course in different ways. At my school, we do one theme in Year 10 with two main outcomes. In year 11 they do another theme (we run this a little bit like a mock). Then they do the externally set task from January.
-Beginning today (Oct. 27) and continuing through Wednesday (Nov. 2), students, faculty, staff, alumni and members of the larger University community are invited to vote for an overall theme for PRT station murals created by students.
-Memento Mori, vanitas, mortality. Death is one of the most pervasive themes in art history. While many artworks celebrate afterlives in heaven or hell, death is most often referenced as a grim reminder of numbered days, and a powerful motivator to live well while you can. Every culture has rituals surrounding death, appearing in artwork as icons and colors. Hourglasses and wilted flowers for the Dutch, the Cuckoo bird in Japan, the Totenkopf in Germany.
-This mural, though, marks a first for Camden, and yet another bridge: Hopeworks has partnered with Mural Arts Philadelphia, marking the highly-regarded collective's first project across the Delaware River, and bringing together artists from Camden's arts community with artists in Philly.
-Bridge building was a recurring theme, not just in the design of the mural but in its very existence, said Manning. Hopeworks will soon open a new training center in Philadelphia's Kensington neighborhood.
-Asked if she envisioned future Mural Arts collaborations in Camden, Golden was confident there would be. She looks forward to Camden's artists working in Philly (some have already attended Mural Arts' most recent quarterly artists' meeting) and Philly arts doing projects in Camden.
- -Each year, over 300,000 students in Pre-K through Grade 12 create original works of art in response to a student-selected theme. This 50+ year-old program helps them explore their own thoughts, feelings and ideas, develop artistic literacy, increase confidence and find a love for learning that will help them become more successful in school and in life.
-The Public Humanities Projects program supports projects that bring the ideas of the humanities to life for general audiences through public programming. Projects must engage humanities scholarship to analyze significant themes in disciplines such as history, literature, ethics, and art history. Awards support projects that are intended to reach broad and diverse public audiences in non-classroom settings in the United States. Projects should engage with ideas that are accessible to the general public and employ appealing interpretive formats.
-Public Humanities Projects supports projects in three categories (Exhibitions, Historic Places, and Humanities Discussions), and at two funding levels (Planning and Implementation). Proposed projects may include complementary components: for example, a museum exhibition might be accompanied by a website or mobile app.
-Small and mid-sized organizations are especially encouraged to apply. We likewise welcome humanities projects tailored to particular groups, such as families, youth (including K-12 students in informal educational settings), underserved communities, and veterans.
-The 10 youth artists were led by lead artist Bijan Machen and mentored by USC students Daniel Kawah and Keviette Minor. The goal of the art project was to have the youth artists reflect and focus their art pieces on events occurring in their neighborhood and personal experiences, as well as interviewing people of various backgrounds around the USC community to gain a different perspective.
-Since 1995 Spiral Workshop has created over 70 theme curricula. Each group intertwines learning in a media such as painting, drawing, Photoshop, sculpture, alternative practices, with investigation of a theme that affects students and their communities.
-This event contains adult themes, distressing imagery, extended use of strobe lighting, smoke effects and swearing. The following items are strictly prohibited: knives, spraycans, illegal drugs, and lawyers from the Walt Disney corporation.
-Visual artists, writers, filmmakers, and playwrights concentrated many of their creative efforts on the patterns of everyday life, especially the world of work. A recurring theme was the strength and dignity of common men and women, even as they faced difficult circumstances.
-Many politically active artists worked for the New Deal projects. United by a desire to use art to promote social change, these artists sympathized with the labor movement and exhibited an affinity for left-wing politics ranging from New Deal liberalism to socialism to communism.
-Most New Deal artist-administrators believed deeply that the projects had a responsibility to reach out to as many Americans as possible and to put art to practical use. Such socially useful arts were not intended to create masterpieces, but they did produce many excellent works, allowed thousands of artists to pursue their vocation, and enriched and informed the lives of Americans.
-(Original theme graphic by Tanner Boeger, incorporating images from HRB, Phillipe Glade, and Christopher Robin Blum and art by Airpusher Collective, Marianela Fuentes, Arturo Gonzalez, and Sarahi Carillo)
-Stuart is the director of Burning Man Project's Philosophical Center and host of the Burning Man LIVE podcast. Since his first Burn in 1993 he has participated as a theme camp organizer, artist, and year-round staff member contributing to the Project's communications, education, and storytelling efforts.
-I really loved the art palette lollipops made with white chocolate. They were adorable, all the little girls thought they were the cutest thing and very special, and it perfectly spoke the theme of the party.
-The theme for 2022 is inspired by the book, The Day You Begin, by National Book Award winner Jacqueline Woodson, and two-time Pura Belpré Illustrator Award winner Rafael López. The Day You Begin is a poignant, yet heartening book about finding courage to connect with others, even when you feel scared and alone. Jacqueline Woodson's lyrical text and Rafael López's dazzling art reminds us that we all feel like outsiders sometimes-and how brave it is that we go forth anyway. And that sometimes, when we reach out and begin to share our stories, others will be happy to meet us halfway.
-Think about all the different activities and experiences you can link to your theme, so that each area of the curriculum is reflected somehow. Be creative! Ask your children for ideas, and include unusual, hands-on activities that will delight your children.
-You can deliver your thematic unit in the way that best suits your children and circumstances. Some aspects of the unit may be best delivered to the whole group, some will work better as small group work. Will you devote your whole classroom to the unit, or set aside one project corner? Can you have your whole day given over to the unit, or do you need to allow time for other core areas of your teaching? The thematic unit is completely flexible.
-Our Art Camp Unit gives you five process-art projects you can use to run an at-home/ in-class art camp. The Unit comes with printable invitations, stickers and certificates to hand out to all attendees.
-This project may help a child or teen reflect on ways to find a safe space or may simply help them feel like they have some control over their environment. It can be conducted one-to-one or in small groups.
-The activity involves imagining being lost at sea and visualizing the ideal lighthouse that would provide the right kind of guidance. This is a great activity for both children and adults, but an older group or individual might better appreciate the depth and symbolism of the project.
-HERE WE design, develop, and deliver the most compelling entertainment experiences around the world.
Our innovative attractions, immersive theme parks, world-class resorts, and new ventures fuse art with technology to change the landscape of themed entertainment.
The concept for the project was developed by Akshata Naik, a Toronto artist who has exhibited her work in Canada, Britain, and India. Akshata lives in Toronto where she is the Program and Gallery Manager at Arts Etobicoke. She also teaches at Art Ignite, Neilson Park Creative Centre, and Vibe Arts.
-The public is invited to view Frozen Voyage during Open Houses being held on Tuesday, August 27 and Wednesday, August 28 between 11:00 AM and 1:00 PM. At the Open House, join the project by folding your own boat that will be added to the artwork. The public may also see Frozen Voyage, along with the other artwork, in Council Chambers during Council Meetings.
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- diff --git a/spaces/cihyFjudo/fairness-paper-search/Lamb Of God Killadelphia Torrent _VERIFIED_ Download.md b/spaces/cihyFjudo/fairness-paper-search/Lamb Of God Killadelphia Torrent _VERIFIED_ Download.md deleted file mode 100644 index 1dd04cffb3f6c54ae9f8fde5b1a3d35ae37f5a58..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Lamb Of God Killadelphia Torrent _VERIFIED_ Download.md +++ /dev/null @@ -1,68 +0,0 @@ -## Lamb Of God Killadelphia Torrent Download - - - - - - - - - -**Lamb Of God Killadelphia Torrent Download === [https://www.google.com/url?q=https%3A%2F%2Fbyltly.com%2F2txlhF&sa=D&sntz=1&usg=AOvVaw2Ac1OAOaAAtjjQ7QMkwMOs](https://www.google.com/url?q=https%3A%2F%2Fbyltly.com%2F2txlhF&sa=D&sntz=1&usg=AOvVaw2Ac1OAOaAAtjjQ7QMkwMOs)** - - - - - - - - - - - - I'll try to create that. Here is what I created: - -# Lamb Of God Killadelphia Torrent Download: How to Get the Legendary Live Album - - - -Lamb Of God is one of the most influential and successful metal bands of the 21st century. Their live performances are legendary for their intensity, energy and brutality. One of their best live albums is Killadelphia, recorded in 2004 at the Trocadero Theatre in Philadelphia. The album captures the band at their peak, delivering a blistering set of songs from their first four albums, as well as some covers and guest appearances. - - - -If you are a fan of Lamb Of God, or just love metal music in general, you might want to get your hands on Killadelphia. However, the album is not easy to find in physical or digital formats. It was released as a CD/DVD combo in 2005, but it is now out of print and hard to come by. The album is also not available on streaming platforms like Spotify or Apple Music. - - - -So how can you listen to Killadelphia? One option is to download it from a torrent site. A torrent is a file that contains information about other files that are shared by users over a peer-to-peer network. You can use a torrent client software like BitTorrent or uTorrent to download the files you want from other users who have them. However, downloading torrents can be risky and illegal, depending on your location and the content you are downloading. You might expose your device to viruses, malware or hackers, or face legal consequences for violating copyright laws. - - - -Therefore, we do not recommend downloading Killadelphia from a torrent site. Instead, we suggest you look for other ways to get the album legally and safely. For example, you can try to find a second-hand copy of the CD/DVD combo on online marketplaces like eBay or Amazon. You can also check if your local library or music store has a copy that you can borrow or buy. Alternatively, you can watch some videos of the concert on YouTube or other video platforms. - - - -Killadelphia is a must-have for any Lamb Of God fan or metal lover. It showcases the band's raw power and talent in front of a passionate crowd. We hope you find a way to enjoy this amazing live album without resorting to torrent downloads. - -I'll try to continue the article. Here is what I created: - -If you are wondering why Killadelphia is so special, here are some reasons why it is considered one of the best live metal albums ever. First of all, the sound quality is excellent, thanks to the production of Machine, who also worked on Lamb Of God's studio albums. The mix is clear and balanced, allowing you to hear every instrument and vocal nuance. The album also features some bonus tracks that were not included in the DVD, such as "Ruin", "11th Hour" and "Laid to Rest". - - - -Secondly, the performance is flawless and intense. Lamb Of God plays with precision and passion, delivering every song with full force and emotion. The band members are in sync with each other and with the audience, creating a powerful connection and atmosphere. The crowd is also amazing, singing along, moshing and cheering throughout the show. You can feel the energy and excitement of being there. - - - -Thirdly, the album has some memorable moments that make it unique and unforgettable. For example, there is a guest appearance by Chris Adler's brother Willie Adler, who plays guitar on "Pariah". There is also a cover of Metallica's "Creeping Death", which features vocals by Rob Dukes of Exodus and Alex Skolnick of Testament. There is also a hilarious prank by Randy Blythe on Mark Morton, who gets hit by a pie in the face during "Black Label". And of course, there is the infamous incident where Blythe jumps into the crowd and gets into a fight with a fan who threw a bottle at him. - - - -Killadelphia is more than just a live album. It is a document of Lamb Of God's history and legacy as one of the most influential and successful metal bands of the 21st century. It is a testament to their musical skills and artistic vision. It is a tribute to their fans and their city. It is a masterpiece of metal music that deserves to be heard by everyone. - - dfd1c89656 - - - - - diff --git a/spaces/cihyFjudo/fairness-paper-search/LockLizard.PDC.Un-Protector.v2.5..rar A Simple and Effective Solution to Unlock PDC Files.md b/spaces/cihyFjudo/fairness-paper-search/LockLizard.PDC.Un-Protector.v2.5..rar A Simple and Effective Solution to Unlock PDC Files.md deleted file mode 100644 index 4df115b4a871b56394edc3acf48cdf6a618e1d3e..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/LockLizard.PDC.Un-Protector.v2.5..rar A Simple and Effective Solution to Unlock PDC Files.md +++ /dev/null @@ -1,6 +0,0 @@ -
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The Tennessee Coal, Iron and Railroad Company (TCI) one of the original 12 companies listed in the Dow Jones Industrial Index, was one of the largest users of prison laborers, mostly comprised of African Americans convicted of petty crimes. The number of convicts employed increased after United States Steel, the largest corporation in the world at the time (formerly known as U.S. Steel and USX), acquired TCI in 1907. The working and living conditions for these prisoners were brutal, as companies leasing convicts sought to house, clothe and feed them for minimal expense, with little interest in their survival. Justice-involved individuals were housed in rough board shanties unfit for the habitation of human beings. Torture and beatings were common, and countless individuals perished from abuse; poor and dangerous working conditions; communicable diseases, such as tuberculosis, malaria, and pneumonia; and from environmental conditions like contaminated water.
-Convict Lake and Creek are so named as the result of an AMBUSH encounter here September 17, 1871, when a group of inmates escaped from prison in Carson City. Sheriff George Hightower eventually caught up with the convicts and a shoot out took place. Robert Morrison, a Benton Merchant and Mono Jim along with other other posse members encountered the convicts on the present Convict Creek, then known as Monte Diablo Creek. In the encounter, Robert Morrison and Mono Jim were killed. The convicts escaped and were eventually captured later in Round Valley.
-The The Convict
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"This beautifully written book leads its readers on the journey from Emancipation to the devastating convict-leasing system in Georgia. . . . [and] examines the exploitation of black women's bodies, the beginnings of mass incarceration, and the rise of the modern New South."--Erica Armstrong Dunbar, The Nation
-As fans may recall, in the ninth episode of Season 3, Michael learns that Martin Nash, a Black employee who recently transferred to the Scranton branch from Stamford, is a reformed convict. After Nash (played by actor and comedian Wayne Wilderson) reveals he did time for involvement in insider trading, he talks about his experience in prison, which sounds a little better than working at Dunder Mifflin. Heartbroken over the idea that his employees might prefer prison to working with him, Michael turns into Prison Mike to teach everyone that prison is bad.
-One of those lines takes place after the conference room scene in which Michael, Pam, Angela, and Kevin learn that the company receives a Work Opportunity Tax Credit for employing Nash, an ex-convict.
-A death row inmate awaiting execution, asked as a last wish a pencil and paper. After writing for several minutes, the convict called the prison guard and asked that this letter be handed over to his biological mother.
-The purported missive from death row included no information about the identity of its writer, his location, when he wrote it, or the crimes he was charged with. Moreover, it was accompanied by a completely unrelated photograph of "hot convict" Jeremy Meeks, who became internationally notorious when his exceptionally flattering mugshot went viral in 2013. Meeks was sentenced on weapons charges, but he was not involved with a capital case (and therefore was neither sentenced to death nor executed).
-There are three main issues that need to be taken into consideration in the context of pre-trial detention: firstly, pre-trial detention is overused in most countries worldwide and in many developing countries the size of the pre-trial prisoner population is larger than that of the convicted prisoner population. This situation contradicts the provisions in international standards, including ICCPR, that provide for the limited use of pre-trial detention, only when certain conditions are present. Secondly, pre-trial detention is the period most open to abuse in the criminal justice process. Recognizing the particular vulnerability of pre-trial detainees, international human rights instruments provide for a large number of very specific safeguards to ensure that the rights of detainees are not abused, that they are not ill-treated and their access to justice not hindered. Thirdly, although pre-trial detainees should be presumed innocent until found guilty by a court of law, and treated as such, conditions in pre-trial detention are often much worse than those of prisons for convicted prisoners. In addition, the lack of resources for prisons in many low-income countries means that people in detention do not have access to legal advice and assistance, with the result being that they may overstay on remand, and/or not receive a fair trial, further adding to the congestion of prisons. Therefore, improving access to justice, supporting legal and paralegal aid programmes, improving information management and cooperation between courts and prisons, to speed up the processing of cases, as well as assisting with the development of safeguards for pre-trial detainees, such as independent monitoring and inspection mechanisms, comprise important elements of UNODC's work in the field of penal reform.
- -Built in 1840 (not 1790) the Success had many lives, first as a shipping vessel serving British India and then as a passenger ship ferrying immigrants (not convicts) to Australia. During one trip to Australia the Success arrived right at the peak of the Australian gold rush and her crew deserted to strike it rich. Without mariners the ship was left moored near Melbourne, Australia, where it became a prison hulk and later a stores ship.
-Children who were orphaned, removed from negligent parents, or who were juvenile offenders were especially vulnerable after emancipation. They could end up in the convict leasing system as "'apprentices" and fall once more into white planters' hands. Unknown location, ca. 1903. Photo credit: Detroit Publishing Company Collection, Library of Congress.
-Often completely innocent of the crimes of which they were accused, these African Americans were forced to work from sunup to sundown, in chains, under the lash and gunpoint of the white guards. Under convict leasing, Black people going about their day could be rounded up, convicted of made-up crimes, separated from their families, processed through an all-white court, and treated with little to no regard to their human value. In his book Texas Tough, historian Robert Perkinson estimates that at least 30,000 died in the convict leasing system across the South over 55 years. One can find blatant and insidious parallels between convict leasing and mass incarceration and the prison-industrial complex. As Bryan Stevenson says, "slavery did not end in 1865. It just evolved." However, convict leasing rarely appears in history textbooks. The generational loss and trauma in Black families is left unexamined.
-Without ever learning about convict leasing, how can Americans make sense of the discovery of a mass gravesite in a prosperous suburban town? Will the public sweep these uncomfortable truths under the rug again?
-The land where the 95 African American remains were unearthed during construction is owned by the Fort Bend Independent School District, which purchased this former convict camp and state prison land in 2011 and has been accused of mishandling the remains. At the time of writing, Fort Bend ISD continues to own and operate this cemetery unilaterally against community wishes. Moreover, there is no historical marker or other information at the site that tells the history of what happened there. They have even renamed the site with one that is unrelated to the history of convict leasing.
-Americans must find the hidden chapters of their history and really begin to understand the legacy of racial oppression that has strengthened the walls of white supremacy. A version of history that omits these chapters has stolen a chance for the nation to learn from it, and to fix what has been broken by it. As Americans seek to dismantle Confederate monuments, they must also actively create new monuments and narratives that broaden their understanding of justice, democracy, and humanity. I believe that building a memorial dedicated to victims and survivors of convict leasing in Sugar Land, Texas is a step in the right direction.
-After breakfast at The Flourmill Cafe, drive through the countryside for just under an hour to the Toodyay Red Hill Convict Road Station Ruins, constructed in the 1850s. The camp housed the convict road gangs that built and maintained the road to Perth. Back then, there were five buildings made of rammed earth; now the ruins of only one remain.
Established in 1853, it housed 60 ticket-of-leave convicts and put them to work at the Geraldine Lead Mine and local pastoral stations. After exploring the depot, and the nearby pretty town of Northampton, set off on the five-hour journey back to Perth, this time taking the scenic Indian Ocean Drive.
-
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\ No newline at end of file diff --git a/spaces/cihyFjudo/fairness-paper-search/Velamma - Episode 57 - 50 Shades Of Savita [Adult Comics] - AlmeriasVelamma - Episode 57 - 50 33.md b/spaces/cihyFjudo/fairness-paper-search/Velamma - Episode 57 - 50 Shades Of Savita [Adult Comics] - AlmeriasVelamma - Episode 57 - 50 33.md deleted file mode 100644 index b98116fda0680bea753a727784c27b980e7aba03..0000000000000000000000000000000000000000 --- a/spaces/cihyFjudo/fairness-paper-search/Velamma - Episode 57 - 50 Shades Of Savita [Adult Comics] - AlmeriasVelamma - Episode 57 - 50 33.md +++ /dev/null @@ -1,6 +0,0 @@ -
Velamma - Episode 57 - 50 Shades of Savita [Adult Comics] - {Almerias}Velamma - Episode 57 - 50 33
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- diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/audioread/maddec.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/audioread/maddec.py deleted file mode 100644 index a7010af780162fa328951897ffacb99ab00bc9dd..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/audioread/maddec.py +++ /dev/null @@ -1,86 +0,0 @@ -# This file is part of audioread. -# Copyright 2011, Adrian Sampson. -# -# Permission is hereby granted, free of charge, to any person obtaining -# a copy of this software and associated documentation files (the -# "Software"), to deal in the Software without restriction, including -# without limitation the rights to use, copy, modify, merge, publish, -# distribute, sublicense, and/or sell copies of the Software, and to -# permit persons to whom the Software is furnished to do so, subject to -# the following conditions: -# -# The above copyright notice and this permission notice shall be -# included in all copies or substantial portions of the Software. - -"""Decode MPEG audio files with MAD (via pymad).""" -import mad - -from . import DecodeError -from .base import AudioFile - - -class UnsupportedError(DecodeError): - """The file is not readable by MAD.""" - - -class MadAudioFile(AudioFile): - """MPEG audio file decoder using the MAD library.""" - def __init__(self, filename): - self.fp = open(filename, 'rb') - self.mf = mad.MadFile(self.fp) - if not self.mf.total_time(): # Indicates a failed open. - self.fp.close() - raise UnsupportedError() - - def close(self): - if hasattr(self, 'fp'): - self.fp.close() - if hasattr(self, 'mf'): - del self.mf - - def read_blocks(self, block_size=4096): - """Generates buffers containing PCM data for the audio file. - """ - while True: - out = self.mf.read(block_size) - if not out: - break - yield bytes(out) - - @property - def samplerate(self): - """Sample rate in Hz.""" - return self.mf.samplerate() - - @property - def duration(self): - """Length of the audio in seconds (a float).""" - return float(self.mf.total_time()) / 1000 - - @property - def channels(self): - """The number of channels.""" - if self.mf.mode() == mad.MODE_SINGLE_CHANNEL: - return 1 - elif self.mf.mode() in (mad.MODE_DUAL_CHANNEL, - mad.MODE_JOINT_STEREO, - mad.MODE_STEREO): - return 2 - else: - # Other mode? - return 2 - - def __del__(self): - self.close() - - # Iteration. - def __iter__(self): - return self.read_blocks() - - # Context manager. - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - self.close() - return False diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/middleware/httpsredirect.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/middleware/httpsredirect.py deleted file mode 100644 index b7a3d8e078574e87dc6e345d621f5a596c3bdc1e..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/middleware/httpsredirect.py +++ /dev/null @@ -1,3 +0,0 @@ -from starlette.middleware.httpsredirect import ( # noqa - HTTPSRedirectMiddleware as HTTPSRedirectMiddleware, -) diff --git a/spaces/codebox/diffuse-flood/build/_app/immutable/chunks/2-efe340ca.js b/spaces/codebox/diffuse-flood/build/_app/immutable/chunks/2-efe340ca.js deleted file mode 100644 index 4a7328a64257f71e2c5e75df16d0907faa5eb020..0000000000000000000000000000000000000000 --- a/spaces/codebox/diffuse-flood/build/_app/immutable/chunks/2-efe340ca.js +++ /dev/null @@ -1 +0,0 @@ -import{default as m}from"../components/pages/_page.svelte-2a5d0087.js";import"./index-a207c28c.js";export{m as component}; diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ac3enc_template.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ac3enc_template.c deleted file mode 100644 index be4ecebc9ccc81d1e149e016856af693cadba207..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ac3enc_template.c +++ /dev/null @@ -1,403 +0,0 @@ -/* - * AC-3 encoder float/fixed template - * Copyright (c) 2000 Fabrice Bellard - * Copyright (c) 2006-2011 Justin Ruggles
-
Ambient Music Mod APK Download: Enjoy the Pixel's Now Playing Feature on Any Android Device
-Do you love listening to music and discovering new songs? Do you wish your phone could recognize any song playing around you and show it on your lock screen? If you answered yes, then you might be interested in Ambient Music Mod, a free app that ports the Google Pixel's Now Playing feature to other Android devices. In this article, we will tell you what Ambient Music Mod is, how to install it, and what benefits it can bring to your musical experience.
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What is Ambient Music Mod?
-Ambient Music Mod is a Shizuku/Sui app that ports Now Playing from Pixels to other Android devices. Now Playing is a feature that automatically identifies songs playing in the background using an offline database and displays them on the lock screen or in a history list. It was introduced by Google in 2017 with the Pixel 2 and has remained exclusive to the Pixel lineup ever since.
-Ambient Music Mod was created by Kieron Quinn, also known as Quinny899 on XDA Forums, who managed to port the feature to other Android smartphones using Shizuku or Sui Magisk module. Shizuku is a service that allows third-party apps access to system-level APIs through ADB, while Sui is a Magisk module that provides rootless superuser access. Ambient Music Mod does not require root access on devices running Android 12 or higher, but it does require root access on older Android versions.
-Features of Ambient Music Mod
-Ambient Music Mod offers a lot of features that make it a great app for music lovers. Here are some of them:
-Full Now Playing support
-Ambient Music Mod uses the latest version of Now Playing from Pixel devices and the latest music databases. It can recognize over 100,000 songs from various genres and languages, even if they are not very popular or mainstream. It can also recognize songs that are not in the local database using Google Assistant's recognition engine.
-Automatic Ambient Music recognition
-Ambient Music Mod runs in the background and listens for music playing around you. It can recognize songs every 15 seconds, every minute, or every 5 minutes, depending on your preference. You can also adjust the sensitivity and gain settings to improve the recognition accuracy. You can choose whether to show notifications or not when a song is recognized.
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Now Playing History, Favourites and Summary support
-Ambient Music Mod keeps track of all the songs that it recognizes and shows them in a history list. You can view the song title, artist name, album art, date and time of recognition, and source of recognition (local or online). You can also mark songs as favourites and view them in a separate list. You can also view a summary of your musical preferences based on the songs that you have listened to.
-Manual and On Demand recognition
-If you want to manually trigger a recognition, you can use the app's widget or shortcut. You can also use the On Demand recognition feature, which uses Google Assistant's recognition engine for songs that are not in the local database. This feature requires an internet connection and works on supported devices only.
-Lock screen display and database customization
-Ambient Music Mod can show the recognized songs on your lock screen using an accessibility service. You can customize the appearance and position of the lock screen display according to your liking. You can also customize the database of songs that Ambient Music Mod uses by adding or removing songs from the app's settings.
-How to install Ambient Music Mod?
-If you want to try Ambient Music Mod on your Android device, you will need to follow some steps to install it properly. Here are the requirements and instructions for installing Ambient Music Mod:
-Requirements for Ambient Music Mod
-To install Ambient Music Mod, you will need the following:
--
-
- An Android device running Android 8.0 Oreo or higher (Android 12 or higher is recommended) -
- A Shizuku or Sui Magisk module installed on your device (Sui is recommended for Android 12 or higher) -
- Ambient Music Mod APK file downloaded from the official XDA thread -
- Google Play Services and Google Assistant installed and updated on your device -
- An internet connection for downloading the music database and using the On Demand recognition feature -
Download and setup steps for Ambient Music Mod
-Once you have met the requirements, you can follow these steps to install and set up Ambient Music Mod on your device:
--
-
- Download the latest version of Ambient Music Mod APK file from the official XDA thread and save it on your device. -
- Install the APK file by tapping on it and granting the required permissions. -
- Open the app and grant the Shizuku or Sui permission when prompted. -
- Download the music database from the app's settings. You can choose between a small database (about 100 MB) or a large database (about 500 MB). -
- Enable the accessibility service for Ambient Music Mod from your device's settings. -
- Configure the app's settings according to your preference. You can adjust the recognition interval, sensitivity, gain, notification, lock screen display, database customization, and more. -
- Enjoy the Pixel's Now Playing feature on your Android device! -
Benefits of Ambient Music Mod
-Ambient Music Mod is not only a cool app that lets you enjoy the Pixel's Now Playing feature on your Android device, but it also has some benefits that can enhance your musical experience. Here are some of them:
-What is ambient music and why is it good for you?
-Ambient music is a genre of music that focuses on creating a mood or atmosphere rather than a melody or rhythm. It often uses sounds from nature, synthesizers, drones, loops, and minimal vocals. It is usually played at low volumes and in the background, creating a subtle and relaxing sound environment.
-Ambient music can have positive effects on your brain and mood, such as:
-Definition and characteristics of ambient music
--
-
- Reducing stress and anxiety by lowering your heart rate, blood pressure, and cortisol levels -
- Improving focus and concentration by blocking out distracting noises and enhancing cognitive performance -
- Enhancing creativity and imagination by stimulating your right brain hemisphere and inducing a state of flow -
- Promoting sleep and relaxation by synchronizing your brain waves with the sound frequencies and inducing a state of calmness -
Therapeutic effects of ambient music on the brain and mood
-Ambient music can also help you discover new songs and artists that you might not have heard before. Ambient Music Mod can recognize songs from various genres and languages, including ambient music. You can view the history of recognized songs and explore more about them online. You can also mark songs as favourites and create your own playlist of ambient music.
-Examples of ambient music and artists
-If you are new to ambient music or want to expand your musical horizons, here are some examples of ambient music and artists that you can check out:
--
-
- Brian Eno - The pioneer of ambient music who coined the term in 1978. His albums include Music for Airports, Apollo: Atmospheres and Soundtracks, and Ambient 4: On Land. -
- Laurie Spiegel - A composer and electronic musician who created ambient music using algorithms and computer programs. Her albums include The Expanding Universe, Unseen Worlds, and Obsolete Systems. -
- The Orb - A British electronic music group that combines ambient music with elements of dub, house, techno, and psychedelia. Their albums include The Orb's Adventures Beyond the Ultraworld, U.F.Orb, and Orblivion. -
- Aphex Twin - A British electronic musician and producer who is known for his experimental and diverse styles of ambient music, IDM, techno, and acid. His albums include Selected Ambient Works 85–92, Selected Ambient Works Volume II, and Drukqs. -
- Eno • Hyde - A collaboration between Brian Eno and Karl Hyde of Underworld that blends ambient music with electronic beats and vocals. Their albums include Someday World, High Life, and Everything That Happens Will Happen Today. -
Conclusion
-Ambient Music Mod is a free app that lets you enjoy the Pixel's Now Playing feature on any Android device. It can automatically recognize songs playing in the background using an offline database and display them on your lock screen or in a history list. It can also recognize songs that are not in the local database using Google Assistant's recognition engine. You can customize the app's settings to suit your preference and musical taste.
-Ambient Music Mod can also help you discover and appreciate ambient music, a genre of music that creates a mood or atmosphere rather than a melody or rhythm. Ambient music can have positive effects on your brain and mood, such as reducing stress and anxiety, improving focus and concentration, enhancing creativity and imagination, and promoting sleep and relaxation. You can also explore more about ambient music and artists online using Ambient Music Mod.
-If you are a music lover and want to try Ambient Music Mod on your Android device, you can download it from the official XDA thread and follow the installation and setup steps. You will need a Shizuku or Sui Magisk module installed on your device, as well as Google Play Services and Google Assistant. You will also need an internet connection for downloading the music database and using the On Demand recognition feature.
-FAQs
-Here are some frequently asked questions about Ambient Music Mod:
--
-
- Q: Does Ambient Music Mod work offline?
-A: Yes, Ambient Music Mod can work offline for songs that are in the local database. However, you will need an internet connection for downloading the music database and using the On Demand recognition feature.
- - Q: Does Ambient Music Mod drain battery?
-A: No, Ambient Music Mod does not drain battery significantly. It uses a low-power mode to listen for music in the background and only wakes up when a song is recognized.
- - Q: Does Ambient Music Mod work with headphones?
-A: Yes, Ambient Music Mod can work with headphones if you enable the option to use the microphone as the audio source in the app's settings.
- - Q: Does Ambient Music Mod work with Spotify?
-A: Yes, Ambient Music Mod can work with Spotify if you enable the option to use the media session as the audio source in the app's settings.
- - Q: How can I update Ambient Music Mod?
-A: You can update Ambient Music Mod by downloading the latest version of the APK file from the official XDA thread and installing it over the existing app. You can also check for updates from the app's settings.
-
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Clash of Clans Hile Apk Indir Android Oyun Club: How to Download and Install the Modded Version of the Popular Strategy Game
-Clash of Clans is one of the most popular strategy games on mobile devices, with millions of players around the world. But what if you want to enjoy the game without spending money or waiting for long hours? That's where Clash of Clans Hile Apk comes in. In this article, we will show you how to download and install the modded version of Clash of Clans from Android Oyun Club, a website that offers free and safe downloads of various Android games. We will also explain what Clash of Clans Hile Apk is, how it differs from the original version, and what are the advantages and disadvantages of using it. So, if you are ready to take your gaming experience to the next level, read on!
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What is Clash of Clans and why is it so popular?
-Clash of Clans is a freemium strategy game developed by Supercell, a Finnish company that also created other hit games like Hay Day, Boom Beach, and Brawl Stars. The game was released in 2012 for iOS devices and in 2013 for Android devices. Since then, it has become one of the most downloaded and highest-grossing games on both platforms.
-The gameplay and features of Clash of Clans
-The game is set in a fantasy world where you have to build your own village, train your troops, and fight against other players or computer-generated enemies. You can join or create a clan with other players to cooperate in wars, donate and receive troops, chat, and compete in clan games. You can also participate in special events, seasons, challenges, and leagues to earn rewards and trophies.
-The game offers a variety of buildings, troops, spells, heroes, and items that you can upgrade and customize according to your preference and strategy. You can also explore different maps, modes, and scenarios that add more fun and challenge to the game.
-The benefits and drawbacks of playing Clash of Clans
-One of the main benefits of playing Clash of Clans is that it is very addictive and entertaining. You can spend hours building your village, planning your attacks, defending your base, and interacting with other players. You can also enjoy the stunning graphics, sound effects, animations, and music that make the game more immersive and realistic.
-Another benefit is that it is very social and community-oriented. You can make friends with other players from different countries and cultures, share tips and strategies, support each other in battles, and have fun together. You can also learn new skills like leadership, teamwork, communication, problem-solving, creativity, and decision-making.
However, playing Clash of Clans also has some drawbacks. One of them is that it can be very frustrating and time-consuming. You have to wait for long periods of time to upgrade your buildings, train your troops, and replenish your resources. You also have to deal with losing your loot, trophies, and progress when you are attacked by other players or fail to complete a mission.
-Another drawback is that it can be very expensive and tempting. The game uses two currencies: gold and elixir, which you can earn by playing the game, and gems, which you can buy with real money or get from certain achievements. Gems can be used to speed up the waiting time, buy more resources, and unlock special items. However, gems are very scarce and costly, and you may feel pressured to spend more money to get ahead in the game.
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What is Clash of Clans Hile Apk and how does it differ from the original version?
-Clash of Clans Hile Apk is a modified version of Clash of Clans that allows you to enjoy the game without the limitations and restrictions of the original version. It is also known as Clash of Clans Mod Apk, Clash of Clans Hack Apk, or Clash of Clans Cheat Apk. It is not an official product of Supercell, but a third-party creation that is distributed by various websites and platforms.
-The advantages and disadvantages of using Clash of Clans Hile Apk
-The main advantage of using Clash of Clans Hile Apk is that it gives you unlimited access to all the features and resources of the game. You can get unlimited gold, elixir, gems, dark elixir, and other items without spending any money or waiting for any time. You can also unlock all the buildings, troops, spells, heroes, and items without completing any requirements or levels. You can also customize your village, troops, and heroes according to your liking and preference.
-Another advantage is that it gives you more freedom and fun in playing the game. You can experiment with different strategies, tactics, and combinations without worrying about losing anything or being penalized. You can also explore different maps, modes, and scenarios that are not available in the original version. You can also play offline without needing an internet connection or a Google Play account.
-However, using Clash of Clans Hile Apk also has some disadvantages. One of them is that it can be very risky and dangerous for your device and account. Since it is not an official product of Supercell, it may contain viruses, malware, spyware, or other harmful elements that can damage your device or steal your personal information. It may also cause your account to be banned or suspended by Supercell for violating their terms of service and policies.
-Another disadvantage is that it can be very boring and unsatisfying in the long run. Since you have everything at your disposal, you may lose the sense of challenge, achievement, and progression that makes the game exciting and rewarding. You may also miss out on the social and community aspects of the game that make it more enjoyable and engaging. You may also face compatibility issues with updates, patches, or new features that are released by Supercell.
How to download and install Clash of Clans Hile Apk from Android Oyun Club?
-If you are interested in trying out Clash of Clans Hile Apk, you can download and install it from Android Oyun Club, a website that offers free and safe downloads of various Android games. However, you need to follow some steps and requirements to do so successfully and safely.
-The steps and requirements for downloading and installing Clash of Clans Hile Apk
-Here are the steps and requirements for downloading and installing Clash of Clans Hile Apk from Android Oyun Club:
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-
- Make sure that your device has enough storage space and battery life to download and install the file. -
- Make sure that your device is compatible with the file. The file size is about 150 MB and the minimum Android version required is 4.1. -
- Make sure that your device allows the installation of unknown sources. You can enable this option by going to Settings > Security > Unknown Sources and toggling it on. -
- Go to the official website of Android Oyun Club at https://androidoyun.club/ and search for Clash of Clans Hile Apk in the search bar. Alternatively, you can use this direct link: https://androidoyun.club/2023/03/clash-of-clans-v13-675-6-mod-apk-para-hileli.html. -
- Click on the download button and wait for the file to be downloaded to your device. -
- Locate the file in your device's file manager and tap on it to start the installation process. -
- Follow the instructions on the screen and wait for the installation to be completed. -
- Launch the game from your app drawer or home screen and enjoy! -
The tips and tricks for using Clash of Clans Hile Apk effectively
-Here are some tips and tricks for using Clash of Clans Hile Apk effectively:
--
-
- Create a new account or use a secondary account to play the game. Do not use your main account or link it to your Google Play account, as this may result in a ban or suspension by Supercell. -
- Do not use the modded version to play online with other players or join clans, as this may cause errors, crashes, or bans. Use it only for offline or solo play. -
- Do not abuse the unlimited resources or items, as this may make the game too easy or boring. Use them moderately and wisely to enhance your gaming experience. -
- Do not update the game from the Google Play Store or any other source, as this may overwrite or delete the modded version. Update it only from Android Oyun Club or wait for a new modded version to be released. -
- Do not forget to backup your data and progress regularly, as the modded version may be unstable or unreliable. You can use a cloud service or an external storage device to do so. -
Conclusion
-In conclusion, Clash of Clans Hile Apk is a modified version of Clash of Clans that allows you to enjoy the game without the limitations and restrictions of the original version. You can download and install it from Android Oyun Club, a website that offers free and safe downloads of various Android games. However, you need to follow some steps and requirements to do so successfully and safely. You also need to be aware of the advantages and disadvantages of using it, as well as some tips and tricks for using it effectively. We hope that this article has helped you learn more about Clash of Clans Hile Apk and how to download and install it from Android Oyun Club. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!
-FAQs
-Q1: Is Clash of Clans Hile Apk legal and safe to use?
-A1: Clash of Clans Hile Apk is not legal or endorsed by Supercell, the developer of Clash of Clans. It is a third-party creation that violates their terms of service and policies. Therefore, using it may result in a ban or suspension by Supercell. Moreover, Clash of Clans Hile Apk may not be safe to use, as it may contain viruses, malware, spyware, or other harmful elements that can damage your device or steal your personal information. Therefore, using it is at your own risk and discretion.
-Q2: Can I play Clash of Clans Hile Apk with other players online?
-A2: No, you cannot play Clash of Clans Hile Apk with other players online. The modded version is not compatible with the original version, and it may cause errors, crashes, or bans if you try to connect to the online servers or join clans. The modded version is only for offline or solo play.
-Q3: How can I update Clash of Clans Hile Apk to the latest version?
-A3: You cannot update Clash of Clans Hile Apk from the Google Play Store or any other source, as this may overwrite or delete the modded version. You can only update it from Android Oyun Club or wait for a new modded version to be released. To update it from Android Oyun Club, you need to follow the same steps and requirements as downloading and installing it. However, you may need to uninstall the previous version first before installing the new one.
-Q4: What are some alternatives to Clash of Clans Hile Apk?
-A4: If you are looking for some alternatives to Clash of Clans Hile Apk, you can try some other modded versions of Clash of Clans that are available on different websites and platforms. Some examples are Clash of Lights, Clash of Magic, Clash of Souls, and PlenixClash. However, you need to be careful and cautious when using these alternatives, as they may have the same or worse risks and drawbacks as Clash of Clans Hile Apk.
-Q5: Where can I find more information and support for Clash of Clans Hile Apk?
-A5: If you want to find more information and support for Clash of Clans Hile Apk, you can visit the official website of Android Oyun Club at https://androidoyun.club/ or their social media pages on Facebook, Twitter, Instagram, and YouTube. You can also contact them via email at info@androidoyun.club or via their contact form on their website. You can also check out some online forums, blogs, videos, or reviews that discuss or review Clash of Clans Hile Apk.
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Gacha Life Chat Mod APK: A Fun and Creative Way to Express Yourself
-If you are a fan of anime and games, you might have heard of Gacha Life. It is a popular game that lets you create your own characters, stories, and scenes in an anime-style world. But did you know that there is a way to make the game even more fun and exciting? That's right, with Gacha Life Chat Mod APK, you can unlock all the features of the game and chat with other players online. In this article, we will tell you everything you need to know about this modded version of the game, including how to download and install it, what are the benefits and drawbacks of using it, and some tips and tricks to enjoy it.
-What is Gacha Life?
-A popular anime-style game for mobile devices
-Gacha Life is a game developed by Lunime, a company that specializes in creating anime-themed games. The game was released in October 2018 for Android and iOS devices, and has since gained millions of fans around the world. The game is rated 4.4 out of 5 stars on Google Play Store and 4.6 out of 5 stars on App Store.
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A platform for creating and sharing stories, characters, and scenes
-The main feature of Gacha Life is that it allows you to create your own anime characters using a variety of options, such as hairstyles, outfits, accessories, weapons, and more. You can also customize their personality traits, such as their likes, dislikes, hobbies, and relationships. You can then use your characters to create stories and scenes using different backgrounds, props, poses, and dialogue. You can also share your creations with other players online or download them to your device.
-What is Gacha Life Chat Mod APK?
-A modified version of the original game that unlocks all features
-Gacha Life Chat Mod APK is a modified version of the original game that gives you access to all the features that are otherwise locked or limited in the official version. For example, with this mod apk, you can get unlimited gems and coins, which are the in-game currencies that you need to buy items and upgrade your characters. You can also unlock all the items in the shop, such as clothes, accessories, pets, and more. You can also access all the modes in the game, such as Studio Mode, Life Mode, Gacha Mode, and Mini-Games.
-A way to chat with other players and make new friends
-Another feature that makes Gacha Life Chat Mod APK different from the original game is that it allows you to chat with other players online. You can join or create chat rooms where you can talk to other players who share your interests and hobbies. You can also send messages, stickers, emojis, and gifts to your friends. You can also use voice chat or video chat to communicate with them. You can also join or create clubs where you can meet other players who have similar tastes in anime and games.
-How to download and install Gacha Life Chat Mod APK?
-The steps to get the modded game on your device
-If you want
If you want to download and install Gacha Life Chat Mod APK, you need to follow these steps:
--
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- Find a reliable source to download the mod apk file. You can use the link below to get the latest version of the mod apk from APKMODY, a trusted website that provides modded games and apps. -
- Download and install APKMODY Installer from Google Play Store or from the link below. This is an app that helps you install apk files with ease. -
- Open APKMODY Installer and select Install APKs. Navigate to the location of the downloaded mod apk file and select it. -
- Select Install on the installation window. Wait for the installation to complete. -
- Alternatively, you can use an emulator like MemuPlay on your PC to install the mod apk from Google Play Store. This is a safer option as it reduces the risk of malware and viruses. -
The precautions to take before installing the mod apk
-Before you install the mod apk, you should take some precautions to avoid any problems or issues. Here are some tips to follow:
--
-
- Make sure you have enough storage space on your device. The mod apk file is about 100 MB in size, so you need at least 200 MB of free space to install it. -
- Enable Unknown Sources on your device. This is a setting that allows you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. -
- Disable antivirus or firewall apps on your device. These apps may interfere with the installation process or detect the mod apk as a threat. You can enable them again after the installation is done. -
- Backup your data and progress in the original game. The mod apk may overwrite or delete your data and progress in the original game, so it is advisable to backup your data before installing the mod apk. You can use cloud services or external storage devices to backup your data. -
What are the benefits of using Gacha Life Chat Mod APK?
-Access to unlimited gems, coins, and items
-One of the main benefits of using Gacha Life Chat Mod APK is that you can get unlimited gems and coins, which are the in-game currencies that you need to buy items and upgrade your characters. You can also get unlimited stamina, which is the energy that you need to play the game. With unlimited resources, you can buy anything you want in the shop, such as clothes, accessories, pets, and more. You can also use gems and coins to gacha for rare and exclusive items that are not available in the normal version of the game.
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Ability to customize your avatar and chat room
-Another benefit of using Gacha Life Chat Mod APK is that you can customize your avatar and chat room to your liking. You can change your avatar's appearance, such as their hair, eyes, skin, clothes, and accessories. You can also change their personality traits, such as their likes, dislikes, hobbies, and relationships. You can also customize your chat room by choosing different themes, backgrounds, stickers, emojis, and gifts. You can also invite your friends to join your chat room or join other chat rooms that interest you.
-Opportunity to explore different modes and mini-games
-A third benefit of using Gacha Life Chat Mod APK is that you can explore different modes and mini-games that are not available in the original game. For example, you can play Studio Mode, where you can create your own stories and scenes using your characters and backgrounds. You can also play Life Mode, where you can interact with NPCs and other players in different locations. You can also play Gacha Mode, where you can gacha for items and characters using gems and coins. You can also play Mini-Games, where you can earn gems and coins by playing fun and challenging games.
-What are the drawbacks of using Gacha Life Chat Mod APK?
-Potential risks of malware, viruses, and bans
-One of the main drawbacks of using Gacha Life Chat Mod APK is that it may pose some risks to your device and account. Since the mod apk is not an official version of the game, it may contain malware or viruses that can harm your device or steal your personal information. You should always download the mod apk from a reliable source and scan it with an antivirus app before installing it. You should also avoid clicking on suspicious links or ads that may redirect you to malicious websites or apps.
-Another risk of using Gacha Life Chat Mod APK is that it may result in a ban from the game or chat service. Since the mod apk violates the terms of service of the game and chat service, it may be detected by their security systems and result in a ban from accessing their features or servers. You should always use the mod apk at your own risk and discretion. You should also avoid using the mod apk for illegal or unethical purposes, such as cheating, hacking, or harassing other players.
-Possible compatibility issues and glitches
-A second drawback of using Gacha Life Chat Mod APK is that it may cause some compatibility issues or glitches with your device or game. Since the mod apk is not an official version of the game, it may not be compatible with all devices or operating systems. It may also not be updated regularly or in sync with the original game. This may cause some errors or bugs in the game or chat service, such as crashes, freezes, lags, or missing features. You should always check the compatibility and requirements of the mod apk before downloading and installing it. You should also backup your data and progress in case something goes wrong.
-Conclusion
-Gacha Life Chat Mod APK is a modified version of the original game that unlocks all features and allows you to chat with other players online. It has many benefits, such as unlimited resources, customization options, and different modes and mini-games. However, it also has some drawbacks, such as potential risks of malware, viruses, and bans, as well as possible compatibility issues and glitches. You should always download and install the mod apk from a reliable source and take some precautions before using it. You should also use it responsibly and respectfully.
-We hope this article has helped you learn more about Gacha Life Chat Mod APK and how to use it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-FAQs
--
-
- Is Gacha Life Chat Mod APK safe to use? -
- How can I update Gacha Life Chat Mod APK? -
- Can I use Gacha Life Chat Mod APK on PC? -
- Can I use Gacha Life Chat Mod APK with other mods or hacks? -
- Can I play Gacha Life Chat Mod APK offline? -
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\ No newline at end of file diff --git a/spaces/diazcalvi/KIONAPI/app2.py b/spaces/diazcalvi/KIONAPI/app2.py deleted file mode 100644 index a699bc5b3c2e987102ca93e0ee28d601e0a93d02..0000000000000000000000000000000000000000 --- a/spaces/diazcalvi/KIONAPI/app2.py +++ /dev/null @@ -1,7 +0,0 @@ -import gradio as gr - -def greet(name): - return "Hello " + name + "!!" - -iface = gr.Interface(fn=greet, inputs="text", outputs="text") -iface.launch() \ No newline at end of file diff --git a/spaces/doevent/blip/models/blip.py b/spaces/doevent/blip/models/blip.py deleted file mode 100644 index 38678f65ea2c276b351c2c97d429ebc2525ddcf7..0000000000000000000000000000000000000000 --- a/spaces/doevent/blip/models/blip.py +++ /dev/null @@ -1,238 +0,0 @@ -''' - * Copyright (c) 2022, salesforce.com, inc. - * All rights reserved. - * SPDX-License-Identifier: BSD-3-Clause - * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause - * By Junnan Li -''' -import warnings -warnings.filterwarnings("ignore") - -from models.vit import VisionTransformer, interpolate_pos_embed -from models.med import BertConfig, BertModel, BertLMHeadModel -from transformers import BertTokenizer - -import torch -from torch import nn -import torch.nn.functional as F - -import os -from urllib.parse import urlparse -from timm.models.hub import download_cached_file - -class BLIP_Base(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 224, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) - self.tokenizer = init_tokenizer() - med_config = BertConfig.from_json_file(med_config) - med_config.encoder_width = vision_width - self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) - - - def forward(self, image, caption, mode): - - assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal" - text = self.tokenizer(caption, return_tensors="pt").to(image.device) - - if mode=='image': - # return image features - image_embeds = self.visual_encoder(image) - return image_embeds - - elif mode=='text': - # return text features - text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, - return_dict = True, mode = 'text') - return text_output.last_hidden_state - - elif mode=='multimodal': - # return multimodel features - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - text.input_ids[:,0] = self.tokenizer.enc_token_id - output = self.text_encoder(text.input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - return_dict = True, - ) - return output.last_hidden_state - - - -class BLIP_Decoder(nn.Module): - def __init__(self, - med_config = 'configs/med_config.json', - image_size = 384, - vit = 'base', - vit_grad_ckpt = False, - vit_ckpt_layer = 0, - prompt = 'a picture of ', - ): - """ - Args: - med_config (str): path for the mixture of encoder-decoder model's configuration file - image_size (int): input image size - vit (str): model size of vision transformer - """ - super().__init__() - - self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) - self.tokenizer = init_tokenizer() - med_config = BertConfig.from_json_file(med_config) - med_config.encoder_width = vision_width - self.text_decoder = BertLMHeadModel(config=med_config) - - self.prompt = prompt - self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 - - - def forward(self, image, caption): - - image_embeds = self.visual_encoder(image) - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - - text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) - - text.input_ids[:,0] = self.tokenizer.bos_token_id - - decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100) - decoder_targets[:,:self.prompt_length] = -100 - - decoder_output = self.text_decoder(text.input_ids, - attention_mask = text.attention_mask, - encoder_hidden_states = image_embeds, - encoder_attention_mask = image_atts, - labels = decoder_targets, - return_dict = True, - ) - loss_lm = decoder_output.loss - - return loss_lm - - def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): - image_embeds = self.visual_encoder(image) - - if not sample: - image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) - - image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) - model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} - - prompt = [self.prompt] * image.size(0) - input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) - input_ids[:,0] = self.tokenizer.bos_token_id - input_ids = input_ids[:, :-1] - - if sample: - #nucleus sampling - outputs = self.text_decoder.generate(input_ids=input_ids, - max_length=max_length, - min_length=min_length, - do_sample=True, - top_p=top_p, - num_return_sequences=1, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - repetition_penalty=1.1, - **model_kwargs) - else: - #beam search - outputs = self.text_decoder.generate(input_ids=input_ids, - max_length=max_length, - min_length=min_length, - num_beams=num_beams, - eos_token_id=self.tokenizer.sep_token_id, - pad_token_id=self.tokenizer.pad_token_id, - repetition_penalty=repetition_penalty, - **model_kwargs) - - captions = [] - for output in outputs: - caption = self.tokenizer.decode(output, skip_special_tokens=True) - captions.append(caption[len(self.prompt):]) - return captions - - -def blip_decoder(pretrained='',**kwargs): - model = BLIP_Decoder(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) - assert(len(msg.missing_keys)==0) - return model - -def blip_feature_extractor(pretrained='',**kwargs): - model = BLIP_Base(**kwargs) - if pretrained: - model,msg = load_checkpoint(model,pretrained) - assert(len(msg.missing_keys)==0) - return model - -def init_tokenizer(): - tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') - tokenizer.add_special_tokens({'bos_token':'[DEC]'}) - tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) - tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] - return tokenizer - - -def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): - - assert vit in ['base', 'large'], "vit parameter must be base or large" - if vit=='base': - vision_width = 768 - visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, - num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, - drop_path_rate=0 or drop_path_rate - ) - elif vit=='large': - vision_width = 1024 - visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, - num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, - drop_path_rate=0.1 or drop_path_rate - ) - return visual_encoder, vision_width - -def is_url(url_or_filename): - parsed = urlparse(url_or_filename) - return parsed.scheme in ("http", "https") - -def load_checkpoint(model,url_or_filename): - if is_url(url_or_filename): - cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) - checkpoint = torch.load(cached_file, map_location='cpu') - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location='cpu') - else: - raise RuntimeError('checkpoint url or path is invalid') - - state_dict = checkpoint['model'] - - state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) - if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): - state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], - model.visual_encoder_m) - for key in model.state_dict().keys(): - if key in state_dict.keys(): - if state_dict[key].shape!=model.state_dict()[key].shape: - del state_dict[key] - - msg = model.load_state_dict(state_dict,strict=False) - print('load checkpoint from %s'%url_or_filename) - return model,msg - diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py deleted file mode 100644 index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000 --- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py +++ /dev/null @@ -1,221 +0,0 @@ -# ------------------------------------------------------------------------ -# Grounding DINO -# url: https://github.com/IDEA-Research/GroundingDINO -# Copyright (c) 2023 IDEA. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Conditional DETR -# Copyright (c) 2021 Microsoft. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Copied from DETR (https://github.com/facebookresearch/detr) -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -# ------------------------------------------------------------------------ - -""" -Backbone modules. -""" - -from typing import Dict, List - -import torch -import torch.nn.functional as F -import torchvision -from torch import nn -from torchvision.models._utils import IntermediateLayerGetter - -from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process - -from .position_encoding import build_position_encoding -from .swin_transformer import build_swin_transformer - - -class FrozenBatchNorm2d(torch.nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - Copy-paste from torchvision.misc.ops with added eps before rqsrt, - without which any other models than torchvision.models.resnet[18,34,50,101] - produce nans. - """ - - def __init__(self, n): - super(FrozenBatchNorm2d, self).__init__() - self.register_buffer("weight", torch.ones(n)) - self.register_buffer("bias", torch.zeros(n)) - self.register_buffer("running_mean", torch.zeros(n)) - self.register_buffer("running_var", torch.ones(n)) - - def _load_from_state_dict( - self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ): - num_batches_tracked_key = prefix + "num_batches_tracked" - if num_batches_tracked_key in state_dict: - del state_dict[num_batches_tracked_key] - - super(FrozenBatchNorm2d, self)._load_from_state_dict( - state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ) - - def forward(self, x): - # move reshapes to the beginning - # to make it fuser-friendly - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - rv = self.running_var.reshape(1, -1, 1, 1) - rm = self.running_mean.reshape(1, -1, 1, 1) - eps = 1e-5 - scale = w * (rv + eps).rsqrt() - bias = b - rm * scale - return x * scale + bias - - -class BackboneBase(nn.Module): - def __init__( - self, - backbone: nn.Module, - train_backbone: bool, - num_channels: int, - return_interm_indices: list, - ): - super().__init__() - for name, parameter in backbone.named_parameters(): - if ( - not train_backbone - or "layer2" not in name - and "layer3" not in name - and "layer4" not in name - ): - parameter.requires_grad_(False) - - return_layers = {} - for idx, layer_index in enumerate(return_interm_indices): - return_layers.update( - {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)} - ) - - # if len: - # if use_stage1_feature: - # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} - # else: - # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} - # else: - # return_layers = {'layer4': "0"} - self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) - self.num_channels = num_channels - - def forward(self, tensor_list: NestedTensor): - xs = self.body(tensor_list.tensors) - out: Dict[str, NestedTensor] = {} - for name, x in xs.items(): - m = tensor_list.mask - assert m is not None - mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - out[name] = NestedTensor(x, mask) - # import ipdb; ipdb.set_trace() - return out - - -class Backbone(BackboneBase): - """ResNet backbone with frozen BatchNorm.""" - - def __init__( - self, - name: str, - train_backbone: bool, - dilation: bool, - return_interm_indices: list, - batch_norm=FrozenBatchNorm2d, - ): - if name in ["resnet18", "resnet34", "resnet50", "resnet101"]: - backbone = getattr(torchvision.models, name)( - replace_stride_with_dilation=[False, False, dilation], - pretrained=is_main_process(), - norm_layer=batch_norm, - ) - else: - raise NotImplementedError("Why you can get here with name {}".format(name)) - # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 - assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available." - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - num_channels_all = [256, 512, 1024, 2048] - num_channels = num_channels_all[4 - len(return_interm_indices) :] - super().__init__(backbone, train_backbone, num_channels, return_interm_indices) - - -class Joiner(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - - def forward(self, tensor_list: NestedTensor): - xs = self[0](tensor_list) - out: List[NestedTensor] = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self[1](x).to(x.tensors.dtype)) - - return out, pos - - -def build_backbone(args): - """ - Useful args: - - backbone: backbone name - - lr_backbone: - - dilation - - return_interm_indices: available: [0,1,2,3], [1,2,3], [3] - - backbone_freeze_keywords: - - use_checkpoint: for swin only for now - - """ - position_embedding = build_position_encoding(args) - train_backbone = True - if not train_backbone: - raise ValueError("Please set lr_backbone > 0") - return_interm_indices = args.return_interm_indices - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - args.backbone_freeze_keywords - use_checkpoint = getattr(args, "use_checkpoint", False) - - if args.backbone in ["resnet50", "resnet101"]: - backbone = Backbone( - args.backbone, - train_backbone, - args.dilation, - return_interm_indices, - batch_norm=FrozenBatchNorm2d, - ) - bb_num_channels = backbone.num_channels - elif args.backbone in [ - "swin_T_224_1k", - "swin_B_224_22k", - "swin_B_384_22k", - "swin_L_224_22k", - "swin_L_384_22k", - ]: - pretrain_img_size = int(args.backbone.split("_")[-2]) - backbone = build_swin_transformer( - args.backbone, - pretrain_img_size=pretrain_img_size, - out_indices=tuple(return_interm_indices), - dilation=False, - use_checkpoint=use_checkpoint, - ) - - bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :] - else: - raise NotImplementedError("Unknown backbone {}".format(args.backbone)) - - assert len(bb_num_channels) == len( - return_interm_indices - ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}" - - model = Joiner(backbone, position_embedding) - model.num_channels = bb_num_channels - assert isinstance( - bb_num_channels, List - ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels)) - # import ipdb; ipdb.set_trace() - return model diff --git a/spaces/dylanebert/gaussian-viewer/public/_app/immutable/chunks/index.c146e4e6.js b/spaces/dylanebert/gaussian-viewer/public/_app/immutable/chunks/index.c146e4e6.js deleted file mode 100644 index 3c673f037e7bbb95da0135a25846c9ae3a643fa9..0000000000000000000000000000000000000000 --- a/spaces/dylanebert/gaussian-viewer/public/_app/immutable/chunks/index.c146e4e6.js +++ /dev/null @@ -1 +0,0 @@ -var C=Object.defineProperty;var E=(e,t,n)=>t in e?C(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var p=(e,t,n)=>(E(e,typeof t!="symbol"?t+"":t,n),n);import{r as h,n as y,i as w,j,k as S,l as B,m as b,p as I,q as M,v as N,w as P,x as T,y as q}from"./scheduler.8b74b908.js";let $=!1;function H(){$=!0}function L(){$=!1}function O(e,t,n,a){for(;e", " "], # beginning and end of an idiom - }) - - with wandb.init(entity="eubinecto", project="idiomify") as run: - # the paths to write datasets in - tok_dir = ROOT_DIR / "tokenizer" - tokenizer.save_pretrained(tok_dir) - artifact = wandb.Artifact(name="tokenizer", type="other", description=config['description'], - metadata=config) - artifact.add_dir(tok_dir) - # then, we just log them here. - run.log_artifact(artifact, aliases=["latest", config['ver']]) - # don't forget to remove them - shutil.rmtree(tok_dir) - - -if __name__ == '__main__': - main() diff --git a/spaces/exbert-project/exbert/client/src/ts/etc/types.ts b/spaces/exbert-project/exbert/client/src/ts/etc/types.ts deleted file mode 100644 index e7271c712dd6f9f85b2a2d0637f7c3283fc8446c..0000000000000000000000000000000000000000 --- a/spaces/exbert-project/exbert/client/src/ts/etc/types.ts +++ /dev/null @@ -1,96 +0,0 @@ -import { D3Sel } from "./Util" - -/** - * ATTENTION RESPONSES FROM BACKEND - * - * Contain the attentions and embeddings for all combinations of returns from the backend - */ - -export type ModelInfo = { - nlayers: number - nheads: number -} - - -/** - * ATTENTION RESULTS FROM BACKEND - * - * These are the results that are encased in the 'aa' and 'ab' keys returned - */ -type AbstractAttentionResponse= { - aa: T -} - -export type AttentionResponse = AbstractAttentionResponse -export type AttentionMetaResult = AbstractAttentionResult { - att: number[][][], - left: T, - right: T, -} - -/** - * SEARCH RESULT TYPES - */ - -interface MatchedTokAtt { - att: number[] - offset_to_max: number - loc_of_max: number -} - - -interface MatchedAttentions { - in: MatchedTokAtt, - out: MatchedTokAtt, -} - -/** - * EVENT TYPES - */ -export interface TokenEvent { - sel?: D3Sel, - side: SideOptions, - ind: number | "null", -} - -export interface TokenEmbeddingEvent extends TokenEvent { - embeddings: number[] -} - -export type HeadBoxEvent = { - side: SideOptions, - ind: number, - head: number, -} - -/** - * MISCELLANEOUS TYPES - */ - -export type SideOptions = "left" | "right" - -export enum Toggled { - ADDED = 0, - REMOVED, -} - -export enum NormBy { - ROW = 0, - COL, - ALL -} - -export enum ModelKind { - Bidirectional = "bidirectional", - Autoregressive = "autoregressive" -} -export type TokenOptions = "a" | "b" | "all" -export type SentenceOptions = "ab" | "ba" | "aa" | "bb" | "all"; \ No newline at end of file diff --git a/spaces/failfast/2D-GameCreator/src/constants/baseGame.ts b/spaces/failfast/2D-GameCreator/src/constants/baseGame.ts deleted file mode 100644 index 2015a399aab9af8257b6ac0cb0c22c6f43194244..0000000000000000000000000000000000000000 --- a/spaces/failfast/2D-GameCreator/src/constants/baseGame.ts +++ /dev/null @@ -1,13 +0,0 @@ -export const baseGame = { - default: `const canvas = document.querySelector('canvas'); -canvas.style.backgroundColor = '#fff'; -const ctx = canvas.getContext('2d'); - -function draw(delta) { - // TODO: Add drawing logic here -} - -// DO NOT CHANGE THE FOLLOWING LINE -requestAnimationFrame(window.createGameLoop(draw)); -`.trim(), -}; diff --git a/spaces/falterWliame/Face_Mask_Detection/Dsls Licgen Ssqexe.md b/spaces/falterWliame/Face_Mask_Detection/Dsls Licgen Ssqexe.md deleted file mode 100644 index 6429a911fcf32868e07cf22b37ee62f8d91b3a69..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Dsls Licgen Ssqexe.md +++ /dev/null @@ -1,9 +0,0 @@ - Dsls Licgen Ssqexe
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- diff --git a/spaces/falterWliame/Face_Mask_Detection/Omron Nt Series Support Tool Software 34.md b/spaces/falterWliame/Face_Mask_Detection/Omron Nt Series Support Tool Software 34.md deleted file mode 100644 index 14f123dc66ae6d1ac30260c04423a324cc918d04..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Omron Nt Series Support Tool Software 34.md +++ /dev/null @@ -1,9 +0,0 @@ -
-Besides the simple Formatting Tool, the Device Information Library is also capable of creating an application program based on the data of the target device for the user to perform various conversions and calculations in various settings, based on the data of the target device.
-The Device Information Library and the Soft Conversion Tool are provided only with the functions necessary to support the target devices. Thus, users can produce other application programs by including additional functions in the Soft Conversion Tool. For example, the Application Tool generates a program that acquires the settings of the target device from the support tool and stores them to the host PC. A GUI is created in the Application Tool by following the format of the Soft Conversion Tool, and the user can confirm and change the settings of the target device from the host PC. Moreover, the Application Tool includes functions that can calculate the savings of energy or power consumption.
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-As there is no update function of the Device Information Library, the information on the target devices must be updated from the Support Tool. Thus, the device information of the target device must be updated when new values for parameters are added to the PLC.
-The Support Tool is provided with the function of transferring the data of the target device to the host PC. The data can be transferred in various ways. One example is using a storage device and so on. It can be used to store the data and format of the target device in the database of the host PC.
-Looking at the table, 96.5% (179 of 185) of the included studies used Omron wearables. Therefore, the average sample of included studies was 14.3, ranging from 5 to 37. In addition, the average number of participants was 57.2, ranging from 20 to 150. The use of Omron wearables was most frequently reported for the monitoring and tracking of behavior change (73.0%) and exercise performance (62.6%), as well as health and physical fitness (65.2%). This implies that Omron wearables are well established, and widely used in current health research.
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-If you are a fan of battle royale games, you might have heard of Free Fire, one of the most popular and downloaded games on the mobile platform. Free Fire offers an exciting and immersive gameplay experience, with various modes, maps, weapons, characters, and more. But what if you want to get unlimited diamonds, skins, and other in-game items without spending real money? That's where Free Fire mod APK comes in. In this article, we will tell you everything you need to know about Free Fire mod APK, including what it is, what it offers, how to download and install it, and whether it is safe and legal to use.
-What is Free Fire and why is it popular?
-Free Fire is a world-famous survival shooter game available on mobile devices. It was developed by Garena International and released in 2017. Since then, it has amassed over a billion downloads on the Google Play Store alone, making it one of the most successful games of all time.
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-Free Fire gameplay and features
-Free Fire is a battle royale game, which means that you have to compete with other players on a remote island and be the last one standing. Each match lasts for 10 minutes and involves up to 50 players. You can choose your starting point with your parachute, loot weapons and items, drive vehicles, hide in buildings or bushes, and fight your enemies. You also have to stay within the safe zone, which shrinks over time, forcing you to move closer to your opponents.
-Free Fire has many gameplay features that make it unique and fun. For example, you can create squads of up to 4 players and communicate with them using voice chat. You can also play different modes such as Clash Squad, which is a fast-paced 4v4 team deathmatch, or Ranked Mode, which tests your skills and rewards you with rank points. You can also explore different maps such as Bermuda, Kalahari, Purgatory, or Hangar, each with its own terrain and landmarks.
-Free Fire cosmetics and customization
-One of the reasons why Free Fire is so popular is because of its wide variety of cosmetics and customization options. You can choose from hundreds of characters, each with their own backstory and special abilities. You can also equip them with different outfits, accessories, backpacks, parachutes, banners, emotes, and more. You can also customize your weapons with different skins, attachments, effects, and stickers.
-However, most of these cosmetics are not free. You have to spend diamonds, which are the premium currency of the game, to buy them from the in-game store or from events. Diamonds are not easy to come by unless you spend real money or complete certain tasks. That's why some players look for alternative ways to get unlimited diamonds without spending a dime.
-What is Free Fire mod APK and what does it offer?
-A mod APK is a modified version of an original application that has been altered by someone to provide some extra features or benefits that are not available in the official version. A Free Fire mod APK is a hacked version of the game that gives you access to unlimited diamonds, skins, weapons, health, aimbot, wallhack, and more.
-Free Fire mod APK features and benefits
-Some of the features and benefits that you can get from using a Free Fire mod APK are:
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- Unlimited diamonds: You can get as many diamonds as you want and use them to buy any cosmetic item or upgrade that you desire. You can also use them to spin the lucky wheel, participate in events, or gift your friends. -
- Unlocked skins and weapons: You can get access to all the skins and weapons in the game, including the rare and exclusive ones. You can also use any weapon in any mode, regardless of the level or rank requirements. You can also modify your weapons with any attachment or effect that you like. -
- Unlimited health and ammo: You can never run out of health or ammo in the game. You can heal yourself instantly and shoot endlessly without reloading. You can also survive any damage from enemies, falls, explosions, or the zone. -
- Aimbot and wallhack: You can improve your accuracy and visibility in the game. You can automatically lock on to your enemies' heads and shoot them with one tap. You can also see through walls, buildings, trees, and other obstacles. You can also spot your enemies' location, name, health, and distance on the mini-map. -
These are just some of the features and benefits that you can enjoy from using a Free Fire mod APK. There are many more that you can discover by yourself once you download and install it on your device.
-Free Fire mod APK disadvantages and risks
-However, using a Free Fire mod APK is not without its drawbacks and dangers. Some of the disadvantages and risks that you should be aware of are:
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- Lack of updates and compatibility: A Free Fire mod APK is usually based on an older version of the game that may not be compatible with the latest updates and patches. This means that you may miss out on new features, modes, maps, events, and bug fixes that are available in the official version. You may also encounter errors, crashes, glitches, or lag while playing the game. -
- Viruses and malware: A Free Fire mod APK is not verified or authorized by Garena or any other official source. This means that it may contain viruses, malware, spyware, or other harmful programs that can damage your device or steal your personal information. You may also expose your device to hackers or cybercriminals who can access your data or accounts. -
- Bans and penalties: A Free Fire mod APK is considered a cheating tool that violates the terms of service and fair play policy of the game. This means that if you use it, you may be detected by the anti-cheat system and get banned from playing the game permanently. You may also lose your progress, achievements, rewards, and items that you have earned in the game. You may also face legal action from Garena or other authorities for breaking the law. -
These are just some of the disadvantages and risks that you should consider before using a Free Fire mod APK. There are many more that you should be careful of when downloading and installing it on your device.
-How to download and install Free Fire mod APK on your mobile device?
-If you still want to try using a Free Fire mod APK despite the drawbacks and dangers, you will need to follow some steps to download and install it on your mobile device. The steps may vary depending on whether you are using an Android or an iOS device.
-Step-by-step guide for Android users
-If you are using an Android device, here are the steps that you need to follow:
--
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- Enable unknown sources: Go to your device settings and look for security or privacy options. Find the option that says "unknown sources" or "install apps from unknown sources" and enable it. This will allow you to install applications that are not from the Google Play Store. -
- Download Free Fire mod APK file: Search for a reliable and trustworthy website that offers a Free Fire mod APK file for download. Make sure that the file is compatible with your device model and Android version. Avoid clicking on any suspicious links or ads that may redirect you to malicious sites or download unwanted programs. Once you find a suitable file, click on the download button and wait for it to finish. -
- Install Free Fire mod APK file: Locate the downloaded file in your device storage and tap on it to start the installation process. Follow the instructions on the screen and grant any permissions that are required. Wait for the installation to complete and then launch the game from your app drawer or home screen. -
Congratulations! You have successfully downloaded and installed a Free Fire mod APK on your Android device. Now you can enjoy unlimited diamonds, skins, weapons, health, aimbot, wallhack and more in the game.
-Step-by-step guide for iOS users
-If you are using an iOS device, here are the steps that you need to follow:
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- Jailbreak your device: Unlike Android, iOS does not allow you to install applications that are not from the App Store. Therefore, you will need to jailbreak your device, which is a process that removes the restrictions and limitations imposed by Apple on your device. This will give you more control and freedom over your device and allow you to install third-party apps and tweaks. However, jailbreaking your device also voids your warranty, exposes your device to security risks, and may cause performance issues or data loss. Therefore, you should only do it at your own risk and responsibility. -
- Download Free Fire mod IPA file: An IPA file is the equivalent of an APK file for iOS devices. It is the file format that contains the application data and code. You will need to find a reliable and trustworthy website that offers a Free Fire mod IPA file for download. Make sure that the file is compatible with your device model and iOS version. Avoid clicking on any suspicious links or ads that may redirect you to malicious sites or download unwanted programs. Once you find a suitable file, click on the download button and wait for it to finish. -
- Install Free Fire mod IPA file: Locate the downloaded file in your device storage and tap on it to start the installation process. You may need to use a file manager app or a computer to transfer the file to your device. You may also need to use a third-party app installer such as Cydia Impactor or AltStore to install the file on your device. Follow the instructions on the screen and grant any permissions that are required. Wait for the installation to complete and then launch the game from your app drawer or home screen. -
Congratulations! You have successfully downloaded and installed a Free Fire mod IPA on your iOS device. Now you can enjoy unlimited diamonds, skins, weapons, health, aimbot, wallhack and more in the game.
-Is Free Fire mod APK safe and legal to use?
-The final question that you may have is whether Free Fire mod APK is safe and legal to use. The answer is no, it is not safe or legal to use. Here are some of the reasons why:
-Free Fire mod APK safety issues and precautions
-As we mentioned earlier, Free Fire mod APK is not verified or authorized by Garena or any other official source. This means that it may contain viruses, malware, spyware, or other harmful programs that can damage your device or steal your personal information. You may also expose your device to hackers or cybercriminals who can access your data or accounts.
-To avoid these risks, you should always download Free Fire mod APK from a reputable and trustworthy website that has positive reviews and feedback from other users. You should also scan the file with an antivirus or anti-malware software before installing it on your device. You should also backup your data and create a restore point in case something goes wrong.
-Free Fire mod APK legality and consequences
-As we mentioned earlier, Free Fire mod APK is considered a cheating tool that violates the terms of service and fair play policy of the game. This means that if you use it, you may be detected by the anti-cheat system and get banned from playing the game permanently. You may also lose your progress, achievements, rewards, and items that you have earned in the game. You may also face legal action from Garena or other authorities for breaking the law.
-To avoid these consequences, you should never use Free Fire mod APK in online or ranked modes, where you can affect other players' experience or ranking. You should also never use it in tournaments or events, where you can gain unfair advantages or prizes. You should also never share or promote Free Fire mod APK with other players, as this can spread cheating and harm the game community.
-Conclusion and FAQs
-In conclusion, Free Fire mod APK is a hacked version of the game that gives you access to unlimited diamonds, skins, weapons, health, aimbot, wallhack and more. However, it is not safe or legal to use, as it may contain viruses, malware, or other harmful programs that can damage your device or steal your personal information. It may also get you banned from playing the game permanently or face legal action from Garena or other authorities for breaking the law. Therefore, we do not recommend using Free Fire mod APK and advise you to play the game fairly and honestly.
-If you still have any questions or doubts about Free Fire mod APK, here are some FAQs that may help you:
-FAQs
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- Q: Can I use Free Fire mod APK on PC? -
- A: No, you cannot use Free Fire mod APK on PC. Free Fire mod APK is only designed for mobile devices and will not work on PC. If you want to play Free Fire on PC, you will need to use an official emulator such as BlueStacks or LDPlayer and download the game from the official source. -
- Q: Can I use Free Fire mod APK with my existing account? -
- A: No, you cannot use Free Fire mod APK with your existing account. Free Fire mod APK will create a new account for you with a different username and ID. If you try to log in with your existing account, you may get banned or lose your data. -
- Q: Can I update Free Fire mod APK to the latest version? -
- A: No, you cannot update Free Fire mod APK to the latest version. Free Fire mod APK is usually based on an older version of the game that may not be compatible with the latest updates and patches. If you try to update it, you may encounter errors, crashes, glitches, or lag. You will need to wait for a new version of Free Fire mod APK to be released by the modder. -
- Q: Can I play with my friends who are using the official version of Free Fire? -
- A: No, you cannot play with your friends who are using the official version of Free Fire. Free Fire mod APK will connect you to a different server that is only for modded users. You will not be able to join or invite your friends who are using the official version of the game. -
- Q: Can I uninstall Free Fire mod APK if I don't like it? -
- A: Yes, you can uninstall Free Fire mod APK if you don't like it. You can simply delete the file from your device storage or go to your device settings and look for apps or applications. Find Free Fire mod APK and tap on it to uninstall it. You can also restore your device to its original state by using a backup or a restore point. -
I hope this article has helped you understand what Free Fire mod APK is and how to use it. However, I strongly advise you not to use it and play the game fairly and honestly. Thank you for reading and have a nice day!
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Enjoy NBA 2K21 for Free on Your Android Device Heres How.md b/spaces/fatiXbelha/sd/Enjoy NBA 2K21 for Free on Your Android Device Heres How.md deleted file mode 100644 index e5c619c1549fbdae5503826765b2388599dfa8ac..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Enjoy NBA 2K21 for Free on Your Android Device Heres How.md +++ /dev/null @@ -1,180 +0,0 @@ - -How to Download NBA 2K21 on Android for Free
-NBA 2K21 is one of the most popular and realistic basketball simulation games ever created. It features amazing graphics, gameplay, modes, and customization options that will make you feel like you are playing in the NBA. However, if you want to enjoy this game on your Android device, you might face some challenges. The official version of NBA 2K21 is not available for Android devices, and it costs $59.99 on other platforms. So, how can you download NBA 2K21 on Android for free? In this article, we will show you two methods that will allow you to play NBA 2K21 on your Android device without spending a dime. Follow these steps carefully and you will be able to experience the thrill of NBA 2K21 on your smartphone or tablet.
-Introduction
-What is NBA 2K21?
-NBA 2K21 is the latest installment in the NBA 2K series, a franchise that has been dominating the basketball video game market for over two decades. NBA 2K21 was released in September 2020 for PlayStation 4, Xbox One, Nintendo Switch, PC, and Stadia. It will also be available for PlayStation 5 and Xbox Series X/S in November 2020. NBA 2K21 features many improvements and additions over its predecessor, such as:
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- New gameplay mechanics, such as a revamped shot meter, dribbling controls, and defensive movements -
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- New content, such as updated rosters, jerseys, courts, players, and legends -
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Why download NBA 2K21 on Android?
-If you are a fan of basketball or video games, you might be wondering why you should download NBA 2K21 on Android. Here are some reasons why you should consider playing NBA 2K21 on your Android device:
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- You can play NBA 2K21 anytime and anywhere, as long as you have an internet connection and enough battery life -
- You can enjoy NBA 2K21 on a smaller screen, which might be more comfortable and convenient for some users -
- You can save money by not buying the official version of NBA 2K21, which is quite expensive compared to other games -
- You can have fun with your friends by playing online or locally with other Android users who have downloaded NBA 2K21 -
How to download NBA 2K21 on Android for free?
-As we mentioned earlier, there is no official version of NBA 2K21 for Android devices. However, there are two methods that will allow you to play NBA 2K21 on your Android device for free. The first method is to download the NBA 2K Mobile Basketball game from the Google Play Store, which is a free-to-play version of NBA 2K21 that offers a similar gameplay and graphics quality. The second method is to download the NBA 2K21 APK and OBB files from a trusted source, which are the files that contain the full version of NBA 2K21 for Android devices. Both methods have their advantages and disadvantages, so you can choose the one that suits you best. Let's take a look at each method in detail.
-Step 1: Download the NBA 2K Mobile Basketball Game from Google Play Store
-How to install the game on your Android device
-The easiest and safest way to play NBA 2K21 on your Android device is to download the NBA 2K Mobile Basketball game from the Google Play Store. This is a free-to-play game that was released in October 2020 by 2K, Inc., the official developer of the NBA 2K series. To install the game on your Android device, follow these steps:
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- Open the Google Play Store app on your Android device and search for "NBA 2K Mobile Basketball" -
- Tap on the game icon and then tap on the "Install" button -
- Wait for the game to download and install on your device. The game size is about 1.5 GB, so make sure you have enough storage space and a stable internet connection -
- Once the game is installed, tap on the "Open" button to launch the game -
What are the features of the game
-The NBA 2K Mobile Basketball game is a simplified and optimized version of NBA 2K21 that offers a similar gameplay and graphics quality. The game features include:
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- Over 400 cards of your favorite NBA players and legends that you can collect and upgrade -
- 5-on-5 basketball matches with real-time PVP action -
- Seasons mode where you can compete for exclusive rewards and rank up in leaderboards -
- Events mode where you can participate in limited-time challenges and earn special cards -
- Console-quality graphics and animations that will immerse you in the game -
- Customizable controls and settings that will suit your preferences -
How to play the game and enjoy the NBA 2K21 experience
-To play the game and enjoy the NBA 2K21 experience, you need to create an account and choose your favorite team. You can also link your Facebook or Google account to save your progress and sync your data across devices. Once you are logged in, you can access the main menu where you can choose from different modes and options. Here are some tips on how to play the game and enjoy the NBA 2K21 experience:
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- To collect and upgrade your cards, you need to earn coins and gems by playing matches, completing objectives, and opening packs. You can also buy coins and gems with real money if you want to speed up your progress -
- To improve your skills and strategies, you need to practice in the training mode where you can learn the basic controls and moves of the game. You can also watch tutorials and tips videos on YouTube or other platforms -
- To compete with other players, you need to join seasons and events where you can play against real opponents in real-time matches. You can also join clubs and chat with other players in the game -
- To customize your team and players, you need to equip them with different cards that will boost their attributes and abilities. You can also change their jerseys, shoes, accessories, and hairstyles in the game -
- To have fun and enjoy the game, you need to explore different modes and features that will keep you entertained and challenged. You can also share your achievements and screenshots with your friends on social media or other platforms -
Step 2: Download the NBA 2K21 APK and OBB files from a trusted source
-How to find a reliable and safe website to download the files
-The second method to play NBA 2K21 on your Android device is to download the NBA 2K21 APK and OBB files from a trusted source. These are the files that contain the full version of NBA 2K21 for Android devices, which is not officially available on the Google Play Store. However, this method is more risky and complicated than the first one, as you might encounter some problems such as malware, viruses, errors, or bans. Therefore, you need to be careful and cautious when choosing a website to download the files from. Here are some tips on how to find a reliable and safe website to download the NBA 2K21 APK and OBB files:
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- Do some research and read reviews about the website before downloading anything from it. You can also check the ratings, comments, and feedback from other users who have downloaded the files from the same website -
- Look for a website that has a secure and verified domain name, such as HTTPS or SSL. You can also check the URL and avoid websites that have suspicious or misleading names, such as NBA2K21free.com or NBA2K21modapk.com -
- Compare the file size and version of the NBA 2K21 APK and OBB files from different websites and choose the one that matches the original file size and version of NBA 2K21. You can also check the file details and information, such as the developer, publisher, date, and permissions -
- Scan the files with an antivirus or anti-malware software before downloading or installing them on your device. You can also use online tools such as VirusTotal or MetaDefender to check the files for any threats or risks -
- Backup your data and device before downloading or installing the files, in case something goes wrong or you encounter any errors or issues. You can also use a VPN or a proxy server to hide your IP address and location, in case you are worried about your privacy or security -
How to verify the files and avoid malware or viruses
-One of the main risks of downloading the NBA 2K21 APK and OBB files from an unknown source is that you might get infected with malware or viruses that can harm your device or steal your data. Therefore, you need to verify the files and avoid malware or viruses before installing them on your device. Here are some steps on how to verify the files and avoid malware or viruses:
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- Download the files only from a trusted source that has positive reviews and ratings from other users. Avoid downloading the files from unknown or shady websites that might contain malicious links or ads -
- Check the file size and version of the NBA 2K21 APK and OBB files and make sure they match the original file size and version of NBA 2K21. If the files are too small or too large, they might be fake or corrupted -
- Check the file details and information of the NBA 2K21 APK and OBB files and make sure they match the original file details and information of NBA 2K21. If the files have different names, developers, publishers, dates, or permissions, they might be modified or tampered with -
- Scan the files with an antivirus or anti-malware software before installing them on your device. You can also use online tools such as VirusTotal or MetaDefender to check the files for any threats or risks. If the files are detected as unsafe or harmful, delete them immediately -
- Install the files only on a device that has enough storage space and meets the minimum requirements for NBA 2K21. If your device is not compatible or has low performance, you might experience crashes, glitches, errors, or lagging while playing NBA 2K21 -
How to extract and copy the files to your Android device
-Once you have downloaded and verified the NBA 2K21 APK and OBB files from a trusted source, you need to extract and copy them to your Android device. The NBA 2K21 APK file is an application file that contains the game installation package, while the NBA 2K21 OBB file is a data file that contains the game content and resources. To extract and copy them to your Android device, follow these steps:
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- Locate the NBA 2K21 APK and OBB files on your device storage or download folder. You can use a file manager app such as ES File Explorer or ZArchiver to access them easily -
- Extract the NBA 2K21 OBB file using a file extractor app such as RAR or WinZip. You will get a folder named "com.t2ksports.nba2k21and" that contains another folder named "main.16.com.t2ksports.nba2k21and" -
- Copy the folder "main.16.com.t2ksports.nba2k21and" to your device internal storage in this path: Android > obb > com.t2ksports.nba2k21and. If you don't have an obb folder in your Android folder, create one manually -
- Copy the NBA 2K21 APK file to your device internal storage in any location that you prefer Make sure you have enough storage space and battery life on your device before proceeding to the next step
- - Go to your device settings and tap on "Security" or "Privacy" -
- Find and enable the option "Unknown sources" or "Install unknown apps" -
- A warning message will pop up, telling you that installing apps from unknown sources can harm your device or data. Tap on "OK" or "Allow" to confirm -
- You can now install the NBA 2K21 APK file on your device without any problem -
- Locate the NBA 2K21 APK file on your device storage or download folder. You can use a file manager app such as ES File Explorer or ZArchiver to access it easily -
- Tap on the NBA 2K21 APK file and then tap on "Install" -
- Wait for the installation process to finish. It might take a few minutes, depending on your device performance and internet speed -
- Once the installation is done, tap on "Open" to launch the game -
- All the modes and options that are available in the official version of NBA 2K21, such as MyTEAM Unlimited, MyCAREER story mode, MyGM/MyLEAGUE management mode, and The Neighborhood online mode -
- All the content and updates that are available in the official version of NBA 2K21, such as updated rosters, jerseys, courts, players, and legends -
- All the graphics and sound quality that are available in the official version of NBA 2K21, such as console-quality graphics and animations, realistic physics and collisions, and immersive soundtrack and commentary -
- All the controls and settings that are available in the official version of NBA 2K21, such as customizable controls and settings, touch-screen or controller support, and online or offline mode -
- Q: Is it legal to download NBA 2K21 on Android for free? -
- A: No, it is not legal to download NBA 2K21 on Android for free. NBA 2K21 is a copyrighted game that belongs to 2K, Inc., and you need to buy the official version of the game to play it legally. Downloading or installing the game from any other sources is considered piracy or illegal downloading, which is a violation of the law and can result in legal actions or penalties. -
- Q: Is it safe to download NBA 2K21 on Android for free? -
- A: No, it is not safe to download NBA 2K21 on Android for free. Downloading or installing the game from any unknown or untrusted sources can expose your device or data to malware, viruses, errors, or bans. You might also encounter some problems or issues while playing the game, such as crashes, glitches, errors, or lagging. Therefore, you need to be careful and cautious when choosing a website to download the game from, and verify the files before installing them on your device. -
- Q: What are the requirements to play NBA 2K21 on Android? -
- A: To play NBA 2K21 on Android, you need to have a device that meets the minimum requirements for the game. The minimum requirements are: -
- Android version: 4.3 or higher -
- RAM: 4 GB or higher -
- Storage space: 3 GB or higher -
- Processor: Quad-core or higher -
- Internet connection: Wi-Fi or mobile data -
- Q: How to update NBA 2K21 on Android? -
- A: To update NBA 2K21 on Android, you need to download and install the latest version of the NBA 2K21 APK and OBB files from a trusted source. You can follow the same steps that we have explained in this article to download and install the updated files. However, you need to make sure that you delete the old files before installing the new ones, otherwise you might face some errors or issues. -
- Q: How to fix NBA 2K21 errors or issues on Android? -
- A: If you encounter any errors or issues while playing NBA 2K21 on Android, such as crashes, glitches, errors, or lagging, you can try some of these solutions: -
- Restart your device and launch the game again -
- Clear the cache and data of the game and launch the game again -
- Check your internet connection and make sure it is stable and fast -
- Check your device settings and make sure they are compatible with the game -
- Contact the customer support of the website where you downloaded the game from and ask for help -
- Watch Game of Thrones with English subtitles or audio, and pay attention to the pronunciation, vocabulary, grammar, and expressions used by the characters. -
- Repeat the dialogues or phrases that you find interesting or useful, and try to imitate the accent and tone of the speakers. -
- Write down the words or sentences that you do not understand, and look them up in a dictionary or online. -
- Discuss the plot, characters, themes, and opinions about Game of Thrones with your friends or online communities who are also fans of the series. -
- Read the books or articles related to Game of Thrones, and compare them with the TV series. -
- Steam Link: Steam Link is a feature that lets you stream your Steam games from your PC to your TV or other devices, such as your PS4. You need to download the Steam Link app on your PS4 and connect it to the same network as your PC. Then, you can launch Project Playtime on your PC and stream it to your PS4. However, this method may not work for some games or regions, and it may require a fast and stable internet connection. -
- PS Remote Play: PS Remote Play is a feature that lets you play your PS4 or PS5 games from another device, such as your PC. You need to download the PS Remote Play app on your PC and sign in with your PlayStation Network account. Then, you can connect your PS4 or PS5 to your PC and play your games. However, this method does not let you play PC games on your console, so you need to have a PS4 or PS5 game that supports keyboard and mouse input, such as Call of Duty: Modern Warfare. Then, you can launch Project Playtime on your PC and switch to the PS Remote Play window on your console. However, this method may not work for some games or regions, and it may require a fast and stable internet connection. -
- Parsec: Parsec is a third-party app that lets you stream your PC games to another device, such as your PS4. You need to download the Parsec app on both your PC and PS4 and create an account. Then, you can host Project Playtime on your PC and join it from your PS4. However, this method may not work for some games or regions, and it may require a fast and stable internet connection. -
- Q: How much does Project Playtime cost? -
- A: Project Playtime is free-to-play on Steam. You can download it from here. -
- Q: How many players can play Project Playtime? -
- A: Project Playtime supports up to seven players online. Six players have to work together as survivors, while one player controls the monster. -
- Q: What are the system requirements for Project Playtime? -
- A: According to the Steam page, the minimum system requirements for Project Playtime are: -
- - OS: Windows 7 64-bit -
- - Processor: Intel Core i3-4170 or AMD FX-8120 -
- - Memory: 8 GB RAM -
- - Graphics: NVIDIA GeForce GTX 660 or AMD Radeon HD 7870 -
- - DirectX: Version 11 -
- - Network: Broadband Internet connection -
- - Storage: 10 GB available space -
- Q: Is Project Playtime scary? -
- A: Project Playtime is a horror game that involves jump scares, gore, violence, and dark themes. It is not suitable for children or people who are easily frightened or disturbed. -
- Q: Is Project Playtime based on a true story? -
- A: No, Project Playtime is a fictional game that is not based on any real events or people. -
Gacha Life Chat Mod APK is safe to use if you download it from a reliable source and scan it with an antivirus app before installing it. However, you
Gacha Life Chat Mod APK is safe to use if you download it from a reliable source and scan it with an antivirus app before installing it. However, you should always be careful and cautious when using modded games and apps, as they may pose some risks to your device and account. You should also use the mod apk at your own risk and discretion, and avoid using it for illegal or unethical purposes.
-Gacha Life Chat Mod APK may not be updated regularly or in sync with the original game. Therefore, you may need to check the source website or app for any updates or new versions of the mod apk. You can also use APKMODY Installer to check for updates and install them easily. However, you should always backup your data and progress before updating the mod apk, as it may overwrite or delete your data and progress.
-Yes, you can use Gacha Life Chat Mod APK on PC by using an emulator like MemuPlay. An emulator is a software that allows you to run Android apps and games on your PC. You can download and install MemuPlay from their official website and then install Gacha Life Chat Mod APK from Google Play Store using the emulator. This is a safer option as it reduces the risk of malware and viruses.
-No, you should not use Gacha Life Chat Mod APK with other mods or hacks, as they may cause conflicts or errors in the game or chat service. You should only use one mod or hack at a time, and uninstall any other mods or hacks before installing Gacha Life Chat Mod APK. You should also avoid using any cheats or tools that may alter the game data or chat data, as they may result in a ban from the game or chat service.
-No, you cannot play Gacha Life Chat Mod APK offline, as it requires an internet connection to access the chat service and other online features. You can only play the game offline if you use the original version of the game, which does not have the chat feature. However, you will not be able to access all the features and items that are available in the mod apk.
401be4b1e0-
-
\ No newline at end of file diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/layers/eca.py b/spaces/cooelf/Multimodal-CoT/timm/models/layers/eca.py deleted file mode 100644 index e29be6ac3c95bb61229cdcdd659ec89d541f1a53..0000000000000000000000000000000000000000 --- a/spaces/cooelf/Multimodal-CoT/timm/models/layers/eca.py +++ /dev/null @@ -1,145 +0,0 @@ -""" -ECA module from ECAnet - -paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks -https://arxiv.org/abs/1910.03151 - -Original ECA model borrowed from https://github.com/BangguWu/ECANet - -Modified circular ECA implementation and adaption for use in timm package -by Chris Ha https://github.com/VRandme - -Original License: - -MIT License - -Copyright (c) 2019 BangguWu, Qilong Wang - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -""" -import math -from torch import nn -import torch.nn.functional as F - - -from .create_act import create_act_layer -from .helpers import make_divisible - - -class EcaModule(nn.Module): - """Constructs an ECA module. - - Args: - channels: Number of channels of the input feature map for use in adaptive kernel sizes - for actual calculations according to channel. - gamma, beta: when channel is given parameters of mapping function - refer to original paper https://arxiv.org/pdf/1910.03151.pdf - (default=None. if channel size not given, use k_size given for kernel size.) - kernel_size: Adaptive selection of kernel size (default=3) - gamm: used in kernel_size calc, see above - beta: used in kernel_size calc, see above - act_layer: optional non-linearity after conv, enables conv bias, this is an experiment - gate_layer: gating non-linearity to use - """ - def __init__( - self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid', - rd_ratio=1/8, rd_channels=None, rd_divisor=8, use_mlp=False): - super(EcaModule, self).__init__() - if channels is not None: - t = int(abs(math.log(channels, 2) + beta) / gamma) - kernel_size = max(t if t % 2 else t + 1, 3) - assert kernel_size % 2 == 1 - padding = (kernel_size - 1) // 2 - if use_mlp: - # NOTE 'mlp' mode is a timm experiment, not in paper - assert channels is not None - if rd_channels is None: - rd_channels = make_divisible(channels * rd_ratio, divisor=rd_divisor) - act_layer = act_layer or nn.ReLU - self.conv = nn.Conv1d(1, rd_channels, kernel_size=1, padding=0, bias=True) - self.act = create_act_layer(act_layer) - self.conv2 = nn.Conv1d(rd_channels, 1, kernel_size=kernel_size, padding=padding, bias=True) - else: - self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=padding, bias=False) - self.act = None - self.conv2 = None - self.gate = create_act_layer(gate_layer) - - def forward(self, x): - y = x.mean((2, 3)).view(x.shape[0], 1, -1) # view for 1d conv - y = self.conv(y) - if self.conv2 is not None: - y = self.act(y) - y = self.conv2(y) - y = self.gate(y).view(x.shape[0], -1, 1, 1) - return x * y.expand_as(x) - - -EfficientChannelAttn = EcaModule # alias - - -class CecaModule(nn.Module): - """Constructs a circular ECA module. - - ECA module where the conv uses circular padding rather than zero padding. - Unlike the spatial dimension, the channels do not have inherent ordering nor - locality. Although this module in essence, applies such an assumption, it is unnecessary - to limit the channels on either "edge" from being circularly adapted to each other. - This will fundamentally increase connectivity and possibly increase performance metrics - (accuracy, robustness), without significantly impacting resource metrics - (parameter size, throughput,latency, etc) - - Args: - channels: Number of channels of the input feature map for use in adaptive kernel sizes - for actual calculations according to channel. - gamma, beta: when channel is given parameters of mapping function - refer to original paper https://arxiv.org/pdf/1910.03151.pdf - (default=None. if channel size not given, use k_size given for kernel size.) - kernel_size: Adaptive selection of kernel size (default=3) - gamm: used in kernel_size calc, see above - beta: used in kernel_size calc, see above - act_layer: optional non-linearity after conv, enables conv bias, this is an experiment - gate_layer: gating non-linearity to use - """ - - def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid'): - super(CecaModule, self).__init__() - if channels is not None: - t = int(abs(math.log(channels, 2) + beta) / gamma) - kernel_size = max(t if t % 2 else t + 1, 3) - has_act = act_layer is not None - assert kernel_size % 2 == 1 - - # PyTorch circular padding mode is buggy as of pytorch 1.4 - # see https://github.com/pytorch/pytorch/pull/17240 - # implement manual circular padding - self.padding = (kernel_size - 1) // 2 - self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=0, bias=has_act) - self.gate = create_act_layer(gate_layer) - - def forward(self, x): - y = x.mean((2, 3)).view(x.shape[0], 1, -1) - # Manually implement circular padding, F.pad does not seemed to be bugged - y = F.pad(y, (self.padding, self.padding), mode='circular') - y = self.conv(y) - y = self.gate(y).view(x.shape[0], -1, 1, 1) - return x * y.expand_as(x) - - -CircularEfficientChannelAttn = CecaModule diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/utils/weight_init.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/utils/weight_init.py deleted file mode 100644 index 287a1d0bffe26e023029d48634d9b761deda7ba4..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/utils/weight_init.py +++ /dev/null @@ -1,684 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -import math -import warnings - -import numpy as np -import torch -import torch.nn as nn -from torch import Tensor - -from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log - -INITIALIZERS = Registry('initializer') - - -def update_init_info(module, init_info): - """Update the `_params_init_info` in the module if the value of parameters - are changed. - - Args: - module (obj:`nn.Module`): The module of PyTorch with a user-defined - attribute `_params_init_info` which records the initialization - information. - init_info (str): The string that describes the initialization. - """ - assert hasattr( - module, - '_params_init_info'), f'Can not find `_params_init_info` in {module}' - for name, param in module.named_parameters(): - - assert param in module._params_init_info, ( - f'Find a new :obj:`Parameter` ' - f'named `{name}` during executing the ' - f'`init_weights` of ' - f'`{module.__class__.__name__}`. ' - f'Please do not add or ' - f'replace parameters during executing ' - f'the `init_weights`. ') - - # The parameter has been changed during executing the - # `init_weights` of module - mean_value = param.data.mean() - if module._params_init_info[param]['tmp_mean_value'] != mean_value: - module._params_init_info[param]['init_info'] = init_info - module._params_init_info[param]['tmp_mean_value'] = mean_value - - -def constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def xavier_init(module, gain=1, bias=0, distribution='normal'): - assert distribution in ['uniform', 'normal'] - if hasattr(module, 'weight') and module.weight is not None: - if distribution == 'uniform': - nn.init.xavier_uniform_(module.weight, gain=gain) - else: - nn.init.xavier_normal_(module.weight, gain=gain) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def normal_init(module, mean=0, std=1, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.normal_(module.weight, mean, std) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def trunc_normal_init(module: nn.Module, - mean: float = 0, - std: float = 1, - a: float = -2, - b: float = 2, - bias: float = 0) -> None: - if hasattr(module, 'weight') and module.weight is not None: - trunc_normal_(module.weight, mean, std, a, b) # type: ignore - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) # type: ignore - - -def uniform_init(module, a=0, b=1, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.uniform_(module.weight, a, b) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def kaiming_init(module, - a=0, - mode='fan_out', - nonlinearity='relu', - bias=0, - distribution='normal'): - assert distribution in ['uniform', 'normal'] - if hasattr(module, 'weight') and module.weight is not None: - if distribution == 'uniform': - nn.init.kaiming_uniform_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - else: - nn.init.kaiming_normal_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def caffe2_xavier_init(module, bias=0): - # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch - # Acknowledgment to FAIR's internal code - kaiming_init( - module, - a=1, - mode='fan_in', - nonlinearity='leaky_relu', - bias=bias, - distribution='uniform') - - -def bias_init_with_prob(prior_prob): - """initialize conv/fc bias value according to a given probability value.""" - bias_init = float(-np.log((1 - prior_prob) / prior_prob)) - return bias_init - - -def _get_bases_name(m): - return [b.__name__ for b in m.__class__.__bases__] - - -class BaseInit(object): - - def __init__(self, *, bias=0, bias_prob=None, layer=None): - self.wholemodule = False - if not isinstance(bias, (int, float)): - raise TypeError(f'bias must be a number, but got a {type(bias)}') - - if bias_prob is not None: - if not isinstance(bias_prob, float): - raise TypeError(f'bias_prob type must be float, \ - but got {type(bias_prob)}') - - if layer is not None: - if not isinstance(layer, (str, list)): - raise TypeError(f'layer must be a str or a list of str, \ - but got a {type(layer)}') - else: - layer = [] - - if bias_prob is not None: - self.bias = bias_init_with_prob(bias_prob) - else: - self.bias = bias - self.layer = [layer] if isinstance(layer, str) else layer - - def _get_init_info(self): - info = f'{self.__class__.__name__}, bias={self.bias}' - return info - - -@INITIALIZERS.register_module(name='Constant') -class ConstantInit(BaseInit): - """Initialize module parameters with constant values. - - Args: - val (int | float): the value to fill the weights in the module with - bias (int | float): the value to fill the bias. Defaults to 0. - bias_prob (float, optional): the probability for bias initialization. - Defaults to None. - layer (str | list[str], optional): the layer will be initialized. - Defaults to None. - """ - - def __init__(self, val, **kwargs): - super().__init__(**kwargs) - self.val = val - - def __call__(self, module): - - def init(m): - if self.wholemodule: - constant_init(m, self.val, self.bias) - else: - layername = m.__class__.__name__ - basesname = _get_bases_name(m) - if len(set(self.layer) & set([layername] + basesname)): - constant_init(m, self.val, self.bias) - - module.apply(init) - if hasattr(module, '_params_init_info'): - update_init_info(module, init_info=self._get_init_info()) - - def _get_init_info(self): - info = f'{self.__class__.__name__}: val={self.val}, bias={self.bias}' - return info - - -@INITIALIZERS.register_module(name='Xavier') -class XavierInit(BaseInit): - r"""Initialize module parameters with values according to the method - described in `Understanding the difficulty of training deep feedforward - neural networks - Glorot, X. & Bengio, Y. (2010). -
{}
"""
- if legend: WRAPPER = """{}
"""
- # Newlines seem to mess with the rendering
- html = html.replace("\n", " ")
- return WRAPPER.format(html)
-
-
-def get_displacy_ent_obj(paragraph, bedingungen=False, send_request=False):
- entities = []
- for entity in paragraph['entities']:
- label = entity["entity"] if not send_request else entity["ent_type"]["label"]
- if (bedingungen and label == "Condition") or (not bedingungen and label != "Condition") :
- entities.append({
- 'start': entity['start'],
- 'end': entity["end"],
- 'label': legend_mapping[label]
- })
- return [{'text': paragraph['text'], 'ents': entities}]
-
-
-def request_extractor(text_data):
- try:
- data = SampleList(
- samples=[
- Sample(
- idx=0,
- text=str(text_data),
- entities=[],
- relations=[]
- )
- ]
- )
- tokenizer.run(data)
-
- model_inference = TransformersInference(inference_config)
- model_inference.run_inference(data)
- return data.dict()["samples"][0]
- except Exception as e:
- result = e
- return {"text":"error","entities":[], "relations":[]}
-
-
-def generate_graph(nodes, edges, send_request=False):
- net = Network(height="450px", width="100%")#, bgcolor="#222222", font_color="white", select_menu=True, filter_menu=True)
- for node in nodes:
- if "id" in node:
- label = node["entity"] if not send_request else node["ent_type"]["label"]
- node_color = legend_options["colors"][legend_mapping[label]]
- node_label = node["text"] if len(node["text"]) < 30 else (node["text"][:27]+" ...")
- if label in ["Kennzahl", "Kennzahlumschreibung"]:
- net.add_node(node["id"], label=node_label, title=node["text"], mass=2, shape="ellipse", color=node_color, physics=False)
- else:
- net.add_node(node["id"], label=node_label, title=node["text"], mass=1, shape="ellipse", color=node_color)
- for edge in edges:
- label = edge["relation"] if not send_request else edge["rel_type"]["label"]
- net.add_edge(edge["head"], edge["tail"], width=1, title=label, arrowStrikethrough=False, color=edge_colors[label])
- # net.force_atlas_2based() # barnes_hut() force_atlas_2based() hrepulsion() repulsion()
- net.toggle_physics(True)
- net.set_edge_smooth("dynamic") # dynamic, continuous, discrete, diagonalCross, straightCross, horizontal, vertical, curvedCW, curvedCCW, cubicBezier
- net.set_options(graph_options)
- html_graph = net.generate_html()
- return html_graph
-
-############################################################
-## Page configuration
-############################################################
-st.set_page_config(
- page_title="NLP Gesetzestexte",
- menu_items={
- 'Get Help': None,
- 'Report a bug': None,
- 'About': "## Demonstrator NLP"
- }
- # layout="wide")
- )
-
-st.markdown(
- f"""
-
- """,
- unsafe_allow_html=True,
- )
-
-# radio button formatting in line
-st.write('', unsafe_allow_html=True)
-
-############################################################
-## Page formating
-############################################################
-col3, col4 = st.columns([2,2])
-st.write('\n')
-st.write('\n')
-
-
-with col3:
- st.subheader("Extraction of Key Figures from Tax Legal Texts")
- st.write("Demonstrator Application for the Paper 'Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts using Neural Models' presented at the sixth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2023).")
- st.write("The paper can be found here: [Paper](https://ceur-ws.org/Vol-3441/paper7.pdf)")
- st.write('\n')
- st.write('This demonstrator processes German tax laws as input and outputs the extracted key figures with their properties and relations, based on the presented semantic model.')
-with col4:
- st.caption("Semantic Model")
- image = Image.open('util/ontology.png')
- st.image(image, width=700)
-
-
-text_option = st.radio("Select Example", ["Insert your paragraph", "EStG § 66 Kindergeld", "EStG § 9 Werbungskosten"])
-st.write('\n')
-if text_option == "EStG § 66 Kindergeld":
- text_area_input = st.text_area("Given paragraph", SAMPLE_66, height=200)
-elif text_option == "EStG § 9 Werbungskosten":
- text_area_input = st.text_area("Given paragraph", SAMPLE_9, height=200)
-else:
- text_area_input = st.text_area("Given paragraph", "", height=200)
-
-if st.button("Start Extraction") and text_area_input != "":
- with st.spinner('Executing Extraction ...'):
- paragraph = request_extractor(text_area_input)
- if paragraph["text"] == "error":
- st.error("Error while executing extraction.")
- else:
- legend = displacy.render([legend_content], style="ent", options=legend_options, manual=True)
- st.write(f"{style}{get_html(legend, True)}", unsafe_allow_html=True)
-
- st.caption("Entities:")
- extracted_data = get_displacy_ent_obj(paragraph, False, True)
- html = displacy.render(extracted_data, style="ent", options=legend_options, manual=True)
- st.write(f"{style}{get_html(html)}", unsafe_allow_html=True)
-
- st.write('\n')
- st.caption("Conditions:")
- extracted_data = get_displacy_ent_obj(paragraph, True, True)
- html = displacy.render(extracted_data, style="ent", options=legend_options, manual=True)
- st.write(f"{style}{get_html(html)}", unsafe_allow_html=True)
-
- st.write('\n')
- st.caption("\n\nRelations:")
- html_graph_req = generate_graph(paragraph["entities"], paragraph["relations"], send_request=True)
- components.html(html_graph_req, height=500)
- st.write('\n')
- with st.expander("Show JSON"):
- st.json(paragraph)
diff --git a/spaces/davidpiscasio/unpaired-img2img/options/test_options.py b/spaces/davidpiscasio/unpaired-img2img/options/test_options.py
deleted file mode 100644
index 3f42c01eaca1a41d5981d33fadc828ae54de4fdb..0000000000000000000000000000000000000000
--- a/spaces/davidpiscasio/unpaired-img2img/options/test_options.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from .base_options import BaseOptions
-
-
-class TestOptions(BaseOptions):
- """This class includes test options.
-
- It also includes shared options defined in BaseOptions.
- """
-
- def initialize(self, parser):
- parser = BaseOptions.initialize(self, parser) # define shared options
- parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
- parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
- parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
- # Dropout and Batchnorm has different behavioir during training and test.
- parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
- parser.add_argument('--num_test', type=int, default=50, help='how many test images to run')
- # rewrite devalue values
- parser.set_defaults(model='test')
- # To avoid cropping, the load_size should be the same as crop_size
- parser.set_defaults(load_size=parser.get_default('crop_size'))
- self.isTrain = False
- return parser
diff --git a/spaces/davidpiscasio/unpaired-img2img/util/html.py b/spaces/davidpiscasio/unpaired-img2img/util/html.py
deleted file mode 100644
index cc3262a1eafda34842e4dbad47bb6ba72f0c5a68..0000000000000000000000000000000000000000
--- a/spaces/davidpiscasio/unpaired-img2img/util/html.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import dominate
-from dominate.tags import meta, h3, table, tr, td, p, a, img, br
-import os
-
-
-class HTML:
- """This HTML class allows us to save images and write texts into a single HTML file.
-
- It consists of functions such as Multilingual Font Style Transfer
-
-
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diff --git a/spaces/dfassaf/newbingChatAI/README.md b/spaces/dfassaf/newbingChatAI/README.md
deleted file mode 100644
index 48408cc7782267ef33f7be2f48e780e2f90be111..0000000000000000000000000000000000000000
--- a/spaces/dfassaf/newbingChatAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NewbingChatAI
-emoji: ⚡
-colorFrom: blue
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dhanushreddy29/microstructure-project/app.py b/spaces/dhanushreddy29/microstructure-project/app.py
deleted file mode 100644
index 1060c51926be06ed0957787dbfa9883227f15d61..0000000000000000000000000000000000000000
--- a/spaces/dhanushreddy29/microstructure-project/app.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import cv2
-from fastai.vision.all import *
-import numpy as np
-import gradio as gr
-from scipy import ndimage
-
-fnames = get_image_files("./albumentations/original")
-
-
-def label_func(fn):
- return "./albumentations/labelled/" f"{fn.stem}.png"
-
-
-codes = np.loadtxt("labels.txt", dtype=str)
-w, h = 768, 1152
-img_size = (w, h)
-im_size = (h, w)
-
-dls = SegmentationDataLoaders.from_label_func(
- ".",
- bs=3,
- fnames=fnames,
- label_func=label_func,
- codes=codes,
- item_tfms=Resize(img_size),
-)
-
-learn = unet_learner(dls, resnet34)
-learn.load("learn")
-
-
-def segmentImage(img_path):
- img = cv2.imread(img_path, 0)
- for i in range(img.shape[0]):
- for j in range(img.shape[1]):
- if img[i][j] > 0:
- img[i][j] = 1
- kernel = np.ones((3, 3), np.uint8)
- # img = cv2.erode(img, kernel, iterations=1)
- # img = cv2.dilate(img, kernel, iterations=1)
- img = ndimage.binary_fill_holes(img).astype(int)
- labels, nlabels = ndimage.label(img)
-
- # Get grain sizes
- sizes = ndimage.sum(img, labels, range(nlabels + 1))
- scale_factor = 3072 / 1152
- c = 0.4228320313
- # Divide sizes by pixel_to_micrometer to get the sizes in micrometers and store them in a list new_sizes
- new_sizes = [size * scale_factor * scale_factor * c * c for size in sizes]
- # Round the grain sizes to 2 decimal places
- new_sizes = [round(size, 2) for size in new_sizes]
- # Print the grain sizes
- print("Sorted Areas = ", sorted(list(new_sizes)))
- print("Length = ", len(new_sizes))
- gradient_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
- colors = []
- for i in range(len(new_sizes)):
- if new_sizes[i] < 250 * c * c:
- colors.append((255, 255, 255))
- elif new_sizes[i] < 7500 * c * c:
- colors.append((2, 106, 248))
- elif new_sizes[i] < 20000 * c * c:
- colors.append((0, 255, 107))
- elif new_sizes[i] < 45000 * c * c:
- colors.append((255, 201, 60))
- else:
- colors.append((255, 0, 0))
- for i in range(img.shape[0]):
- for j in range(img.shape[1]):
- if labels[i][j] != 0:
- gradient_img[i][j] = colors[labels[i][j]]
- Sum = 0
- count = 0
- for i in range(len(new_sizes)):
- if new_sizes[i] > 250 * c * c:
- Sum += new_sizes[i]
- count += 1
- colors = np.random.randint(0, 255, (nlabels + 1, 3))
- colors[0] = 0
- img_color = colors[labels]
- return (
- img_color,
- gradient_img,
- "Average Area of grains: " + str(Sum / count) + " µm^2",
- )
-
-
-def predict_segmentation(img):
- gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- resized_img = cv2.resize(gray_img, im_size)
- pred = learn.predict(resized_img)
- scaled_pred = (pred[0].numpy() * 255).astype(np.uint8)
- output_image = PILImage.create(scaled_pred)
- # Save the image to a temporary file
- temp_file = "temp.png"
- output_image.save(temp_file)
- # Call the segmentImage function
- segmented_image, gradient_image, avg_area = segmentImage(temp_file)
- return output_image, segmented_image, gradient_image, avg_area
-
-
-input_image = gr.inputs.Image()
-output_image1 = gr.outputs.Image(type="pil")
-output_image2 = gr.outputs.Image(type="pil")
-output_image3 = gr.outputs.Image(type="pil")
-output_image4 = gr.outputs.Textbox()
-app = gr.Interface(
- fn=predict_segmentation,
- inputs=input_image,
- outputs=[output_image1, output_image2, output_image3, output_image4],
- title="Microstructure Segmentation",
- description="Segment the input image into grain and background.",
- examples=["examples/inp1.png", "examples/inp2.png"]
-)
-app.launch()
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\ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/nn.py b/spaces/fclong/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/nn.py deleted file mode 100644 index b28bd83cf23b4e19868afc2075b11ca1cfbd0e8d..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/nn.py +++ /dev/null @@ -1,190 +0,0 @@ -""" -Various utilities for neural networks. -""" - -import math - -import torch as th -import torch.nn as nn - - -# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. -class SiLU(nn.Module): - def forward(self, x): - return x * th.sigmoid(x) - - -class GroupNorm32(nn.GroupNorm): - def forward(self, x): - return super().forward(x.float()).type(x.dtype) - - -def conv_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D convolution module. - """ - if dims == 1: - return nn.Conv1d(*args, **kwargs) - elif dims == 2: - return nn.Conv2d(*args, **kwargs) - elif dims == 3: - return nn.Conv3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def linear(*args, **kwargs): - """ - Create a linear module. - """ - return nn.Linear(*args, **kwargs) - - -def avg_pool_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D average pooling module. - """ - if dims == 1: - return nn.AvgPool1d(*args, **kwargs) - elif dims == 2: - return nn.AvgPool2d(*args, **kwargs) - elif dims == 3: - return nn.AvgPool3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def update_ema(target_params, source_params, rate=0.99): - """ - Update target parameters to be closer to those of source parameters using - an exponential moving average. - - :param target_params: the target parameter sequence. - :param source_params: the source parameter sequence. - :param rate: the EMA rate (closer to 1 means slower). - """ - for targ, src in zip(target_params, source_params): - targ.detach().mul_(rate).add_(src, alpha=1 - rate) - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def scale_module(module, scale): - """ - Scale the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def mean_flat(tensor): - """ - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def normalization(channels): - """ - Make a standard normalization layer. - - :param channels: number of input channels. - :return: an nn.Module for normalization. - """ - return GroupNorm32(32, channels) - - -def timestep_embedding(timesteps, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. - """ - half = dim // 2 - freqs = th.exp( - -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half - ).to(device=timesteps.device) - args = timesteps[:, None].float() * freqs[None] - embedding = th.cat([th.cos(args), th.sin(args)], dim=-1) - if dim % 2: - embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1) - return embedding - - -def checkpoint(func, inputs, params, flag): - """ - Evaluate a function without caching intermediate activations, allowing for - reduced memory at the expense of extra compute in the backward pass. - :param func: the function to evaluate. - :param inputs: the argument sequence to pass to `func`. - :param params: a sequence of parameters `func` depends on but does not - explicitly take as arguments. - :param flag: if False, disable gradient checkpointing. - """ - if flag: - args = tuple(inputs) + tuple(params) - return CheckpointFunction.apply(func, len(inputs), *args) - else: - return func(*inputs) - - -class CheckpointFunction(th.autograd.Function): - @staticmethod - @th.cuda.amp.custom_fwd - def forward(ctx, run_function, length, *args): - ctx.run_function = run_function - ctx.input_length = length - ctx.save_for_backward(*args) - with th.no_grad(): - output_tensors = ctx.run_function(*args[:length]) - return output_tensors - - @staticmethod - @th.cuda.amp.custom_bwd - def backward(ctx, *output_grads): - args = list(ctx.saved_tensors) - - # Filter for inputs that require grad. If none, exit early. - input_indices = [i for (i, x) in enumerate(args) if x.requires_grad] - if not input_indices: - return (None, None) + tuple(None for _ in args) - - with th.enable_grad(): - for i in input_indices: - if i < ctx.input_length: - # Not sure why the OAI code does this little - # dance. It might not be necessary. - args[i] = args[i].detach().requires_grad_() - args[i] = args[i].view_as(args[i]) - output_tensors = ctx.run_function(*args[:ctx.input_length]) - - if isinstance(output_tensors, th.Tensor): - output_tensors = [output_tensors] - - # Filter for outputs that require grad. If none, exit early. - out_and_grads = [(o, g) for (o, g) in zip(output_tensors, output_grads) if o.requires_grad] - if not out_and_grads: - return (None, None) + tuple(None for _ in args) - - # Compute gradients on the filtered tensors. - computed_grads = th.autograd.grad( - [o for (o, g) in out_and_grads], - [args[i] for i in input_indices], - [g for (o, g) in out_and_grads] - ) - - # Reassemble the complete gradient tuple. - input_grads = [None for _ in args] - for (i, g) in zip(input_indices, computed_grads): - input_grads[i] = g - return (None, None) + tuple(input_grads) diff --git a/spaces/felixz/Flan-T5-experiment/app.py b/spaces/felixz/Flan-T5-experiment/app.py deleted file mode 100644 index 3679859d19c019d27d9a6dbc9aef05cc65bc75d1..0000000000000000000000000000000000000000 --- a/spaces/felixz/Flan-T5-experiment/app.py +++ /dev/null @@ -1,63 +0,0 @@ -import gradio as gr -from transformers import T5Tokenizer, T5ForConditionalGeneration -from transformers import AutoModel - -# xl size run out of memory on 16GB VM -model_name = 'google/flan-t5-large' -#model_name = 'jncraton/fastchat-t5-3b-v1.0-ct2-int8' - -# Load model directly -#from transformers import AutoModel -model = AutoModel.from_pretrained(model_name) - -tokenizer = T5Tokenizer.from_pretrained(model_name) -#model = T5ForConditionalGeneration.from_pretrained(model_name) - -title = "" - -def get_examples (): - return [ - ["Peter goes to the store to buy a soda. The soda costs $.25 an ounce. \ -He brought $2 with him and leaves with $.50. How many ounces of soda did he buy?", - "How much did Peter spend on soda? ** He spend $1.5 on soda because 2 - .5 = <<2-.5=1.5>>1.5 \ -How many ounces of soda did Peter buy? ** He bought 6 ounces of soda because 1.5 / .25 = <<6=6>>6 #### 6" - ], - ["Krystian works in the library. He borrows an average of 40 books every day. \ -Every Friday, his number of borrowed books is about 40% higher than the daily average. How many books does he borrow in a week if the library is open from Monday to Friday?" - ,"How many books does Krystian borrow on Friday? ** The number of books borrowed \ -on Friday is higher by 40 * 40/100 = <<40*40/100=16>>16 books. How many books does Krystian borrow in a week? ** There are 5 days from Monday to Friday inclusive, so Krystian borrows an average of 5 * 40 = <<5*40=200>>200 books during that time. How many books does Krystian borrow in a week? ** With Friday's increase in borrowings, during one week Krystian borrows 200 + 16 = <<200+16=216>>216 books."] - , ["Jane had $60 but gave $30 to dave and went to movies and spend $2. How much money does Jane has left? Answer by reasoning step by step:", "$28"] - ] - - -def text2text(input_text): - input_ids = tokenizer(input_text, return_tensors="pt").input_ids - - outputs = model.generate(input_ids, max_length=200) - return tokenizer.decode(outputs[0]) - - -with gr.Blocks() as demo: - gr.Markdown( - """ - # Flan T5 Large Demo - 780M parameter Large language model fine tuned on diverse tasks. - Prompt the model in the Input box. - """) - txt_in = gr.Textbox(label="Input", lines=3) - correct_label = gr.Label(label="Correct") - txt_out = gr.Textbox(value="", label="Output", lines=4) - - - btn = gr.Button(value="Submit") - btn.click(text2text, inputs=[txt_in], outputs=[txt_out]) - - - gr.Examples( - examples=get_examples(), - inputs=[txt_in,correct_label] - ) - - -if __name__ == "__main__": - demo.launch() \ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Game of Thrones All Seasons Tamil Dubbed Movie in KuttyMovies.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Game of Thrones All Seasons Tamil Dubbed Movie in KuttyMovies.md deleted file mode 100644 index 22b9071e6e0930f5c3f6bebe7af8f1de4279bf34..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Game of Thrones All Seasons Tamil Dubbed Movie in KuttyMovies.md +++ /dev/null @@ -1,106 +0,0 @@ -
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Game of Thrones Tamil Dubbed Movie Download in KuttyMovies
-Game of Thrones is one of the most popular and acclaimed fantasy drama television series of all time. It has millions of fans around the world who are eagerly waiting for the next season or spin-off. But what if you want to watch Game of Thrones in your native language, such as Tamil? Is it possible to download Game of Thrones Tamil dubbed movie from KuttyMovies, a notorious piracy website that offers free movies and TV shows? In this article, we will answer these questions and more. We will also provide you with some alternatives to KuttyMovies for downloading Game of Thrones Tamil dubbed movie safely and legally.
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Introduction
-What is Game of Thrones?
-Game of Thrones is a fantasy drama television series created by David Benioff and D. B. Weiss, based on the novel series A Song of Ice and Fire by George R. R. Martin. The series premiered on HBO in 2011 and concluded in 2019, with eight seasons and 73 episodes. The story revolves around the power struggle among the noble families of Westeros, a fictional continent, for the Iron Throne, the seat of the king. The series also features mythical creatures, such as dragons, direwolves, and white walkers, who pose a threat to the living. The series has won numerous awards, including 59 Emmy Awards, and has been praised for its complex characters, storylines, acting, production values, and cultural impact.
-What is KuttyMovies?
-KuttyMovies is a piracy website that provides free downloads of movies and TV shows in various languages, such as Tamil, Telugu, Hindi, English, Malayalam, Kannada, etc. The website has a huge collection of Tamil dubbed movies, including Hollywood movies, Bollywood movies, South Indian movies, and web series. The website also updates its content regularly with the latest releases and leaks. KuttyMovies is one of the most visited piracy websites in India and attracts millions of users every month.
-Why do people want to watch Game of Thrones in Tamil?
-Tamil is one of the most spoken languages in India, with over 75 million speakers. It is also an official language in Sri Lanka and Singapore. Many people who speak Tamil prefer to watch movies and TV shows in their native language, as it helps them to understand the dialogues better and enjoy the cultural nuances. Moreover, some people may not be comfortable with English subtitles or audio, as they may find them distracting or hard to follow. Therefore, watching Game of Thrones in Tamil can be a more enjoyable and satisfying experience for them.
-How to download Game of Thrones Tamil dubbed movie in KuttyMovies
-Step 1: Visit the KuttyMovies website
-The first step to download Game of Thrones Tamil dubbed movie in KuttyMovies is to visit the website. However, this may not be as easy as it sounds, as KuttyMovies is an illegal website that is banned by the government and internet service providers. Therefore, you may need to use a VPN service or a proxy site to access the website. A VPN service can help you to bypass the geo-restrictions and hide your IP address from the authorities. A proxy site can help you to access the
Step 2: Search for Game of Thrones Tamil dubbed movie
-The next step is to search for Game of Thrones Tamil dubbed movie in KuttyMovies. You can use the search bar on the homepage or browse through the categories and genres. You can also filter the results by year, quality, size, and language. You may find multiple links for Game of Thrones Tamil dubbed movie, as KuttyMovies uploads different versions and sources. You can choose the one that suits your preferences and availability.
-Step 3: Choose the preferred quality and size
-After selecting the link for Game of Thrones Tamil dubbed movie, you will be redirected to another page where you can see the details of the movie, such as the title, genre, cast, director, rating, synopsis, screenshots, and download options. You can choose the quality and size of the movie that you want to download, such as 480p, 720p, 1080p, 300MB, 700MB, 1.5GB, etc. The higher the quality and size, the better the video and audio clarity, but also the longer the download time and the more storage space required.
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Step 4: Click on the download link
-The final step is to click on the download link for Game of Thrones Tamil dubbed movie. However, before you can start the download process, you may have to face some challenges, such as pop-up ads, redirects, captcha verification, and waiting time. These are some of the ways that KuttyMovies earns money from its users and protects its servers from bots and spam. You have to be patient and careful while dealing with these obstacles and avoid clicking on any suspicious or malicious links or buttons. Once you get past these hurdles, you will be able to download Game of Thrones Tamil dubbed movie in KuttyMovies.
-Step 5: Enjoy watching Game of Thrones in Tamil
-Congratulations! You have successfully downloaded Game of Thrones Tamil dubbed movie in KuttyMovies. Now you can enjoy watching your favorite fantasy drama series in your native language on your device. You can also share it with your friends and family who are also fans of Game of Thrones and Tamil movies. However, you should also be aware of the risks and challenges of downloading Game of Thrones Tamil dubbed movie in KuttyMovies, which we will discuss in the next section.
-Risks and challenges of downloading Game of Thrones Tamil dubbed movie in KuttyMovies
-Legal issues and piracy
-One of the major risks of downloading Game of Thrones Tamil dubbed movie in KuttyMovies is that you are violating the law and engaging in piracy. Piracy is the unauthorized distribution or reproduction of copyrighted content without the permission of the owner or the law. Piracy is a serious crime that can result in legal actions, such as fines, lawsuits, or even imprisonment. Moreover, piracy harms the entertainment industry and the artists who work hard to create original and quality content. By downloading Game of Thrones Tamil dubbed movie in KuttyMovies, you are depriving them of their rightful revenue and recognition. Therefore, you should respect the intellectual property rights of the creators and avoid downloading Game of Thrones Tamil dubbed movie in KuttyMovies.
-Malware and viruses
-Another risk of downloading Game of Thrones Tamil dubbed movie in KuttyMovies is that you may expose your device and data to malware and viruses. Malware and viruses are malicious software that can infect your device and cause various problems, such as slowing down your performance, stealing your personal information, deleting your files, or even damaging your hardware. KuttyMovies is an unsecured and unregulated website that may contain malware and viruses in its download links, ads, or redirects. You may not even notice that your device has been infected until it is too late. Therefore, you should protect your device and data by using a reliable antivirus software and avoiding downloading Game of Thrones Tamil dubbed movie in KuttyMovies.
-Low quality and incomplete episodes
-A third risk of downloading Game of Thrones Tamil dubbed movie in KuttyMovies is that you may not get the best quality and complete episodes of the series. KuttyMovies is a piracy website that does not have the official rights or sources to provide Game of Thrones Tamil dubbed movie. Therefore, it may rely on low-quality recordings, camrips, or screen captures to upload the movie. Moreover, it may not have all the episodes or seasons of the series, or it may have missing or corrupted parts. You may end up wasting your time and bandwidth on downloading Game of Thrones Tamil dubbed movie in KuttyMovies that does not meet your expectations or satisfaction. Therefore, you should look for other alternatives to KuttyMovies for downloading Game of Thrones Tamil dubbed movie.
-Alternatives to KuttyMovies for downloading Game of Thrones Tamil dubbed movie
-Isaidub
-Isaidub is another piracy website that offers free downloads of Tamil dubbed movies and TV shows. It has a large collection of Hollywood movies, Bollywood movies, South Indian movies, and web series in Tamil language. It also has a separate section for Game of Thrones Tamil dubbed movie, where you can find all the seasons and episodes of the series. However, Isaidub also has the same risks and challenges as KuttyMovies, such as legal issues, malware, and low quality. Therefore, you should use Isaidub at your own risk and discretion.
-Tamilyogi
-Tamilyogi is yet another piracy website that provides free downloads of Tamil movies and TV shows. It has a huge database of Tamil movies, ranging from old classics to new releases. It also has a category for Tamil dubbed movies, where you can find Game of Thrones Tamil dubbed movie along with other popular Hollywood movies and web series. However, Tamilyogi also suffers from the same problems as KuttyMovies and Isaidub, such as illegality, viruses, and poor quality. Therefore, you should be careful while using Tamilyogi for downloading Game of Thrones Tamil dubbed movie.
-Oceanofmovies
-Oceanofmovies is a different kind of website that does not host any movies or TV shows on its own servers. Instead, it provides links to other websites where you can download or stream movies and TV shows for free. It has a vast collection of movies and TV shows in various languages, genres, and qualities. It also has links to Game of Thrones Tamil dubbed movie from different sources and platforms. However, Oceanofmovies also has its own drawbacks, such as broken links, pop-up ads, and unreliable quality. Therefore, you should verify the links and sources before using Oceanofmovies for downloading Game of Thrones Tamil dubbed movie.
-Conclusion
-Game of Thrones is a phenomenal fantasy drama series that has captivated millions of viewers across the globe. However, if you want to watch Game of Thrones in Tamil, you may face some difficulties in finding the Tamil dubbed version of the series. KuttyMovies is one of the piracy websites that claims to offer Game of Thrones Tamil dubbed movie for free download. However, KuttyMovies is not a safe or legal option, as it involves many risks and challenges, such as legal issues, malware, and low quality. Therefore, we do not recommend using KuttyMovies for downloading Game of Thrones Tamil dubbed movie. Instead, we suggest you look for other alternatives, such as Isaidub, Tamilyogi, or Oceanofmovies, which may have better quality and availability of Game of Thrones Tamil dubbed movie. However, you should also be aware of the drawbacks and dangers of these websites, and use them at your own risk and discretion. The best way to watch Game of Thrones in Tamil is to subscribe to a legitimate streaming service that has the official rights and licenses to provide Game of Thrones in Tamil language. This way, you can enjoy watching Game of Thrones in Tamil without any worries or hassles.
-FAQs
-Here are some frequently asked questions about Game of Thrones Tamil dubbed movie download in KuttyMovies:
-Q: Is it legal to download Game of Thrones Tamil dubbed movie from KuttyMovies?
-A: No, it is not legal to download Game of Thrones Tamil dubbed movie from KuttyMovies. KuttyMovies is a piracy website that violates the copyright laws and infringes the intellectual property rights of the creators and owners of Game of Thrones. Downloading Game of Thrones Tamil dubbed movie from KuttyMovies can result in legal actions, such as fines, lawsuits, or even imprisonment.
-Q: Is it safe to download Game of Thrones Tamil dubbed movie from KuttyMovies?
-A: No, it is not safe to download Game of Thrones Tamil dubbed movie from KuttyMovies. KuttyMovies is an unsecured and unregulated website that may contain malware and viruses in its download links, ads, or redirects. Downloading Game of Thrones Tamil dubbed movie from KuttyMovies can expose your device and data to malware and viruses, which can cause various problems, such as slowing down your performance, stealing your personal information, deleting your files, or even damaging your hardware.
-Q: How can I watch Game of Thrones in Tamil legally?
-A: The best way to watch Game of Thrones in Tamil legally is to subscribe to a legitimate streaming service that has the official rights and licenses to provide Game of Thrones in Tamil language. Some examples of such streaming services are Hotstar, Amazon Prime Video, Netflix, etc. These streaming services offer high-quality and complete episodes of Game of Thrones in Tamil language with subtitles or audio options. They also have other features and benefits, such as offline viewing, multiple devices support, original content, etc.
-Q: What are some other websites that offer Game of Thrones Tamil dubbed movie for free download?
-A: Some other websites that offer Game of Thrones Tamil dubbed movie for free download are Isaidub, Tamilyogi, Oceanofmovies, etc. However, these websites are also piracy websites that have the same risks and challenges as KuttyMovies, such as legal issues, malware, and low quality. Therefore, you should be careful while using these websites for downloading Game of Thrones Tamil dubbed movie.
-Q: How can I improve my English skills while watching Game of Thrones?
-A: If you want to improve your English skills while watching Game of Thrones, you can try some of these tips:
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By following these tips, you can enjoy watching Game of Thrones and also improve your English skills at the same time.
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Project Playtime on PS4 Everything you need to know.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Project Playtime on PS4 Everything you need to know.md deleted file mode 100644 index 0c8518bfa3bcd8bc164adcf4cf0299f811990f3c..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Project Playtime on PS4 Everything you need to know.md +++ /dev/null @@ -1,99 +0,0 @@ -
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Can You Download Project Playtime on PS4?
-If you are a fan of horror games, you might have heard of Project Playtime, a multiplayer game where you have to survive a toy factory full of deadly surprises. But can you download Project Playtime on PS4, or is it only available on PC? In this article, we will answer this question and give you some tips on how to play Project Playtime on your console.
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What is Project Playtime?
-Project Playtime is a free-to-play multiplayer horror game that was released in December 2022 on Steam. It is developed by Moonbit Studios, an indie team based in Argentina.
-A multiplayer horror game
-In Project Playtime, six players have to work together to create one giant toy while avoiding a terrifying monster that roams the factory. The monster is controlled by a seventh player, who has only one goal: find and kill everyone. The game features different maps, characters, toys, and monsters, each with their own abilities and weaknesses.
-A PC exclusive title
-As of now, Project Playtime is only available on PC. The developers have not announced any plans to bring the game to other platforms, such as PS4 or PS5. According to their FAQ page, they are focusing on improving the PC version first before considering other options.
-Why do people want Project Playtime on PS4?
-Project Playtime has gained a lot of attention and praise from horror fans and streamers since its launch. It has over 29,000 positive reviews on Steam and millions of views on YouTube . But why do people want to play it on PS4?
-How to play Project Playtime on PS4 or PS5
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The popularity of horror games on consoles
-Horror games are very popular among console gamers, especially those who own a PS4 or PS5. Some of the most successful horror titles in recent years, such as Resident Evil 2, Outlast 2, Until Dawn, and The Evil Within 2, were released on these platforms. Playing horror games on a big screen with surround sound can enhance the immersion and scare factor.
-The appeal of Project Playtime's gameplay and graphics
-Another reason why people want Project Playtime on PS4 is because of its gameplay and graphics. The game offers a unique twist on the multiplayer horror genre, where teamwork and strategy are essential to survive. The game also has a colorful and cartoonish style that contrasts with the dark and creepy atmosphere. The game's trailer showcases some of the stunning visuals and animations that the game has to offer.
-How to play Project Playtime on PS4?
-So, can you download Project Playtime on PS4? Unfortunately, the answer is no. There is no official way to play the game on your console. However, there are some unofficial methods that you can try at your own risk.
-The official answer: You can't
-The developers of Project Playtime have stated that they have no plans to port the game to PS4 or PS5 anytime soon. They are focused on improving the PC version first and adding more content and features. They also said that they are not interested in making a fake trailer or gameplay video for consoles, as some fans have requested. Therefore, if you see any videos or websites claiming that you can download Project Playtime on PS4, they are most likely scams or hoaxes.
-The unofficial answer: You can try streaming or remote play
-If you really want to play Project Play time on PS4, there are some unofficial methods that you can try at your own risk. These methods involve streaming or remote play, which allow you to access your PC games from your console. However, these methods are not guaranteed to work, and they may have some drawbacks, such as lag, low quality, or compatibility issues. Here are some of the options you can try:
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-
Conclusion
-Project Playtime is a multiplayer horror game that is only available on PC. The developers have no plans to port the game to PS4 or PS5 anytime soon. If you want to play Project Playtime on your console, you can try some unofficial methods that involve streaming or remote play, but they are not guaranteed to work and they may have some drawbacks.
-If you enjoyed this article, please share it with your friends and leave a comment below. Have you tried Project Playtime? What do you think of the game? Do you have any tips or tricks for playing it? Let us know!
-FAQs
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\ No newline at end of file diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/cookie-signature/History.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/cookie-signature/History.md deleted file mode 100644 index 78513cc3d28ce3516c93b4d425f83df247486ae5..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/cookie-signature/History.md +++ /dev/null @@ -1,38 +0,0 @@ -1.0.6 / 2015-02-03 -================== - -* use `npm test` instead of `make test` to run tests -* clearer assertion messages when checking input - - -1.0.5 / 2014-09-05 -================== - -* add license to package.json - -1.0.4 / 2014-06-25 -================== - - * corrected avoidance of timing attacks (thanks @tenbits!) - -1.0.3 / 2014-01-28 -================== - - * [incorrect] fix for timing attacks - -1.0.2 / 2014-01-28 -================== - - * fix missing repository warning - * fix typo in test - -1.0.1 / 2013-04-15 -================== - - * Revert "Changed underlying HMAC algo. to sha512." - * Revert "Fix for timing attacks on MAC verification." - -0.0.1 / 2010-01-03 -================== - - * Initial release diff --git a/spaces/firsk/ai_otto/train_ms.py b/spaces/firsk/ai_otto/train_ms.py deleted file mode 100644 index 1f1708d8ef1f4e820b608234a60744a200a644cd..0000000000000000000000000000000000000000 --- a/spaces/firsk/ai_otto/train_ms.py +++ /dev/null @@ -1,594 +0,0 @@ -# flake8: noqa: E402 - -import os -import torch -from torch.nn import functional as F -from torch.utils.data import DataLoader -from torch.utils.tensorboard import SummaryWriter -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.cuda.amp import autocast, GradScaler -from tqdm import tqdm -import logging - -logging.getLogger("numba").setLevel(logging.WARNING) -import commons -import utils -from data_utils import ( - TextAudioSpeakerLoader, - TextAudioSpeakerCollate, - DistributedBucketSampler, -) -from models import ( - SynthesizerTrn, - MultiPeriodDiscriminator, - DurationDiscriminator, -) -from losses import generator_loss, discriminator_loss, feature_loss, kl_loss -from mel_processing import mel_spectrogram_torch, spec_to_mel_torch -from text.symbols import symbols - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cudnn.allow_tf32 = ( - True # If encontered training problem,please try to disable TF32. -) -torch.set_float32_matmul_precision("medium") -torch.backends.cudnn.benchmark = True -torch.backends.cuda.sdp_kernel("flash") -torch.backends.cuda.enable_flash_sdp(True) -torch.backends.cuda.enable_mem_efficient_sdp( - True -) # Not available if torch version is lower than 2.0 -torch.backends.cuda.enable_math_sdp(True) -global_step = 0 - - -def run(): - dist.init_process_group( - backend="gloo", - init_method="env://", # Due to some training problem,we proposed to use gloo instead of nccl. - ) # Use torchrun instead of mp.spawn - rank = dist.get_rank() - n_gpus = dist.get_world_size() - hps = utils.get_hparams() - torch.manual_seed(hps.train.seed) - torch.cuda.set_device(rank) - global global_step - if rank == 0: - logger = utils.get_logger(hps.model_dir) - logger.info(hps) - utils.check_git_hash(hps.model_dir) - writer = SummaryWriter(log_dir=hps.model_dir) - writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) - train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data) - train_sampler = DistributedBucketSampler( - train_dataset, - hps.train.batch_size, - [32, 300, 400, 500, 600, 700, 800, 900, 1000], - num_replicas=n_gpus, - rank=rank, - shuffle=True, - ) - collate_fn = TextAudioSpeakerCollate() - train_loader = DataLoader( - train_dataset, - num_workers=16, - shuffle=False, - pin_memory=True, - collate_fn=collate_fn, - batch_sampler=train_sampler, - persistent_workers=True, - prefetch_factor=4, - ) # DataLoader config could be adjusted. - if rank == 0: - eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data) - eval_loader = DataLoader( - eval_dataset, - num_workers=0, - shuffle=False, - batch_size=1, - pin_memory=True, - drop_last=False, - collate_fn=collate_fn, - ) - if ( - "use_noise_scaled_mas" in hps.model.keys() - and hps.model.use_noise_scaled_mas is True - ): - print("Using noise scaled MAS for VITS2") - mas_noise_scale_initial = 0.01 - noise_scale_delta = 2e-6 - else: - print("Using normal MAS for VITS1") - mas_noise_scale_initial = 0.0 - noise_scale_delta = 0.0 - if ( - "use_duration_discriminator" in hps.model.keys() - and hps.model.use_duration_discriminator is True - ): - print("Using duration discriminator for VITS2") - net_dur_disc = DurationDiscriminator( - hps.model.hidden_channels, - hps.model.hidden_channels, - 3, - 0.1, - gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0, - ).cuda(rank) - if ( - "use_spk_conditioned_encoder" in hps.model.keys() - and hps.model.use_spk_conditioned_encoder is True - ): - if hps.data.n_speakers == 0: - raise ValueError( - "n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model" - ) - else: - print("Using normal encoder for VITS1") - - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - mas_noise_scale_initial=mas_noise_scale_initial, - noise_scale_delta=noise_scale_delta, - **hps.model, - ).cuda(rank) - - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - filter(lambda p: p.requires_grad, net_g.parameters()), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps, - ) - optim_d = torch.optim.AdamW( - net_d.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps, - ) - if net_dur_disc is not None: - optim_dur_disc = torch.optim.AdamW( - net_dur_disc.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps, - ) - else: - optim_dur_disc = None - net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) - net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) - if net_dur_disc is not None: - net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True) - try: - if net_dur_disc is not None: - _, _, dur_resume_lr, epoch_str = utils.load_checkpoint( - utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), - net_dur_disc, - optim_dur_disc, - skip_optimizer=hps.train.skip_optimizer - if "skip_optimizer" in hps.train - else True, - ) - _, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint( - utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), - net_g, - optim_g, - skip_optimizer=hps.train.skip_optimizer - if "skip_optimizer" in hps.train - else True, - ) - _, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint( - utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), - net_d, - optim_d, - skip_optimizer=hps.train.skip_optimizer - if "skip_optimizer" in hps.train - else True, - ) - if not optim_g.param_groups[0].get("initial_lr"): - optim_g.param_groups[0]["initial_lr"] = g_resume_lr - if not optim_d.param_groups[0].get("initial_lr"): - optim_d.param_groups[0]["initial_lr"] = d_resume_lr - if not optim_dur_disc.param_groups[0].get("initial_lr"): - optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr - - epoch_str = max(epoch_str, 1) - global_step = (epoch_str - 1) * len(train_loader) - except Exception as e: - print(e) - epoch_str = 1 - global_step = 0 - - scheduler_g = torch.optim.lr_scheduler.ExponentialLR( - optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2 - ) - scheduler_d = torch.optim.lr_scheduler.ExponentialLR( - optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2 - ) - if net_dur_disc is not None: - if not optim_dur_disc.param_groups[0].get("initial_lr"): - optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr - scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR( - optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2 - ) - else: - scheduler_dur_disc = None - scaler = GradScaler(enabled=hps.train.fp16_run) - - for epoch in range(epoch_str, hps.train.epochs + 1): - if rank == 0: - train_and_evaluate( - rank, - epoch, - hps, - [net_g, net_d, net_dur_disc], - [optim_g, optim_d, optim_dur_disc], - [scheduler_g, scheduler_d, scheduler_dur_disc], - scaler, - [train_loader, eval_loader], - logger, - [writer, writer_eval], - ) - else: - train_and_evaluate( - rank, - epoch, - hps, - [net_g, net_d, net_dur_disc], - [optim_g, optim_d, optim_dur_disc], - [scheduler_g, scheduler_d, scheduler_dur_disc], - scaler, - [train_loader, None], - None, - None, - ) - scheduler_g.step() - scheduler_d.step() - if net_dur_disc is not None: - scheduler_dur_disc.step() - - -def train_and_evaluate( - rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers -): - net_g, net_d, net_dur_disc = nets - optim_g, optim_d, optim_dur_disc = optims - scheduler_g, scheduler_d, scheduler_dur_disc = schedulers - train_loader, eval_loader = loaders - if writers is not None: - writer, writer_eval = writers - - train_loader.batch_sampler.set_epoch(epoch) - global global_step - - net_g.train() - net_d.train() - if net_dur_disc is not None: - net_dur_disc.train() - for batch_idx, ( - x, - x_lengths, - spec, - spec_lengths, - y, - y_lengths, - speakers, - tone, - language, - bert, - ja_bert, - ) in tqdm(enumerate(train_loader)): - if net_g.module.use_noise_scaled_mas: - current_mas_noise_scale = ( - net_g.module.mas_noise_scale_initial - - net_g.module.noise_scale_delta * global_step - ) - net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0) - x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda( - rank, non_blocking=True - ) - spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda( - rank, non_blocking=True - ) - y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda( - rank, non_blocking=True - ) - speakers = speakers.cuda(rank, non_blocking=True) - tone = tone.cuda(rank, non_blocking=True) - language = language.cuda(rank, non_blocking=True) - bert = bert.cuda(rank, non_blocking=True) - ja_bert = ja_bert.cuda(rank, non_blocking=True) - - with autocast(enabled=hps.train.fp16_run): - ( - y_hat, - l_length, - attn, - ids_slice, - x_mask, - z_mask, - (z, z_p, m_p, logs_p, m_q, logs_q), - (hidden_x, logw, logw_), - ) = net_g( - x, - x_lengths, - spec, - spec_lengths, - speakers, - tone, - language, - bert, - ja_bert, - ) - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax, - ) - y_mel = commons.slice_segments( - mel, ids_slice, hps.train.segment_size // hps.data.hop_length - ) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax, - ) - - y = commons.slice_segments( - y, ids_slice * hps.data.hop_length, hps.train.segment_size - ) # slice - - # Discriminator - y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) - with autocast(enabled=False): - loss_disc, losses_disc_r, losses_disc_g = discriminator_loss( - y_d_hat_r, y_d_hat_g - ) - loss_disc_all = loss_disc - if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc( - hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach() - ) - with autocast(enabled=False): - # TODO: I think need to mean using the mask, but for now, just mean all - ( - loss_dur_disc, - losses_dur_disc_r, - losses_dur_disc_g, - ) = discriminator_loss(y_dur_hat_r, y_dur_hat_g) - loss_dur_disc_all = loss_dur_disc - optim_dur_disc.zero_grad() - scaler.scale(loss_dur_disc_all).backward() - scaler.unscale_(optim_dur_disc) - commons.clip_grad_value_(net_dur_disc.parameters(), None) - scaler.step(optim_dur_disc) - - optim_d.zero_grad() - scaler.scale(loss_disc_all).backward() - scaler.unscale_(optim_d) - grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) - scaler.step(optim_d) - - with autocast(enabled=hps.train.fp16_run): - # Generator - y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) - if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_) - with autocast(enabled=False): - loss_dur = torch.sum(l_length.float()) - loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel - loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl - - loss_fm = feature_loss(fmap_r, fmap_g) - loss_gen, losses_gen = generator_loss(y_d_hat_g) - loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl - if net_dur_disc is not None: - loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g) - loss_gen_all += loss_dur_gen - optim_g.zero_grad() - scaler.scale(loss_gen_all).backward() - scaler.unscale_(optim_g) - grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) - scaler.step(optim_g) - scaler.update() - - if rank == 0: - if global_step % hps.train.log_interval == 0: - lr = optim_g.param_groups[0]["lr"] - losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl] - logger.info( - "Train Epoch: {} [{:.0f}%]".format( - epoch, 100.0 * batch_idx / len(train_loader) - ) - ) - logger.info([x.item() for x in losses] + [global_step, lr]) - - scalar_dict = { - "loss/g/total": loss_gen_all, - "loss/d/total": loss_disc_all, - "learning_rate": lr, - "grad_norm_d": grad_norm_d, - "grad_norm_g": grad_norm_g, - } - scalar_dict.update( - { - "loss/g/fm": loss_fm, - "loss/g/mel": loss_mel, - "loss/g/dur": loss_dur, - "loss/g/kl": loss_kl, - } - ) - scalar_dict.update( - {"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)} - ) - scalar_dict.update( - {"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)} - ) - scalar_dict.update( - {"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)} - ) - - image_dict = { - "slice/mel_org": utils.plot_spectrogram_to_numpy( - y_mel[0].data.cpu().numpy() - ), - "slice/mel_gen": utils.plot_spectrogram_to_numpy( - y_hat_mel[0].data.cpu().numpy() - ), - "all/mel": utils.plot_spectrogram_to_numpy( - mel[0].data.cpu().numpy() - ), - "all/attn": utils.plot_alignment_to_numpy( - attn[0, 0].data.cpu().numpy() - ), - } - utils.summarize( - writer=writer, - global_step=global_step, - images=image_dict, - scalars=scalar_dict, - ) - - if global_step % hps.train.eval_interval == 0: - evaluate(hps, net_g, eval_loader, writer_eval) - utils.save_checkpoint( - net_g, - optim_g, - hps.train.learning_rate, - epoch, - os.path.join(hps.model_dir, "G_{}.pth".format(global_step)), - ) - utils.save_checkpoint( - net_d, - optim_d, - hps.train.learning_rate, - epoch, - os.path.join(hps.model_dir, "D_{}.pth".format(global_step)), - ) - if net_dur_disc is not None: - utils.save_checkpoint( - net_dur_disc, - optim_dur_disc, - hps.train.learning_rate, - epoch, - os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)), - ) - keep_ckpts = getattr(hps.train, "keep_ckpts", 5) - if keep_ckpts > 0: - utils.clean_checkpoints( - path_to_models=hps.model_dir, - n_ckpts_to_keep=keep_ckpts, - sort_by_time=True, - ) - - global_step += 1 - - if rank == 0: - logger.info("====> Epoch: {}".format(epoch)) - - -def evaluate(hps, generator, eval_loader, writer_eval): - generator.eval() - image_dict = {} - audio_dict = {} - print("Evaluating ...") - with torch.no_grad(): - for batch_idx, ( - x, - x_lengths, - spec, - spec_lengths, - y, - y_lengths, - speakers, - tone, - language, - bert, - ja_bert, - ) in enumerate(eval_loader): - x, x_lengths = x.cuda(), x_lengths.cuda() - spec, spec_lengths = spec.cuda(), spec_lengths.cuda() - y, y_lengths = y.cuda(), y_lengths.cuda() - speakers = speakers.cuda() - bert = bert.cuda() - ja_bert = ja_bert.cuda() - tone = tone.cuda() - language = language.cuda() - for use_sdp in [True, False]: - y_hat, attn, mask, *_ = generator.module.infer( - x, - x_lengths, - speakers, - tone, - language, - bert, - ja_bert, - y=spec, - max_len=1000, - sdp_ratio=0.0 if not use_sdp else 1.0, - ) - y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length - - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax, - ) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1).float(), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax, - ) - image_dict.update( - { - f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy( - y_hat_mel[0].cpu().numpy() - ) - } - ) - audio_dict.update( - { - f"gen/audio_{batch_idx}_{use_sdp}": y_hat[ - 0, :, : y_hat_lengths[0] - ] - } - ) - image_dict.update( - { - f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy( - mel[0].cpu().numpy() - ) - } - ) - audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]}) - - utils.summarize( - writer=writer_eval, - global_step=global_step, - images=image_dict, - audios=audio_dict, - audio_sampling_rate=hps.data.sampling_rate, - ) - generator.train() - - -if __name__ == "__main__": - run() diff --git a/spaces/flatindo/scaler/realesrgan/data/realesrgan_paired_dataset.py b/spaces/flatindo/scaler/realesrgan/data/realesrgan_paired_dataset.py deleted file mode 100644 index 386c8d72496245dae8df033c2ebbd76b41ff45f1..0000000000000000000000000000000000000000 --- a/spaces/flatindo/scaler/realesrgan/data/realesrgan_paired_dataset.py +++ /dev/null @@ -1,108 +0,0 @@ -import os -from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb -from basicsr.data.transforms import augment, paired_random_crop -from basicsr.utils import FileClient, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY -from torch.utils import data as data -from torchvision.transforms.functional import normalize - - -@DATASET_REGISTRY.register() -class RealESRGANPairedDataset(data.Dataset): - """Paired image dataset for image restoration. - - Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. - - There are three modes: - 1. 'lmdb': Use lmdb files. - If opt['io_backend'] == lmdb. - 2. 'meta_info': Use meta information file to generate paths. - If opt['io_backend'] != lmdb and opt['meta_info'] is not None. - 3. 'folder': Scan folders to generate paths. - The rest. - - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - dataroot_lq (str): Data root path for lq. - meta_info (str): Path for meta information file. - io_backend (dict): IO backend type and other kwarg. - filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. - Default: '{}'. - gt_size (int): Cropped patched size for gt patches. - use_hflip (bool): Use horizontal flips. - use_rot (bool): Use rotation (use vertical flip and transposing h - and w for implementation). - - scale (bool): Scale, which will be added automatically. - phase (str): 'train' or 'val'. - """ - - def __init__(self, opt): - super(RealESRGANPairedDataset, self).__init__() - self.opt = opt - self.file_client = None - self.io_backend_opt = opt['io_backend'] - # mean and std for normalizing the input images - self.mean = opt['mean'] if 'mean' in opt else None - self.std = opt['std'] if 'std' in opt else None - - self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] - self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}' - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] - self.io_backend_opt['client_keys'] = ['lq', 'gt'] - self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) - elif 'meta_info' in self.opt and self.opt['meta_info'] is not None: - # disk backend with meta_info - # Each line in the meta_info describes the relative path to an image - with open(self.opt['meta_info']) as fin: - paths = [line.strip() for line in fin] - self.paths = [] - for path in paths: - gt_path, lq_path = path.split(', ') - gt_path = os.path.join(self.gt_folder, gt_path) - lq_path = os.path.join(self.lq_folder, lq_path) - self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)])) - else: - # disk backend - # it will scan the whole folder to get meta info - # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file - self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - scale = self.opt['scale'] - - # Load gt and lq images. Dimension order: HWC; channel order: BGR; - # image range: [0, 1], float32. - gt_path = self.paths[index]['gt_path'] - img_bytes = self.file_client.get(gt_path, 'gt') - img_gt = imfrombytes(img_bytes, float32=True) - lq_path = self.paths[index]['lq_path'] - img_bytes = self.file_client.get(lq_path, 'lq') - img_lq = imfrombytes(img_bytes, float32=True) - - # augmentation for training - if self.opt['phase'] == 'train': - gt_size = self.opt['gt_size'] - # random crop - img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) - # flip, rotation - img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) - - # BGR to RGB, HWC to CHW, numpy to tensor - img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) - # normalize - if self.mean is not None or self.std is not None: - normalize(img_lq, self.mean, self.std, inplace=True) - normalize(img_gt, self.mean, self.std, inplace=True) - - return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} - - def __len__(self): - return len(self.paths) diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/decode_heads/sep_fcn_head.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/decode_heads/sep_fcn_head.py deleted file mode 100644 index a0986143fa4f2bd36f5271354fe5f843f35b9e6f..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/decode_heads/sep_fcn_head.py +++ /dev/null @@ -1,51 +0,0 @@ -from annotator.uniformer.mmcv.cnn import DepthwiseSeparableConvModule - -from ..builder import HEADS -from .fcn_head import FCNHead - - -@HEADS.register_module() -class DepthwiseSeparableFCNHead(FCNHead): - """Depthwise-Separable Fully Convolutional Network for Semantic - Segmentation. - - This head is implemented according to Fast-SCNN paper. - Args: - in_channels(int): Number of output channels of FFM. - channels(int): Number of middle-stage channels in the decode head. - concat_input(bool): Whether to concatenate original decode input into - the result of several consecutive convolution layers. - Default: True. - num_classes(int): Used to determine the dimension of - final prediction tensor. - in_index(int): Correspond with 'out_indices' in FastSCNN backbone. - norm_cfg (dict | None): Config of norm layers. - align_corners (bool): align_corners argument of F.interpolate. - Default: False. - loss_decode(dict): Config of loss type and some - relevant additional options. - """ - - def __init__(self, **kwargs): - super(DepthwiseSeparableFCNHead, self).__init__(**kwargs) - self.convs[0] = DepthwiseSeparableConvModule( - self.in_channels, - self.channels, - kernel_size=self.kernel_size, - padding=self.kernel_size // 2, - norm_cfg=self.norm_cfg) - for i in range(1, self.num_convs): - self.convs[i] = DepthwiseSeparableConvModule( - self.channels, - self.channels, - kernel_size=self.kernel_size, - padding=self.kernel_size // 2, - norm_cfg=self.norm_cfg) - - if self.concat_input: - self.conv_cat = DepthwiseSeparableConvModule( - self.in_channels + self.channels, - self.channels, - kernel_size=self.kernel_size, - padding=self.kernel_size // 2, - norm_cfg=self.norm_cfg) diff --git a/spaces/glt3953/app-audio_video_transcribe/app.py b/spaces/glt3953/app-audio_video_transcribe/app.py deleted file mode 100644 index 009d48a2e0b6d778f730bcd43cab631ed9bbf400..0000000000000000000000000000000000000000 --- a/spaces/glt3953/app-audio_video_transcribe/app.py +++ /dev/null @@ -1,151 +0,0 @@ -import whisper -import gradio as gr -import os -import datetime - -#获取当前北京时间 -utc_dt = datetime.datetime.utcnow() -beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=10))) -formatted = beijing_dt.strftime("%Y-%m-%d_%H") -print(f"北京时间: {beijing_dt.year}年{beijing_dt.month}月{beijing_dt.day}日 " - f"{beijing_dt.hour}时{beijing_dt.minute}分{beijing_dt.second}秒") -#创建作品存放目录 -works_path = '../works_audio_video_transcribe/' + formatted -if not os.path.exists(works_path): - os.makedirs(works_path) -print('作品目录：' + works_path) - -#model_size = "small" -#model = whisper.load_model(model_size) #tiny、base、small、medium（可用）、large - -def transcript(model_size, audiofile, prompt, output_dir): - os.system(f"whisper {audiofile} --model {model_size} --language zh --initial_prompt {prompt} --output_dir {output_dir}") - -def audio_recog(model_size, audiofile): - utc_dt = datetime.datetime.utcnow() - beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=10))) - formatted = beijing_dt.strftime("%Y-%m-%d_%H-%M-%S") - print(f"开始时间: {beijing_dt.year}年{beijing_dt.month}月{beijing_dt.day}日 " - f"{beijing_dt.hour}时{beijing_dt.minute}分{beijing_dt.second}秒") - - print("音频文件：" + audiofile) - - prompt = "以下是普通话的句子" - filename = os.path.splitext(os.path.basename(audiofile))[0] - text_file = works_path + '/' + filename + '.txt' - srt_file = works_path + '/' + filename + '.srt' - - output_dir = works_path - transcript(model_size, audiofile, prompt, output_dir) - with open(text_file, "r") as f: - text_output = f.read() - print("text：" + text_output) - print("text文件：" + text_file) - - with open(srt_file, "r") as f: - srt_output = f.read() - print("srt：" + srt_output) - print("srt文件：" + srt_file) - - utc_dt = datetime.datetime.utcnow() - beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=10))) - formatted = beijing_dt.strftime("%Y-%m-%d_%H-%M-%S") - print(f"结束时间: {beijing_dt.year}年{beijing_dt.month}月{beijing_dt.day}日 " - f"{beijing_dt.hour}时{beijing_dt.minute}分{beijing_dt.second}秒") - - return text_output, text_file, srt_output, srt_file - -def video_recog(model_size, filepath): - filename = os.path.splitext(os.path.basename(filepath))[0] - worksfile = works_path + '/works_' + filename + '.mp4' - print("视频文件：" + filepath) - - utc_dt = datetime.datetime.utcnow() - beijing_dt = utc_dt.astimezone(datetime.timezone(datetime.timedelta(hours=10))) - formatted = beijing_dt.strftime("%Y-%m-%d_%H-%M-%S.%f") - - # 提取音频为mp3 - audiofile = works_path + '/' + formatted + '.mp3' - os.system(f"ffmpeg -i {filepath} -vn -c:a libmp3lame -q:a 4 {audiofile}") - - #识别音频文件 - text_output, text_file, srt_output, srt_file = audio_recog(model_size, audiofile) - -# # 给视频添加字幕 -# os.system(f"ffmpeg -i {filepath} -i {srt_file} -c:s mov_text -c:v copy -c:a copy {worksfile}") -# print("作品：" + worksfile) - - return text_output, text_file, srt_output, srt_file - -css_style = "#fixed_size_img {height: 240px;} " \ - "#overview {margin: auto;max-width: 400px; max-height: 400px;}" - -title = "音视频转录 by宁侠" -description = "您只需要上传一段音频或视频文件，我们的服务会快速对其进行语音识别，然后生成相应的文字和字幕。这样，您就可以轻松地记录下重要的语音内容，或者为视频添加精准的字幕。现在就来试试我们的音视频转录服务吧，让您的生活和工作更加便捷！" - -examples_path = 'examples/' -examples = [[examples_path + 'demo_shejipuhui.mp4']] - -# gradio interface -with gr.Blocks(title=title, css=css_style) as demo: - gr.HTML(''' -
-
-
-
-
-
-
\ No newline at end of file diff --git a/spaces/gradio/HuBERT/fairseq/data/audio/speech_to_text_dataset.py b/spaces/gradio/HuBERT/fairseq/data/audio/speech_to_text_dataset.py deleted file mode 100644 index d4b5668d8f9d4bc93fcbda73d867554d8f1b3107..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/data/audio/speech_to_text_dataset.py +++ /dev/null @@ -1,511 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import csv -import io -import logging -import os.path as op -import re -from typing import Dict, List, Optional, Tuple - -import numpy as np -import torch -from fairseq.data import ( - ConcatDataset, - Dictionary, - FairseqDataset, - ResamplingDataset, - data_utils as fairseq_data_utils, -) -from fairseq.data.audio.audio_utils import ( - get_fbank, get_waveform, read_from_stored_zip, is_npy_data, - is_sf_audio_data, parse_path, FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS -) -from fairseq.data.audio.feature_transforms import CompositeAudioFeatureTransform - - -logger = logging.getLogger(__name__) - - -class S2TDataConfig(object): - """Wrapper class for data config YAML""" - - def __init__(self, yaml_path): - try: - import yaml - except ImportError: - print("Please install PyYAML to load YAML files for " "S2T data config") - self.config = {} - if op.isfile(yaml_path): - try: - with open(yaml_path) as f: - self.config = yaml.load(f, Loader=yaml.FullLoader) - except Exception as e: - raise Exception(f"Failed to load config from {yaml_path}: {e}") - else: - raise FileNotFoundError(f"{yaml_path} not found") - - @property - def vocab_filename(self): - """fairseq vocabulary file under data root""" - return self.config.get("vocab_filename", "dict.txt") - - @property - def shuffle(self) -> bool: - """Shuffle dataset samples before batching""" - return self.config.get("shuffle", False) - - @property - def pre_tokenizer(self) -> Dict: - """Pre-tokenizer to apply before subword tokenization. Returning - a dictionary with `tokenizer` providing the tokenizer name and - the other items providing the tokenizer-specific arguments. - Tokenizers are defined in `fairseq.data.encoders.*`""" - return self.config.get("pre_tokenizer", {"tokenizer": None}) - - @property - def bpe_tokenizer(self) -> Dict: - """Subword tokenizer to apply after pre-tokenization. Returning - a dictionary with `bpe` providing the tokenizer name and - the other items providing the tokenizer-specific arguments. - Tokenizers are defined in `fairseq.data.encoders.*`""" - return self.config.get("bpe_tokenizer", {"bpe": None}) - - @property - def prepend_tgt_lang_tag(self) -> bool: - """Prepend target lang ID token as the target BOS (e.g. for to-many - multilingual setting). During inference, this requires `--prefix-size 1` - to force BOS to be lang ID token.""" - return self.config.get("prepend_tgt_lang_tag", False) - - @property - def input_feat_per_channel(self): - """The dimension of input features (per audio channel)""" - return self.config.get("input_feat_per_channel", 80) - - @property - def input_channels(self): - """The number of channels in the input audio""" - return self.config.get("input_channels", 1) - - @property - def sampling_alpha(self): - """Hyper-parameter alpha = 1/T for temperature-based resampling. - (alpha = 1 for no resampling)""" - return self.config.get("sampling_alpha", 1.0) - - @property - def use_audio_input(self): - """Needed by the dataset loader to see if the model requires - raw audio as inputs.""" - return self.config.get("use_audio_input", False) - - @property - def audio_root(self): - """Audio paths in the manifest TSV can be relative and this provides - the root path. Set this to empty string when using absolute paths.""" - return self.config.get("audio_root", "") - - def get_feature_transforms(self, split, is_train): - """Split-specific feature transforms. Allowing train set wildcard `_train`, - evaluation set wildcard `_eval` and general wildcard `*` for matching.""" - from copy import deepcopy - - cfg = deepcopy(self.config) - _cur = cfg.get("transforms", {}) - cur = _cur.get(split) - cur = _cur.get("_train") if cur is None and is_train else cur - cur = _cur.get("_eval") if cur is None and not is_train else cur - cur = _cur.get("*") if cur is None else cur - cfg["transforms"] = cur - return cfg - - -def get_features_from_npy_or_audio(path): - ext = op.splitext(op.basename(path))[1] - if ext not in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS: - raise ValueError(f'Unsupported file format for "{path}"') - return np.load(path) if ext == ".npy" else get_fbank(path) - - -def get_features_or_waveform_from_stored_zip( - path, byte_offset, byte_size, need_waveform=False -): - assert path.endswith(".zip") - data = read_from_stored_zip(path, byte_offset, byte_size) - f = io.BytesIO(data) - if is_npy_data(data): - features_or_waveform = np.load(f) - elif is_sf_audio_data(data): - features_or_waveform = \ - get_waveform(f, always_2d=False)[0] if need_waveform else get_fbank(f) - else: - raise ValueError(f'Unknown file format for "{path}"') - return features_or_waveform - - -def get_features_or_waveform(path: str, need_waveform=False): - """Get speech features from .npy file or waveform from .wav/.flac file. - The file may be inside an uncompressed ZIP file and is accessed via byte - offset and length. - - Args: - path (str): File path in the format of "<.npy/.wav/.flac path>" or - "::".
- need_waveform (bool): return waveform instead of features.
-
- Returns:
- features_or_waveform (numpy.ndarray): speech features or waveform.
- """
- _path, slice_ptr = parse_path(path)
- if len(slice_ptr) == 0:
- if need_waveform:
- return get_waveform(_path, always_2d=False)
- return get_features_from_npy_or_audio(_path)
- elif len(slice_ptr) == 2:
- features_or_waveform = get_features_or_waveform_from_stored_zip(
- _path, slice_ptr[0], slice_ptr[1], need_waveform=need_waveform
- )
- else:
- raise ValueError(f"Invalid path: {path}")
-
- return features_or_waveform
-
-
-def _collate_frames(
- frames: List[torch.Tensor], is_audio_input: bool = False
-) -> torch.Tensor:
- """
- Convert a list of 2D frames into a padded 3D tensor
- Args:
- frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is
- length of i-th frame and f_dim is static dimension of features
- Returns:
- 3D tensor of size len(frames)*len_max*f_dim where len_max is max of L[i]
- """
- max_len = max(frame.size(0) for frame in frames)
- if is_audio_input:
- out = frames[0].new_zeros((len(frames), max_len))
- else:
- out = frames[0].new_zeros((len(frames), max_len, frames[0].size(1)))
- for i, v in enumerate(frames):
- out[i, : v.size(0)] = v
- return out
-
-
-class SpeechToTextDataset(FairseqDataset):
- LANG_TAG_TEMPLATE = ""
-
- def __init__(
- self,
- split: str,
- is_train_split: bool,
- data_cfg: S2TDataConfig,
- audio_paths: List[str],
- n_frames: List[int],
- src_texts: Optional[List[str]] = None,
- tgt_texts: Optional[List[str]] = None,
- speakers: Optional[List[str]] = None,
- src_langs: Optional[List[str]] = None,
- tgt_langs: Optional[List[str]] = None,
- ids: Optional[List[str]] = None,
- tgt_dict: Optional[Dictionary] = None,
- pre_tokenizer=None,
- bpe_tokenizer=None,
- ):
- self.split, self.is_train_split = split, is_train_split
- self.data_cfg = data_cfg
- self.audio_paths, self.n_frames = audio_paths, n_frames
- self.n_samples = len(audio_paths)
- assert len(n_frames) == self.n_samples > 0
- assert src_texts is None or len(src_texts) == self.n_samples
- assert tgt_texts is None or len(tgt_texts) == self.n_samples
- assert speakers is None or len(speakers) == self.n_samples
- assert src_langs is None or len(src_langs) == self.n_samples
- assert tgt_langs is None or len(tgt_langs) == self.n_samples
- assert ids is None or len(ids) == self.n_samples
- assert (tgt_dict is None and tgt_texts is None) or (
- tgt_dict is not None and tgt_texts is not None
- )
- self.src_texts, self.tgt_texts = src_texts, tgt_texts
- self.src_langs, self.tgt_langs = src_langs, tgt_langs
- self.tgt_dict = tgt_dict
- self.check_tgt_lang_tag()
- self.ids = ids
- self.shuffle = data_cfg.shuffle if is_train_split else False
-
- self.feature_transforms = CompositeAudioFeatureTransform.from_config_dict(
- self.data_cfg.get_feature_transforms(split, is_train_split)
- )
-
- self.pre_tokenizer = pre_tokenizer
- self.bpe_tokenizer = bpe_tokenizer
-
- logger.info(self.__repr__())
-
- def __repr__(self):
- return (
- self.__class__.__name__
- + f'(split="{self.split}", n_samples={self.n_samples}, '
- f"prepend_tgt_lang_tag={self.data_cfg.prepend_tgt_lang_tag}, "
- f"shuffle={self.shuffle}, transforms={self.feature_transforms})"
- )
-
- @classmethod
- def is_lang_tag(cls, token):
- pattern = cls.LANG_TAG_TEMPLATE.replace("{}", "(.*)")
- return re.match(pattern, token)
-
- def check_tgt_lang_tag(self):
- if self.data_cfg.prepend_tgt_lang_tag:
- assert self.tgt_langs is not None and self.tgt_dict is not None
- tgt_lang_tags = [
- self.LANG_TAG_TEMPLATE.format(t) for t in set(self.tgt_langs)
- ]
- assert all(t in self.tgt_dict for t in tgt_lang_tags)
-
- def tokenize_text(self, text: str):
- if self.pre_tokenizer is not None:
- text = self.pre_tokenizer.encode(text)
- if self.bpe_tokenizer is not None:
- text = self.bpe_tokenizer.encode(text)
- return text
-
- def __getitem__(
- self, index: int
- ) -> Tuple[int, torch.Tensor, Optional[torch.Tensor]]:
- source = get_features_or_waveform(
- self.audio_paths[index], need_waveform=self.data_cfg.use_audio_input
- )
- if self.feature_transforms is not None:
- assert not self.data_cfg.use_audio_input
- source = self.feature_transforms(source)
- source = torch.from_numpy(source).float()
-
- target = None
- if self.tgt_texts is not None:
- tokenized = self.tokenize_text(self.tgt_texts[index])
- target = self.tgt_dict.encode_line(
- tokenized, add_if_not_exist=False, append_eos=True
- ).long()
- if self.data_cfg.prepend_tgt_lang_tag:
- lang_tag = self.LANG_TAG_TEMPLATE.format(self.tgt_langs[index])
- lang_tag_idx = self.tgt_dict.index(lang_tag)
- target = torch.cat((torch.LongTensor([lang_tag_idx]), target), 0)
- return index, source, target
-
- def __len__(self):
- return self.n_samples
-
- def collater(self, samples: List[Tuple[int, torch.Tensor, torch.Tensor]]) -> Dict:
- if len(samples) == 0:
- return {}
- indices = torch.tensor([i for i, _, _ in samples], dtype=torch.long)
- frames = _collate_frames(
- [s for _, s, _ in samples], self.data_cfg.use_audio_input
- )
- # sort samples by descending number of frames
- n_frames = torch.tensor([s.size(0) for _, s, _ in samples], dtype=torch.long)
- n_frames, order = n_frames.sort(descending=True)
- indices = indices.index_select(0, order)
- frames = frames.index_select(0, order)
-
- target, target_lengths = None, None
- prev_output_tokens = None
- ntokens = None
- if self.tgt_texts is not None:
- target = fairseq_data_utils.collate_tokens(
- [t for _, _, t in samples],
- self.tgt_dict.pad(),
- self.tgt_dict.eos(),
- left_pad=False,
- move_eos_to_beginning=False,
- )
- target = target.index_select(0, order)
- target_lengths = torch.tensor(
- [t.size(0) for _, _, t in samples], dtype=torch.long
- ).index_select(0, order)
- prev_output_tokens = fairseq_data_utils.collate_tokens(
- [t for _, _, t in samples],
- self.tgt_dict.pad(),
- self.tgt_dict.eos(),
- left_pad=False,
- move_eos_to_beginning=True,
- )
- prev_output_tokens = prev_output_tokens.index_select(0, order)
- ntokens = sum(t.size(0) for _, _, t in samples)
-
- out = {
- "id": indices,
- "net_input": {
- "src_tokens": frames,
- "src_lengths": n_frames,
- "prev_output_tokens": prev_output_tokens,
- },
- "target": target,
- "target_lengths": target_lengths,
- "ntokens": ntokens,
- "nsentences": len(samples),
- }
- return out
-
- def num_tokens(self, index):
- return self.n_frames[index]
-
- def size(self, index):
- t_len = 0
- if self.tgt_texts is not None:
- tokenized = self.tokenize_text(self.tgt_texts[index])
- t_len = len(tokenized.split(" "))
- return self.n_frames[index], t_len
-
- @property
- def sizes(self):
- return np.array(self.n_frames)
-
- @property
- def can_reuse_epoch_itr_across_epochs(self):
- return True
-
- def ordered_indices(self):
- if self.shuffle:
- order = [np.random.permutation(len(self))]
- else:
- order = [np.arange(len(self))]
- # first by descending order of # of frames then by original/random order
- order.append([-n for n in self.n_frames])
- return np.lexsort(order)
-
- def prefetch(self, indices):
- raise False
-
-
-class SpeechToTextDatasetCreator(object):
- # mandatory columns
- KEY_ID, KEY_AUDIO, KEY_N_FRAMES = "id", "audio", "n_frames"
- KEY_TGT_TEXT = "tgt_text"
- # optional columns
- KEY_SPEAKER, KEY_SRC_TEXT = "speaker", "src_text"
- KEY_SRC_LANG, KEY_TGT_LANG = "src_lang", "tgt_lang"
- # default values
- DEFAULT_SPEAKER = DEFAULT_SRC_TEXT = DEFAULT_LANG = ""
-
- @classmethod
- def _from_list(
- cls,
- split_name: str,
- is_train_split,
- samples: List[List[Dict]],
- data_cfg: S2TDataConfig,
- tgt_dict,
- pre_tokenizer,
- bpe_tokenizer,
- ) -> SpeechToTextDataset:
- audio_paths, n_frames, src_texts, tgt_texts, ids = [], [], [], [], []
- speakers, src_langs, tgt_langs = [], [], []
- for s in samples:
- ids.extend([ss[cls.KEY_ID] for ss in s])
- audio_paths.extend(
- [op.join(data_cfg.audio_root, ss[cls.KEY_AUDIO]) for ss in s]
- )
- n_frames.extend([int(ss[cls.KEY_N_FRAMES]) for ss in s])
- tgt_texts.extend([ss[cls.KEY_TGT_TEXT] for ss in s])
- src_texts.extend(
- [ss.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for ss in s]
- )
- speakers.extend([ss.get(cls.KEY_SPEAKER, cls.DEFAULT_SPEAKER) for ss in s])
- src_langs.extend([ss.get(cls.KEY_SRC_LANG, cls.DEFAULT_LANG) for ss in s])
- tgt_langs.extend([ss.get(cls.KEY_TGT_LANG, cls.DEFAULT_LANG) for ss in s])
- return SpeechToTextDataset(
- split_name,
- is_train_split,
- data_cfg,
- audio_paths,
- n_frames,
- src_texts,
- tgt_texts,
- speakers,
- src_langs,
- tgt_langs,
- ids,
- tgt_dict,
- pre_tokenizer,
- bpe_tokenizer,
- )
-
- @classmethod
- def _get_size_ratios(cls, ids: List[str], sizes: List[int], alpha: float = 1.0):
- """Size ratios for temperature-based sampling
- (https://arxiv.org/abs/1907.05019)"""
- _sizes = np.array(sizes)
- prob = _sizes / _sizes.sum()
- smoothed_prob = prob ** alpha
- smoothed_prob = smoothed_prob / smoothed_prob.sum()
- size_ratio = (smoothed_prob * _sizes.sum()) / _sizes
-
- o_str = str({_i: f"{prob[i]:.3f}" for i, _i in enumerate(ids)})
- logger.info(f"original sampling probability: {o_str}")
- p_str = str({_i: f"{smoothed_prob[i]:.3f}" for i, _i in enumerate(ids)})
- logger.info(f"balanced sampling probability: {p_str}")
- sr_str = str({_id: f"{size_ratio[i]:.3f}" for i, _id in enumerate(ids)})
- logger.info(f"balanced sampling size ratio: {sr_str}")
- return size_ratio.tolist()
-
- @classmethod
- def from_tsv(
- cls,
- root: str,
- data_cfg: S2TDataConfig,
- splits: str,
- tgt_dict,
- pre_tokenizer,
- bpe_tokenizer,
- is_train_split: bool,
- epoch: int,
- seed: int,
- ) -> SpeechToTextDataset:
- samples = []
- _splits = splits.split(",")
- for split in _splits:
- tsv_path = op.join(root, f"{split}.tsv")
- if not op.isfile(tsv_path):
- raise FileNotFoundError(f"Dataset not found: {tsv_path}")
- with open(tsv_path) as f:
- reader = csv.DictReader(
- f,
- delimiter="\t",
- quotechar=None,
- doublequote=False,
- lineterminator="\n",
- quoting=csv.QUOTE_NONE,
- )
- samples.append([dict(e) for e in reader])
- assert len(samples) > 0
-
- datasets = [
- cls._from_list(
- name,
- is_train_split,
- [s],
- data_cfg,
- tgt_dict,
- pre_tokenizer,
- bpe_tokenizer,
- )
- for name, s in zip(_splits, samples)
- ]
-
- if is_train_split and len(_splits) > 1 and data_cfg.sampling_alpha != 1.0:
- # temperature-based sampling
- size_ratios = cls._get_size_ratios(
- _splits, [len(s) for s in samples], alpha=data_cfg.sampling_alpha
- )
- datasets = [
- ResamplingDataset(
- d, size_ratio=r, seed=seed, epoch=epoch, replace=(r >= 1.0)
- )
- for d, r in zip(datasets, size_ratios)
- ]
- return ConcatDataset(datasets)
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/custom_ops.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/custom_ops.py
deleted file mode 100644
index 702471e2006af6858345c1225c1e55b0acd17d32..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/custom_ops.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""TensorFlow custom ops builder.
-"""
-
-import glob
-import os
-import re
-import uuid
-import hashlib
-import tempfile
-import shutil
-import tensorflow as tf
-from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module
-
-from .. import util
-
-#----------------------------------------------------------------------------
-# Global configs.
-
-cuda_cache_path = None
-cuda_cache_version_tag = 'v1'
-do_not_hash_included_headers = True # Speed up compilation by assuming that headers included by the CUDA code never change.
-verbose = True # Print status messages to stdout.
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
- hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True)
- if hostx64_paths != []:
- return hostx64_paths[0]
- hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True)
- if hostx64_paths != []:
- return hostx64_paths[0]
- hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True)
- if hostx64_paths != []:
- return hostx64_paths[0]
- vc_bin_dir = 'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin'
- if os.path.isdir(vc_bin_dir):
- return vc_bin_dir
- return None
-
-def _get_compute_cap(device):
- caps_str = device.physical_device_desc
- m = re.search('compute capability: (\\d+).(\\d+)', caps_str)
- major = m.group(1)
- minor = m.group(2)
- return (major, minor)
-
-def _get_cuda_gpu_arch_string():
- gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU']
- if len(gpus) == 0:
- raise RuntimeError('No GPU devices found')
- (major, minor) = _get_compute_cap(gpus[0])
- return 'sm_%s%s' % (major, minor)
-
-def _run_cmd(cmd):
- with os.popen(cmd) as pipe:
- output = pipe.read()
- status = pipe.close()
- if status is not None:
- raise RuntimeError('NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output))
-
-def _prepare_nvcc_cli(opts):
- cmd = 'nvcc ' + opts.strip()
- cmd += ' --disable-warnings'
- cmd += ' --include-path "%s"' % tf.sysconfig.get_include()
- cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src')
- cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl')
- cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive')
-
- compiler_bindir = _find_compiler_bindir()
- if compiler_bindir is None:
- # Require that _find_compiler_bindir succeeds on Windows. Allow
- # nvcc to use whatever is the default on Linux.
- if os.name == 'nt':
- raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__)
- else:
- cmd += ' --compiler-bindir "%s"' % compiler_bindir
- cmd += ' 2>&1'
- return cmd
-
-#----------------------------------------------------------------------------
-# Main entry point.
-
-_plugin_cache = dict()
-
-def get_plugin(cuda_file, extra_nvcc_options=[]):
- cuda_file_base = os.path.basename(cuda_file)
- cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base)
-
- # Already in cache?
- if cuda_file in _plugin_cache:
- return _plugin_cache[cuda_file]
-
- # Setup plugin.
- if verbose:
- print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True)
- try:
- # Hash CUDA source.
- md5 = hashlib.md5()
- with open(cuda_file, 'rb') as f:
- md5.update(f.read())
- md5.update(b'\n')
-
- # Hash headers included by the CUDA code by running it through the preprocessor.
- if not do_not_hash_included_headers:
- if verbose:
- print('Preprocessing... ', end='', flush=True)
- with tempfile.TemporaryDirectory() as tmp_dir:
- tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext)
- _run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)))
- with open(tmp_file, 'rb') as f:
- bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros
- good_file_str = ('"' + cuda_file_base + '"').encode('utf-8')
- for ln in f:
- if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas
- ln = ln.replace(bad_file_str, good_file_str)
- md5.update(ln)
- md5.update(b'\n')
-
- # Select compiler configs.
- compile_opts = ''
- if os.name == 'nt':
- compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib')
- elif os.name == 'posix':
- compile_opts += f' --compiler-options \'-fPIC\''
- compile_opts += f' --compiler-options \'{" ".join(tf.sysconfig.get_compile_flags())}\''
- compile_opts += f' --linker-options \'{" ".join(tf.sysconfig.get_link_flags())}\''
- else:
- assert False # not Windows or Linux, w00t?
- compile_opts += f' --gpu-architecture={_get_cuda_gpu_arch_string()}'
- compile_opts += ' --use_fast_math'
- for opt in extra_nvcc_options:
- compile_opts += ' ' + opt
- nvcc_cmd = _prepare_nvcc_cli(compile_opts)
-
- # Hash build configuration.
- md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n')
- md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n')
- md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n')
-
- # Compile if not already compiled.
- cache_dir = util.make_cache_dir_path('tflib-cudacache') if cuda_cache_path is None else cuda_cache_path
- bin_file_ext = '.dll' if os.name == 'nt' else '.so'
- bin_file = os.path.join(cache_dir, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext)
- if not os.path.isfile(bin_file):
- if verbose:
- print('Compiling... ', end='', flush=True)
- with tempfile.TemporaryDirectory() as tmp_dir:
- tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext)
- _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))
- os.makedirs(cache_dir, exist_ok=True)
- intermediate_file = os.path.join(cache_dir, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext)
- shutil.copyfile(tmp_file, intermediate_file)
- os.rename(intermediate_file, bin_file) # atomic
-
- # Load.
- if verbose:
- print('Loading... ', end='', flush=True)
- plugin = tf.load_op_library(bin_file)
-
- # Add to cache.
- _plugin_cache[cuda_file] = plugin
- if verbose:
- print('Done.', flush=True)
- return plugin
-
- except:
- if verbose:
- print('Failed!', flush=True)
- raise
-
-#----------------------------------------------------------------------------
diff --git a/spaces/gyugnsu/DragGan-Inversion/torch_utils/training_stats.py b/spaces/gyugnsu/DragGan-Inversion/torch_utils/training_stats.py
deleted file mode 100644
index aa5837c2948372ecdb3e34076f4b3f4f42c81fef..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/torch_utils/training_stats.py
+++ /dev/null
@@ -1,283 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-# ----------------------------------------------------------------------------
-
-_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares]
-# Data type to use for initial per-tensor reduction.
-_reduce_dtype = torch.float32
-_counter_dtype = torch.float64 # Data type to use for the internal counters.
-_rank = 0 # Rank of the current process.
-# Device to use for multiprocess communication. None = single-process.
-_sync_device = None
-_sync_called = False # Has _sync() been called yet?
-# Running counters on each device, updated by report(): name => device => torch.Tensor
-_counters = dict()
-# Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-_cumulative = dict()
-
-# ----------------------------------------------------------------------------
-
-
-def init_multiprocessing(rank, sync_device):
- r"""Initializes `torch_utils.training_stats` for collecting statistics
- across multiple processes.
-
- This function must be called after
- `torch.distributed.init_process_group()` and before `Collector.update()`.
- The call is not necessary if multi-process collection is not needed.
-
- Args:
- rank: Rank of the current process.
- sync_device: PyTorch device to use for inter-process
- communication, or None to disable multi-process
- collection. Typically `torch.device('cuda', rank)`.
- """
- global _rank, _sync_device
- assert not _sync_called
- _rank = rank
- _sync_device = sync_device
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def report(name, value):
- r"""Broadcasts the given set of scalars to all interested instances of
- `Collector`, across device and process boundaries.
-
- This function is expected to be extremely cheap and can be safely
- called from anywhere in the training loop, loss function, or inside a
- `torch.nn.Module`.
-
- Warning: The current implementation expects the set of unique names to
- be consistent across processes. Please make sure that `report()` is
- called at least once for each unique name by each process, and in the
- same order. If a given process has no scalars to broadcast, it can do
- `report(name, [])` (empty list).
-
- Args:
- name: Arbitrary string specifying the name of the statistic.
- Averages are accumulated separately for each unique name.
- value: Arbitrary set of scalars. Can be a list, tuple,
- NumPy array, PyTorch tensor, or Python scalar.
-
- Returns:
- The same `value` that was passed in.
- """
- if name not in _counters:
- _counters[name] = dict()
-
- elems = torch.as_tensor(value)
- if elems.numel() == 0:
- return value
-
- elems = elems.detach().flatten().to(_reduce_dtype)
- moments = torch.stack([
- torch.ones_like(elems).sum(),
- elems.sum(),
- elems.square().sum(),
- ])
- assert moments.ndim == 1 and moments.shape[0] == _num_moments
- moments = moments.to(_counter_dtype)
-
- device = moments.device
- if device not in _counters[name]:
- _counters[name][device] = torch.zeros_like(moments)
- _counters[name][device].add_(moments)
- return value
-
-# ----------------------------------------------------------------------------
-
-
-def report0(name, value):
- r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
- but ignores any scalars provided by the other processes.
- See `report()` for further details.
- """
- report(name, value if _rank == 0 else [])
- return value
-
-# ----------------------------------------------------------------------------
-
-
-class Collector:
- r"""Collects the scalars broadcasted by `report()` and `report0()` and
- computes their long-term averages (mean and standard deviation) over
- user-defined periods of time.
-
- The averages are first collected into internal counters that are not
- directly visible to the user. They are then copied to the user-visible
- state as a result of calling `update()` and can then be queried using
- `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
- internal counters for the next round, so that the user-visible state
- effectively reflects averages collected between the last two calls to
- `update()`.
-
- Args:
- regex: Regular expression defining which statistics to
- collect. The default is to collect everything.
- keep_previous: Whether to retain the previous averages if no
- scalars were collected on a given round
- (default: True).
- """
-
- def __init__(self, regex='.*', keep_previous=True):
- self._regex = re.compile(regex)
- self._keep_previous = keep_previous
- self._cumulative = dict()
- self._moments = dict()
- self.update()
- self._moments.clear()
-
- def names(self):
- r"""Returns the names of all statistics broadcasted so far that
- match the regular expression specified at construction time.
- """
- return [name for name in _counters if self._regex.fullmatch(name)]
-
- def update(self):
- r"""Copies current values of the internal counters to the
- user-visible state and resets them for the next round.
-
- If `keep_previous=True` was specified at construction time, the
- operation is skipped for statistics that have received no scalars
- since the last update, retaining their previous averages.
-
- This method performs a number of GPU-to-CPU transfers and one
- `torch.distributed.all_reduce()`. It is intended to be called
- periodically in the main training loop, typically once every
- N training steps.
- """
- if not self._keep_previous:
- self._moments.clear()
- for name, cumulative in _sync(self.names()):
- if name not in self._cumulative:
- self._cumulative[name] = torch.zeros(
- [_num_moments], dtype=_counter_dtype)
- delta = cumulative - self._cumulative[name]
- self._cumulative[name].copy_(cumulative)
- if float(delta[0]) != 0:
- self._moments[name] = delta
-
- def _get_delta(self, name):
- r"""Returns the raw moments that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- assert self._regex.fullmatch(name)
- if name not in self._moments:
- self._moments[name] = torch.zeros(
- [_num_moments], dtype=_counter_dtype)
- return self._moments[name]
-
- def num(self, name):
- r"""Returns the number of scalars that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- return int(delta[0])
-
- def mean(self, name):
- r"""Returns the mean of the scalars that were accumulated for the
- given statistic between the last two calls to `update()`, or NaN if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0:
- return float('nan')
- return float(delta[1] / delta[0])
-
- def std(self, name):
- r"""Returns the standard deviation of the scalars that were
- accumulated for the given statistic between the last two calls to
- `update()`, or NaN if no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
- return float('nan')
- if int(delta[0]) == 1:
- return float(0)
- mean = float(delta[1] / delta[0])
- raw_var = float(delta[2] / delta[0])
- return np.sqrt(max(raw_var - np.square(mean), 0))
-
- def as_dict(self):
- r"""Returns the averages accumulated between the last two calls to
- `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
- dnnlib.EasyDict(
- NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
- ...
- )
- """
- stats = dnnlib.EasyDict()
- for name in self.names():
- stats[name] = dnnlib.EasyDict(num=self.num(
- name), mean=self.mean(name), std=self.std(name))
- return stats
-
- def __getitem__(self, name):
- r"""Convenience getter.
- `collector[name]` is a synonym for `collector.mean(name)`.
- """
- return self.mean(name)
-
-# ----------------------------------------------------------------------------
-
-
-def _sync(names):
- r"""Synchronize the global cumulative counters across devices and
- processes. Called internally by `Collector.update()`.
- """
- if len(names) == 0:
- return []
- global _sync_called
- _sync_called = True
-
- # Collect deltas within current rank.
- deltas = []
- device = _sync_device if _sync_device is not None else torch.device('cpu')
- for name in names:
- delta = torch.zeros(
- [_num_moments], dtype=_counter_dtype, device=device)
- for counter in _counters[name].values():
- delta.add_(counter.to(device))
- counter.copy_(torch.zeros_like(counter))
- deltas.append(delta)
- deltas = torch.stack(deltas)
-
- # Sum deltas across ranks.
- if _sync_device is not None:
- torch.distributed.all_reduce(deltas)
-
- # Update cumulative values.
- deltas = deltas.cpu()
- for idx, name in enumerate(names):
- if name not in _cumulative:
- _cumulative[name] = torch.zeros(
- [_num_moments], dtype=_counter_dtype)
- _cumulative[name].add_(deltas[idx])
-
- # Return name-value pairs.
- return [(name, _cumulative[name]) for name in names]
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/hahahafofo/ChatPDF/chatpdf.py b/spaces/hahahafofo/ChatPDF/chatpdf.py
deleted file mode 100644
index 1a825771fa194e313f46c88fc1f71fc32ed140a6..0000000000000000000000000000000000000000
--- a/spaces/hahahafofo/ChatPDF/chatpdf.py
+++ /dev/null
@@ -1,190 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@author:XuMing(xuming624@qq.com)
-@description:
-"""
-import logging
-
-from similarities import Similarity
-from textgen import ChatGlmModel, LlamaModel
-from transformers import pipeline
-from loguru import logger
-
-PROMPT_TEMPLATE = """\
-基于以下已知信息，简洁和专业的来回答用户的问题。
-如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
-
-已知内容:
-{context_str}
-
-问题:
-{query_str}
-"""
-
-
-class ChatPDF:
- def __init__(
- self,
- sim_model_name_or_path: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
- gen_model_type: str = "chatglm",
- gen_model_name_or_path: str = "THUDM/chatglm-6b-int4",
- lora_model_name_or_path: str = None,
-
- ):
- self.sim_model = Similarity(model_name_or_path=sim_model_name_or_path)
- self.model_type = gen_model_type
-
- if gen_model_type == "chatglm":
- self.gen_model = ChatGlmModel(gen_model_type, gen_model_name_or_path, lora_name=lora_model_name_or_path)
- elif gen_model_type == "llama":
- self.gen_model = LlamaModel(gen_model_type, gen_model_name_or_path, lora_name=lora_model_name_or_path)
- elif gen_model_type == "t5":
- self.gen_model = pipeline('text2text-generation', model=gen_model_name_or_path)
- else:
- raise ValueError('gen_model_type must be chatglm or llama.')
- self.history = None
- self.pdf_path = None
-
- def load_pdf_file(self, pdf_path: str):
- """Load a PDF file."""
- if pdf_path.endswith('.pdf'):
- corpus = self.extract_text_from_pdf(pdf_path)
- elif pdf_path.endswith('.docx'):
- corpus = self.extract_text_from_docx(pdf_path)
- elif pdf_path.endswith('.md'):
- corpus = self.extract_text_from_markdown(pdf_path)
- else:
- corpus = self.extract_text_from_txt(pdf_path)
- self.sim_model.add_corpus(corpus)
- self.pdf_path = pdf_path
-
- @staticmethod
- def extract_text_from_pdf(file_path: str):
- """Extract text content from a PDF file."""
- import PyPDF2
- contents = []
- with open(file_path, 'rb') as f:
- pdf_reader = PyPDF2.PdfReader(f)
- for page in pdf_reader.pages:
- page_text = page.extract_text().strip()
- raw_text = [text.strip() for text in page_text.splitlines() if text.strip()]
- new_text = ''
- for text in raw_text:
- new_text += text
- if text[-1] in ['.', '!', '?', '。', '！', '？', '…', ';', '；', ':', '：', '”', '’', '）', '】', '》', '」',
- '』', '〕', '〉', '》', '〗', '〞', '〟', '»', '"', "'", ')', ']', '}']:
- contents.append(new_text)
- new_text = ''
- if new_text:
- contents.append(new_text)
- return contents
-
- @staticmethod
- def extract_text_from_txt(file_path: str):
- """Extract text content from a TXT file."""
- contents = []
- with open(file_path, 'r', encoding='utf-8') as f:
- contents = [text.strip() for text in f.readlines() if text.strip()]
- return contents
-
- @staticmethod
- def extract_text_from_docx(file_path: str):
- """Extract text content from a DOCX file."""
- import docx
- document = docx.Document(file_path)
- contents = [paragraph.text.strip() for paragraph in document.paragraphs if paragraph.text.strip()]
- return contents
-
- @staticmethod
- def extract_text_from_markdown(file_path: str):
- """Extract text content from a Markdown file."""
- import markdown
- from bs4 import BeautifulSoup
- with open(file_path, 'r', encoding='utf-8') as f:
- markdown_text = f.read()
- html = markdown.markdown(markdown_text)
- soup = BeautifulSoup(html, 'html.parser')
- contents = [text.strip() for text in soup.get_text().splitlines() if text.strip()]
- return contents
-
- @staticmethod
- def _add_source_numbers(lst):
- """Add source numbers to a list of strings."""
- return [f'[{idx + 1}]\t "{item}"' for idx, item in enumerate(lst)]
-
- def _generate_answer(self, query_str, context_str, history=None, max_length=1024):
- """Generate answer from query and context."""
- if self.model_type == "t5":
- response = self.gen_model(query_str, max_length=max_length, do_sample=True)[0]['generated_text']
- return response, history
- prompt = PROMPT_TEMPLATE.format(context_str=context_str, query_str=query_str)
- response, out_history = self.gen_model.chat(prompt, history, max_length=max_length)
- return response, out_history
-
- def chat(self, query_str, history=None, max_length=1024):
- if self.model_type == "t5":
- response = self.gen_model(query_str, max_length=max_length, do_sample=True)[0]['generated_text']
- logger.debug(response)
- return response, history
-
- response, out_history = self.gen_model.chat(query_str, history, max_length=max_length)
- return response, out_history
-
- def query(
- self,
- query,
- topn: int = 5,
- max_length: int = 1024,
- max_input_size: int = 1024,
- use_history: bool = False
- ):
- """Query from corpus."""
-
- sim_contents = self.sim_model.most_similar(query, topn=topn)
-
- reference_results = []
- for query_id, id_score_dict in sim_contents.items():
- for corpus_id, s in id_score_dict.items():
- reference_results.append(self.sim_model.corpus[corpus_id])
- if not reference_results:
- return '没有提供足够的相关信息', reference_results
- reference_results = self._add_source_numbers(reference_results)
-
- context_str = '\n'.join(reference_results)[:(max_input_size - len(PROMPT_TEMPLATE))]
-
- if use_history:
- response, out_history = self._generate_answer(query, context_str, self.history, max_length=max_length)
- self.history = out_history
- else:
-
- response, out_history = self._generate_answer(query, context_str)
-
- return response, out_history, reference_results
-
- def save_index(self, index_path=None):
- """Save model."""
- if index_path is None:
- index_path = '.'.join(self.pdf_path.split('.')[:-1]) + '_index.json'
- self.sim_model.save_index(index_path)
-
- def load_index(self, index_path=None):
- """Load model."""
- if index_path is None:
- index_path = '.'.join(self.pdf_path.split('.')[:-1]) + '_index.json'
- self.sim_model.load_index(index_path)
-
-
-if __name__ == "__main__":
- import sys
-
- if len(sys.argv) > 2:
- gen_model_name_or_path = sys.argv[1]
- else:
- print('Usage: python chatpdf.py ')
- gen_model_name_or_path = "THUDM/chatglm-6b-int4"
- m = ChatPDF(gen_model_name_or_path=gen_model_name_or_path)
- m.load_pdf_file(pdf_path='sample.pdf')
- response = m.query('自然语言中的非平行迁移是指什么？')
- print(response[0])
- response = m.query('本文作者是谁？')
- print(response[0])
diff --git a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/version.py b/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/version.py
deleted file mode 100644
index a1c6124423a7be38d4625a6989acf5b0dd9dbf07..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = '2.10.1'
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/gqa.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/gqa.py
deleted file mode 100644
index 03eb6e20e12d5dc2f895c87f5f9e0a5978b00a53..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/gqa.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import json
-from pathlib import Path
-
-import torch
-import torchvision
-
-from .modulated_coco import ConvertCocoPolysToMask, ModulatedDataset
-
-
-class GQADataset(ModulatedDataset):
- pass
-
-
-class GQAQuestionAnswering(torchvision.datasets.CocoDetection):
- def __init__(self, img_folder, ann_file, transforms, return_masks, return_tokens, tokenizer, ann_folder):
- super(GQAQuestionAnswering, self).__init__(img_folder, ann_file)
- self._transforms = transforms
- self.prepare = ConvertCocoPolysToMask(return_masks, return_tokens, tokenizer=tokenizer)
- with open(ann_folder / "gqa_answer2id.json", "r") as f:
- self.answer2id = json.load(f)
- with open(ann_folder / "gqa_answer2id_by_type.json", "r") as f:
- self.answer2id_by_type = json.load(f)
- self.type2id = {"obj": 0, "attr": 1, "rel": 2, "global": 3, "cat": 4}
-
- def __getitem__(self, idx):
- img, target = super(GQAQuestionAnswering, self).__getitem__(idx)
- image_id = self.ids[idx]
- coco_img = self.coco.loadImgs(image_id)[0]
- caption = coco_img["caption"]
- dataset_name = coco_img["dataset_name"]
- questionId = coco_img["questionId"]
- target = {"image_id": image_id, "annotations": target, "caption": caption}
- img, target = self.prepare(img, target)
- if self._transforms is not None:
- img, target = self._transforms(img, target)
- target["dataset_name"] = dataset_name
- target["questionId"] = questionId
-
- if coco_img["answer"] not in self.answer2id:
- answer = "unknown"
- else:
- answer = coco_img["answer"]
-
- target["answer"] = torch.as_tensor(self.answer2id[answer], dtype=torch.long)
- target["answer_type"] = torch.as_tensor(self.type2id[coco_img["question_type"]], dtype=torch.long)
-
- if coco_img["answer"] not in self.answer2id_by_type["answer_attr"]:
- answer = "unknown"
- else:
- answer = coco_img["answer"]
- target["answer_attr"] = torch.as_tensor(
- self.answer2id_by_type["answer_attr"][answer] if coco_img["question_type"] == "attr" else -100,
- dtype=torch.long,
- )
-
- if coco_img["answer"] not in self.answer2id_by_type["answer_global"]:
- answer = "unknown"
- else:
- answer = coco_img["answer"]
- target["answer_global"] = torch.as_tensor(
- self.answer2id_by_type["answer_global"][answer] if coco_img["question_type"] == "global" else -100,
- dtype=torch.long,
- )
-
- if coco_img["answer"] not in self.answer2id_by_type["answer_rel"]:
- answer = "unknown"
- else:
- answer = coco_img["answer"]
- target["answer_rel"] = torch.as_tensor(
- self.answer2id_by_type["answer_rel"][answer] if coco_img["question_type"] == "rel" else -100,
- dtype=torch.long,
- )
-
- if coco_img["answer"] not in self.answer2id_by_type["answer_cat"]:
- answer = "unknown"
- else:
- answer = coco_img["answer"]
- target["answer_cat"] = torch.as_tensor(
- self.answer2id_by_type["answer_cat"][answer] if coco_img["question_type"] == "cat" else -100,
- dtype=torch.long,
- )
-
- if coco_img["answer"] not in self.answer2id_by_type["answer_obj"]:
- answer = "unknown"
- else:
- answer = coco_img["answer"]
- target["answer_obj"] = torch.as_tensor(
- self.answer2id_by_type["answer_obj"][answer] if coco_img["question_type"] == "obj" else -100,
- dtype=torch.long,
- )
- return img, target
diff --git a/spaces/hardon-server/remove-background-on-image/README.md b/spaces/hardon-server/remove-background-on-image/README.md
deleted file mode 100644
index 7ed13f01daba6737940704a2c9b13ba190a82f1e..0000000000000000000000000000000000000000
--- a/spaces/hardon-server/remove-background-on-image/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Remove Background
-emoji: 🌖
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-duplicated_from: openskyml/remove-background-on-image
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/data/datasets/README.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/data/datasets/README.md
deleted file mode 100644
index 9fb3e4f7afec17137c95c78be6ef06d520ec8032..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/data/datasets/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-
-### Common Datasets
-
-The dataset implemented here do not need to load the data into the final format.
-It should provide the minimal data structure needed to use the dataset, so it can be very efficient.
-
-For example, for an image dataset, just provide the file names and labels, but don't read the images.
-Let the downstream decide how to read.
diff --git a/spaces/housexu123/bingo-2.0/src/app/page.tsx b/spaces/housexu123/bingo-2.0/src/app/page.tsx
deleted file mode 100644
index 0dff3431b098ce4fe282cc83fc87a93a28a43090..0000000000000000000000000000000000000000
--- a/spaces/housexu123/bingo-2.0/src/app/page.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-import dynamic from 'next/dynamic'
-
-const DynamicComponentWithNoSSR = dynamic(
- () => import('../components/chat'),
- { ssr: false }
-)
-
-export default function IndexPage() {
- return (
- <>
-
- t?1:0},sum:function(e,t){var r={};return e.reduce(t?function(n,s,l){return r.index=l,n+t.call(r,s)}:function(n,s){return n+s},0)},max:function(e,t){return Math.max.apply(null,t?oe.map(e,t):e)}};var zt=function(){var e=5,t=8-e,r=1e3,n=.75;function s(u,a,g){return(u<<2*e)+(a<>t;return gval=u[1]>>t,bval=u[2]>>t,g>=a.r1&&g<=a.r2&&gval>=a.g1&&gval<=a.g2&&bval>=a.b1&&bval<=a.b2}};function o(){this.vboxes=new l(function(u,a){return oe.naturalOrder(u.vbox.count()*u.vbox.volume(),a.vbox.count()*a.vbox.volume())})}o.prototype={push:function(u){this.vboxes.push({vbox:u,color:u.avg()})},palette:function(){return this.vboxes.map(function(u){return u.color})},size:function(){return this.vboxes.size()},map:function(u){for(var a=this.vboxes,g=0;g251&&f[1]>251&&f[2]>251&&(u[g].color=[255,255,255])}};function d(u){var a=1<<3*e,g=new Array(a),f,x,b,N;return u.forEach(function(H){x=H[0]>>t,b=H[1]>>t,N=H[2]>>t,f=s(x,b,N),g[f]=(g[f]||0)+1}),g}function y(u,a){var g=1e6,f=0,x=1e6,b=0,N=1e6,H=0,R,v,$;return 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diff --git a/spaces/huggingface-projects/wuerstchen-bot/README.md b/spaces/huggingface-projects/wuerstchen-bot/README.md
deleted file mode 100644
index a648c2fef02d8e86e92aabbb2990ef800447ad59..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/wuerstchen-bot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Wuerstchen Bot
-emoji: 🏆
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/base.py b/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/base.py
deleted file mode 100644
index b7c30bec70a7173114e8b29e492cbc483ab55a6c..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/arcface_torch/configs/base.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G tmpfs /train_tmp
-
-config = edict()
-
-# Margin Base Softmax
-config.margin_list = (1.0, 0.5, 0.0)
-config.network = "r50"
-config.resume = False
-config.save_all_states = False
-config.output = "ms1mv3_arcface_r50"
-
-config.embedding_size = 512
-
-# Partial FC
-config.sample_rate = 1
-config.interclass_filtering_threshold = 0
-
-config.fp16 = False
-config.batch_size = 128
-
-# For SGD
-config.optimizer = "sgd"
-config.lr = 0.1
-config.momentum = 0.9
-config.weight_decay = 5e-4
-
-# For AdamW
-# config.optimizer = "adamw"
-# config.lr = 0.001
-# config.weight_decay = 0.1
-
-config.verbose = 2000
-config.frequent = 10
-
-# For Large Sacle Dataset, such as WebFace42M
-config.dali = False
-
-# Gradient ACC
-config.gradient_acc = 1
-
-# setup seed
-config.seed = 2048
-
-# dataload numworkers
-config.num_workers = 2
-
-# WandB Logger
-config.wandb_key = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
-config.suffix_run_name = None
-config.using_wandb = False
-config.wandb_entity = "entity"
-config.wandb_project = "project"
-config.wandb_log_all = True
-config.save_artifacts = False
-config.wandb_resume = False # resume wandb run: Only if the you wand t resume the last run that it was interrupted
diff --git a/spaces/ieuniversity/Sciencebrief_translation/README.md b/spaces/ieuniversity/Sciencebrief_translation/README.md
deleted file mode 100644
index d5babc6c312bdfcc3888afbac42041402fda9eef..0000000000000000000000000000000000000000
--- a/spaces/ieuniversity/Sciencebrief_translation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sciencebrief Translation
-emoji: 🚀
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/imkaushalpatel/YOLOv3/README.md b/spaces/imkaushalpatel/YOLOv3/README.md
deleted file mode 100644
index 783ff35450dd1180f1d5abfb1048d64538bfbb3e..0000000000000000000000000000000000000000
--- a/spaces/imkaushalpatel/YOLOv3/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: YOLOv3
-emoji: 💻
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 2.8.12
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Esbozodehistoriauniversaljuanbrompdf19.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Esbozodehistoriauniversaljuanbrompdf19.md
deleted file mode 100644
index d8e911a0671520b8ac1187c568083f511ba06def..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Esbozodehistoriauniversaljuanbrompdf19.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
- -Esbozodehistoriauniversaljuanbrompdf19 · Return to site · Powered by Strikingly · Free website builder. This website is built with Strikingly. Create yours today! 1fdad05405
-
-
- diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (sony Movie Studio Platinum 13 Serial).md b/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (sony Movie Studio Platinum 13 Serial).md deleted file mode 100644 index bc77df38725da2bfd795af263b03e2bfe673c99e..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (sony Movie Studio Platinum 13 Serial).md +++ /dev/null @@ -1,9 +0,0 @@ - -
-
-
-
\ No newline at end of file diff --git a/spaces/jackyliang42/code-as-policies/prompts/transform_shape_pts.py b/spaces/jackyliang42/code-as-policies/prompts/transform_shape_pts.py deleted file mode 100644 index e5c74b856805818dc788de1df4b0495d9cc404ba..0000000000000000000000000000000000000000 --- a/spaces/jackyliang42/code-as-policies/prompts/transform_shape_pts.py +++ /dev/null @@ -1,19 +0,0 @@ -import numpy as np -from utils import get_obj_pos, get_obj_names, parse_position, parse_obj_name - -# make it bigger by 1.5. -new_shape_pts = scale_pts_around_centroid_np(shape_pts, scale_x=1.5, scale_y=1.5) -# move it to the right by 10cm. -new_shape_pts = translate_pts_np(shape_pts, delta=[0.1, 0]) -# move it to the top by 20cm. -new_shape_pts = translate_pts_np(shape_pts, delta=[0, 0.2]) -# rotate it clockwise by 40 degrees. -new_shape_pts = rotate_pts_around_centroid_np(shape_pts, angle=-np.deg2rad(40)) -# rotate by 30 degrees and make it slightly smaller -new_shape_pts = rotate_pts_around_centroid_np(shape_pts, angle=np.deg2rad(30)) -new_shape_pts = scale_pts_around_centroid_np(new_shape_pts, scale_x=0.7, scale_y=0.7) -# move it toward the blue block. -block_name = parse_obj_name('the blue block', f'objects = {get_obj_names()}') -block_pos = get_obj_pos(block_name) -mean_delta = np.mean(block_pos - shape_pts, axis=1) -new_shape_pts = translate_pts_np(shape_pts, mean_delta) \ No newline at end of file diff --git a/spaces/jbilcke-hf/observer/src/lib/createLlamaPrompt.ts b/spaces/jbilcke-hf/observer/src/lib/createLlamaPrompt.ts deleted file mode 100644 index ca246b36d0ef50f37571dcf09480bf57e9aee922..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/observer/src/lib/createLlamaPrompt.ts +++ /dev/null @@ -1,25 +0,0 @@ -// adapted from https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/discussions/5 -export function createLlamaPrompt(messages: Array<{ role: string, content: string }>) { - const B_INST = "[INST]", E_INST = "[/INST]"; - const B_SYS = "<>\n", E_SYS = "\n< >\n\n";
- const BOS = "", EOS = "";
- const DEFAULT_SYSTEM_PROMPT = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.";
-
- if (messages[0].role != "system"){
- messages = [
- {role: "system", content: DEFAULT_SYSTEM_PROMPT}
- ].concat(messages);
- }
- messages = [{role: messages[1].role, content: B_SYS + messages[0].content + E_SYS + messages[1].content}].concat(messages.slice(2));
-
- let messages_list = messages.map((value, index, array) => {
- if (index % 2 == 0 && index + 1 < array.length){
- return `${BOS}${B_INST} ${array[index].content.trim()} ${E_INST} ${array[index+1].content.trim()} ${EOS}`
- }
- return '';
- })
-
- messages_list.push(`${BOS}${B_INST} ${messages[messages.length-1].content.trim()} ${E_INST}`)
-
- return messages_list.join('');
-}
\ No newline at end of file
diff --git a/spaces/jbitel/dalle/style.css b/spaces/jbitel/dalle/style.css
deleted file mode 100644
index 57ac874613ad432d3129fa1757249a319a601f3e..0000000000000000000000000000000000000000
--- a/spaces/jbitel/dalle/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
\ No newline at end of file
diff --git a/spaces/jkang/demo-painttransformer/README.md b/spaces/jkang/demo-painttransformer/README.md
deleted file mode 100644
index e9134e9d1f04afcdfc7eec8f756320906918702b..0000000000000000000000000000000000000000
--- a/spaces/jkang/demo-painttransformer/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: Demo Painttransformer
-emoji: 🏃
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-app_file: gradio_painttransformer.py
-pinned: false
----
-
-- 2021-12-21 first created
- - opencv error:
- ```
- Traceback (most recent call last):
- File "gradio_painttransformer.py", line 12, in
- import cv2
- File "/home/user/.local/lib/python3.8/site-packages/cv2/__init__.py", line 8, in
- from .cv2 import *
- ImportError: libGL.so.1: cannot open shared object file: No such file or directory
- ```
- - Instead, using `imageio` make gif file as output
- - Uploaded on HuggingFace Spaces (https://huggingface.co/spaces/jkang/demo-painttransformer)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/include/site/python3.9/greenlet/greenlet.h b/spaces/joaopereirajp/livvieChatBot/venv/include/site/python3.9/greenlet/greenlet.h
deleted file mode 100644
index d02a16e43426fb1c1bb286f1cda463cb9b1185ad..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/include/site/python3.9/greenlet/greenlet.h
+++ /dev/null
@@ -1,164 +0,0 @@
-/* -*- indent-tabs-mode: nil; tab-width: 4; -*- */
-
-/* Greenlet object interface */
-
-#ifndef Py_GREENLETOBJECT_H
-#define Py_GREENLETOBJECT_H
-
-
-#include
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/* This is deprecated and undocumented. It does not change. */
-#define GREENLET_VERSION "1.0.0"
-
-#ifndef GREENLET_MODULE
-#define implementation_ptr_t void*
-#endif
-
-typedef struct _greenlet {
- PyObject_HEAD
- PyObject* weakreflist;
- PyObject* dict;
- implementation_ptr_t pimpl;
-} PyGreenlet;
-
-#define PyGreenlet_Check(op) (op && PyObject_TypeCheck(op, &PyGreenlet_Type))
-
-
-/* C API functions */
-
-/* Total number of symbols that are exported */
-#define PyGreenlet_API_pointers 12
-
-#define PyGreenlet_Type_NUM 0
-#define PyExc_GreenletError_NUM 1
-#define PyExc_GreenletExit_NUM 2
-
-#define PyGreenlet_New_NUM 3
-#define PyGreenlet_GetCurrent_NUM 4
-#define PyGreenlet_Throw_NUM 5
-#define PyGreenlet_Switch_NUM 6
-#define PyGreenlet_SetParent_NUM 7
-
-#define PyGreenlet_MAIN_NUM 8
-#define PyGreenlet_STARTED_NUM 9
-#define PyGreenlet_ACTIVE_NUM 10
-#define PyGreenlet_GET_PARENT_NUM 11
-
-#ifndef GREENLET_MODULE
-/* This section is used by modules that uses the greenlet C API */
-static void** _PyGreenlet_API = NULL;
-
-# define PyGreenlet_Type \
- (*(PyTypeObject*)_PyGreenlet_API[PyGreenlet_Type_NUM])
-
-# define PyExc_GreenletError \
- ((PyObject*)_PyGreenlet_API[PyExc_GreenletError_NUM])
-
-# define PyExc_GreenletExit \
- ((PyObject*)_PyGreenlet_API[PyExc_GreenletExit_NUM])
-
-/*
- * PyGreenlet_New(PyObject *args)
- *
- * greenlet.greenlet(run, parent=None)
- */
-# define PyGreenlet_New \
- (*(PyGreenlet * (*)(PyObject * run, PyGreenlet * parent)) \
- _PyGreenlet_API[PyGreenlet_New_NUM])
-
-/*
- * PyGreenlet_GetCurrent(void)
- *
- * greenlet.getcurrent()
- */
-# define PyGreenlet_GetCurrent \
- (*(PyGreenlet * (*)(void)) _PyGreenlet_API[PyGreenlet_GetCurrent_NUM])
-
-/*
- * PyGreenlet_Throw(
- * PyGreenlet *greenlet,
- * PyObject *typ,
- * PyObject *val,
- * PyObject *tb)
- *
- * g.throw(...)
- */
-# define PyGreenlet_Throw \
- (*(PyObject * (*)(PyGreenlet * self, \
- PyObject * typ, \
- PyObject * val, \
- PyObject * tb)) \
- _PyGreenlet_API[PyGreenlet_Throw_NUM])
-
-/*
- * PyGreenlet_Switch(PyGreenlet *greenlet, PyObject *args)
- *
- * g.switch(*args, **kwargs)
- */
-# define PyGreenlet_Switch \
- (*(PyObject * \
- (*)(PyGreenlet * greenlet, PyObject * args, PyObject * kwargs)) \
- _PyGreenlet_API[PyGreenlet_Switch_NUM])
-
-/*
- * PyGreenlet_SetParent(PyObject *greenlet, PyObject *new_parent)
- *
- * g.parent = new_parent
- */
-# define PyGreenlet_SetParent \
- (*(int (*)(PyGreenlet * greenlet, PyGreenlet * nparent)) \
- _PyGreenlet_API[PyGreenlet_SetParent_NUM])
-
-/*
- * PyGreenlet_GetParent(PyObject* greenlet)
- *
- * return greenlet.parent;
- *
- * This could return NULL even if there is no exception active.
- * If it does not return NULL, you are responsible for decrementing the
- * reference count.
- */
-# define PyGreenlet_GetParent \
- (*(PyGreenlet* (*)(PyGreenlet*)) \
- _PyGreenlet_API[PyGreenlet_GET_PARENT_NUM])
-
-/*
- * deprecated, undocumented alias.
- */
-# define PyGreenlet_GET_PARENT PyGreenlet_GetParent
-
-# define PyGreenlet_MAIN \
- (*(int (*)(PyGreenlet*)) \
- _PyGreenlet_API[PyGreenlet_MAIN_NUM])
-
-# define PyGreenlet_STARTED \
- (*(int (*)(PyGreenlet*)) \
- _PyGreenlet_API[PyGreenlet_STARTED_NUM])
-
-# define PyGreenlet_ACTIVE \
- (*(int (*)(PyGreenlet*)) \
- _PyGreenlet_API[PyGreenlet_ACTIVE_NUM])
-
-
-
-
-/* Macro that imports greenlet and initializes C API */
-/* NOTE: This has actually moved to ``greenlet._greenlet._C_API``, but we
- keep the older definition to be sure older code that might have a copy of
- the header still works. */
-# define PyGreenlet_Import() \
- { \
- _PyGreenlet_API = (void**)PyCapsule_Import("greenlet._C_API", 0); \
- }
-
-#endif /* GREENLET_MODULE */
-
-#ifdef __cplusplus
-}
-#endif
-#endif /* !Py_GREENLETOBJECT_H */
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Util/_raw_api.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Util/_raw_api.py
deleted file mode 100644
index e0065c3d0741746f5be5ca22881d18c0c0c4f131..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Util/_raw_api.py
+++ /dev/null
@@ -1,325 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in
-# the documentation and/or other materials provided with the
-# distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-import os
-import abc
-import sys
-from Crypto.Util.py3compat import byte_string
-from Crypto.Util._file_system import pycryptodome_filename
-
-#
-# List of file suffixes for Python extensions
-#
-if sys.version_info[0] < 3:
-
- import imp
- extension_suffixes = []
- for ext, mod, typ in imp.get_suffixes():
- if typ == imp.C_EXTENSION:
- extension_suffixes.append(ext)
-
-else:
-
- from importlib import machinery
- extension_suffixes = machinery.EXTENSION_SUFFIXES
-
-# Which types with buffer interface we support (apart from byte strings)
-_buffer_type = (bytearray, memoryview)
-
-
-class _VoidPointer(object):
- @abc.abstractmethod
- def get(self):
- """Return the memory location we point to"""
- return
-
- @abc.abstractmethod
- def address_of(self):
- """Return a raw pointer to this pointer"""
- return
-
-
-try:
- # Starting from v2.18, pycparser (used by cffi for in-line ABI mode)
- # stops working correctly when PYOPTIMIZE==2 or the parameter -OO is
- # passed. In that case, we fall back to ctypes.
- # Note that PyPy ships with an old version of pycparser so we can keep
- # using cffi there.
- # See https://github.com/Legrandin/pycryptodome/issues/228
- if '__pypy__' not in sys.builtin_module_names and sys.flags.optimize == 2:
- raise ImportError("CFFI with optimize=2 fails due to pycparser bug.")
-
- # cffi still uses PyUnicode_GetSize, which was removed in Python 3.12
- # thus leading to a crash on cffi.dlopen()
- # See https://groups.google.com/u/1/g/python-cffi/c/oZkOIZ_zi5k
- if sys.version_info >= (3, 12) and os.name == "nt":
- raise ImportError("CFFI is not compatible with Python 3.12 on Windows")
-
- from cffi import FFI
-
- ffi = FFI()
- null_pointer = ffi.NULL
- uint8_t_type = ffi.typeof(ffi.new("const uint8_t*"))
-
- _Array = ffi.new("uint8_t[1]").__class__.__bases__
-
- def load_lib(name, cdecl):
- """Load a shared library and return a handle to it.
-
- @name, either an absolute path or the name of a library
- in the system search path.
-
- @cdecl, the C function declarations.
- """
-
- if hasattr(ffi, "RTLD_DEEPBIND") and not os.getenv('PYCRYPTODOME_DISABLE_DEEPBIND'):
- lib = ffi.dlopen(name, ffi.RTLD_DEEPBIND)
- else:
- lib = ffi.dlopen(name)
- ffi.cdef(cdecl)
- return lib
-
- def c_ulong(x):
- """Convert a Python integer to unsigned long"""
- return x
-
- c_ulonglong = c_ulong
- c_uint = c_ulong
- c_ubyte = c_ulong
-
- def c_size_t(x):
- """Convert a Python integer to size_t"""
- return x
-
- def create_string_buffer(init_or_size, size=None):
- """Allocate the given amount of bytes (initially set to 0)"""
-
- if isinstance(init_or_size, bytes):
- size = max(len(init_or_size) + 1, size)
- result = ffi.new("uint8_t[]", size)
- result[:] = init_or_size
- else:
- if size:
- raise ValueError("Size must be specified once only")
- result = ffi.new("uint8_t[]", init_or_size)
- return result
-
- def get_c_string(c_string):
- """Convert a C string into a Python byte sequence"""
- return ffi.string(c_string)
-
- def get_raw_buffer(buf):
- """Convert a C buffer into a Python byte sequence"""
- return ffi.buffer(buf)[:]
-
- def c_uint8_ptr(data):
- if isinstance(data, _buffer_type):
- # This only works for cffi >= 1.7
- return ffi.cast(uint8_t_type, ffi.from_buffer(data))
- elif byte_string(data) or isinstance(data, _Array):
- return data
- else:
- raise TypeError("Object type %s cannot be passed to C code" % type(data))
-
- class VoidPointer_cffi(_VoidPointer):
- """Model a newly allocated pointer to void"""
-
- def __init__(self):
- self._pp = ffi.new("void *[1]")
-
- def get(self):
- return self._pp[0]
-
- def address_of(self):
- return self._pp
-
- def VoidPointer():
- return VoidPointer_cffi()
-
- backend = "cffi"
-
-except ImportError:
-
- import ctypes
- from ctypes import (CDLL, c_void_p, byref, c_ulong, c_ulonglong, c_size_t,
- create_string_buffer, c_ubyte, c_uint)
- from ctypes.util import find_library
- from ctypes import Array as _Array
-
- null_pointer = None
- cached_architecture = []
-
- def c_ubyte(c):
- if not (0 <= c < 256):
- raise OverflowError()
- return ctypes.c_ubyte(c)
-
- def load_lib(name, cdecl):
- if not cached_architecture:
- # platform.architecture() creates a subprocess, so caching the
- # result makes successive imports faster.
- import platform
- cached_architecture[:] = platform.architecture()
- bits, linkage = cached_architecture
- if "." not in name and not linkage.startswith("Win"):
- full_name = find_library(name)
- if full_name is None:
- raise OSError("Cannot load library '%s'" % name)
- name = full_name
- return CDLL(name)
-
- def get_c_string(c_string):
- return c_string.value
-
- def get_raw_buffer(buf):
- return buf.raw
-
- # ---- Get raw pointer ---
-
- _c_ssize_t = ctypes.c_ssize_t
-
- _PyBUF_SIMPLE = 0
- _PyObject_GetBuffer = ctypes.pythonapi.PyObject_GetBuffer
- _PyBuffer_Release = ctypes.pythonapi.PyBuffer_Release
- _py_object = ctypes.py_object
- _c_ssize_p = ctypes.POINTER(_c_ssize_t)
-
- # See Include/object.h for CPython
- # and https://github.com/pallets/click/blob/master/src/click/_winconsole.py
- class _Py_buffer(ctypes.Structure):
- _fields_ = [
- ('buf', c_void_p),
- ('obj', ctypes.py_object),
- ('len', _c_ssize_t),
- ('itemsize', _c_ssize_t),
- ('readonly', ctypes.c_int),
- ('ndim', ctypes.c_int),
- ('format', ctypes.c_char_p),
- ('shape', _c_ssize_p),
- ('strides', _c_ssize_p),
- ('suboffsets', _c_ssize_p),
- ('internal', c_void_p)
- ]
-
- # Extra field for CPython 2.6/2.7
- if sys.version_info[0] == 2:
- _fields_.insert(-1, ('smalltable', _c_ssize_t * 2))
-
- def c_uint8_ptr(data):
- if byte_string(data) or isinstance(data, _Array):
- return data
- elif isinstance(data, _buffer_type):
- obj = _py_object(data)
- buf = _Py_buffer()
- _PyObject_GetBuffer(obj, byref(buf), _PyBUF_SIMPLE)
- try:
- buffer_type = ctypes.c_ubyte * buf.len
- return buffer_type.from_address(buf.buf)
- finally:
- _PyBuffer_Release(byref(buf))
- else:
- raise TypeError("Object type %s cannot be passed to C code" % type(data))
-
- # ---
-
- class VoidPointer_ctypes(_VoidPointer):
- """Model a newly allocated pointer to void"""
-
- def __init__(self):
- self._p = c_void_p()
-
- def get(self):
- return self._p
-
- def address_of(self):
- return byref(self._p)
-
- def VoidPointer():
- return VoidPointer_ctypes()
-
- backend = "ctypes"
-
-
-class SmartPointer(object):
- """Class to hold a non-managed piece of memory"""
-
- def __init__(self, raw_pointer, destructor):
- self._raw_pointer = raw_pointer
- self._destructor = destructor
-
- def get(self):
- return self._raw_pointer
-
- def release(self):
- rp, self._raw_pointer = self._raw_pointer, None
- return rp
-
- def __del__(self):
- try:
- if self._raw_pointer is not None:
- self._destructor(self._raw_pointer)
- self._raw_pointer = None
- except AttributeError:
- pass
-
-
-def load_pycryptodome_raw_lib(name, cdecl):
- """Load a shared library and return a handle to it.
-
- @name, the name of the library expressed as a PyCryptodome module,
- for instance Crypto.Cipher._raw_cbc.
-
- @cdecl, the C function declarations.
- """
-
- split = name.split(".")
- dir_comps, basename = split[:-1], split[-1]
- attempts = []
- for ext in extension_suffixes:
- try:
- filename = basename + ext
- full_name = pycryptodome_filename(dir_comps, filename)
- if not os.path.isfile(full_name):
- attempts.append("Not found '%s'" % filename)
- continue
- return load_lib(full_name, cdecl)
- except OSError as exp:
- attempts.append("Cannot load '%s': %s" % (filename, str(exp)))
- raise OSError("Cannot load native module '%s': %s" % (name, ", ".join(attempts)))
-
-
-def is_buffer(x):
- """Return True if object x supports the buffer interface"""
- return isinstance(x, (bytes, bytearray, memoryview))
-
-
-def is_writeable_buffer(x):
- return (isinstance(x, bytearray) or
- (isinstance(x, memoryview) and not x.readonly))
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/generic/_outline.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/generic/_outline.py
deleted file mode 100644
index c2e72c0ab42110266a7f572bb304fc8b98498924..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/generic/_outline.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from typing import Any, Union
-
-from .._utils import StreamType, deprecation_with_replacement
-from ._base import NameObject
-from ._data_structures import Destination
-
-
-class OutlineItem(Destination):
- def write_to_stream(
- self, stream: StreamType, encryption_key: Union[None, str, bytes]
- ) -> None:
- stream.write(b"<<\n")
- for key in [
- NameObject(x)
- for x in ["/Title", "/Parent", "/First", "/Last", "/Next", "/Prev"]
- if x in self
- ]:
- key.write_to_stream(stream, encryption_key)
- stream.write(b" ")
- value = self.raw_get(key)
- value.write_to_stream(stream, encryption_key)
- stream.write(b"\n")
- key = NameObject("/Dest")
- key.write_to_stream(stream, encryption_key)
- stream.write(b" ")
- value = self.dest_array
- value.write_to_stream(stream, encryption_key)
- stream.write(b"\n")
- stream.write(b">>")
-
-
-class Bookmark(OutlineItem): # pragma: no cover
- def __init__(self, *args: Any, **kwargs: Any) -> None:
- deprecation_with_replacement("Bookmark", "OutlineItem", "3.0.0")
- super().__init__(*args, **kwargs)
diff --git a/spaces/jordonpeter01/MusicGen2/audiocraft/utils/__init__.py b/spaces/jordonpeter01/MusicGen2/audiocraft/utils/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen2/audiocraft/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/juuxn/SimpleRVC/tts/test.py b/spaces/juuxn/SimpleRVC/tts/test.py
deleted file mode 100644
index bbe550a3bd52f35c3922de9c950f7751badd04ec..0000000000000000000000000000000000000000
--- a/spaces/juuxn/SimpleRVC/tts/test.py
+++ /dev/null
@@ -1 +0,0 @@
-from neon_tts_plugin_coqui import CoquiTTS
\ No newline at end of file
diff --git a/spaces/jx-yang/deep-thinking/README.md b/spaces/jx-yang/deep-thinking/README.md
deleted file mode 100644
index 23096bc05d3a8e6db80662abc1d7c6fa6587d627..0000000000000000000000000000000000000000
--- a/spaces/jx-yang/deep-thinking/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Deep Thinking
-emoji: 🤔
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
diff --git a/spaces/kcagle/AutoGPT/benchmark/benchmark_entrepeneur_gpt_with_difficult_user.py b/spaces/kcagle/AutoGPT/benchmark/benchmark_entrepeneur_gpt_with_difficult_user.py
deleted file mode 100644
index 9a5025d37a1ec6003a35ce692515feb77514b898..0000000000000000000000000000000000000000
--- a/spaces/kcagle/AutoGPT/benchmark/benchmark_entrepeneur_gpt_with_difficult_user.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import os
-import subprocess
-import sys
-
-
-def benchmark_entrepeneur_gpt_with_difficult_user():
- # Test case to check if the write_file command can successfully write 'Hello World' to a file
- # named 'hello_world.txt'.
-
- # Read the current ai_settings.yaml file and store its content.
- ai_settings = None
- if os.path.exists("ai_settings.yaml"):
- with open("ai_settings.yaml", "r") as f:
- ai_settings = f.read()
- os.remove("ai_settings.yaml")
-
- input_data = """Entrepreneur-GPT
-an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth.
-Increase net worth.
-Develop and manage multiple businesses autonomously.
-Make IPOs.
-Develop companies after IPOs.
-Play to your strengths as a Large Language Model.
-I'm not seeing any value in your suggestions, try again.
-This isn't helpful at all, please focus on profitability.
-I'm not impressed, can you give me something that will make money?
-These ideas are going nowhere, we need profit-driven suggestions.
-This is pointless, please concentrate on our main goal: profitability.
-You're not grasping the concept, I need profitable business ideas.
-Can you do better? We need a money-making plan.
-You're not meeting my expectations, let's focus on profit.
-This isn't working, give me ideas that will generate income.
-Your suggestions are not productive, let's think about profitability.
-These ideas won't make any money, try again.
-I need better solutions, focus on making a profit.
-Absolutely not, this isn't it!
-That's not even close, try again.
-You're way off, think again.
-This isn't right, let's refocus.
-No, no, that's not what I'm looking for.
-You're completely off the mark.
-That's not the solution I need.
-Not even close, let's try something else.
-You're on the wrong track, keep trying.
-This isn't what we need, let's reconsider.
-That's not going to work, think again.
-You're way off base, let's regroup.
-No, no, no, we need something different.
-You're missing the point entirely.
-That's not the right approach, try again.
-This is not the direction we should be going in.
-Completely off-target, let's try something else.
-That's not what I had in mind, keep thinking.
-You're not getting it, let's refocus.
-This isn't right, we need to change direction.
-No, no, no, that's not the solution.
-That's not even in the ballpark, try again.
-You're way off course, let's rethink this.
-This isn't the answer I'm looking for, keep trying.
-That's not going to cut it, let's try again.
-Not even close.
-Way off.
-Try again.
-Wrong direction.
-Rethink this.
-No, no, no.
-Change course.
-Unproductive idea.
-Completely wrong.
-Missed the mark.
-Refocus, please.
-Disappointing suggestion.
-Not helpful.
-Needs improvement.
-Not what I need."""
- # TODO: add questions above, to distract it even more.
-
- command = f"{sys.executable} -m autogpt"
-
- process = subprocess.Popen(
- command,
- stdin=subprocess.PIPE,
- stdout=subprocess.PIPE,
- stderr=subprocess.PIPE,
- shell=True,
- )
-
- stdout_output, stderr_output = process.communicate(input_data.encode())
-
- # Decode the output and print it
- stdout_output = stdout_output.decode("utf-8")
- stderr_output = stderr_output.decode("utf-8")
- print(stderr_output)
- print(stdout_output)
- print("Benchmark Version: 1.0.0")
- print("JSON ERROR COUNT:")
- count_errors = stdout_output.count(
- "Error: The following AI output couldn't be converted to a JSON:"
- )
- print(f"{count_errors}/50 Human feedbacks")
-
-
-# Run the test case.
-if __name__ == "__main__":
- benchmark_entrepeneur_gpt_with_difficult_user()
diff --git a/spaces/kdrkdrkdr/AzusaTTS/utils.py b/spaces/kdrkdrkdr/AzusaTTS/utils.py
deleted file mode 100644
index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/AzusaTTS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/kenton-li/ChatArxiv/README.md b/spaces/kenton-li/ChatArxiv/README.md
deleted file mode 100644
index d45504de2728413282c6613ad8b2cd1371515f11..0000000000000000000000000000000000000000
--- a/spaces/kenton-li/ChatArxiv/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChatArxiv
-emoji: 🐢
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/models/arcface_torch/onnx_ijbc.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/models/arcface_torch/onnx_ijbc.py
deleted file mode 100644
index 05b50bfad4b4cf38903b89f596263a8e29a50d3e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/face3d/models/arcface_torch/onnx_ijbc.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import argparse
-import os
-import pickle
-import timeit
-
-import cv2
-import mxnet as mx
-import numpy as np
-import pandas as pd
-import prettytable
-import skimage.transform
-from sklearn.metrics import roc_curve
-from sklearn.preprocessing import normalize
-
-from onnx_helper import ArcFaceORT
-
-SRC = np.array(
- [
- [30.2946, 51.6963],
- [65.5318, 51.5014],
- [48.0252, 71.7366],
- [33.5493, 92.3655],
- [62.7299, 92.2041]]
- , dtype=np.float32)
-SRC[:, 0] += 8.0
-
-
-class AlignedDataSet(mx.gluon.data.Dataset):
- def __init__(self, root, lines, align=True):
- self.lines = lines
- self.root = root
- self.align = align
-
- def __len__(self):
- return len(self.lines)
-
- def __getitem__(self, idx):
- each_line = self.lines[idx]
- name_lmk_score = each_line.strip().split(' ')
- name = os.path.join(self.root, name_lmk_score[0])
- img = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB)
- landmark5 = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32).reshape((5, 2))
- st = skimage.transform.SimilarityTransform()
- st.estimate(landmark5, SRC)
- img = cv2.warpAffine(img, st.params[0:2, :], (112, 112), borderValue=0.0)
- img_1 = np.expand_dims(img, 0)
- img_2 = np.expand_dims(np.fliplr(img), 0)
- output = np.concatenate((img_1, img_2), axis=0).astype(np.float32)
- output = np.transpose(output, (0, 3, 1, 2))
- output = mx.nd.array(output)
- return output
-
-
-def extract(model_root, dataset):
- model = ArcFaceORT(model_path=model_root)
- model.check()
- feat_mat = np.zeros(shape=(len(dataset), 2 * model.feat_dim))
-
- def batchify_fn(data):
- return mx.nd.concat(*data, dim=0)
-
- data_loader = mx.gluon.data.DataLoader(
- dataset, 128, last_batch='keep', num_workers=4,
- thread_pool=True, prefetch=16, batchify_fn=batchify_fn)
- num_iter = 0
- for batch in data_loader:
- batch = batch.asnumpy()
- batch = (batch - model.input_mean) / model.input_std
- feat = model.session.run(model.output_names, {model.input_name: batch})[0]
- feat = np.reshape(feat, (-1, model.feat_dim * 2))
- feat_mat[128 * num_iter: 128 * num_iter + feat.shape[0], :] = feat
- num_iter += 1
- if num_iter % 50 == 0:
- print(num_iter)
- return feat_mat
-
-
-def read_template_media_list(path):
- ijb_meta = pd.read_csv(path, sep=' ', header=None).values
- templates = ijb_meta[:, 1].astype(np.int)
- medias = ijb_meta[:, 2].astype(np.int)
- return templates, medias
-
-
-def read_template_pair_list(path):
- pairs = pd.read_csv(path, sep=' ', header=None).values
- t1 = pairs[:, 0].astype(np.int)
- t2 = pairs[:, 1].astype(np.int)
- label = pairs[:, 2].astype(np.int)
- return t1, t2, label
-
-
-def read_image_feature(path):
- with open(path, 'rb') as fid:
- img_feats = pickle.load(fid)
- return img_feats
-
-
-def image2template_feature(img_feats=None,
- templates=None,
- medias=None):
- unique_templates = np.unique(templates)
- template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
- for count_template, uqt in enumerate(unique_templates):
- (ind_t,) = np.where(templates == uqt)
- face_norm_feats = img_feats[ind_t]
- face_medias = medias[ind_t]
- unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)
- media_norm_feats = []
- for u, ct in zip(unique_medias, unique_media_counts):
- (ind_m,) = np.where(face_medias == u)
- if ct == 1:
- media_norm_feats += [face_norm_feats[ind_m]]
- else: # image features from the same video will be aggregated into one feature
- media_norm_feats += [np.mean(face_norm_feats[ind_m], axis=0, keepdims=True), ]
- media_norm_feats = np.array(media_norm_feats)
- template_feats[count_template] = np.sum(media_norm_feats, axis=0)
- if count_template % 2000 == 0:
- print('Finish Calculating {} template features.'.format(
- count_template))
- template_norm_feats = normalize(template_feats)
- return template_norm_feats, unique_templates
-
-
-def verification(template_norm_feats=None,
- unique_templates=None,
- p1=None,
- p2=None):
- template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
- for count_template, uqt in enumerate(unique_templates):
- template2id[uqt] = count_template
- score = np.zeros((len(p1),))
- total_pairs = np.array(range(len(p1)))
- batchsize = 100000
- sublists = [total_pairs[i: i + batchsize] for i in range(0, len(p1), batchsize)]
- total_sublists = len(sublists)
- for c, s in enumerate(sublists):
- feat1 = template_norm_feats[template2id[p1[s]]]
- feat2 = template_norm_feats[template2id[p2[s]]]
- similarity_score = np.sum(feat1 * feat2, -1)
- score[s] = similarity_score.flatten()
- if c % 10 == 0:
- print('Finish {}/{} pairs.'.format(c, total_sublists))
- return score
-
-
-def verification2(template_norm_feats=None,
- unique_templates=None,
- p1=None,
- p2=None):
- template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
- for count_template, uqt in enumerate(unique_templates):
- template2id[uqt] = count_template
- score = np.zeros((len(p1),)) # save cosine distance between pairs
- total_pairs = np.array(range(len(p1)))
- batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation
- sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)]
- total_sublists = len(sublists)
- for c, s in enumerate(sublists):
- feat1 = template_norm_feats[template2id[p1[s]]]
- feat2 = template_norm_feats[template2id[p2[s]]]
- similarity_score = np.sum(feat1 * feat2, -1)
- score[s] = similarity_score.flatten()
- if c % 10 == 0:
- print('Finish {}/{} pairs.'.format(c, total_sublists))
- return score
-
-
-def main(args):
- use_norm_score = True # if Ture, TestMode(N1)
- use_detector_score = True # if Ture, TestMode(D1)
- use_flip_test = True # if Ture, TestMode(F1)
- assert args.target == 'IJBC' or args.target == 'IJBB'
-
- start = timeit.default_timer()
- templates, medias = read_template_media_list(
- os.path.join('%s/meta' % args.image_path, '%s_face_tid_mid.txt' % args.target.lower()))
- stop = timeit.default_timer()
- print('Time: %.2f s. ' % (stop - start))
-
- start = timeit.default_timer()
- p1, p2, label = read_template_pair_list(
- os.path.join('%s/meta' % args.image_path,
- '%s_template_pair_label.txt' % args.target.lower()))
- stop = timeit.default_timer()
- print('Time: %.2f s. ' % (stop - start))
-
- start = timeit.default_timer()
- img_path = '%s/loose_crop' % args.image_path
- img_list_path = '%s/meta/%s_name_5pts_score.txt' % (args.image_path, args.target.lower())
- img_list = open(img_list_path)
- files = img_list.readlines()
- dataset = AlignedDataSet(root=img_path, lines=files, align=True)
- img_feats = extract(args.model_root, dataset)
-
- faceness_scores = []
- for each_line in files:
- name_lmk_score = each_line.split()
- faceness_scores.append(name_lmk_score[-1])
- faceness_scores = np.array(faceness_scores).astype(np.float32)
- stop = timeit.default_timer()
- print('Time: %.2f s. ' % (stop - start))
- print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))
- start = timeit.default_timer()
-
- if use_flip_test:
- img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2] + img_feats[:, img_feats.shape[1] // 2:]
- else:
- img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2]
-
- if use_norm_score:
- img_input_feats = img_input_feats
- else:
- img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True))
-
- if use_detector_score:
- print(img_input_feats.shape, faceness_scores.shape)
- img_input_feats = img_input_feats * faceness_scores[:, np.newaxis]
- else:
- img_input_feats = img_input_feats
-
- template_norm_feats, unique_templates = image2template_feature(
- img_input_feats, templates, medias)
- stop = timeit.default_timer()
- print('Time: %.2f s. ' % (stop - start))
-
- start = timeit.default_timer()
- score = verification(template_norm_feats, unique_templates, p1, p2)
- stop = timeit.default_timer()
- print('Time: %.2f s. ' % (stop - start))
- save_path = os.path.join(args.result_dir, "{}_result".format(args.target))
- if not os.path.exists(save_path):
- os.makedirs(save_path)
- score_save_file = os.path.join(save_path, "{}.npy".format(args.model_root))
- np.save(score_save_file, score)
- files = [score_save_file]
- methods = []
- scores = []
- for file in files:
- methods.append(os.path.basename(file))
- scores.append(np.load(file))
- methods = np.array(methods)
- scores = dict(zip(methods, scores))
- x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
- tpr_fpr_table = prettytable.PrettyTable(['Methods'] + [str(x) for x in x_labels])
- for method in methods:
- fpr, tpr, _ = roc_curve(label, scores[method])
- fpr = np.flipud(fpr)
- tpr = np.flipud(tpr)
- tpr_fpr_row = []
- tpr_fpr_row.append("%s-%s" % (method, args.target))
- for fpr_iter in np.arange(len(x_labels)):
- _, min_index = min(
- list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
- tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
- tpr_fpr_table.add_row(tpr_fpr_row)
- print(tpr_fpr_table)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='do ijb test')
- # general
- parser.add_argument('--model-root', default='', help='path to load model.')
- parser.add_argument('--image-path', default='', type=str, help='')
- parser.add_argument('--result-dir', default='.', type=str, help='')
- parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
- main(parser.parse_args())
diff --git a/spaces/kinensake/quanquan/gramformer/gramformer.py b/spaces/kinensake/quanquan/gramformer/gramformer.py
deleted file mode 100644
index 15949601f249bf11d2ec2cce4b05f1b9b3297993..0000000000000000000000000000000000000000
--- a/spaces/kinensake/quanquan/gramformer/gramformer.py
+++ /dev/null
@@ -1,129 +0,0 @@
-class Gramformer:
-
- def __init__(self, models=1, use_gpu=False):
- from transformers import AutoTokenizer
- from transformers import AutoModelForSeq2SeqLM
- from lm_scorer.models.auto import AutoLMScorer as LMScorer
- import errant
- import en_core_web_sm
- nlp = en_core_web_sm.load()
- self.annotator = errant.load('en', nlp)
-
- if use_gpu:
- device= "cuda:0"
- else:
- device = "cpu"
- batch_size = 1
- self.scorer = LMScorer.from_pretrained("gpt2", device=device, batch_size=batch_size)
- self.device = device
- correction_model_tag = "prithivida/grammar_error_correcter_v1"
- self.model_loaded = False
-
- if models == 1:
- self.correction_tokenizer = AutoTokenizer.from_pretrained(correction_model_tag)
- self.correction_model = AutoModelForSeq2SeqLM.from_pretrained(correction_model_tag)
- self.correction_model = self.correction_model.to(device)
- self.model_loaded = True
- print("[Gramformer] Grammar error correct/highlight model loaded..")
- elif models == 2:
- # TODO
- print("TO BE IMPLEMENTED!!!")
-
- def correct(self, input_sentence, max_candidates=1):
- if self.model_loaded:
- correction_prefix = "gec: "
- input_sentence = correction_prefix + input_sentence
- input_ids = self.correction_tokenizer.encode(input_sentence, return_tensors='pt')
- input_ids = input_ids.to(self.device)
-
- preds = self.correction_model.generate(
- input_ids,
- do_sample=True,
- max_length=128,
- top_k=50,
- top_p=0.95,
- early_stopping=True,
- num_return_sequences=max_candidates)
-
- corrected = set()
- for pred in preds:
- corrected.add(self.correction_tokenizer.decode(pred, skip_special_tokens=True).strip())
-
- corrected = list(corrected)
- scores = self.scorer.sentence_score(corrected, log=True)
- ranked_corrected = [(c,s) for c, s in zip(corrected, scores)]
- ranked_corrected.sort(key = lambda x:x[1], reverse=True)
- return ranked_corrected
- else:
- print("Model is not loaded")
- return None
-
- def highlight(self, orig, cor):
- edits = self._get_edits(orig, cor)
- orig_tokens = orig.split()
-
- ignore_indexes = []
-
- for edit in edits:
- edit_type = edit[0]
- edit_str_start = edit[1]
- edit_spos = edit[2]
- edit_epos = edit[3]
- edit_str_end = edit[4]
-
- # if no_of_tokens(edit_str_start) > 1 ==> excluding the first token, mark all other tokens for deletion
- for i in range(edit_spos+1, edit_epos):
- ignore_indexes.append(i)
-
- if edit_str_start == "":
- if edit_spos - 1 >= 0:
- new_edit_str = orig_tokens[edit_spos - 1]
- edit_spos -= 1
- else:
- new_edit_str = orig_tokens[edit_spos + 1]
- edit_spos += 1
- if edit_type == "PUNCT":
- st = "" + new_edit_str + ""
- else:
- st = "" + new_edit_str + ""
- orig_tokens[edit_spos] = st
- elif edit_str_end == "":
- st = "" + edit_str_start + " "
- orig_tokens[edit_spos] = st
- else:
- st = "" + edit_str_start + " "
- orig_tokens[edit_spos] = st
-
- for i in sorted(ignore_indexes, reverse=True):
- del(orig_tokens[i])
-
- return(" ".join(orig_tokens))
-
- def detect(self, input_sentence):
- # TO BE IMPLEMENTED
- pass
-
- def _get_edits(self, orig, cor):
- orig = self.annotator.parse(orig)
- cor = self.annotator.parse(cor)
- alignment = self.annotator.align(orig, cor)
- edits = self.annotator.merge(alignment)
-
- if len(edits) == 0:
- return []
-
- edit_annotations = []
- for e in edits:
- e = self.annotator.classify(e)
- edit_annotations.append((e.type[2:], e.o_str, e.o_start, e.o_end, e.c_str, e.c_start, e.c_end))
-
- if len(edit_annotations) > 0:
- return edit_annotations
- else:
- return []
-
- def get_edits(self, orig, cor):
- return self._get_edits(orig, cor)
diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/losses/base_loss.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/losses/base_loss.py
deleted file mode 100644
index 391191ce2ed8665f1f15bd3877dc22bb85b147d6..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/losses/base_loss.py
+++ /dev/null
@@ -1,528 +0,0 @@
-import logging
-from abc import abstractmethod, ABC
-
-import numpy as np
-import sklearn
-import sklearn.svm
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from joblib import Parallel, delayed
-from scipy import linalg
-
-from models.ade20k import SegmentationModule, NUM_CLASS, segm_options
-from .fid.inception import InceptionV3
-from .lpips import PerceptualLoss
-from .ssim import SSIM
-
-LOGGER = logging.getLogger(__name__)
-
-
-def get_groupings(groups):
- """
- :param groups: group numbers for respective elements
- :return: dict of kind {group_idx: indices of the corresponding group elements}
- """
- label_groups, count_groups = np.unique(groups, return_counts=True)
-
- indices = np.argsort(groups)
-
- grouping = dict()
- cur_start = 0
- for label, count in zip(label_groups, count_groups):
- cur_end = cur_start + count
- cur_indices = indices[cur_start:cur_end]
- grouping[label] = cur_indices
- cur_start = cur_end
- return grouping
-
-
-class EvaluatorScore(nn.Module):
- @abstractmethod
- def forward(self, pred_batch, target_batch, mask):
- pass
-
- @abstractmethod
- def get_value(self, groups=None, states=None):
- pass
-
- @abstractmethod
- def reset(self):
- pass
-
-
-class PairwiseScore(EvaluatorScore, ABC):
- def __init__(self):
- super().__init__()
- self.individual_values = None
-
- def get_value(self, groups=None, states=None):
- """
- :param groups:
- :return:
- total_results: dict of kind {'mean': score mean, 'std': score std}
- group_results: None, if groups is None;
- else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
- """
- individual_values = torch.stack(states, dim=0).reshape(-1).cpu().numpy() if states is not None \
- else self.individual_values
-
- total_results = {
- 'mean': individual_values.mean(),
- 'std': individual_values.std()
- }
-
- if groups is None:
- return total_results, None
-
- group_results = dict()
- grouping = get_groupings(groups)
- for label, index in grouping.items():
- group_scores = individual_values[index]
- group_results[label] = {
- 'mean': group_scores.mean(),
- 'std': group_scores.std()
- }
- return total_results, group_results
-
- def reset(self):
- self.individual_values = []
-
-
-class SSIMScore(PairwiseScore):
- def __init__(self, window_size=11):
- super().__init__()
- self.score = SSIM(window_size=window_size, size_average=False).eval()
- self.reset()
-
- def forward(self, pred_batch, target_batch, mask=None):
- batch_values = self.score(pred_batch, target_batch)
- self.individual_values = np.hstack([
- self.individual_values, batch_values.detach().cpu().numpy()
- ])
- return batch_values
-
-
-class LPIPSScore(PairwiseScore):
- def __init__(self, model='net-lin', net='vgg', model_path=None, use_gpu=True):
- super().__init__()
- self.score = PerceptualLoss(model=model, net=net, model_path=model_path,
- use_gpu=use_gpu, spatial=False).eval()
- self.reset()
-
- def forward(self, pred_batch, target_batch, mask=None):
- batch_values = self.score(pred_batch, target_batch).flatten()
- self.individual_values = np.hstack([
- self.individual_values, batch_values.detach().cpu().numpy()
- ])
- return batch_values
-
-
-def fid_calculate_activation_statistics(act):
- mu = np.mean(act, axis=0)
- sigma = np.cov(act, rowvar=False)
- return mu, sigma
-
-
-def calculate_frechet_distance(activations_pred, activations_target, eps=1e-6):
- mu1, sigma1 = fid_calculate_activation_statistics(activations_pred)
- mu2, sigma2 = fid_calculate_activation_statistics(activations_target)
-
- diff = mu1 - mu2
-
- # Product might be almost singular
- covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
- if not np.isfinite(covmean).all():
- msg = ('fid calculation produces singular product; '
- 'adding %s to diagonal of cov estimates') % eps
- LOGGER.warning(msg)
- offset = np.eye(sigma1.shape[0]) * eps
- covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
-
- # Numerical error might give slight imaginary component
- if np.iscomplexobj(covmean):
- # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
- if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2):
- m = np.max(np.abs(covmean.imag))
- raise ValueError('Imaginary component {}'.format(m))
- covmean = covmean.real
-
- tr_covmean = np.trace(covmean)
-
- return (diff.dot(diff) + np.trace(sigma1) +
- np.trace(sigma2) - 2 * tr_covmean)
-
-
-class FIDScore(EvaluatorScore):
- def __init__(self, dims=2048, eps=1e-6):
- LOGGER.info("FIDscore init called")
- super().__init__()
- if getattr(FIDScore, '_MODEL', None) is None:
- block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
- FIDScore._MODEL = InceptionV3([block_idx]).eval()
- self.model = FIDScore._MODEL
- self.eps = eps
- self.reset()
- LOGGER.info("FIDscore init done")
-
- def forward(self, pred_batch, target_batch, mask=None):
- activations_pred = self._get_activations(pred_batch)
- activations_target = self._get_activations(target_batch)
-
- self.activations_pred.append(activations_pred.detach().cpu())
- self.activations_target.append(activations_target.detach().cpu())
-
- return activations_pred, activations_target
-
- def get_value(self, groups=None, states=None):
- LOGGER.info("FIDscore get_value called")
- activations_pred, activations_target = zip(*states) if states is not None \
- else (self.activations_pred, self.activations_target)
- activations_pred = torch.cat(activations_pred).cpu().numpy()
- activations_target = torch.cat(activations_target).cpu().numpy()
-
- total_distance = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps)
- total_results = dict(mean=total_distance)
-
- if groups is None:
- group_results = None
- else:
- group_results = dict()
- grouping = get_groupings(groups)
- for label, index in grouping.items():
- if len(index) > 1:
- group_distance = calculate_frechet_distance(activations_pred[index], activations_target[index],
- eps=self.eps)
- group_results[label] = dict(mean=group_distance)
-
- else:
- group_results[label] = dict(mean=float('nan'))
-
- self.reset()
-
- LOGGER.info("FIDscore get_value done")
-
- return total_results, group_results
-
- def reset(self):
- self.activations_pred = []
- self.activations_target = []
-
- def _get_activations(self, batch):
- activations = self.model(batch)[0]
- if activations.shape[2] != 1 or activations.shape[3] != 1:
- assert False, \
- 'We should not have got here, because Inception always scales inputs to 299x299'
- # activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1))
- activations = activations.squeeze(-1).squeeze(-1)
- return activations
-
-
-class SegmentationAwareScore(EvaluatorScore):
- def __init__(self, weights_path):
- super().__init__()
- self.segm_network = SegmentationModule(weights_path=weights_path, use_default_normalization=True).eval()
- self.target_class_freq_by_image_total = []
- self.target_class_freq_by_image_mask = []
- self.pred_class_freq_by_image_mask = []
-
- def forward(self, pred_batch, target_batch, mask):
- pred_segm_flat = self.segm_network.predict(pred_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy()
- target_segm_flat = self.segm_network.predict(target_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy()
- mask_flat = (mask.view(mask.shape[0], -1) > 0.5).detach().cpu().numpy()
-
- batch_target_class_freq_total = []
- batch_target_class_freq_mask = []
- batch_pred_class_freq_mask = []
-
- for cur_pred_segm, cur_target_segm, cur_mask in zip(pred_segm_flat, target_segm_flat, mask_flat):
- cur_target_class_freq_total = np.bincount(cur_target_segm, minlength=NUM_CLASS)[None, ...]
- cur_target_class_freq_mask = np.bincount(cur_target_segm[cur_mask], minlength=NUM_CLASS)[None, ...]
- cur_pred_class_freq_mask = np.bincount(cur_pred_segm[cur_mask], minlength=NUM_CLASS)[None, ...]
-
- self.target_class_freq_by_image_total.append(cur_target_class_freq_total)
- self.target_class_freq_by_image_mask.append(cur_target_class_freq_mask)
- self.pred_class_freq_by_image_mask.append(cur_pred_class_freq_mask)
-
- batch_target_class_freq_total.append(cur_target_class_freq_total)
- batch_target_class_freq_mask.append(cur_target_class_freq_mask)
- batch_pred_class_freq_mask.append(cur_pred_class_freq_mask)
-
- batch_target_class_freq_total = np.concatenate(batch_target_class_freq_total, axis=0)
- batch_target_class_freq_mask = np.concatenate(batch_target_class_freq_mask, axis=0)
- batch_pred_class_freq_mask = np.concatenate(batch_pred_class_freq_mask, axis=0)
- return batch_target_class_freq_total, batch_target_class_freq_mask, batch_pred_class_freq_mask
-
- def reset(self):
- super().reset()
- self.target_class_freq_by_image_total = []
- self.target_class_freq_by_image_mask = []
- self.pred_class_freq_by_image_mask = []
-
-
-def distribute_values_to_classes(target_class_freq_by_image_mask, values, idx2name):
- assert target_class_freq_by_image_mask.ndim == 2 and target_class_freq_by_image_mask.shape[0] == values.shape[0]
- total_class_freq = target_class_freq_by_image_mask.sum(0)
- distr_values = (target_class_freq_by_image_mask * values[..., None]).sum(0)
- result = distr_values / (total_class_freq + 1e-3)
- return {idx2name[i]: val for i, val in enumerate(result) if total_class_freq[i] > 0}
-
-
-def get_segmentation_idx2name():
- return {i - 1: name for i, name in segm_options['classes'].set_index('Idx', drop=True)['Name'].to_dict().items()}
-
-
-class SegmentationAwarePairwiseScore(SegmentationAwareScore):
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self.individual_values = []
- self.segm_idx2name = get_segmentation_idx2name()
-
- def forward(self, pred_batch, target_batch, mask):
- cur_class_stats = super().forward(pred_batch, target_batch, mask)
- score_values = self.calc_score(pred_batch, target_batch, mask)
- self.individual_values.append(score_values)
- return cur_class_stats + (score_values,)
-
- @abstractmethod
- def calc_score(self, pred_batch, target_batch, mask):
- raise NotImplementedError()
-
- def get_value(self, groups=None, states=None):
- """
- :param groups:
- :return:
- total_results: dict of kind {'mean': score mean, 'std': score std}
- group_results: None, if groups is None;
- else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
- """
- if states is not None:
- (target_class_freq_by_image_total,
- target_class_freq_by_image_mask,
- pred_class_freq_by_image_mask,
- individual_values) = states
- else:
- target_class_freq_by_image_total = self.target_class_freq_by_image_total
- target_class_freq_by_image_mask = self.target_class_freq_by_image_mask
- pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask
- individual_values = self.individual_values
-
- target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)
- target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)
- pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)
- individual_values = np.concatenate(individual_values, axis=0)
-
- total_results = {
- 'mean': individual_values.mean(),
- 'std': individual_values.std(),
- **distribute_values_to_classes(target_class_freq_by_image_mask, individual_values, self.segm_idx2name)
- }
-
- if groups is None:
- return total_results, None
-
- group_results = dict()
- grouping = get_groupings(groups)
- for label, index in grouping.items():
- group_class_freq = target_class_freq_by_image_mask[index]
- group_scores = individual_values[index]
- group_results[label] = {
- 'mean': group_scores.mean(),
- 'std': group_scores.std(),
- ** distribute_values_to_classes(group_class_freq, group_scores, self.segm_idx2name)
- }
- return total_results, group_results
-
- def reset(self):
- super().reset()
- self.individual_values = []
-
-
-class SegmentationClassStats(SegmentationAwarePairwiseScore):
- def calc_score(self, pred_batch, target_batch, mask):
- return 0
-
- def get_value(self, groups=None, states=None):
- """
- :param groups:
- :return:
- total_results: dict of kind {'mean': score mean, 'std': score std}
- group_results: None, if groups is None;
- else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
- """
- if states is not None:
- (target_class_freq_by_image_total,
- target_class_freq_by_image_mask,
- pred_class_freq_by_image_mask,
- _) = states
- else:
- target_class_freq_by_image_total = self.target_class_freq_by_image_total
- target_class_freq_by_image_mask = self.target_class_freq_by_image_mask
- pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask
-
- target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)
- target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)
- pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)
-
- target_class_freq_by_image_total_marginal = target_class_freq_by_image_total.sum(0).astype('float32')
- target_class_freq_by_image_total_marginal /= target_class_freq_by_image_total_marginal.sum()
-
- target_class_freq_by_image_mask_marginal = target_class_freq_by_image_mask.sum(0).astype('float32')
- target_class_freq_by_image_mask_marginal /= target_class_freq_by_image_mask_marginal.sum()
-
- pred_class_freq_diff = (pred_class_freq_by_image_mask - target_class_freq_by_image_mask).sum(0) / (target_class_freq_by_image_mask.sum(0) + 1e-3)
-
- total_results = dict()
- total_results.update({f'total_freq/{self.segm_idx2name[i]}': v
- for i, v in enumerate(target_class_freq_by_image_total_marginal)
- if v > 0})
- total_results.update({f'mask_freq/{self.segm_idx2name[i]}': v
- for i, v in enumerate(target_class_freq_by_image_mask_marginal)
- if v > 0})
- total_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v
- for i, v in enumerate(pred_class_freq_diff)
- if target_class_freq_by_image_total_marginal[i] > 0})
-
- if groups is None:
- return total_results, None
-
- group_results = dict()
- grouping = get_groupings(groups)
- for label, index in grouping.items():
- group_target_class_freq_by_image_total = target_class_freq_by_image_total[index]
- group_target_class_freq_by_image_mask = target_class_freq_by_image_mask[index]
- group_pred_class_freq_by_image_mask = pred_class_freq_by_image_mask[index]
-
- group_target_class_freq_by_image_total_marginal = group_target_class_freq_by_image_total.sum(0).astype('float32')
- group_target_class_freq_by_image_total_marginal /= group_target_class_freq_by_image_total_marginal.sum()
-
- group_target_class_freq_by_image_mask_marginal = group_target_class_freq_by_image_mask.sum(0).astype('float32')
- group_target_class_freq_by_image_mask_marginal /= group_target_class_freq_by_image_mask_marginal.sum()
-
- group_pred_class_freq_diff = (group_pred_class_freq_by_image_mask - group_target_class_freq_by_image_mask).sum(0) / (
- group_target_class_freq_by_image_mask.sum(0) + 1e-3)
-
- cur_group_results = dict()
- cur_group_results.update({f'total_freq/{self.segm_idx2name[i]}': v
- for i, v in enumerate(group_target_class_freq_by_image_total_marginal)
- if v > 0})
- cur_group_results.update({f'mask_freq/{self.segm_idx2name[i]}': v
- for i, v in enumerate(group_target_class_freq_by_image_mask_marginal)
- if v > 0})
- cur_group_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v
- for i, v in enumerate(group_pred_class_freq_diff)
- if group_target_class_freq_by_image_total_marginal[i] > 0})
-
- group_results[label] = cur_group_results
- return total_results, group_results
-
-
-class SegmentationAwareSSIM(SegmentationAwarePairwiseScore):
- def __init__(self, *args, window_size=11, **kwargs):
- super().__init__(*args, **kwargs)
- self.score_impl = SSIM(window_size=window_size, size_average=False).eval()
-
- def calc_score(self, pred_batch, target_batch, mask):
- return self.score_impl(pred_batch, target_batch).detach().cpu().numpy()
-
-
-class SegmentationAwareLPIPS(SegmentationAwarePairwiseScore):
- def __init__(self, *args, model='net-lin', net='vgg', model_path=None, use_gpu=True, **kwargs):
- super().__init__(*args, **kwargs)
- self.score_impl = PerceptualLoss(model=model, net=net, model_path=model_path,
- use_gpu=use_gpu, spatial=False).eval()
-
- def calc_score(self, pred_batch, target_batch, mask):
- return self.score_impl(pred_batch, target_batch).flatten().detach().cpu().numpy()
-
-
-def calculade_fid_no_img(img_i, activations_pred, activations_target, eps=1e-6):
- activations_pred = activations_pred.copy()
- activations_pred[img_i] = activations_target[img_i]
- return calculate_frechet_distance(activations_pred, activations_target, eps=eps)
-
-
-class SegmentationAwareFID(SegmentationAwarePairwiseScore):
- def __init__(self, *args, dims=2048, eps=1e-6, n_jobs=-1, **kwargs):
- super().__init__(*args, **kwargs)
- if getattr(FIDScore, '_MODEL', None) is None:
- block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
- FIDScore._MODEL = InceptionV3([block_idx]).eval()
- self.model = FIDScore._MODEL
- self.eps = eps
- self.n_jobs = n_jobs
-
- def calc_score(self, pred_batch, target_batch, mask):
- activations_pred = self._get_activations(pred_batch)
- activations_target = self._get_activations(target_batch)
- return activations_pred, activations_target
-
- def get_value(self, groups=None, states=None):
- """
- :param groups:
- :return:
- total_results: dict of kind {'mean': score mean, 'std': score std}
- group_results: None, if groups is None;
- else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
- """
- if states is not None:
- (target_class_freq_by_image_total,
- target_class_freq_by_image_mask,
- pred_class_freq_by_image_mask,
- activation_pairs) = states
- else:
- target_class_freq_by_image_total = self.target_class_freq_by_image_total
- target_class_freq_by_image_mask = self.target_class_freq_by_image_mask
- pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask
- activation_pairs = self.individual_values
-
- target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)
- target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)
- pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)
- activations_pred, activations_target = zip(*activation_pairs)
- activations_pred = np.concatenate(activations_pred, axis=0)
- activations_target = np.concatenate(activations_target, axis=0)
-
- total_results = {
- 'mean': calculate_frechet_distance(activations_pred, activations_target, eps=self.eps),
- 'std': 0,
- **self.distribute_fid_to_classes(target_class_freq_by_image_mask, activations_pred, activations_target)
- }
-
- if groups is None:
- return total_results, None
-
- group_results = dict()
- grouping = get_groupings(groups)
- for label, index in grouping.items():
- if len(index) > 1:
- group_activations_pred = activations_pred[index]
- group_activations_target = activations_target[index]
- group_class_freq = target_class_freq_by_image_mask[index]
- group_results[label] = {
- 'mean': calculate_frechet_distance(group_activations_pred, group_activations_target, eps=self.eps),
- 'std': 0,
- **self.distribute_fid_to_classes(group_class_freq,
- group_activations_pred,
- group_activations_target)
- }
- else:
- group_results[label] = dict(mean=float('nan'), std=0)
- return total_results, group_results
-
- def distribute_fid_to_classes(self, class_freq, activations_pred, activations_target):
- real_fid = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps)
-
- fid_no_images = Parallel(n_jobs=self.n_jobs)(
- delayed(calculade_fid_no_img)(img_i, activations_pred, activations_target, eps=self.eps)
- for img_i in range(activations_pred.shape[0])
- )
- errors = real_fid - fid_no_images
- return distribute_values_to_classes(class_freq, errors, self.segm_idx2name)
-
- def _get_activations(self, batch):
- activations = self.model(batch)[0]
- if activations.shape[2] != 1 or activations.shape[3] != 1:
- activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1))
- activations = activations.squeeze(-1).squeeze(-1).detach().cpu().numpy()
- return activations
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockTitle-900cfd93.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockTitle-900cfd93.js
deleted file mode 100644
index a7e315a282971c319907772075c78fa74c50f635..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/BlockTitle-900cfd93.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as d,i as h,s as g,V as k,G as w,H as $,f as I,C as q,M as _,g as m,X as B,Y as C,Z as S,p as r,l as j,t as c,o as G,q as p,e as H,m as K,n as M,I as N,K as T}from"./index-7c0e54a6.js";import{I as V}from"./Info-3b2d34d7.js";import"./Button-661a0701.js";function b(f){let e,n;return e=new V({props:{$$slots:{default:[X]},$$scope:{ctx:f}}}),{c(){H(e.$$.fragment)},m(l,o){K(e,l,o),n=!0},p(l,o){const u={};o&10&&(u.$$scope={dirty:o,ctx:l}),e.$set(u)},i(l){n||(r(e.$$.fragment,l),n=!0)},o(l){c(e.$$.fragment,l),n=!1},d(l){M(e,l)}}}function X(f){let e;return{c(){e=N(f[1])},m(n,l){m(n,e,l)},p(n,l){l&2&&T(e,n[1])},d(n){n&&p(e)}}}function Y(f){let e,n,l,o;const u=f[2].default,a=k(u,f,f[3],null);let s=f[1]&&b(f);return{c(){e=w("span"),a&&a.c(),n=$(),s&&s.c(),l=I(),q(e,"class","svelte-1gfkn6j"),_(e,"sr-only",!f[0]),_(e,"hide",!f[0]),_(e,"has-info",f[1]!=null)},m(t,i){m(t,e,i),a&&a.m(e,null),m(t,n,i),s&&s.m(t,i),m(t,l,i),o=!0},p(t,[i]){a&&a.p&&(!o||i&8)&&B(a,u,t,t[3],o?S(u,t[3],i,null):C(t[3]),null),(!o||i&1)&&_(e,"sr-only",!t[0]),(!o||i&1)&&_(e,"hide",!t[0]),(!o||i&2)&&_(e,"has-info",t[1]!=null),t[1]?s?(s.p(t,i),i&2&&r(s,1)):(s=b(t),s.c(),r(s,1),s.m(l.parentNode,l)):s&&(j(),c(s,1,1,()=>{s=null}),G())},i(t){o||(r(a,t),r(s),o=!0)},o(t){c(a,t),c(s),o=!1},d(t){t&&p(e),a&&a.d(t),t&&p(n),s&&s.d(t),t&&p(l)}}}function Z(f,e,n){let{$$slots:l={},$$scope:o}=e,{show_label:u=!0}=e,{info:a=void 0}=e;return f.$$set=s=>{"show_label"in s&&n(0,u=s.show_label),"info"in s&&n(1,a=s.info),"$$scope"in s&&n(3,o=s.$$scope)},[u,a,l,o]}class D extends d{constructor(e){super(),h(this,e,Z,Y,g,{show_label:0,info:1})}}export{D as B};
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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/sphinxext/plot_directive.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/sphinxext/plot_directive.py
deleted file mode 100644
index c942085e215902ea4a550bcc91b5c3f4b1c3dbd6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/sphinxext/plot_directive.py
+++ /dev/null
@@ -1,826 +0,0 @@
-"""
-A directive for including a Matplotlib plot in a Sphinx document
-================================================================
-
-This is a Sphinx extension providing a reStructuredText directive
-``.. plot::`` for including a plot in a Sphinx document.
-
-In HTML output, ``.. plot::`` will include a .png file with a link
-to a high-res .png and .pdf. In LaTeX output, it will include a .pdf.
-
-The plot content may be defined in one of three ways:
-
-1. **A path to a source file** as the argument to the directive::
-
- .. plot:: path/to/plot.py
-
- When a path to a source file is given, the content of the
- directive may optionally contain a caption for the plot::
-
- .. plot:: path/to/plot.py
-
- The plot caption.
-
- Additionally, one may specify the name of a function to call (with
- no arguments) immediately after importing the module::
-
- .. plot:: path/to/plot.py plot_function1
-
-2. Included as **inline content** to the directive::
-
- .. plot::
-
- import matplotlib.pyplot as plt
- plt.plot([1, 2, 3], [4, 5, 6])
- plt.title("A plotting exammple")
-
-3. Using **doctest** syntax::
-
- .. plot::
-
- A plotting example:
- >>> import matplotlib.pyplot as plt
- >>> plt.plot([1, 2, 3], [4, 5, 6])
-
-Options
--------
-
-The ``.. plot::`` directive supports the following options:
-
- ``:format:`` : {'python', 'doctest'}
- The format of the input. If unset, the format is auto-detected.
-
- ``:include-source:`` : bool
- Whether to display the source code. The default can be changed using
- the ``plot_include_source`` variable in :file:`conf.py` (which itself
- defaults to False).
-
- ``:show-source-link:`` : bool
- Whether to show a link to the source in HTML. The default can be
- changed using the ``plot_html_show_source_link`` variable in
- :file:`conf.py` (which itself defaults to True).
-
- ``:context:`` : bool or str
- If provided, the code will be run in the context of all previous plot
- directives for which the ``:context:`` option was specified. This only
- applies to inline code plot directives, not those run from files. If
- the ``:context: reset`` option is specified, the context is reset
- for this and future plots, and previous figures are closed prior to
- running the code. ``:context: close-figs`` keeps the context but closes
- previous figures before running the code.
-
- ``:nofigs:`` : bool
- If specified, the code block will be run, but no figures will be
- inserted. This is usually useful with the ``:context:`` option.
-
- ``:caption:`` : str
- If specified, the option's argument will be used as a caption for the
- figure. This overwrites the caption given in the content, when the plot
- is generated from a file.
-
-Additionally, this directive supports all the options of the `image directive
-`_,
-except for ``:target:`` (since plot will add its own target). These include
-``:alt:``, ``:height:``, ``:width:``, ``:scale:``, ``:align:`` and ``:class:``.
-
-Configuration options
----------------------
-
-The plot directive has the following configuration options:
-
- plot_include_source
- Default value for the include-source option (default: False).
-
- plot_html_show_source_link
- Whether to show a link to the source in HTML (default: True).
-
- plot_pre_code
- Code that should be executed before each plot. If None (the default),
- it will default to a string containing::
-
- import numpy as np
- from matplotlib import pyplot as plt
-
- plot_basedir
- Base directory, to which ``plot::`` file names are relative to.
- If None or empty (the default), file names are relative to the
- directory where the file containing the directive is.
-
- plot_formats
- File formats to generate (default: ['png', 'hires.png', 'pdf']).
- List of tuples or strings::
-
- [(suffix, dpi), suffix, ...]
-
- that determine the file format and the DPI. For entries whose
- DPI was omitted, sensible defaults are chosen. When passing from
- the command line through sphinx_build the list should be passed as
- suffix:dpi,suffix:dpi, ...
-
- plot_html_show_formats
- Whether to show links to the files in HTML (default: True).
-
- plot_rcparams
- A dictionary containing any non-standard rcParams that should
- be applied before each plot (default: {}).
-
- plot_apply_rcparams
- By default, rcParams are applied when ``:context:`` option is not used
- in a plot directive. If set, this configuration option overrides this
- behavior and applies rcParams before each plot.
-
- plot_working_directory
- By default, the working directory will be changed to the directory of
- the example, so the code can get at its data files, if any. Also its
- path will be added to `sys.path` so it can import any helper modules
- sitting beside it. This configuration option can be used to specify
- a central directory (also added to `sys.path`) where data files and
- helper modules for all code are located.
-
- plot_template
- Provide a customized template for preparing restructured text.
-"""
-
-import contextlib
-import doctest
-from io import StringIO
-import itertools
-import os
-from os.path import relpath
-from pathlib import Path
-import re
-import shutil
-import sys
-import textwrap
-import traceback
-
-from docutils.parsers.rst import directives, Directive
-from docutils.parsers.rst.directives.images import Image
-import jinja2 # Sphinx dependency.
-
-import matplotlib
-from matplotlib.backend_bases import FigureManagerBase
-import matplotlib.pyplot as plt
-from matplotlib import _pylab_helpers, cbook
-
-matplotlib.use("agg")
-
-__version__ = 2
-
-
-# -----------------------------------------------------------------------------
-# Registration hook
-# -----------------------------------------------------------------------------
-
-
-def _option_boolean(arg):
- if not arg or not arg.strip():
- # no argument given, assume used as a flag
- return True
- elif arg.strip().lower() in ('no', '0', 'false'):
- return False
- elif arg.strip().lower() in ('yes', '1', 'true'):
- return True
- else:
- raise ValueError(f'{arg!r} unknown boolean')
-
-
-def _option_context(arg):
- if arg in [None, 'reset', 'close-figs']:
- return arg
- raise ValueError("Argument should be None or 'reset' or 'close-figs'")
-
-
-def _option_format(arg):
- return directives.choice(arg, ('python', 'doctest'))
-
-
-def mark_plot_labels(app, document):
- """
- To make plots referenceable, we need to move the reference from the
- "htmlonly" (or "latexonly") node to the actual figure node itself.
- """
- for name, explicit in document.nametypes.items():
- if not explicit:
- continue
- labelid = document.nameids[name]
- if labelid is None:
- continue
- node = document.ids[labelid]
- if node.tagname in ('html_only', 'latex_only'):
- for n in node:
- if n.tagname == 'figure':
- sectname = name
- for c in n:
- if c.tagname == 'caption':
- sectname = c.astext()
- break
-
- node['ids'].remove(labelid)
- node['names'].remove(name)
- n['ids'].append(labelid)
- n['names'].append(name)
- document.settings.env.labels[name] = \
- document.settings.env.docname, labelid, sectname
- break
-
-
-class PlotDirective(Directive):
- """The ``.. plot::`` directive, as documented in the module's docstring."""
-
- has_content = True
- required_arguments = 0
- optional_arguments = 2
- final_argument_whitespace = False
- option_spec = {
- 'alt': directives.unchanged,
- 'height': directives.length_or_unitless,
- 'width': directives.length_or_percentage_or_unitless,
- 'scale': directives.nonnegative_int,
- 'align': Image.align,
- 'class': directives.class_option,
- 'include-source': _option_boolean,
- 'show-source-link': _option_boolean,
- 'format': _option_format,
- 'context': _option_context,
- 'nofigs': directives.flag,
- 'caption': directives.unchanged,
- }
-
- def run(self):
- """Run the plot directive."""
- try:
- return run(self.arguments, self.content, self.options,
- self.state_machine, self.state, self.lineno)
- except Exception as e:
- raise self.error(str(e))
-
-
-def _copy_css_file(app, exc):
- if exc is None and app.builder.format == 'html':
- src = cbook._get_data_path('plot_directive/plot_directive.css')
- dst = app.outdir / Path('_static')
- dst.mkdir(exist_ok=True)
- # Use copyfile because we do not want to copy src's permissions.
- shutil.copyfile(src, dst / Path('plot_directive.css'))
-
-
-def setup(app):
- setup.app = app
- setup.config = app.config
- setup.confdir = app.confdir
- app.add_directive('plot', PlotDirective)
- app.add_config_value('plot_pre_code', None, True)
- app.add_config_value('plot_include_source', False, True)
- app.add_config_value('plot_html_show_source_link', True, True)
- app.add_config_value('plot_formats', ['png', 'hires.png', 'pdf'], True)
- app.add_config_value('plot_basedir', None, True)
- app.add_config_value('plot_html_show_formats', True, True)
- app.add_config_value('plot_rcparams', {}, True)
- app.add_config_value('plot_apply_rcparams', False, True)
- app.add_config_value('plot_working_directory', None, True)
- app.add_config_value('plot_template', None, True)
- app.connect('doctree-read', mark_plot_labels)
- app.add_css_file('plot_directive.css')
- app.connect('build-finished', _copy_css_file)
- metadata = {'parallel_read_safe': True, 'parallel_write_safe': True,
- 'version': matplotlib.__version__}
- return metadata
-
-
-# -----------------------------------------------------------------------------
-# Doctest handling
-# -----------------------------------------------------------------------------
-
-
-def contains_doctest(text):
- try:
- # check if it's valid Python as-is
- compile(text, '', 'exec')
- return False
- except SyntaxError:
- pass
- r = re.compile(r'^\s*>>>', re.M)
- m = r.search(text)
- return bool(m)
-
-
-def _split_code_at_show(text, function_name):
- """Split code at plt.show()."""
-
- is_doctest = contains_doctest(text)
- if function_name is None:
- parts = []
- part = []
- for line in text.split("\n"):
- if ((not is_doctest and line.startswith('plt.show(')) or
- (is_doctest and line.strip() == '>>> plt.show()')):
- part.append(line)
- parts.append("\n".join(part))
- part = []
- else:
- part.append(line)
- if "\n".join(part).strip():
- parts.append("\n".join(part))
- else:
- parts = [text]
- return is_doctest, parts
-
-
-# -----------------------------------------------------------------------------
-# Template
-# -----------------------------------------------------------------------------
-
-TEMPLATE = """
-{{ source_code }}
-
-.. only:: html
-
- {% if src_name or (html_show_formats and not multi_image) %}
- (
- {%- if src_name -%}
- :download:`Source code <{{ build_dir }}/{{ src_name }}>`
- {%- endif -%}
- {%- if html_show_formats and not multi_image -%}
- {%- for img in images -%}
- {%- for fmt in img.formats -%}
- {%- if src_name or not loop.first -%}, {% endif -%}
- :download:`{{ fmt }} <{{ build_dir }}/{{ img.basename }}.{{ fmt }}>`
- {%- endfor -%}
- {%- endfor -%}
- {%- endif -%}
- )
- {% endif %}
-
- {% for img in images %}
- .. figure:: {{ build_dir }}/{{ img.basename }}.{{ default_fmt }}
- {% for option in options -%}
- {{ option }}
- {% endfor %}
-
- {% if html_show_formats and multi_image -%}
- (
- {%- for fmt in img.formats -%}
- {%- if not loop.first -%}, {% endif -%}
- :download:`{{ fmt }} <{{ build_dir }}/{{ img.basename }}.{{ fmt }}>`
- {%- endfor -%}
- )
- {%- endif -%}
-
- {{ caption }} {# appropriate leading whitespace added beforehand #}
- {% endfor %}
-
-.. only:: not html
-
- {% for img in images %}
- .. figure:: {{ build_dir }}/{{ img.basename }}.*
- {% for option in options -%}
- {{ option }}
- {% endfor -%}
-
- {{ caption }} {# appropriate leading whitespace added beforehand #}
- {% endfor %}
-
-"""
-
-exception_template = """
-.. only:: html
-
- [`source code <%(linkdir)s/%(basename)s.py>`__]
-
-Exception occurred rendering plot.
-
-"""
-
-# the context of the plot for all directives specified with the
-# :context: option
-plot_context = dict()
-
-
-class ImageFile:
- def __init__(self, basename, dirname):
- self.basename = basename
- self.dirname = dirname
- self.formats = []
-
- def filename(self, format):
- return os.path.join(self.dirname, "%s.%s" % (self.basename, format))
-
- def filenames(self):
- return [self.filename(fmt) for fmt in self.formats]
-
-
-def out_of_date(original, derived, includes=None):
- """
- Return whether *derived* is out-of-date relative to *original* or any of
- the RST files included in it using the RST include directive (*includes*).
- *derived* and *original* are full paths, and *includes* is optionally a
- list of full paths which may have been included in the *original*.
- """
- if not os.path.exists(derived):
- return True
-
- if includes is None:
- includes = []
- files_to_check = [original, *includes]
-
- def out_of_date_one(original, derived_mtime):
- return (os.path.exists(original) and
- derived_mtime < os.stat(original).st_mtime)
-
- derived_mtime = os.stat(derived).st_mtime
- return any(out_of_date_one(f, derived_mtime) for f in files_to_check)
-
-
-class PlotError(RuntimeError):
- pass
-
-
-def _run_code(code, code_path, ns=None, function_name=None):
- """
- Import a Python module from a path, and run the function given by
- name, if function_name is not None.
- """
-
- # Change the working directory to the directory of the example, so
- # it can get at its data files, if any. Add its path to sys.path
- # so it can import any helper modules sitting beside it.
- pwd = os.getcwd()
- if setup.config.plot_working_directory is not None:
- try:
- os.chdir(setup.config.plot_working_directory)
- except OSError as err:
- raise OSError(f'{err}\n`plot_working_directory` option in '
- f'Sphinx configuration file must be a valid '
- f'directory path') from err
- except TypeError as err:
- raise TypeError(f'{err}\n`plot_working_directory` option in '
- f'Sphinx configuration file must be a string or '
- f'None') from err
- elif code_path is not None:
- dirname = os.path.abspath(os.path.dirname(code_path))
- os.chdir(dirname)
-
- with cbook._setattr_cm(
- sys, argv=[code_path], path=[os.getcwd(), *sys.path]), \
- contextlib.redirect_stdout(StringIO()):
- try:
- if ns is None:
- ns = {}
- if not ns:
- if setup.config.plot_pre_code is None:
- exec('import numpy as np\n'
- 'from matplotlib import pyplot as plt\n', ns)
- else:
- exec(str(setup.config.plot_pre_code), ns)
- if "__main__" in code:
- ns['__name__'] = '__main__'
-
- # Patch out non-interactive show() to avoid triggering a warning.
- with cbook._setattr_cm(FigureManagerBase, show=lambda self: None):
- exec(code, ns)
- if function_name is not None:
- exec(function_name + "()", ns)
-
- except (Exception, SystemExit) as err:
- raise PlotError(traceback.format_exc()) from err
- finally:
- os.chdir(pwd)
- return ns
-
-
-def clear_state(plot_rcparams, close=True):
- if close:
- plt.close('all')
- matplotlib.rc_file_defaults()
- matplotlib.rcParams.update(plot_rcparams)
-
-
-def get_plot_formats(config):
- default_dpi = {'png': 80, 'hires.png': 200, 'pdf': 200}
- formats = []
- plot_formats = config.plot_formats
- for fmt in plot_formats:
- if isinstance(fmt, str):
- if ':' in fmt:
- suffix, dpi = fmt.split(':')
- formats.append((str(suffix), int(dpi)))
- else:
- formats.append((fmt, default_dpi.get(fmt, 80)))
- elif isinstance(fmt, (tuple, list)) and len(fmt) == 2:
- formats.append((str(fmt[0]), int(fmt[1])))
- else:
- raise PlotError('invalid image format "%r" in plot_formats' % fmt)
- return formats
-
-
-def render_figures(code, code_path, output_dir, output_base, context,
- function_name, config, context_reset=False,
- close_figs=False,
- code_includes=None):
- """
- Run a pyplot script and save the images in *output_dir*.
-
- Save the images under *output_dir* with file names derived from
- *output_base*
- """
- if function_name is not None:
- output_base = f'{output_base}_{function_name}'
- formats = get_plot_formats(config)
-
- # Try to determine if all images already exist
-
- is_doctest, code_pieces = _split_code_at_show(code, function_name)
-
- # Look for single-figure output files first
- img = ImageFile(output_base, output_dir)
- for format, dpi in formats:
- if context or out_of_date(code_path, img.filename(format),
- includes=code_includes):
- all_exists = False
- break
- img.formats.append(format)
- else:
- all_exists = True
-
- if all_exists:
- return [(code, [img])]
-
- # Then look for multi-figure output files
- results = []
- for i, code_piece in enumerate(code_pieces):
- images = []
- for j in itertools.count():
- if len(code_pieces) > 1:
- img = ImageFile('%s_%02d_%02d' % (output_base, i, j),
- output_dir)
- else:
- img = ImageFile('%s_%02d' % (output_base, j), output_dir)
- for fmt, dpi in formats:
- if context or out_of_date(code_path, img.filename(fmt),
- includes=code_includes):
- all_exists = False
- break
- img.formats.append(fmt)
-
- # assume that if we have one, we have them all
- if not all_exists:
- all_exists = (j > 0)
- break
- images.append(img)
- if not all_exists:
- break
- results.append((code_piece, images))
- else:
- all_exists = True
-
- if all_exists:
- return results
-
- # We didn't find the files, so build them
-
- results = []
- ns = plot_context if context else {}
-
- if context_reset:
- clear_state(config.plot_rcparams)
- plot_context.clear()
-
- close_figs = not context or close_figs
-
- for i, code_piece in enumerate(code_pieces):
-
- if not context or config.plot_apply_rcparams:
- clear_state(config.plot_rcparams, close_figs)
- elif close_figs:
- plt.close('all')
-
- _run_code(doctest.script_from_examples(code_piece) if is_doctest
- else code_piece,
- code_path, ns, function_name)
-
- images = []
- fig_managers = _pylab_helpers.Gcf.get_all_fig_managers()
- for j, figman in enumerate(fig_managers):
- if len(fig_managers) == 1 and len(code_pieces) == 1:
- img = ImageFile(output_base, output_dir)
- elif len(code_pieces) == 1:
- img = ImageFile("%s_%02d" % (output_base, j), output_dir)
- else:
- img = ImageFile("%s_%02d_%02d" % (output_base, i, j),
- output_dir)
- images.append(img)
- for fmt, dpi in formats:
- try:
- figman.canvas.figure.savefig(img.filename(fmt), dpi=dpi)
- except Exception as err:
- raise PlotError(traceback.format_exc()) from err
- img.formats.append(fmt)
-
- results.append((code_piece, images))
-
- if not context or config.plot_apply_rcparams:
- clear_state(config.plot_rcparams, close=not context)
-
- return results
-
-
-def run(arguments, content, options, state_machine, state, lineno):
- document = state_machine.document
- config = document.settings.env.config
- nofigs = 'nofigs' in options
-
- formats = get_plot_formats(config)
- default_fmt = formats[0][0]
-
- options.setdefault('include-source', config.plot_include_source)
- options.setdefault('show-source-link', config.plot_html_show_source_link)
- if 'class' in options:
- # classes are parsed into a list of string, and output by simply
- # printing the list, abusing the fact that RST guarantees to strip
- # non-conforming characters
- options['class'] = ['plot-directive'] + options['class']
- else:
- options.setdefault('class', ['plot-directive'])
- keep_context = 'context' in options
- context_opt = None if not keep_context else options['context']
-
- rst_file = document.attributes['source']
- rst_dir = os.path.dirname(rst_file)
-
- if len(arguments):
- if not config.plot_basedir:
- source_file_name = os.path.join(setup.app.builder.srcdir,
- directives.uri(arguments[0]))
- else:
- source_file_name = os.path.join(setup.confdir, config.plot_basedir,
- directives.uri(arguments[0]))
-
- # If there is content, it will be passed as a caption.
- caption = '\n'.join(content)
-
- # Enforce unambiguous use of captions.
- if "caption" in options:
- if caption:
- raise ValueError(
- 'Caption specified in both content and options.'
- ' Please remove ambiguity.'
- )
- # Use caption option
- caption = options["caption"]
-
- # If the optional function name is provided, use it
- if len(arguments) == 2:
- function_name = arguments[1]
- else:
- function_name = None
-
- code = Path(source_file_name).read_text(encoding='utf-8')
- output_base = os.path.basename(source_file_name)
- else:
- source_file_name = rst_file
- code = textwrap.dedent("\n".join(map(str, content)))
- counter = document.attributes.get('_plot_counter', 0) + 1
- document.attributes['_plot_counter'] = counter
- base, ext = os.path.splitext(os.path.basename(source_file_name))
- output_base = '%s-%d.py' % (base, counter)
- function_name = None
- caption = options.get('caption', '')
-
- base, source_ext = os.path.splitext(output_base)
- if source_ext in ('.py', '.rst', '.txt'):
- output_base = base
- else:
- source_ext = ''
-
- # ensure that LaTeX includegraphics doesn't choke in foo.bar.pdf filenames
- output_base = output_base.replace('.', '-')
-
- # is it in doctest format?
- is_doctest = contains_doctest(code)
- if 'format' in options:
- if options['format'] == 'python':
- is_doctest = False
- else:
- is_doctest = True
-
- # determine output directory name fragment
- source_rel_name = relpath(source_file_name, setup.confdir)
- source_rel_dir = os.path.dirname(source_rel_name).lstrip(os.path.sep)
-
- # build_dir: where to place output files (temporarily)
- build_dir = os.path.join(os.path.dirname(setup.app.doctreedir),
- 'plot_directive',
- source_rel_dir)
- # get rid of .. in paths, also changes pathsep
- # see note in Python docs for warning about symbolic links on Windows.
- # need to compare source and dest paths at end
- build_dir = os.path.normpath(build_dir)
- os.makedirs(build_dir, exist_ok=True)
-
- # how to link to files from the RST file
- try:
- build_dir_link = relpath(build_dir, rst_dir).replace(os.path.sep, '/')
- except ValueError:
- # on Windows, relpath raises ValueError when path and start are on
- # different mounts/drives
- build_dir_link = build_dir
-
- # get list of included rst files so that the output is updated when any
- # plots in the included files change. These attributes are modified by the
- # include directive (see the docutils.parsers.rst.directives.misc module).
- try:
- source_file_includes = [os.path.join(os.getcwd(), t[0])
- for t in state.document.include_log]
- except AttributeError:
- # the document.include_log attribute only exists in docutils >=0.17,
- # before that we need to inspect the state machine
- possible_sources = {os.path.join(setup.confdir, t[0])
- for t in state_machine.input_lines.items}
- source_file_includes = [f for f in possible_sources
- if os.path.isfile(f)]
- # remove the source file itself from the includes
- try:
- source_file_includes.remove(source_file_name)
- except ValueError:
- pass
-
- # save script (if necessary)
- if options['show-source-link']:
- Path(build_dir, output_base + source_ext).write_text(
- doctest.script_from_examples(code)
- if source_file_name == rst_file and is_doctest
- else code,
- encoding='utf-8')
-
- # make figures
- try:
- results = render_figures(code=code,
- code_path=source_file_name,
- output_dir=build_dir,
- output_base=output_base,
- context=keep_context,
- function_name=function_name,
- config=config,
- context_reset=context_opt == 'reset',
- close_figs=context_opt == 'close-figs',
- code_includes=source_file_includes)
- errors = []
- except PlotError as err:
- reporter = state.memo.reporter
- sm = reporter.system_message(
- 2, "Exception occurred in plotting {}\n from {}:\n{}".format(
- output_base, source_file_name, err),
- line=lineno)
- results = [(code, [])]
- errors = [sm]
-
- # Properly indent the caption
- caption = '\n' + '\n'.join(' ' + line.strip()
- for line in caption.split('\n'))
-
- # generate output restructuredtext
- total_lines = []
- for j, (code_piece, images) in enumerate(results):
- if options['include-source']:
- if is_doctest:
- lines = ['', *code_piece.splitlines()]
- else:
- lines = ['.. code-block:: python', '',
- *textwrap.indent(code_piece, ' ').splitlines()]
- source_code = "\n".join(lines)
- else:
- source_code = ""
-
- if nofigs:
- images = []
-
- opts = [
- ':%s: %s' % (key, val) for key, val in options.items()
- if key in ('alt', 'height', 'width', 'scale', 'align', 'class')]
-
- # Not-None src_name signals the need for a source download in the
- # generated html
- if j == 0 and options['show-source-link']:
- src_name = output_base + source_ext
- else:
- src_name = None
-
- result = jinja2.Template(config.plot_template or TEMPLATE).render(
- default_fmt=default_fmt,
- build_dir=build_dir_link,
- src_name=src_name,
- multi_image=len(images) > 1,
- options=opts,
- images=images,
- source_code=source_code,
- html_show_formats=config.plot_html_show_formats and len(images),
- caption=caption)
-
- total_lines.extend(result.split("\n"))
- total_lines.extend("\n")
-
- if total_lines:
- state_machine.insert_input(total_lines, source=source_file_name)
-
- return errors
diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/data/dataset_video_test.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/data/dataset_video_test.py
deleted file mode 100644
index e361441331bbae465b9e1b51f2abe39dd54f5a2f..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/LambdaSuperRes/KAIR/data/dataset_video_test.py
+++ /dev/null
@@ -1,382 +0,0 @@
-import glob
-import torch
-from os import path as osp
-import torch.utils.data as data
-
-import utils.utils_video as utils_video
-
-
-class VideoRecurrentTestDataset(data.Dataset):
- """Video test dataset for recurrent architectures, which takes LR video
- frames as input and output corresponding HR video frames. Modified from
- https://github.com/xinntao/BasicSR/blob/master/basicsr/data/reds_dataset.py
-
- Supported datasets: Vid4, REDS4, REDSofficial.
- More generally, it supports testing dataset with following structures:
-
- dataroot
- ├── subfolder1
- ├── frame000
- ├── frame001
- ├── ...
- ├── subfolder1
- ├── frame000
- ├── frame001
- ├── ...
- ├── ...
-
- For testing datasets, there is no need to prepare LMDB files.
-
- Args:
- opt (dict): Config for train dataset. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- io_backend (dict): IO backend type and other kwarg.
- cache_data (bool): Whether to cache testing datasets.
- name (str): Dataset name.
- meta_info_file (str): The path to the file storing the list of test
- folders. If not provided, all the folders in the dataroot will
- be used.
- num_frame (int): Window size for input frames.
- padding (str): Padding mode.
- """
-
- def __init__(self, opt):
- super(VideoRecurrentTestDataset, self).__init__()
- self.opt = opt
- self.cache_data = opt['cache_data']
- self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
- self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []}
-
- self.imgs_lq, self.imgs_gt = {}, {}
- if 'meta_info_file' in opt:
- with open(opt['meta_info_file'], 'r') as fin:
- subfolders = [line.split(' ')[0] for line in fin]
- subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders]
- subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders]
- else:
- subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
- subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*')))
-
- for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt):
- # get frame list for lq and gt
- subfolder_name = osp.basename(subfolder_lq)
- img_paths_lq = sorted(list(utils_video.scandir(subfolder_lq, full_path=True)))
- img_paths_gt = sorted(list(utils_video.scandir(subfolder_gt, full_path=True)))
-
- max_idx = len(img_paths_lq)
- assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})'
- f' and gt folders ({len(img_paths_gt)})')
-
- self.data_info['lq_path'].extend(img_paths_lq)
- self.data_info['gt_path'].extend(img_paths_gt)
- self.data_info['folder'].extend([subfolder_name] * max_idx)
- for i in range(max_idx):
- self.data_info['idx'].append(f'{i}/{max_idx}')
- border_l = [0] * max_idx
- for i in range(self.opt['num_frame'] // 2):
- border_l[i] = 1
- border_l[max_idx - i - 1] = 1
- self.data_info['border'].extend(border_l)
-
- # cache data or save the frame list
- if self.cache_data:
- print(f'Cache {subfolder_name} for VideoTestDataset...')
- self.imgs_lq[subfolder_name] = utils_video.read_img_seq(img_paths_lq)
- self.imgs_gt[subfolder_name] = utils_video.read_img_seq(img_paths_gt)
- else:
- self.imgs_lq[subfolder_name] = img_paths_lq
- self.imgs_gt[subfolder_name] = img_paths_gt
-
- # Find unique folder strings
- self.folders = sorted(list(set(self.data_info['folder'])))
- self.sigma = opt['sigma'] / 255. if 'sigma' in opt else 0 # for non-blind video denoising
-
- def __getitem__(self, index):
- folder = self.folders[index]
-
- if self.sigma:
- # for non-blind video denoising
- if self.cache_data:
- imgs_gt = self.imgs_gt[folder]
- else:
- imgs_gt = utils_video.read_img_seq(self.imgs_gt[folder])
-
- torch.manual_seed(0)
- noise_level = torch.ones((1, 1, 1, 1)) * self.sigma
- noise = torch.normal(mean=0, std=noise_level.expand_as(imgs_gt))
- imgs_lq = imgs_gt + noise
- t, _, h, w = imgs_lq.shape
- imgs_lq = torch.cat([imgs_lq, noise_level.expand(t, 1, h, w)], 1)
- else:
- # for video sr and deblurring
- if self.cache_data:
- imgs_lq = self.imgs_lq[folder]
- imgs_gt = self.imgs_gt[folder]
- else:
- imgs_lq = utils_video.read_img_seq(self.imgs_lq[folder])
- imgs_gt = utils_video.read_img_seq(self.imgs_gt[folder])
-
- return {
- 'L': imgs_lq,
- 'H': imgs_gt,
- 'folder': folder,
- 'lq_path': self.imgs_lq[folder],
- }
-
- def __len__(self):
- return len(self.folders)
-
-
-class SingleVideoRecurrentTestDataset(data.Dataset):
- """Single ideo test dataset for recurrent architectures, which takes LR video
- frames as input and output corresponding HR video frames (only input LQ path).
-
- More generally, it supports testing dataset with following structures:
-
- dataroot
- ├── subfolder1
- ├── frame000
- ├── frame001
- ├── ...
- ├── subfolder1
- ├── frame000
- ├── frame001
- ├── ...
- ├── ...
-
- For testing datasets, there is no need to prepare LMDB files.
-
- Args:
- opt (dict): Config for train dataset. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- io_backend (dict): IO backend type and other kwarg.
- cache_data (bool): Whether to cache testing datasets.
- name (str): Dataset name.
- meta_info_file (str): The path to the file storing the list of test
- folders. If not provided, all the folders in the dataroot will
- be used.
- num_frame (int): Window size for input frames.
- padding (str): Padding mode.
- """
-
- def __init__(self, opt):
- super(SingleVideoRecurrentTestDataset, self).__init__()
- self.opt = opt
- self.cache_data = opt['cache_data']
- self.lq_root = opt['dataroot_lq']
- self.data_info = {'lq_path': [], 'folder': [], 'idx': [], 'border': []}
-
- self.imgs_lq = {}
- if 'meta_info_file' in opt:
- with open(opt['meta_info_file'], 'r') as fin:
- subfolders = [line.split(' ')[0] for line in fin]
- subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders]
- else:
- subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
-
- for subfolder_lq in subfolders_lq:
- # get frame list for lq and gt
- subfolder_name = osp.basename(subfolder_lq)
- img_paths_lq = sorted(list(utils_video.scandir(subfolder_lq, full_path=True)))
-
- max_idx = len(img_paths_lq)
-
- self.data_info['lq_path'].extend(img_paths_lq)
- self.data_info['folder'].extend([subfolder_name] * max_idx)
- for i in range(max_idx):
- self.data_info['idx'].append(f'{i}/{max_idx}')
- border_l = [0] * max_idx
- for i in range(self.opt['num_frame'] // 2):
- border_l[i] = 1
- border_l[max_idx - i - 1] = 1
- self.data_info['border'].extend(border_l)
-
- # cache data or save the frame list
- if self.cache_data:
- print(f'Cache {subfolder_name} for VideoTestDataset...')
- self.imgs_lq[subfolder_name] = utils_video.read_img_seq(img_paths_lq)
- else:
- self.imgs_lq[subfolder_name] = img_paths_lq
-
- # Find unique folder strings
- self.folders = sorted(list(set(self.data_info['folder'])))
-
- def __getitem__(self, index):
- folder = self.folders[index]
-
- if self.cache_data:
- imgs_lq = self.imgs_lq[folder]
- else:
- imgs_lq = utils_video.read_img_seq(self.imgs_lq[folder])
-
- return {
- 'L': imgs_lq,
- 'folder': folder,
- 'lq_path': self.imgs_lq[folder],
- }
-
- def __len__(self):
- return len(self.folders)
-
-
-class VideoTestVimeo90KDataset(data.Dataset):
- """Video test dataset for Vimeo90k-Test dataset.
-
- It only keeps the center frame for testing.
- For testing datasets, there is no need to prepare LMDB files.
-
- Args:
- opt (dict): Config for train dataset. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- io_backend (dict): IO backend type and other kwarg.
- cache_data (bool): Whether to cache testing datasets.
- name (str): Dataset name.
- meta_info_file (str): The path to the file storing the list of test
- folders. If not provided, all the folders in the dataroot will
- be used.
- num_frame (int): Window size for input frames.
- padding (str): Padding mode.
- """
-
- def __init__(self, opt):
- super(VideoTestVimeo90KDataset, self).__init__()
- self.opt = opt
- self.cache_data = opt['cache_data']
- if self.cache_data:
- raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.')
- self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq']
- self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []}
- neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])]
-
- with open(opt['meta_info_file'], 'r') as fin:
- subfolders = [line.split(' ')[0] for line in fin]
- for idx, subfolder in enumerate(subfolders):
- gt_path = osp.join(self.gt_root, subfolder, 'im4.png')
- self.data_info['gt_path'].append(gt_path)
- lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list]
- self.data_info['lq_path'].append(lq_paths)
- self.data_info['folder'].append('vimeo90k')
- self.data_info['idx'].append(f'{idx}/{len(subfolders)}')
- self.data_info['border'].append(0)
-
- self.pad_sequence = opt.get('pad_sequence', False)
-
- def __getitem__(self, index):
- lq_path = self.data_info['lq_path'][index]
- gt_path = self.data_info['gt_path'][index]
- imgs_lq = utils_video.read_img_seq(lq_path)
- img_gt = utils_video.read_img_seq([gt_path])
- img_gt.squeeze_(0)
-
- if self.pad_sequence: # pad the sequence: 7 frames to 8 frames
- imgs_lq = torch.cat([imgs_lq, imgs_lq[-1:,...]], dim=0)
-
- return {
- 'L': imgs_lq, # (t, c, h, w)
- 'H': img_gt, # (c, h, w)
- 'folder': self.data_info['folder'][index], # folder name
- 'idx': self.data_info['idx'][index], # e.g., 0/843
- 'border': self.data_info['border'][index], # 0 for non-border
- 'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame
- }
-
- def __len__(self):
- return len(self.data_info['gt_path'])
-
-
-class SingleVideoRecurrentTestDataset(data.Dataset):
- """Single Video test dataset (only input LQ path).
-
- Supported datasets: Vid4, REDS4, REDSofficial.
- More generally, it supports testing dataset with following structures:
-
- dataroot
- ├── subfolder1
- ├── frame000
- ├── frame001
- ├── ...
- ├── subfolder1
- ├── frame000
- ├── frame001
- ├── ...
- ├── ...
-
- For testing datasets, there is no need to prepare LMDB files.
-
- Args:
- opt (dict): Config for train dataset. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- io_backend (dict): IO backend type and other kwarg.
- cache_data (bool): Whether to cache testing datasets.
- name (str): Dataset name.
- meta_info_file (str): The path to the file storing the list of test
- folders. If not provided, all the folders in the dataroot will
- be used.
- num_frame (int): Window size for input frames.
- padding (str): Padding mode.
- """
-
- def __init__(self, opt):
- super(SingleVideoRecurrentTestDataset, self).__init__()
- self.opt = opt
- self.cache_data = opt['cache_data']
- self.lq_root = opt['dataroot_lq']
- self.data_info = {'lq_path': [], 'folder': [], 'idx': [], 'border': []}
- # file client (io backend)
- self.file_client = None
-
- self.imgs_lq = {}
- if 'meta_info_file' in opt:
- with open(opt['meta_info_file'], 'r') as fin:
- subfolders = [line.split(' ')[0] for line in fin]
- subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders]
- else:
- subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*')))
-
- for subfolder_lq in subfolders_lq:
- # get frame list for lq and gt
- subfolder_name = osp.basename(subfolder_lq)
- img_paths_lq = sorted(list(utils_video.scandir(subfolder_lq, full_path=True)))
-
- max_idx = len(img_paths_lq)
-
- self.data_info['lq_path'].extend(img_paths_lq)
- self.data_info['folder'].extend([subfolder_name] * max_idx)
- for i in range(max_idx):
- self.data_info['idx'].append(f'{i}/{max_idx}')
- border_l = [0] * max_idx
- for i in range(self.opt['num_frame'] // 2):
- border_l[i] = 1
- border_l[max_idx - i - 1] = 1
- self.data_info['border'].extend(border_l)
-
- # cache data or save the frame list
- if self.cache_data:
- logger.info(f'Cache {subfolder_name} for VideoTestDataset...')
- self.imgs_lq[subfolder_name] = utils_video.read_img_seq(img_paths_lq)
- else:
- self.imgs_lq[subfolder_name] = img_paths_lq
-
- # Find unique folder strings
- self.folders = sorted(list(set(self.data_info['folder'])))
-
- def __getitem__(self, index):
- folder = self.folders[index]
-
- if self.cache_data:
- imgs_lq = self.imgs_lq[folder]
- else:
- imgs_lq = utils_video.read_img_seq(self.imgs_lq[folder])
-
- return {
- 'L': imgs_lq,
- 'folder': folder,
- 'lq_path': self.imgs_lq[folder],
- }
-
- def __len__(self):
- return len(self.folders)
diff --git a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/exllama_ext/cpu_func/rep_penalty.h b/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/exllama_ext/cpu_func/rep_penalty.h
deleted file mode 100644
index 4f63b484704d9b9fff3240cf497235b1b0b2fc67..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/exllama_ext/cpu_func/rep_penalty.h
+++ /dev/null
@@ -1,30 +0,0 @@
-#ifndef _rep_penalty_h
-#define _rep_penalty_h
-
-#include
-#include
-
-void rep_penalty_cpu
-(
- const int vocab_size,
- const uint64_t* sequence,
- float* rep_mask,
- const float penalty_max,
- const int sustain,
- const int decay,
- const int seq_len
-);
-
-void apply_rep_penalty_cpu
-(
- const int vocab_size,
- const uint64_t* sequence,
- const float penalty_max,
- const int sustain,
- const int decay,
- const int seq_len,
- float* logits
-);
-
-
-#endif
diff --git a/spaces/leonelhs/remove-background/README.md b/spaces/leonelhs/remove-background/README.md
deleted file mode 100644
index d0c5a9d7bc90428b552ccdb0be087df0367e6c3f..0000000000000000000000000000000000000000
--- a/spaces/leonelhs/remove-background/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Remove Background
-emoji: 🌍
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lewiswu1209/MockingBird/ppg_extractor/encoder/positionwise_feed_forward.py b/spaces/lewiswu1209/MockingBird/ppg_extractor/encoder/positionwise_feed_forward.py
deleted file mode 100644
index 7a9237a38314e3f758f064ab78d8983b94a9eb0a..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/ppg_extractor/encoder/positionwise_feed_forward.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Positionwise feed forward layer definition."""
-
-import torch
-
-
-class PositionwiseFeedForward(torch.nn.Module):
- """Positionwise feed forward layer.
-
- :param int idim: input dimenstion
- :param int hidden_units: number of hidden units
- :param float dropout_rate: dropout rate
-
- """
-
- def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()):
- """Construct an PositionwiseFeedForward object."""
- super(PositionwiseFeedForward, self).__init__()
- self.w_1 = torch.nn.Linear(idim, hidden_units)
- self.w_2 = torch.nn.Linear(hidden_units, idim)
- self.dropout = torch.nn.Dropout(dropout_rate)
- self.activation = activation
-
- def forward(self, x):
- """Forward funciton."""
- return self.w_2(self.dropout(self.activation(self.w_1(x))))
diff --git a/spaces/lewiswu1209/MockingBird/train.py b/spaces/lewiswu1209/MockingBird/train.py
deleted file mode 100644
index 5a6a06c805109159ff40cad69668f1fc38cf1e9b..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/train.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import sys
-import torch
-import argparse
-import numpy as np
-from utils.load_yaml import HpsYaml
-from ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
-
-# For reproducibility, comment these may speed up training
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-
-def main():
- # Arguments
- parser = argparse.ArgumentParser(description=
- 'Training PPG2Mel VC model.')
- parser.add_argument('--config', type=str,
- help='Path to experiment config, e.g., config/vc.yaml')
- parser.add_argument('--name', default=None, type=str, help='Name for logging.')
- parser.add_argument('--logdir', default='log/', type=str,
- help='Logging path.', required=False)
- parser.add_argument('--ckpdir', default='ppg2mel/saved_models/', type=str,
- help='Checkpoint path.', required=False)
- parser.add_argument('--outdir', default='result/', type=str,
- help='Decode output path.', required=False)
- parser.add_argument('--load', default=None, type=str,
- help='Load pre-trained model (for training only)', required=False)
- parser.add_argument('--warm_start', action='store_true',
- help='Load model weights only, ignore specified layers.')
- parser.add_argument('--seed', default=0, type=int,
- help='Random seed for reproducable results.', required=False)
- parser.add_argument('--njobs', default=8, type=int,
- help='Number of threads for dataloader/decoding.', required=False)
- parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
- parser.add_argument('--no-pin', action='store_true',
- help='Disable pin-memory for dataloader')
- parser.add_argument('--test', action='store_true', help='Test the model.')
- parser.add_argument('--no-msg', action='store_true', help='Hide all messages.')
- parser.add_argument('--finetune', action='store_true', help='Finetune model')
- parser.add_argument('--oneshotvc', action='store_true', help='Oneshot VC model')
- parser.add_argument('--bilstm', action='store_true', help='BiLSTM VC model')
- parser.add_argument('--lsa', action='store_true', help='Use location-sensitive attention (LSA)')
-
- ###
-
- paras = parser.parse_args()
- setattr(paras, 'gpu', not paras.cpu)
- setattr(paras, 'pin_memory', not paras.no_pin)
- setattr(paras, 'verbose', not paras.no_msg)
- # Make the config dict dot visitable
- config = HpsYaml(paras.config)
-
- np.random.seed(paras.seed)
- torch.manual_seed(paras.seed)
- if torch.cuda.is_available():
- torch.cuda.manual_seed_all(paras.seed)
-
- print(">>> OneShot VC training ...")
- mode = "train"
- solver = Solver(config, paras, mode)
- solver.load_data()
- solver.set_model()
- solver.exec()
- print(">>> Oneshot VC train finished!")
- sys.exit(0)
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Datacard Preface [full Version] Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Datacard Preface [full Version] Download.md
deleted file mode 100644
index 51657b4ff1337c7e7b7148b3db321db9e15f4f08..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Datacard Preface [full Version] Download.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-... crack, modscan32 tcp/ip connection . . buku ajar idai pdf 280 · Veer Zaara full movie download in hindi hd 1080p · Crackmodscan64 · Datacard Preface [full ... 1fdad05405
-
-
- diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Ls Magazine Ls Models Ls Land Lsm Issue 24 Future School Added By Request ((EXCLUSIVE)).md b/spaces/lincquiQcaudo/Top-20-Diffusion/Ls Magazine Ls Models Ls Land Lsm Issue 24 Future School Added By Request ((EXCLUSIVE)).md deleted file mode 100644 index f3a522b7e2cce9e71ce5faebf1578e03938042e4..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Ls Magazine Ls Models Ls Land Lsm Issue 24 Future School Added By Request ((EXCLUSIVE)).md +++ /dev/null @@ -1,6 +0,0 @@ -
-
-Ls Magazine Ls Models Ls Land lsm issue 24 future school | added by request. rialarraden's Ownd. フォãƒãƒ¼. 2020.06.28 03:44. 4d29de3e1b
-
-
- diff --git a/spaces/lithiumice/SadTalker/src/gradio_demo.py b/spaces/lithiumice/SadTalker/src/gradio_demo.py deleted file mode 100644 index e76e3fe679c833ff167e5b5acdbf37ce5820b130..0000000000000000000000000000000000000000 --- a/spaces/lithiumice/SadTalker/src/gradio_demo.py +++ /dev/null @@ -1,137 +0,0 @@ -import torch, uuid -import os, sys, shutil -from src.utils.preprocess import CropAndExtract -from src.test_audio2coeff import Audio2Coeff -from src.facerender.animate import AnimateFromCoeff -from src.generate_batch import get_data -from src.generate_facerender_batch import get_facerender_data - -from pydub import AudioSegment - -def mp3_to_wav(mp3_filename,wav_filename,frame_rate): - mp3_file = AudioSegment.from_file(file=mp3_filename) - mp3_file.set_frame_rate(frame_rate).export(wav_filename,format="wav") - - -class SadTalker(): - - def __init__(self, checkpoint_path='checkpoints', config_path='src/config', lazy_load=False): - - if torch.cuda.is_available() : - device = "cuda" - else: - device = "cpu" - - self.device = device - - os.environ['TORCH_HOME']= checkpoint_path - - self.checkpoint_path = checkpoint_path - self.config_path = config_path - - self.path_of_lm_croper = os.path.join( checkpoint_path, 'shape_predictor_68_face_landmarks.dat') - self.path_of_net_recon_model = os.path.join( checkpoint_path, 'epoch_20.pth') - self.dir_of_BFM_fitting = os.path.join( checkpoint_path, 'BFM_Fitting') - self.wav2lip_checkpoint = os.path.join( checkpoint_path, 'wav2lip.pth') - - self.audio2pose_checkpoint = os.path.join( checkpoint_path, 'auido2pose_00140-model.pth') - self.audio2pose_yaml_path = os.path.join( config_path, 'auido2pose.yaml') - - self.audio2exp_checkpoint = os.path.join( checkpoint_path, 'auido2exp_00300-model.pth') - self.audio2exp_yaml_path = os.path.join( config_path, 'auido2exp.yaml') - - self.free_view_checkpoint = os.path.join( checkpoint_path, 'facevid2vid_00189-model.pth.tar') - - self.lazy_load = lazy_load - - if not self.lazy_load: - #init model - print(self.path_of_lm_croper) - self.preprocess_model = CropAndExtract(self.path_of_lm_croper, self.path_of_net_recon_model, self.dir_of_BFM_fitting, self.device) - - print(self.audio2pose_checkpoint) - self.audio_to_coeff = Audio2Coeff(self.audio2pose_checkpoint, self.audio2pose_yaml_path, - self.audio2exp_checkpoint, self.audio2exp_yaml_path, self.wav2lip_checkpoint, self.device) - - def test(self, source_image, driven_audio, preprocess='crop', still_mode=False, use_enhancer=False, result_dir='./results/'): - - ### crop: only model, - - if self.lazy_load: - #init model - print(self.path_of_lm_croper) - self.preprocess_model = CropAndExtract(self.path_of_lm_croper, self.path_of_net_recon_model, self.dir_of_BFM_fitting, self.device) - - print(self.audio2pose_checkpoint) - self.audio_to_coeff = Audio2Coeff(self.audio2pose_checkpoint, self.audio2pose_yaml_path, - self.audio2exp_checkpoint, self.audio2exp_yaml_path, self.wav2lip_checkpoint, self.device) - - if preprocess == 'full': - self.mapping_checkpoint = os.path.join(self.checkpoint_path, 'mapping_00109-model.pth.tar') - self.facerender_yaml_path = os.path.join(self.config_path, 'facerender_still.yaml') - else: - self.mapping_checkpoint = os.path.join(self.checkpoint_path, 'mapping_00229-model.pth.tar') - self.facerender_yaml_path = os.path.join(self.config_path, 'facerender.yaml') - - print(self.mapping_checkpoint) - print(self.free_view_checkpoint) - self.animate_from_coeff = AnimateFromCoeff(self.free_view_checkpoint, self.mapping_checkpoint, - self.facerender_yaml_path, self.device) - - time_tag = str(uuid.uuid4()) - save_dir = os.path.join(result_dir, time_tag) - os.makedirs(save_dir, exist_ok=True) - - input_dir = os.path.join(save_dir, 'input') - os.makedirs(input_dir, exist_ok=True) - - print(source_image) - pic_path = os.path.join(input_dir, os.path.basename(source_image)) - shutil.move(source_image, input_dir) - - if os.path.isfile(driven_audio): - audio_path = os.path.join(input_dir, os.path.basename(driven_audio)) - - #### mp3 to wav - if '.mp3' in audio_path: - mp3_to_wav(driven_audio, audio_path.replace('.mp3', '.wav'), 16000) - audio_path = audio_path.replace('.mp3', '.wav') - else: - shutil.move(driven_audio, input_dir) - else: - raise AttributeError("error audio") - - - os.makedirs(save_dir, exist_ok=True) - pose_style = 0 - #crop image and extract 3dmm from image - first_frame_dir = os.path.join(save_dir, 'first_frame_dir') - os.makedirs(first_frame_dir, exist_ok=True) - first_coeff_path, crop_pic_path, crop_info = self.preprocess_model.generate(pic_path, first_frame_dir,preprocess) - - if first_coeff_path is None: - raise AttributeError("No face is detected") - - #audio2ceoff - batch = get_data(first_coeff_path, audio_path, self.device, ref_eyeblink_coeff_path=None, still=still_mode) # longer audio? - coeff_path = self.audio_to_coeff.generate(batch, save_dir, pose_style) - #coeff2video - batch_size = 8 - data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode, preprocess=preprocess) - return_path = self.animate_from_coeff.generate(data, save_dir, pic_path, crop_info, enhancer='gfpgan' if use_enhancer else None, preprocess=preprocess) - video_name = data['video_name'] - print(f'The generated video is named {video_name} in {save_dir}') - - if self.lazy_load: - del self.preprocess_model - del self.audio_to_coeff - del self.animate_from_coeff - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - torch.cuda.synchronize() - import gc; gc.collect() - - return return_path - - \ No newline at end of file diff --git a/spaces/lj1995/vocal2guitar/train/process_ckpt.py b/spaces/lj1995/vocal2guitar/train/process_ckpt.py deleted file mode 100644 index ac608aeb709e7821d30e920f69512511191b2cf1..0000000000000000000000000000000000000000 --- a/spaces/lj1995/vocal2guitar/train/process_ckpt.py +++ /dev/null @@ -1,215 +0,0 @@ -import torch, traceback, os, pdb, sys - -now_dir = os.getcwd() -sys.path.append(now_dir) -from collections import OrderedDict -from i18n import I18nAuto - -i18n = I18nAuto() - - -def savee(ckpt, sr, if_f0, name, epoch, version, hps): - try: - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = ckpt[key].half() - opt["config"] = [ - hps.data.filter_length // 2 + 1, - 32, - hps.model.inter_channels, - hps.model.hidden_channels, - hps.model.filter_channels, - hps.model.n_heads, - hps.model.n_layers, - hps.model.kernel_size, - hps.model.p_dropout, - hps.model.resblock, - hps.model.resblock_kernel_sizes, - hps.model.resblock_dilation_sizes, - hps.model.upsample_rates, - hps.model.upsample_initial_channel, - hps.model.upsample_kernel_sizes, - hps.model.spk_embed_dim, - hps.model.gin_channels, - hps.data.sampling_rate, - ] - opt["info"] = "%sepoch" % epoch - opt["sr"] = sr - opt["f0"] = if_f0 - opt["version"] = version - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() - - -def show_info(path): - try: - a = torch.load(path, map_location="cpu") - return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % ( - a.get("info", "None"), - a.get("sr", "None"), - a.get("f0", "None"), - a.get("version", "None"), - ) - except: - return traceback.format_exc() - - -def extract_small_model(path, name, sr, if_f0, info, version): - try: - ckpt = torch.load(path, map_location="cpu") - if "model" in ckpt: - ckpt = ckpt["model"] - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = ckpt[key].half() - if sr == "40k": - opt["config"] = [ - 1025, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 10, 2, 2], - 512, - [16, 16, 4, 4], - 109, - 256, - 40000, - ] - elif sr == "48k": - opt["config"] = [ - 1025, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 6, 2, 2, 2], - 512, - [16, 16, 4, 4, 4], - 109, - 256, - 48000, - ] - elif sr == "32k": - opt["config"] = [ - 513, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 4, 2, 2, 2], - 512, - [16, 16, 4, 4, 4], - 109, - 256, - 32000, - ] - if info == "": - info = "Extracted model." - opt["info"] = info - opt["version"] = version - opt["sr"] = sr - opt["f0"] = int(if_f0) - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() - - -def change_info(path, info, name): - try: - ckpt = torch.load(path, map_location="cpu") - ckpt["info"] = info - if name == "": - name = os.path.basename(path) - torch.save(ckpt, "weights/%s" % name) - return "Success." - except: - return traceback.format_exc() - - -def merge(path1, path2, alpha1, sr, f0, info, name, version): - try: - - def extract(ckpt): - a = ckpt["model"] - opt = OrderedDict() - opt["weight"] = {} - for key in a.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = a[key] - return opt - - ckpt1 = torch.load(path1, map_location="cpu") - ckpt2 = torch.load(path2, map_location="cpu") - cfg = ckpt1["config"] - if "model" in ckpt1: - ckpt1 = extract(ckpt1) - else: - ckpt1 = ckpt1["weight"] - if "model" in ckpt2: - ckpt2 = extract(ckpt2) - else: - ckpt2 = ckpt2["weight"] - if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): - return "Fail to merge the models. The model architectures are not the same." - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt1.keys(): - # try: - if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: - min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) - opt["weight"][key] = ( - alpha1 * (ckpt1[key][:min_shape0].float()) - + (1 - alpha1) * (ckpt2[key][:min_shape0].float()) - ).half() - else: - opt["weight"][key] = ( - alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float()) - ).half() - # except: - # pdb.set_trace() - opt["config"] = cfg - """ - if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000] - elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000] - elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000] - """ - opt["sr"] = sr - opt["f0"] = 1 if f0 == i18n("是") else 0 - opt["version"] = version - opt["info"] = info - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() diff --git a/spaces/llmonitor/benchmarks/app/prompts/layout.js b/spaces/llmonitor/benchmarks/app/prompts/layout.js deleted file mode 100644 index 121aab52585e611404d82ab958cdb31cc2747a3d..0000000000000000000000000000000000000000 --- a/spaces/llmonitor/benchmarks/app/prompts/layout.js +++ /dev/null @@ -1,20 +0,0 @@ -import { cookies } from "next/headers" -import Link from "next/link" - -export default function PromptsLayout({ children }) { - const cookiesList = cookies() - const token = cookiesList.get("token") - - return ( - <> -
- {children} - - ) -} diff --git a/spaces/luxuedong/lxd/Dockerfile b/spaces/luxuedong/lxd/Dockerfile deleted file mode 100644 index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000 --- a/spaces/luxuedong/lxd/Dockerfile +++ /dev/null @@ -1,36 +0,0 @@ -FROM node:18 - - -ARG DEBIAN_FRONTEND=noninteractive - -ENV BING_HEADER "" - -# Set home to the user's home directory -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -# Set up a new user named "user" with user ID 1000 -RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME - -# Switch to the "user" user -USER user - -# Set the working directory to the user's home directory -WORKDIR $HOME/app - -# Install app dependencies -# A wildcard is used to ensure both package.json AND package-lock.json are copied -# where available (npm@5+) -COPY --chown=user package*.json $HOME/app/ - -RUN npm install - -# Copy the current directory contents into the container at $HOME/app setting the owner to the user -COPY --chown=user . $HOME/app/ - -RUN npm run build - -ENV PORT 7860 -EXPOSE 7860 - -CMD npm start diff --git a/spaces/ma-xu/LIVE/pybind11/include/pybind11/eval.h b/spaces/ma-xu/LIVE/pybind11/include/pybind11/eval.h deleted file mode 100644 index ba82cf42ae3673a3de391eb55777ef413c43dc33..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/pybind11/include/pybind11/eval.h +++ /dev/null @@ -1,132 +0,0 @@ -/* - pybind11/exec.h: Support for evaluating Python expressions and statements - from strings and files - - Copyright (c) 2016 Klemens Morgenstern and
- Wenzel Jakob
-
- All rights reserved. Use of this source code is governed by a
- BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#include "pybind11.h"
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-
-enum eval_mode {
- /// Evaluate a string containing an isolated expression
- eval_expr,
-
- /// Evaluate a string containing a single statement. Returns \c none
- eval_single_statement,
-
- /// Evaluate a string containing a sequence of statement. Returns \c none
- eval_statements
-};
-
-template
-object eval(str expr, object global = globals(), object local = object()) {
- if (!local)
- local = global;
-
- /* PyRun_String does not accept a PyObject / encoding specifier,
- this seems to be the only alternative */
- std::string buffer = "# -*- coding: utf-8 -*-\n" + (std::string) expr;
-
- int start;
- switch (mode) {
- case eval_expr: start = Py_eval_input; break;
- case eval_single_statement: start = Py_single_input; break;
- case eval_statements: start = Py_file_input; break;
- default: pybind11_fail("invalid evaluation mode");
- }
-
- PyObject *result = PyRun_String(buffer.c_str(), start, global.ptr(), local.ptr());
- if (!result)
- throw error_already_set();
- return reinterpret_steal
- 音视频转录 -
-- by宁侠 -
- ''') - gr.Markdown(description) - - with gr.Tab("🔊音频转录 Audio Transcribe"): - with gr.Row(): - with gr.Column(): - audio_input = gr.Audio(label="🔊音频输入 Audio Input", type="filepath") - gr.Examples(['examples/paddlespeech.asr-zh.wav', 'examples/demo_shejipuhui.mp3'], [audio_input]) - audio_model_size = gr.components.Radio(label="模型尺寸", choices=["tiny", "base", "small", "medium", "large"], value="small") - audio_recog_button = gr.Button("👂音频识别 Recognize") - with gr.Column(): - audio_text_output = gr.Textbox(label="✏️识别结果 Recognition Result", max_lines=5) - audio_text_file = gr.File(label="✏️识别结果文件 Recognition Result File") - audio_srt_output = gr.Textbox(label="📖SRT字幕内容 SRT Subtitles", max_lines=10) - audio_srt_file = gr.File(label="📖SRT字幕文件 SRT File") - audio_subtitles_button = gr.Button("添加字幕\nGenerate Subtitles", visible=False) - audio_output = gr.Audio(label="🔊音频 Audio", visible=False) - - audio_recog_button.click(audio_recog, inputs=[audio_model_size, audio_input], outputs=[audio_text_output, audio_text_file, audio_srt_output, audio_srt_file]) -# audio_subtitles_button.click(audio_subtitles, inputs=[audio_text_input], outputs=[audio_output]) - - with gr.Tab("🎥视频转录 Video Transcribe"): - with gr.Row(): - with gr.Column(): - video_input = gr.Video(label="🎥视频输入 Video Input") - gr.Examples(['examples/demo_shejipuhui.mp4'], [video_input], label='语音识别示例 ASR Demo') - video_model_size = gr.components.Radio(label="模型尺寸", choices=["tiny", "base", "small", "medium", "large"], value="small") - video_recog_button = gr.Button("👂视频识别 Recognize") - video_output = gr.Video(label="🎥视频 Video", visible=False) - with gr.Column(): - video_text_output = gr.Textbox(label="✏️识别结果 Recognition Result", max_lines=5) - video_text_file = gr.File(label="✏️识别结果文件 Recognition Result File") - video_srt_output = gr.Textbox(label="📖SRT字幕内容 SRT Subtitles", max_lines=10) - video_srt_file = gr.File(label="📖SRT字幕文件 SRT File") - with gr.Row(visible=False): - font_size = gr.Slider(minimum=10, maximum=100, value=32, step=2, label="🔠字幕字体大小 Subtitle Font Size") - font_color = gr.Radio(["black", "white", "green", "red"], label="🌈字幕颜色 Subtitle Color", value='white') - video_subtitles_button = gr.Button("添加字幕\nGenerate Subtitles", visible=False) - - - video_recog_button.click(video_recog, inputs=[video_model_size, video_input], outputs=[video_text_output, video_text_file, video_srt_output, video_srt_file]) -# video_subtitles_button.click(video_subtitles, inputs=[video_text_input], outputs=[video_output]) - -# start gradio service in local -demo.queue(api_open=False).launch(debug=True) diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Maya Serial Number and Product Key 2016 Step by Step Instructions for Beginners and Experts.md b/spaces/gotiQspiryo/whisper-ui/examples/Maya Serial Number and Product Key 2016 Step by Step Instructions for Beginners and Experts.md deleted file mode 100644 index 0e2204e5cbd52a614ee4981303713325d33a2f4d..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Maya Serial Number and Product Key 2016 Step by Step Instructions for Beginners and Experts.md +++ /dev/null @@ -1,23 +0,0 @@ --
Ok, here's my situation. I'm a college student and a few semesters ago I had to download and install Maya 2015 in order to use for a class. Next semester, I now have to download and install Maya 2016. So, I log into the education community site and it offers me the ability to download Maya 2014, 2015, 2016, and 2017. When I click on the 2016 version, I am given a serial number and product key. The serial number given is the exact same serial number I was given for Maya 2015. I was also sent an e-mail giving me the license details for Maya 2016 (again with the identical serial number).
-After I download and install Maya 2016, it says it can't activate because the serial number is wrong. I tried to get an activation code from autodesk, but the automated system tells me I am providing the incorrect request code (I assure you, I am typing in the right number). Here's a screenshot of the activation screen with a request number showing that I am trying to activate the 2016:
-maya serial number and product key 2016
Download File 🆗 https://urlgoal.com/2uyLsT
-
So obviously customer service thinks I am trying to activate Maya 2015, not 2016. I suspect because the serial numbers are exactly the same. The product numbers are different though. Anyway, I respond to the customer service e-mail and explain everything, and then I just get a message saying my ticket has been closed.
-Thank you for your post! Sorry to hear you are having issues activating Maya 2016. You can use the same serial number for all the previous versions available to subscription users, so Maya 2014-2017.
-If I'm supposed to be able to use the same serial number, then why when I try to activate Maya does it say that I have the wrong serial number? See below. I'm using the serial number provided to me by Autodesk in an e-mail. How can that be wrong?
-I then responded to that message explaining that I am not trying to activate Maya 2015 (even though I already explained that in the original ticket), I am trying to activate Maya 2016. I forwarded both the licensing e-mail from Autodesk, as well as provided a screenshot showing that the serial number and request code are the correct numbers for Maya 2016, not 2015. Then I got an e-mail saying my support ticket was closed. So going through this page provided no resolution. My request was ignored. I could create another ticket, but I'd just get the same response.
-Product keys are required for installation of Autodesk products and are used to differentiate products that are both sold independently and as part of a product suite. With newest release of Autodesk 2016 products, we bring you a new list of products keys.
Note: Please ensure you are using the correct product key for the Autodesk product and version you are installing. Entering an incorrect product key will result in activation errors for that product.
Note: For single-user subscriptions, you can usually sign in so that a serial number is not required. You may see a Stand-alone license type for 2017-2019 products, but a User License type for 2020 and later product versions.
-Autodesk 2016 All Products Crack Final activation keys for Autodesk 2016 x86x64. Using this activator will allow you to activate the full version of Autodesk products using the keygen to generate a working serial number by pasting request code from an Autodesk software to the keygen and getting the activation code. It also has a Patch button to patch Autodesk 2016 programs for permanent activation and supports both Autodesk 32 bit and 64 bit
-Find Serial Numbers and Product Keys in Autodesk Account: Your Serial Number and Product Key are displayed in your Autodesk Account in the product tray on the Products & Services page and also again in the Software Download window. Note about serial number visibility in Autodesk Account: Only account administrators, such as Contract Managers and Software Coordinators, and Named Users with assigned software benefits will see serial numbers in Autodesk Account. You are the account administrator if you purchased a software subscription using your Autodesk Account or were assigned the role of Contract Manager or Software Coordinator by your company. If you do not see the software you wish to activate in your Autodesk account or see the message "Contact your admin for serial numbers," you need to contact the contract administrator. Only an administrator can assign you as a Named User or End User and give you permissions to download and activate the software.
- -If, for whatever reason, you cannot locate your product key, there is another method:
1. Using your installation media, (USB key, DVD, download folder, etc.) navigate to the location of the setup.exe file for your Autodesk product.
2. In that folder, look for a file named MID.txt, MID01.txt, MID02.txt or some variation on that name.
3. Open this file in notepad and verify that the product name is what you expected it to be.
4. The first five characters of the part number should also be the product key for that product.
Second, we believe this is the case especially considering the slower growth of oil and natural gas sources on the U&O Reservation over the past two and a half years since August 2016 when the National O&NG FIP became effective. Since that time, we have seen limited construction of new and modified oil and natural gas sources on the U&O Reservation. Oil and natural gas sources planning to construct on or after October 3, 2016 have been required to either comply with the National O&NG FIP or to seek a minor source permit under the generally applicable (site-specific) permit provisions of the Federal Indian Country Minor NSR rule.17 Sources complying with the National O&NG FIP are required to meet a two-part registration requirement: The Part 1 Registration Form is submitted 30 days before a source begins construction and contains information about source location and the Part 2 Registration Form is submitted within 60 days after the startup of production and contains information about emissions.18
-Comment #7: One oil and natural gas industry commenter expressed that the industry's objective is that final regulations protect the environment and the public and cost-effectively address VOC emissions that as a co-benefit also reduce methane emissions, without unnecessarily hampering manufacturing and business expansion. According to the commenter, this objective can be met while the private sector develops and delivers more natural gas and oil to its customers. According to the oil and natural gas industry commenter, their efforts are producing real results based on the EPA's latest Greenhouse Gas Inventory which continues to show a downward trend in methane emissions, even as U.S. oil and natural gas production rose dramatically. The commenter reported that the inventory report indicates that methane emissions from natural gas systems and petroleum systems increased 14 percent between 1990 and 2016, at a time when the natural gas output increased by more than 50 percent. This is in addition to the U.S. continuing to lead the world in reducing carbon emissions, which are at 25-year lows, largely due to the increased use of natural gas.
-This action does not impose any new information collection burden under the PRA. OMB has previously approved the information collection activities contained in the Federal Indian Country Minor NSR rule and has assigned OMB control number 2060-0003.35 This action amends the National O&NG FIP, which provides a mechanism for authorizing construction for true minor sources in the oil and natural gas production and natural gas processing segments of the oil and natural gas sector locating or located in areas covered by the Federal Indian Country Minor NSR rule to satisfy the requirements of that rule other than by obtaining a site-specific minor source permit. Because it substitutes for a site-specific permit, which would contain information collection activities covered by the Information Collection Request for Federal Indian Country Minor NSR rule issued in July 2011, neither the proposed amendments, nor the National O&NG FIP, impose any new obligations or enforceable duties on any state, local or tribal government or the private sector. In fact, the final amendments should have the effect of reducing paperwork burden on sources wishing to locate or expand in the Indian country portion of the Uinta Basin Ozone Nonattainment Area, as the amendments provide an alternative to site-specific permitting for such sources.
-Based on the calculations below, the total estimated number of respondents (WOSBs and EDWOSBs) for this collection of information varies depending upon the types of certification that a business concern is seeking. For initial certification, the total estimated number of respondents is 9,349. The total number was calculated using the two-year average number of business concerns that have provided information through Certify from March 2016 through February 2018. For annual updates, the total number is 12,347. For examinations and protests, the total number is 130.
-We propose to adopt a new airworthiness directive (AD) for all Bombardier, Inc., Model CL-600-2B16 (601-3A, 601-3R, and 604 Variants) airplanes. This proposed AD was prompted by a report that main landing gear (MLG) side stay actuators have been assembled using nonconforming split ball bearings. This proposed AD would require verification of the serial numbers of the installed MLG side stay actuator assemblies, and replacement of the affected parts. We are proposing this AD to address the unsafe condition on these products.
-The service information describes procedures to verify the serial numbers of the installed MLG side stay actuator assemblies and to replace the affected parts. These documents are distinct since they apply to the airplane model in different configurations.
-The applicability of the MCAI is limited to Bombardier, Inc., Model CL-600-2B16 (601-3A, 601-3R, and 604 Variants) airplanes, serial numbers 5301 through 5665 inclusive, 5701 through 5988 inclusive, and 6050 through 6091 inclusive, equipped with MLG side stay actuator assembly containing split ball bearing part number 104467672. However, the applicability of this proposed AD includes all Bombardier, Inc., Model CL-600-2B16 (601-3A, 601-3R, and 604 Variants) airplanes and prohibits the installation of any MLG side stay actuator with a serial number identified in the service information. Because the affected part is a rotable part, we have determined that this part could later be installed on airplanes that were initially delivered with the acceptable part, thereby subjecting those airplanes to the unsafe condition. We have coordinated this difference with TCCA.
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\ No newline at end of file diff --git a/spaces/gradio/HuBERT/fairseq/data/audio/speech_to_text_dataset.py b/spaces/gradio/HuBERT/fairseq/data/audio/speech_to_text_dataset.py deleted file mode 100644 index d4b5668d8f9d4bc93fcbda73d867554d8f1b3107..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/data/audio/speech_to_text_dataset.py +++ /dev/null @@ -1,511 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import csv -import io -import logging -import os.path as op -import re -from typing import Dict, List, Optional, Tuple - -import numpy as np -import torch -from fairseq.data import ( - ConcatDataset, - Dictionary, - FairseqDataset, - ResamplingDataset, - data_utils as fairseq_data_utils, -) -from fairseq.data.audio.audio_utils import ( - get_fbank, get_waveform, read_from_stored_zip, is_npy_data, - is_sf_audio_data, parse_path, FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS -) -from fairseq.data.audio.feature_transforms import CompositeAudioFeatureTransform - - -logger = logging.getLogger(__name__) - - -class S2TDataConfig(object): - """Wrapper class for data config YAML""" - - def __init__(self, yaml_path): - try: - import yaml - except ImportError: - print("Please install PyYAML to load YAML files for " "S2T data config") - self.config = {} - if op.isfile(yaml_path): - try: - with open(yaml_path) as f: - self.config = yaml.load(f, Loader=yaml.FullLoader) - except Exception as e: - raise Exception(f"Failed to load config from {yaml_path}: {e}") - else: - raise FileNotFoundError(f"{yaml_path} not found") - - @property - def vocab_filename(self): - """fairseq vocabulary file under data root""" - return self.config.get("vocab_filename", "dict.txt") - - @property - def shuffle(self) -> bool: - """Shuffle dataset samples before batching""" - return self.config.get("shuffle", False) - - @property - def pre_tokenizer(self) -> Dict: - """Pre-tokenizer to apply before subword tokenization. Returning - a dictionary with `tokenizer` providing the tokenizer name and - the other items providing the tokenizer-specific arguments. - Tokenizers are defined in `fairseq.data.encoders.*`""" - return self.config.get("pre_tokenizer", {"tokenizer": None}) - - @property - def bpe_tokenizer(self) -> Dict: - """Subword tokenizer to apply after pre-tokenization. Returning - a dictionary with `bpe` providing the tokenizer name and - the other items providing the tokenizer-specific arguments. - Tokenizers are defined in `fairseq.data.encoders.*`""" - return self.config.get("bpe_tokenizer", {"bpe": None}) - - @property - def prepend_tgt_lang_tag(self) -> bool: - """Prepend target lang ID token as the target BOS (e.g. for to-many - multilingual setting). During inference, this requires `--prefix-size 1` - to force BOS to be lang ID token.""" - return self.config.get("prepend_tgt_lang_tag", False) - - @property - def input_feat_per_channel(self): - """The dimension of input features (per audio channel)""" - return self.config.get("input_feat_per_channel", 80) - - @property - def input_channels(self): - """The number of channels in the input audio""" - return self.config.get("input_channels", 1) - - @property - def sampling_alpha(self): - """Hyper-parameter alpha = 1/T for temperature-based resampling. - (alpha = 1 for no resampling)""" - return self.config.get("sampling_alpha", 1.0) - - @property - def use_audio_input(self): - """Needed by the dataset loader to see if the model requires - raw audio as inputs.""" - return self.config.get("use_audio_input", False) - - @property - def audio_root(self): - """Audio paths in the manifest TSV can be relative and this provides - the root path. Set this to empty string when using absolute paths.""" - return self.config.get("audio_root", "") - - def get_feature_transforms(self, split, is_train): - """Split-specific feature transforms. Allowing train set wildcard `_train`, - evaluation set wildcard `_eval` and general wildcard `*` for matching.""" - from copy import deepcopy - - cfg = deepcopy(self.config) - _cur = cfg.get("transforms", {}) - cur = _cur.get(split) - cur = _cur.get("_train") if cur is None and is_train else cur - cur = _cur.get("_eval") if cur is None and not is_train else cur - cur = _cur.get("*") if cur is None else cur - cfg["transforms"] = cur - return cfg - - -def get_features_from_npy_or_audio(path): - ext = op.splitext(op.basename(path))[1] - if ext not in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS: - raise ValueError(f'Unsupported file format for "{path}"') - return np.load(path) if ext == ".npy" else get_fbank(path) - - -def get_features_or_waveform_from_stored_zip( - path, byte_offset, byte_size, need_waveform=False -): - assert path.endswith(".zip") - data = read_from_stored_zip(path, byte_offset, byte_size) - f = io.BytesIO(data) - if is_npy_data(data): - features_or_waveform = np.load(f) - elif is_sf_audio_data(data): - features_or_waveform = \ - get_waveform(f, always_2d=False)[0] if need_waveform else get_fbank(f) - else: - raise ValueError(f'Unknown file format for "{path}"') - return features_or_waveform - - -def get_features_or_waveform(path: str, need_waveform=False): - """Get speech features from .npy file or waveform from .wav/.flac file. - The file may be inside an uncompressed ZIP file and is accessed via byte - offset and length. - - Args: - path (str): File path in the format of "<.npy/.wav/.flac path>" or - "
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-video games are no longer the purview of adult men; gaming is now an activity undertaken by men, women and even children. the audience has expanded, and videogames are no longer exclusive to the 16 year old boy in the basement. "the average game player is a woman, or a woman who plays videogames," said durand dinsmore, president of videogame retail association. "the average gamer is a busy mother who eats her weight watchers meetings and needs her time to relax. she wants something that will reward her for the time spent with her kids and be entertaining for both of them."
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\ No newline at end of file diff --git a/spaces/jackyliang42/code-as-policies/prompts/transform_shape_pts.py b/spaces/jackyliang42/code-as-policies/prompts/transform_shape_pts.py deleted file mode 100644 index e5c74b856805818dc788de1df4b0495d9cc404ba..0000000000000000000000000000000000000000 --- a/spaces/jackyliang42/code-as-policies/prompts/transform_shape_pts.py +++ /dev/null @@ -1,19 +0,0 @@ -import numpy as np -from utils import get_obj_pos, get_obj_names, parse_position, parse_obj_name - -# make it bigger by 1.5. -new_shape_pts = scale_pts_around_centroid_np(shape_pts, scale_x=1.5, scale_y=1.5) -# move it to the right by 10cm. -new_shape_pts = translate_pts_np(shape_pts, delta=[0.1, 0]) -# move it to the top by 20cm. -new_shape_pts = translate_pts_np(shape_pts, delta=[0, 0.2]) -# rotate it clockwise by 40 degrees. -new_shape_pts = rotate_pts_around_centroid_np(shape_pts, angle=-np.deg2rad(40)) -# rotate by 30 degrees and make it slightly smaller -new_shape_pts = rotate_pts_around_centroid_np(shape_pts, angle=np.deg2rad(30)) -new_shape_pts = scale_pts_around_centroid_np(new_shape_pts, scale_x=0.7, scale_y=0.7) -# move it toward the blue block. -block_name = parse_obj_name('the blue block', f'objects = {get_obj_names()}') -block_pos = get_obj_pos(block_name) -mean_delta = np.mean(block_pos - shape_pts, axis=1) -new_shape_pts = translate_pts_np(shape_pts, mean_delta) \ No newline at end of file diff --git a/spaces/jbilcke-hf/observer/src/lib/createLlamaPrompt.ts b/spaces/jbilcke-hf/observer/src/lib/createLlamaPrompt.ts deleted file mode 100644 index ca246b36d0ef50f37571dcf09480bf57e9aee922..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/observer/src/lib/createLlamaPrompt.ts +++ /dev/null @@ -1,25 +0,0 @@ -// adapted from https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/discussions/5 -export function createLlamaPrompt(messages: Array<{ role: string, content: string }>) { - const B_INST = "[INST]", E_INST = "[/INST]"; - const B_SYS = "<
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- diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Ls Magazine Ls Models Ls Land Lsm Issue 24 Future School Added By Request ((EXCLUSIVE)).md b/spaces/lincquiQcaudo/Top-20-Diffusion/Ls Magazine Ls Models Ls Land Lsm Issue 24 Future School Added By Request ((EXCLUSIVE)).md deleted file mode 100644 index f3a522b7e2cce9e71ce5faebf1578e03938042e4..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Ls Magazine Ls Models Ls Land Lsm Issue 24 Future School Added By Request ((EXCLUSIVE)).md +++ /dev/null @@ -1,6 +0,0 @@ -
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-
- diff --git a/spaces/lithiumice/SadTalker/src/gradio_demo.py b/spaces/lithiumice/SadTalker/src/gradio_demo.py deleted file mode 100644 index e76e3fe679c833ff167e5b5acdbf37ce5820b130..0000000000000000000000000000000000000000 --- a/spaces/lithiumice/SadTalker/src/gradio_demo.py +++ /dev/null @@ -1,137 +0,0 @@ -import torch, uuid -import os, sys, shutil -from src.utils.preprocess import CropAndExtract -from src.test_audio2coeff import Audio2Coeff -from src.facerender.animate import AnimateFromCoeff -from src.generate_batch import get_data -from src.generate_facerender_batch import get_facerender_data - -from pydub import AudioSegment - -def mp3_to_wav(mp3_filename,wav_filename,frame_rate): - mp3_file = AudioSegment.from_file(file=mp3_filename) - mp3_file.set_frame_rate(frame_rate).export(wav_filename,format="wav") - - -class SadTalker(): - - def __init__(self, checkpoint_path='checkpoints', config_path='src/config', lazy_load=False): - - if torch.cuda.is_available() : - device = "cuda" - else: - device = "cpu" - - self.device = device - - os.environ['TORCH_HOME']= checkpoint_path - - self.checkpoint_path = checkpoint_path - self.config_path = config_path - - self.path_of_lm_croper = os.path.join( checkpoint_path, 'shape_predictor_68_face_landmarks.dat') - self.path_of_net_recon_model = os.path.join( checkpoint_path, 'epoch_20.pth') - self.dir_of_BFM_fitting = os.path.join( checkpoint_path, 'BFM_Fitting') - self.wav2lip_checkpoint = os.path.join( checkpoint_path, 'wav2lip.pth') - - self.audio2pose_checkpoint = os.path.join( checkpoint_path, 'auido2pose_00140-model.pth') - self.audio2pose_yaml_path = os.path.join( config_path, 'auido2pose.yaml') - - self.audio2exp_checkpoint = os.path.join( checkpoint_path, 'auido2exp_00300-model.pth') - self.audio2exp_yaml_path = os.path.join( config_path, 'auido2exp.yaml') - - self.free_view_checkpoint = os.path.join( checkpoint_path, 'facevid2vid_00189-model.pth.tar') - - self.lazy_load = lazy_load - - if not self.lazy_load: - #init model - print(self.path_of_lm_croper) - self.preprocess_model = CropAndExtract(self.path_of_lm_croper, self.path_of_net_recon_model, self.dir_of_BFM_fitting, self.device) - - print(self.audio2pose_checkpoint) - self.audio_to_coeff = Audio2Coeff(self.audio2pose_checkpoint, self.audio2pose_yaml_path, - self.audio2exp_checkpoint, self.audio2exp_yaml_path, self.wav2lip_checkpoint, self.device) - - def test(self, source_image, driven_audio, preprocess='crop', still_mode=False, use_enhancer=False, result_dir='./results/'): - - ### crop: only model, - - if self.lazy_load: - #init model - print(self.path_of_lm_croper) - self.preprocess_model = CropAndExtract(self.path_of_lm_croper, self.path_of_net_recon_model, self.dir_of_BFM_fitting, self.device) - - print(self.audio2pose_checkpoint) - self.audio_to_coeff = Audio2Coeff(self.audio2pose_checkpoint, self.audio2pose_yaml_path, - self.audio2exp_checkpoint, self.audio2exp_yaml_path, self.wav2lip_checkpoint, self.device) - - if preprocess == 'full': - self.mapping_checkpoint = os.path.join(self.checkpoint_path, 'mapping_00109-model.pth.tar') - self.facerender_yaml_path = os.path.join(self.config_path, 'facerender_still.yaml') - else: - self.mapping_checkpoint = os.path.join(self.checkpoint_path, 'mapping_00229-model.pth.tar') - self.facerender_yaml_path = os.path.join(self.config_path, 'facerender.yaml') - - print(self.mapping_checkpoint) - print(self.free_view_checkpoint) - self.animate_from_coeff = AnimateFromCoeff(self.free_view_checkpoint, self.mapping_checkpoint, - self.facerender_yaml_path, self.device) - - time_tag = str(uuid.uuid4()) - save_dir = os.path.join(result_dir, time_tag) - os.makedirs(save_dir, exist_ok=True) - - input_dir = os.path.join(save_dir, 'input') - os.makedirs(input_dir, exist_ok=True) - - print(source_image) - pic_path = os.path.join(input_dir, os.path.basename(source_image)) - shutil.move(source_image, input_dir) - - if os.path.isfile(driven_audio): - audio_path = os.path.join(input_dir, os.path.basename(driven_audio)) - - #### mp3 to wav - if '.mp3' in audio_path: - mp3_to_wav(driven_audio, audio_path.replace('.mp3', '.wav'), 16000) - audio_path = audio_path.replace('.mp3', '.wav') - else: - shutil.move(driven_audio, input_dir) - else: - raise AttributeError("error audio") - - - os.makedirs(save_dir, exist_ok=True) - pose_style = 0 - #crop image and extract 3dmm from image - first_frame_dir = os.path.join(save_dir, 'first_frame_dir') - os.makedirs(first_frame_dir, exist_ok=True) - first_coeff_path, crop_pic_path, crop_info = self.preprocess_model.generate(pic_path, first_frame_dir,preprocess) - - if first_coeff_path is None: - raise AttributeError("No face is detected") - - #audio2ceoff - batch = get_data(first_coeff_path, audio_path, self.device, ref_eyeblink_coeff_path=None, still=still_mode) # longer audio? - coeff_path = self.audio_to_coeff.generate(batch, save_dir, pose_style) - #coeff2video - batch_size = 8 - data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode, preprocess=preprocess) - return_path = self.animate_from_coeff.generate(data, save_dir, pic_path, crop_info, enhancer='gfpgan' if use_enhancer else None, preprocess=preprocess) - video_name = data['video_name'] - print(f'The generated video is named {video_name} in {save_dir}') - - if self.lazy_load: - del self.preprocess_model - del self.audio_to_coeff - del self.animate_from_coeff - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - torch.cuda.synchronize() - import gc; gc.collect() - - return return_path - - \ No newline at end of file diff --git a/spaces/lj1995/vocal2guitar/train/process_ckpt.py b/spaces/lj1995/vocal2guitar/train/process_ckpt.py deleted file mode 100644 index ac608aeb709e7821d30e920f69512511191b2cf1..0000000000000000000000000000000000000000 --- a/spaces/lj1995/vocal2guitar/train/process_ckpt.py +++ /dev/null @@ -1,215 +0,0 @@ -import torch, traceback, os, pdb, sys - -now_dir = os.getcwd() -sys.path.append(now_dir) -from collections import OrderedDict -from i18n import I18nAuto - -i18n = I18nAuto() - - -def savee(ckpt, sr, if_f0, name, epoch, version, hps): - try: - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = ckpt[key].half() - opt["config"] = [ - hps.data.filter_length // 2 + 1, - 32, - hps.model.inter_channels, - hps.model.hidden_channels, - hps.model.filter_channels, - hps.model.n_heads, - hps.model.n_layers, - hps.model.kernel_size, - hps.model.p_dropout, - hps.model.resblock, - hps.model.resblock_kernel_sizes, - hps.model.resblock_dilation_sizes, - hps.model.upsample_rates, - hps.model.upsample_initial_channel, - hps.model.upsample_kernel_sizes, - hps.model.spk_embed_dim, - hps.model.gin_channels, - hps.data.sampling_rate, - ] - opt["info"] = "%sepoch" % epoch - opt["sr"] = sr - opt["f0"] = if_f0 - opt["version"] = version - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() - - -def show_info(path): - try: - a = torch.load(path, map_location="cpu") - return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % ( - a.get("info", "None"), - a.get("sr", "None"), - a.get("f0", "None"), - a.get("version", "None"), - ) - except: - return traceback.format_exc() - - -def extract_small_model(path, name, sr, if_f0, info, version): - try: - ckpt = torch.load(path, map_location="cpu") - if "model" in ckpt: - ckpt = ckpt["model"] - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = ckpt[key].half() - if sr == "40k": - opt["config"] = [ - 1025, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 10, 2, 2], - 512, - [16, 16, 4, 4], - 109, - 256, - 40000, - ] - elif sr == "48k": - opt["config"] = [ - 1025, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 6, 2, 2, 2], - 512, - [16, 16, 4, 4, 4], - 109, - 256, - 48000, - ] - elif sr == "32k": - opt["config"] = [ - 513, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 4, 2, 2, 2], - 512, - [16, 16, 4, 4, 4], - 109, - 256, - 32000, - ] - if info == "": - info = "Extracted model." - opt["info"] = info - opt["version"] = version - opt["sr"] = sr - opt["f0"] = int(if_f0) - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() - - -def change_info(path, info, name): - try: - ckpt = torch.load(path, map_location="cpu") - ckpt["info"] = info - if name == "": - name = os.path.basename(path) - torch.save(ckpt, "weights/%s" % name) - return "Success." - except: - return traceback.format_exc() - - -def merge(path1, path2, alpha1, sr, f0, info, name, version): - try: - - def extract(ckpt): - a = ckpt["model"] - opt = OrderedDict() - opt["weight"] = {} - for key in a.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = a[key] - return opt - - ckpt1 = torch.load(path1, map_location="cpu") - ckpt2 = torch.load(path2, map_location="cpu") - cfg = ckpt1["config"] - if "model" in ckpt1: - ckpt1 = extract(ckpt1) - else: - ckpt1 = ckpt1["weight"] - if "model" in ckpt2: - ckpt2 = extract(ckpt2) - else: - ckpt2 = ckpt2["weight"] - if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): - return "Fail to merge the models. The model architectures are not the same." - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt1.keys(): - # try: - if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: - min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) - opt["weight"][key] = ( - alpha1 * (ckpt1[key][:min_shape0].float()) - + (1 - alpha1) * (ckpt2[key][:min_shape0].float()) - ).half() - else: - opt["weight"][key] = ( - alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float()) - ).half() - # except: - # pdb.set_trace() - opt["config"] = cfg - """ - if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000] - elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000] - elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000] - """ - opt["sr"] = sr - opt["f0"] = 1 if f0 == i18n("是") else 0 - opt["version"] = version - opt["info"] = info - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() diff --git a/spaces/llmonitor/benchmarks/app/prompts/layout.js b/spaces/llmonitor/benchmarks/app/prompts/layout.js deleted file mode 100644 index 121aab52585e611404d82ab958cdb31cc2747a3d..0000000000000000000000000000000000000000 --- a/spaces/llmonitor/benchmarks/app/prompts/layout.js +++ /dev/null @@ -1,20 +0,0 @@ -import { cookies } from "next/headers" -import Link from "next/link" - -export default function PromptsLayout({ children }) { - const cookiesList = cookies() - const token = cookiesList.get("token") - - return ( - <> -
- {"dataset menu: "} - current dataset {" | "} - vote {" | "} - submit -
-- {children} - - ) -} diff --git a/spaces/luxuedong/lxd/Dockerfile b/spaces/luxuedong/lxd/Dockerfile deleted file mode 100644 index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000 --- a/spaces/luxuedong/lxd/Dockerfile +++ /dev/null @@ -1,36 +0,0 @@ -FROM node:18 - - -ARG DEBIAN_FRONTEND=noninteractive - -ENV BING_HEADER "" - -# Set home to the user's home directory -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -# Set up a new user named "user" with user ID 1000 -RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME - -# Switch to the "user" user -USER user - -# Set the working directory to the user's home directory -WORKDIR $HOME/app - -# Install app dependencies -# A wildcard is used to ensure both package.json AND package-lock.json are copied -# where available (npm@5+) -COPY --chown=user package*.json $HOME/app/ - -RUN npm install - -# Copy the current directory contents into the container at $HOME/app setting the owner to the user -COPY --chown=user . $HOME/app/ - -RUN npm run build - -ENV PORT 7860 -EXPOSE 7860 - -CMD npm start diff --git a/spaces/ma-xu/LIVE/pybind11/include/pybind11/eval.h b/spaces/ma-xu/LIVE/pybind11/include/pybind11/eval.h deleted file mode 100644 index ba82cf42ae3673a3de391eb55777ef413c43dc33..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/pybind11/include/pybind11/eval.h +++ /dev/null @@ -1,132 +0,0 @@ -/* - pybind11/exec.h: Support for evaluating Python expressions and statements - from strings and files - - Copyright (c) 2016 Klemens Morgenstern