


import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil




class    UpperCAmelCase					:




			def __init__(						self							:		Dict       ,       __magic_name__							:		str = "cpu"       ,       __magic_name__							:		str = "openai/clip-vit-large-patch14"						):







						"""simple docstring"""







						UpperCamelCase         =					device
						UpperCamelCase         =					CLIPTokenizerFast.from_pretrained(__magic_name__						)
						UpperCamelCase         =					[0.48_145_466, 0.4_578_275, 0.40_821_073]
						UpperCamelCase         =					[0.26_862_954, 0.26_130_258, 0.27_577_711]
						UpperCamelCase         =					torchvision.transforms.Normalize(self.image_mean       ,       self.image_std						)
						UpperCamelCase         =					torchvision.transforms.Resize(2_2_4						)
						UpperCamelCase         =					torchvision.transforms.CenterCrop(2_2_4						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					self.resize(__magic_name__						)
						UpperCamelCase         =					self.center_crop(__magic_name__						)
						UpperCamelCase         =					self.normalize(__magic_name__						)
						return images





			def __call__(						self							:		Union[str, Any]       ,       __magic_name__							:		str=None       ,       __magic_name__							:		Any=None       ,       **__magic_name__							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer(text=__magic_name__       ,       **__magic_name__						)
						UpperCamelCase         =					self.preprocess_img(__magic_name__						)
						UpperCamelCase         =					{key: value.to(self.device						) for (key, value) in encoding.items()}
						return encoding




class    UpperCAmelCase					(       nn.Module    ):




			def __init__(						self							:		Tuple       ,       __magic_name__							:		Union[str, Any]=1_0       ,       __magic_name__							:		Union[str, Any]=0.01       ,       __magic_name__							:		str=None       ,       __magic_name__							:		Any=None       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		Optional[Any]=None       ,       __magic_name__							:		Tuple=None       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		Tuple=False       ,       __magic_name__							:		List[str]=True       ,       __magic_name__							:		Union[str, Any]="image"       ,       __magic_name__							:		Union[str, Any]=True       ,       __magic_name__							:		List[str]=False       ,       __magic_name__							:		Any=False       ,       __magic_name__							:		List[Any]=False       ,       ):







						"""simple docstring"""







						super().__init__()
						UpperCamelCase         =					None
						UpperCamelCase         =					device if device else get_device()
						if vqgan:
									UpperCamelCase         =					vqgan
						else:
									UpperCamelCase         =					load_vqgan(self.device       ,       conf_path=__magic_name__       ,       ckpt_path=__magic_name__						)
						self.vqgan.eval()
						if clip:
									UpperCamelCase         =					clip
						else:
									UpperCamelCase         =					CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""						)
						self.clip.to(self.device						)
						UpperCamelCase         =					ProcessorGradientFlow(device=self.device						)

						UpperCamelCase         =					iterations
						UpperCamelCase         =					lr
						UpperCamelCase         =					log
						UpperCamelCase         =					make_grid
						UpperCamelCase         =					return_val
						UpperCamelCase         =					quantize
						UpperCamelCase         =					self.vqgan.decoder.z_shape




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		Any=None       ,       __magic_name__							:		Tuple=5       ,       __magic_name__							:		Any=True						):







						"""simple docstring"""







						UpperCamelCase         =					[]
						if output_path is None:
									UpperCamelCase         =					"""./animation.gif"""
						if input_path is None:
									UpperCamelCase         =					self.save_path
						UpperCamelCase         =					sorted(glob(input_path + """/*"""						)						)
						if not len(__magic_name__						):
									raise ValueError(
									    """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
									    """ function?)"""						)
						if len(__magic_name__						) == 1:
									print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""						)
						UpperCamelCase         =					total_duration / len(__magic_name__						)
						UpperCamelCase         =					[frame_duration] * len(__magic_name__						)
						if extend_frames:
									UpperCamelCase         =					1.5
									UpperCamelCase         =					3
						for file_name in paths:
									if file_name.endswith(""".png"""						):
												images.append(imageio.imread(__magic_name__						)						)
						imageio.mimsave(__magic_name__       ,       __magic_name__       ,       duration=__magic_name__						)
						print(F'gif saved to {output_path}'						)




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		List[Any]=None						):







						"""simple docstring"""







						if not (path or img):
									raise ValueError("""Input either path or tensor"""						)
						if img is not None:
									raise NotImplementedError
						UpperCamelCase         =					preprocess(Image.open(__magic_name__						)       ,       target_image_size=2_5_6						).to(self.device						)
						UpperCamelCase         =					preprocess_vqgan(__magic_name__						)
						UpperCamelCase  ,  *UpperCamelCase         =					self.vqgan.encode(__magic_name__						)
						return z




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.latent.detach().requires_grad_()
						UpperCamelCase         =					base_latent + transform_vector
						if self.quantize:
									UpperCamelCase  ,  *UpperCamelCase         =					self.vqgan.quantize(__magic_name__						)
						else:
									UpperCamelCase         =					trans_latent
						return self.vqgan.decode(__magic_name__						)




			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Optional[Any]=None						):







						"""simple docstring"""







						UpperCamelCase         =					self.clip_preprocessor(text=__magic_name__       ,       images=__magic_name__       ,       return_tensors="""pt"""       ,       padding=__magic_name__						)
						UpperCamelCase         =					self.clip(**__magic_name__						)
						UpperCamelCase         =					clip_outputs.logits_per_image
						if weights is not None:
									UpperCamelCase         =					similarity_logits * weights
						return similarity_logits.sum()




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		Dict       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self._get_clip_similarity(pos_prompts["""prompts"""]       ,       __magic_name__       ,       weights=(1 / pos_prompts["""weights"""])						)
						if neg_prompts:
									UpperCamelCase         =					self._get_clip_similarity(neg_prompts["""prompts"""]       ,       __magic_name__       ,       weights=neg_prompts["""weights"""]						)
						else:
									UpperCamelCase         =					torch.tensor([1]       ,       device=self.device						)
						UpperCamelCase         =					-torch.log(__magic_name__						) + torch.log(__magic_name__						)
						return loss




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		int       ,       __magic_name__							:		int       ,       __magic_name__							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					torch.randn_like(self.latent       ,       requires_grad=__magic_name__       ,       device=self.device						)
						UpperCamelCase         =					torch.optim.Adam([vector]       ,       lr=self.lr						)

						for i in range(self.iterations						):
									optim.zero_grad()
									UpperCamelCase         =					self._add_vector(__magic_name__						)
									UpperCamelCase         =					loop_post_process(__magic_name__						)
									UpperCamelCase         =					self._get_CLIP_loss(__magic_name__       ,       __magic_name__       ,       __magic_name__						)
									print("""CLIP loss"""       ,       __magic_name__						)
									if self.log:
												wandb.log({"""CLIP Loss""": clip_loss}						)
									clip_loss.backward(retain_graph=__magic_name__						)
									optim.step()
									if self.return_val == "image":
												yield custom_to_pil(transformed_img[0]						)
									else:
												yield vector




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		List[Any]						):







						"""simple docstring"""







						wandb.init(reinit=__magic_name__       ,       project="""face-editor"""						)
						wandb.config.update({"""Positive Prompts""": positive_prompts}						)
						wandb.config.update({"""Negative Prompts""": negative_prompts}						)
						wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}						)
						if image_path:
									UpperCamelCase         =					Image.open(__magic_name__						)
									UpperCamelCase         =					image.resize((2_5_6, 2_5_6)						)
									wandb.log("""Original Image"""       ,       wandb.Image(__magic_name__						)						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[Any]						):







						"""simple docstring"""







						if not prompts:
									return []
						UpperCamelCase         =					[]
						UpperCamelCase         =					[]
						if isinstance(__magic_name__       ,       __magic_name__						):
									UpperCamelCase         =					[prompt.strip() for prompt in prompts.split("""|"""						)]
						for prompt in prompts:
									if isinstance(__magic_name__       ,       (tuple, list)						):
												UpperCamelCase         =					prompt[0]
												UpperCamelCase         =					float(prompt[1]						)
									elif ":" in prompt:
												UpperCamelCase  ,  UpperCamelCase         =					prompt.split(""":"""						)
												UpperCamelCase         =					float(__magic_name__						)
									else:
												UpperCamelCase         =					prompt
												UpperCamelCase         =					1.0
									processed_prompts.append(__magic_name__						)
									weights.append(__magic_name__						)
						return {
						    "prompts": processed_prompts,
						    "weights": torch.tensor(__magic_name__       ,       device=self.device						),
						}





			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		Optional[Any]=None       ,       __magic_name__							:		Optional[int]=True       ,       __magic_name__							:		Optional[Any]=False       ,       __magic_name__							:		Any=True       ,       __magic_name__							:		Dict=True       ,       __magic_name__							:		Dict=None       ,       ):







						"""simple docstring"""







						if image_path:
									UpperCamelCase         =					self._get_latent(__magic_name__						)
						else:
									UpperCamelCase         =					torch.randn(self.latent_dim       ,       device=self.device						)
						if self.log:
									self._init_logging(__magic_name__       ,       __magic_name__       ,       __magic_name__						)

						assert pos_prompts, "You must provide at least one positive prompt."
						UpperCamelCase         =					self.process_prompts(__magic_name__						)
						UpperCamelCase         =					self.process_prompts(__magic_name__						)
						if save_final and save_path is None:
									UpperCamelCase         =					os.path.join("""./outputs/"""       ,       """_""".join(pos_prompts["""prompts"""]						)						)
						if not os.path.exists(__magic_name__						):
									os.makedirs(__magic_name__						)
						else:
									UpperCamelCase         =					save_path + """_""" + get_timestamp()
									os.makedirs(__magic_name__						)
						UpperCamelCase         =					save_path

						UpperCamelCase         =					self.vqgan.decode(self.latent						)[0]
						if show_intermediate:
									print("""Original Image"""						)
									show_pil(custom_to_pil(__magic_name__						)						)

						UpperCamelCase         =					loop_post_process(__magic_name__						)
						for iter, transformed_img in enumerate(self._optimize_CLIP(__magic_name__       ,       __magic_name__       ,       __magic_name__						)						):
									if show_intermediate:
												show_pil(__magic_name__						)
									if save_intermediate:
												transformed_img.save(os.path.join(self.save_path       ,       F'iter_{iter:03d}.png'						)						)
									if self.log:
												wandb.log({"""Image""": wandb.Image(__magic_name__						)}						)
						if show_final:
									show_pil(__magic_name__						)
						if save_final:
									transformed_img.save(os.path.join(self.save_path       ,       F'iter_{iter:03d}_final.png'						)						)








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			__snake_case            =   None


__snake_case            =   logging.get_logger(__name__)


__snake_case            =   {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__snake_case            =   {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__snake_case            =   {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
__snake_case            =   ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= ["""input_ids""", """attention_mask"""]
			lowercase						= MBartTokenizer

			lowercase						= []
			lowercase						= []




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		str=None       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		Dict="<s>"       ,       __magic_name__							:		Union[str, Any]="</s>"       ,       __magic_name__							:		List[str]="</s>"       ,       __magic_name__							:		Optional[int]="<s>"       ,       __magic_name__							:		int="<unk>"       ,       __magic_name__							:		str="<pad>"       ,       __magic_name__							:		List[str]="<mask>"       ,       __magic_name__							:		Tuple=None       ,       __magic_name__							:		Dict=None       ,       __magic_name__							:		Dict=None       ,       **__magic_name__							:		Tuple       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					AddedToken(__magic_name__       ,       lstrip=__magic_name__       ,       rstrip=__magic_name__						) if isinstance(__magic_name__       ,       __magic_name__						) else mask_token

						super().__init__(
						    vocab_file=__magic_name__       ,       tokenizer_file=__magic_name__       ,       bos_token=__magic_name__       ,       eos_token=__magic_name__       ,       sep_token=__magic_name__       ,       cls_token=__magic_name__       ,       unk_token=__magic_name__       ,       pad_token=__magic_name__       ,       mask_token=__magic_name__       ,       src_lang=__magic_name__       ,       tgt_lang=__magic_name__       ,       additional_special_tokens=__magic_name__       ,       **__magic_name__       ,       )

						UpperCamelCase         =					vocab_file
						UpperCamelCase         =					False if not self.vocab_file else True

						UpperCamelCase         =					FAIRSEQ_LANGUAGE_CODES.copy()

						if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

						self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}						)
						UpperCamelCase         =					{
						    lang_code: self.convert_tokens_to_ids(__magic_name__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						UpperCamelCase         =					src_lang if src_lang is not None else """en_XX"""
						UpperCamelCase         =					self.convert_tokens_to_ids(self._src_lang						)
						UpperCamelCase         =					tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						return self._src_lang




			@src_lang.setter
			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					[self.sep_token_id]
						UpperCamelCase         =					[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str]       ,       __magic_name__							:		Optional[str]       ,       **__magic_name__							:		Tuple						):







						"""simple docstring"""







						if src_lang is None or tgt_lang is None:
									raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""						)
						UpperCamelCase         =					src_lang
						UpperCamelCase         =					self(__magic_name__       ,       add_special_tokens=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__						)
						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					tgt_lang_id
						return inputs




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		str = "en_XX"       ,       __magic_name__							:		Optional[List[str]] = None       ,       __magic_name__							:		str = "ro_RO"       ,       **__magic_name__							:		int       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					src_lang
						UpperCamelCase         =					tgt_lang
						return super().prepare_seqaseq_batch(__magic_name__       ,       __magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						return self.set_src_lang_special_tokens(self.src_lang						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						return self.set_tgt_lang_special_tokens(self.tgt_lang						)




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,       self.prefix_tokens + self.suffix_tokens						)						)       ,       )




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,       self.prefix_tokens + self.suffix_tokens						)						)       ,       )





			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







						"""simple docstring"""







						if not self.can_save_slow_tokenizer:
									raise ValueError(
									    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
									    """tokenizer."""						)

						if not os.path.isdir(__magic_name__						):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'						)
									return
						UpperCamelCase         =					os.path.join(
						    __magic_name__       ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(__magic_name__						):
									copyfile(self.vocab_file       ,       __magic_name__						)

						return (out_vocab_file,)




