




import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


a_   		=     {
    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						['''layers''', '''blocks''']
    for k in ignore_keys:
        state_dict.pop(lowercase_					,     lowercase_      )


a_   		=     {
    "blocks": "layers",
    "mlp.0": "fc1",
    "mlp.2": "fc2",
    "mlp_ln": "final_layer_norm",
    ".attn.query": ".self_attn.q_proj",
    ".attn.key": ".self_attn.k_proj",
    ".attn.value": ".self_attn.v_proj",
    ".attn_ln": ".self_attn_layer_norm",
    ".attn.out": ".self_attn.out_proj",
    ".cross_attn.query": ".encoder_attn.q_proj",
    ".cross_attn.key": ".encoder_attn.k_proj",
    ".cross_attn.value": ".encoder_attn.v_proj",
    ".cross_attn_ln": ".encoder_attn_layer_norm",
    ".cross_attn.out": ".encoder_attn.out_proj",
    "decoder.ln.": "decoder.layer_norm.",
    "encoder.ln.": "encoder.layer_norm.",
    "token_embedding": "embed_tokens",
    "encoder.positional_embedding": "encoder.embed_positions.weight",
    "decoder.positional_embedding": "decoder.embed_positions.weight",
    "ln_post": "layer_norm",
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						list(s_dict.keys()      )
    for key in keys:
        __UpperCamelCase  		=						key
        for k, v in WHISPER_MAPPING.items():
            if k in key:
                __UpperCamelCase  		=						new_key.replace(lowercase_					,     lowercase_      )

        print(F"{key} -> {new_key}"      )

        __UpperCamelCase  		=						s_dict.pop(lowercase_      )
    return s_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						emb.weight.shape
    __UpperCamelCase  		=						nn.Linear(lowercase_					,     lowercase_					,     bias=lowercase_      )
    __UpperCamelCase  		=						emb.weight.data
    return lin_layer




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	bytes:

    """simple docstring"""


    os.makedirs(lowercase_					,     exist_ok=lowercase_      )
    __UpperCamelCase  		=						os.path.basename(lowercase_      )

    __UpperCamelCase  		=						url.split('''/'''      )[-2]
    __UpperCamelCase  		=						os.path.join(lowercase_					,     lowercase_      )

    if os.path.exists(lowercase_      ) and not os.path.isfile(lowercase_      ):
        raise RuntimeError(F"{download_target} exists and is not a regular file"      )

    if os.path.isfile(lowercase_      ):
        __UpperCamelCase  		=						open(lowercase_					,     '''rb'''      ).read()
        if hashlib.shaaaa(lowercase_      ).hexdigest() == expected_shaaaa:
            return model_bytes
        else:
            warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"      )

    with urllib.request.urlopen(lowercase_      ) as source, open(lowercase_					,     '''wb'''      ) as output:
        with tqdm(
            total=int(source.info().get('''Content-Length'''      )      )					,     ncols=80					,     unit='''iB'''					,     unit_scale=lowercase_					,     unit_divisor=10_24      ) as loop:
            while True:
                __UpperCamelCase  		=						source.read(81_92      )
                if not buffer:
                    break

                output.write(lowercase_      )
                loop.update(len(lowercase_      )      )

    __UpperCamelCase  		=						open(lowercase_					,     '''rb'''      ).read()
    if hashlib.shaaaa(lowercase_      ).hexdigest() != expected_shaaaa:
        raise RuntimeError(
            '''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'''      )

    return model_bytes




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    if ".pt" not in checkpoint_path:
        __UpperCamelCase  		=						_download(_MODELS[checkpoint_path]      )
    else:
        __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    __UpperCamelCase  		=						original_checkpoint['''dims''']
    __UpperCamelCase  		=						original_checkpoint['''model_state_dict''']
    __UpperCamelCase  		=						state_dict['''decoder.token_embedding.weight''']
    remove_ignore_keys_(lowercase_      )
    rename_keys(lowercase_      )
    __UpperCamelCase  		=						True
    __UpperCamelCase  		=						state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

    __UpperCamelCase  		=						WhisperConfig(
        vocab_size=dimensions['''n_vocab''']					,     encoder_ffn_dim=lowercase_					,     decoder_ffn_dim=lowercase_					,     num_mel_bins=dimensions['''n_mels''']					,     d_model=dimensions['''n_audio_state''']					,     max_target_positions=dimensions['''n_text_ctx''']					,     encoder_layers=dimensions['''n_audio_layer''']					,     encoder_attention_heads=dimensions['''n_audio_head''']					,     decoder_layers=dimensions['''n_text_layer''']					,     decoder_attention_heads=dimensions['''n_text_state''']					,     max_source_positions=dimensions['''n_audio_ctx''']					,     )

    __UpperCamelCase  		=						WhisperForConditionalGeneration(lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						model.model.load_state_dict(lowercase_					,     strict=lowercase_      )
    if len(lowercase_      ) > 0 and not set(lowercase_      ) <= {
        "encoder.embed_positions.weights",
        "decoder.embed_positions.weights",
    }:
        raise ValueError(
            '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
            F" but all the following weights are missing {missing}"      )

    if tie_embeds:
        __UpperCamelCase  		=						make_linear_from_emb(model.model.decoder.embed_tokens      )
    else:
        __UpperCamelCase  		=						proj_out_weights

    model.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 # # Required parameters
 parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 a_   		=     parser.parse_args()

 convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)












import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
 print("Googling.....")
 a_   		=     "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
 a_   		=     requests.get(url, headers={"UserAgent": UserAgent().random})
 # res.raise_for_status()
 with open("project1a.html", "wb") as out_file:  # only for knowing the class
  for data in res.iter_content(1_0000):
   out_file.write(data)
 a_   		=     BeautifulSoup(res.text, "html.parser")
 a_   		=     list(soup.select(".eZt8xd"))[:5]

 print(len(links))
 for link in links:
  if link.text == "Maps":
   webbrowser.open(link.get("href"))
  else:
   webbrowser.open(f"""https://google.com{link.get('href')}""")






