




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


_snake_case										=       {
    "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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Union[str, Any]			=						["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(snake_case_,snake_case_     )


_snake_case										=       {
    "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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Tuple			=						list(s_dict.keys()     )
      for key in keys:
            _A							:     List[Any]			=						key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        _A							:     Optional[Any]			=						new_key.replace(snake_case_,snake_case_     )

            print(f'''{key} -> {new_key}'''     )

            _A							:     List[Any]			=						s_dict.pop(snake_case_     )
      return s_dict







def 				lowerCAmelCase_	(     snake_case_     ):
      _A			,   _A							:     List[Any]			=						emb.weight.shape
      _A							:     Union[str, Any]			=						nn.Linear(snake_case_,snake_case_,bias=snake_case_     )
      _A							:     Dict			=						emb.weight.data
      return lin_layer







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      os.makedirs(snake_case_,exist_ok=snake_case_     )
      _A							:     Dict			=						os.path.basename(snake_case_     )

      _A							:     Any			=						url.split("""/"""     )[-2]
      _A							:     int			=						os.path.join(snake_case_,snake_case_     )

      if os.path.exists(snake_case_     ) and not os.path.isfile(snake_case_     ):
            raise RuntimeError(f'''{download_target} exists and is not a regular file'''     )

      if os.path.isfile(snake_case_     ):
            _A							:     List[Any]			=						open(snake_case_,"""rb"""     ).read()
            if hashlib.shaaaa(snake_case_     ).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(snake_case_     ) as source, open(snake_case_,"""wb"""     ) as output:
            with tqdm(
                total=int(source.info().get("""Content-Length"""     )     ),ncols=80,unit="""iB""",unit_scale=snake_case_,unit_divisor=1024     ) as loop:
                  while True:
                        _A							:     Dict			=						source.read(8192     )
                        if not buffer:
                              break

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

      _A							:     Any			=						open(snake_case_,"""rb"""     ).read()
      if hashlib.shaaaa(snake_case_     ).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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if ".pt" not in checkpoint_path:
            _A							:     List[str]			=						_download(_MODELS[checkpoint_path]     )
      else:
            _A							:     Any			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						original_checkpoint["""dims"""]
      _A							:     str			=						original_checkpoint["""model_state_dict"""]
      _A							:     Tuple			=						state_dict["""decoder.token_embedding.weight"""]
      remove_ignore_keys_(snake_case_     )
      rename_keys(snake_case_     )
      _A							:     str			=						True
      _A							:     Optional[Any]			=						state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

      _A							:     Optional[int]			=						WhisperConfig(
          vocab_size=dimensions["""n_vocab"""],encoder_ffn_dim=snake_case_,decoder_ffn_dim=snake_case_,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"""],)

      _A							:     str			=						WhisperForConditionalGeneration(snake_case_     )
      _A			,   _A							:     Union[str, Any]			=						model.model.load_state_dict(snake_case_,strict=snake_case_     )
      if len(snake_case_     ) > 0 and not set(snake_case_     ) <= {
          "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:
            _A							:     Tuple			=						make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            _A							:     Tuple			=						proj_out_weights

      model.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       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.")
  _snake_case										=       parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      while b:
            _A			,   _A							:     List[str]			=						b, a % b
      return a







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      return a if b == 0 else euclidean_gcd_recursive(snake_case_,a % b     )







def 				lowerCAmelCase_	(     ):
      print(f'''euclidean_gcd(3, 5) = {euclidean_gcd(3,5     )}'''     )
      print(f'''euclidean_gcd(5, 3) = {euclidean_gcd(5,3     )}'''     )
      print(f'''euclidean_gcd(1, 3) = {euclidean_gcd(1,3     )}'''     )
      print(f'''euclidean_gcd(3, 6) = {euclidean_gcd(3,6     )}'''     )
      print(f'''euclidean_gcd(6, 3) = {euclidean_gcd(6,3     )}'''     )

      print(f'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3,5     )}'''     )
      print(f'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5,3     )}'''     )
      print(f'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1,3     )}'''     )
      print(f'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3,6     )}'''     )
      print(f'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6,3     )}'''     )


if __name__ == "__main__":
  main()

