

"""simple docstring"""


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					:Optional[Any]    = {
    "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  _lowercase					(    __lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase							)


a					:Any    = {
    "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  _lowercase					(    __lowerCAmelCase							)       ->				Optional[int]:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	list(s_dict.keys()							)
      for key in keys:
            SCREAMING_SNAKE_CASE__   :      int      		=	key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	new_key.replace(__lowerCAmelCase  ,					__lowerCAmelCase							)

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

            SCREAMING_SNAKE_CASE__   :      Dict      		=	s_dict.pop(__lowerCAmelCase							)
      return s_dict




def  _lowercase					(    __lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Any      		=	emb.weight.shape
      SCREAMING_SNAKE_CASE__   :      str      		=	nn.Linear(__lowerCAmelCase  ,					__lowerCAmelCase  ,					bias=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	emb.weight.data
      return lin_layer




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				bytes:
      os.makedirs(__lowerCAmelCase  ,					exist_ok=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.basename(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	url.split("""/"""							)[-2]
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

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

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

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

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

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	WhisperConfig(
          vocab_size=dimensions["""n_vocab"""]  ,					encoder_ffn_dim=__lowerCAmelCase  ,					decoder_ffn_dim=__lowerCAmelCase  ,					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"""]  ,					)

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	WhisperForConditionalGeneration(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	model.model.load_state_dict(__lowerCAmelCase  ,					strict=__lowerCAmelCase							)
      if len(__lowerCAmelCase							) > 0 and not set(__lowerCAmelCase							) <= {
          "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:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	make_linear_from_emb(model.model.decoder.embed_tokens							)
      else:
            SCREAMING_SNAKE_CASE__   :      int      		=	proj_out_weights

      model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
  a					:Tuple    = 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					:int    = parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




