



'''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


lowerCAmelCase:					List[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       lowerCamelCase__       (						_A				):
  a						:					List[str]        =     ['layers', 'blocks']
  for k in ignore_keys:
    state_dict.pop(A__ ,			A__				)


lowerCAmelCase:					int						       =						{
    """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__       (						_A				):
  a						:					Any        =     list(s_dict.keys()				)
  for key in keys:
    a						:					Optional[Any]        =     key
    for k, v in WHISPER_MAPPING.items():
      if k in key:
        a						:					Union[str, Any]        =     new_key.replace(A__ ,			A__				)

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

    a						:					int        =     s_dict.pop(A__				)
  return s_dict
def       lowerCamelCase__       (						_A				):
  a						:					List[str]        =     emb.weight.shape
  a						:					Tuple        =     nn.Linear(A__ ,			A__ ,			bias=A__				)
  a						:					Any        =     emb.weight.data
  return lin_layer
def       lowerCamelCase__       (						_A ,			_A				):
  os.makedirs(A__ ,			exist_ok=A__				)
  a						:					Union[str, Any]        =     os.path.basename(A__				)

  a						:					Any        =     url.split('/'				)[-2]
  a						:					Dict        =     os.path.join(A__ ,			A__				)

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

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

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

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

  a						:					str        =     WhisperConfig(
      vocab_size=dimensions['n_vocab'] ,			encoder_ffn_dim=A__ ,			decoder_ffn_dim=A__ ,			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						:					int        =     WhisperForConditionalGeneration(A__				)
  a						:					Dict        =     model.model.load_state_dict(A__ ,			strict=A__				)
  if len(A__				) > 0 and not set(A__				) <= {
      "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						:					Any        =     make_linear_from_emb(model.model.decoder.embed_tokens				)
  else:
    a						:					Union[str, Any]        =     proj_out_weights

  model.save_pretrained(A__				)


if __name__ == "__main__":
     lowerCAmelCase:					int						       =						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.')
     lowerCAmelCase:					List[str]						       =						parser.parse_args()

     convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


