





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


UpperCAmelCase     :     int							=	{
    '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 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  a__     : Any  =["layers", "blocks"]
  for k in ignore_keys:
    state_dict.pop(UpperCamelCase__	,	UpperCamelCase__						)


UpperCAmelCase     :     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 					_A	(		SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  a__     : List[str]  =list(s_dict.keys()						)
  for key in keys:
    a__     : str  =key
    for k, v in WHISPER_MAPPING.items():
      if k in key:
        a__     : List[str]  =new_key.replace(UpperCamelCase__	,	UpperCamelCase__						)

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

    a__     : Tuple  =s_dict.pop(UpperCamelCase__						)
  return s_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  a__ ,							a__     : Any  =emb.weight.shape
  a__     : Any  =nn.Linear(UpperCamelCase__	,	UpperCamelCase__	,	bias=UpperCamelCase__						)
  a__     : List[Any]  =emb.weight.data
  return lin_layer
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       str						):





  """simple docstring"""



  os.makedirs(UpperCamelCase__	,	exist_ok=UpperCamelCase__						)
  a__     : Any  =os.path.basename(UpperCamelCase__						)

  a__     : List[Any]  =url.split("/"						)[-2]
  a__     : str  =os.path.join(UpperCamelCase__	,	UpperCamelCase__						)

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

  if os.path.isfile(UpperCamelCase__						):
    a__     : Dict  =open(UpperCamelCase__	,	"rb"						).read()
    if hashlib.shaaaa(UpperCamelCase__						).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(UpperCamelCase__						) as source, open(UpperCamelCase__	,	"wb"						) as output:
    with tqdm(
        total=int(source.info().get("Content-Length"						)						)	,	ncols=80	,	unit="iB"	,	unit_scale=UpperCamelCase__	,	unit_divisor=1_024						) as loop:
      while True:
        a__     : str  =source.read(8_192						)
        if not buffer:
          break

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

  a__     : int  =open(UpperCamelCase__	,	"rb"						).read()
  if hashlib.shaaaa(UpperCamelCase__						).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 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  if ".pt" not in checkpoint_path:
    a__     : int  =_download(_MODELS[checkpoint_path]						)
  else:
    a__     : List[str]  =torch.load(UpperCamelCase__	,	map_location="cpu"						)
  a__     : List[str]  =original_checkpoint["dims"]
  a__     : List[Any]  =original_checkpoint["model_state_dict"]
  a__     : Dict  =state_dict["decoder.token_embedding.weight"]
  remove_ignore_keys_(UpperCamelCase__						)
  rename_keys(UpperCamelCase__						)
  a__     : List[Any]  =True
  a__     : Dict  =state_dict["decoder.layers.0.fc1.weight"].shape[0]

  a__     : Optional[int]  =WhisperConfig(
      vocab_size=dimensions["n_vocab"]	,	encoder_ffn_dim=UpperCamelCase__	,	decoder_ffn_dim=UpperCamelCase__	,	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(UpperCamelCase__						)
  a__ ,							a__     : Tuple  =model.model.load_state_dict(UpperCamelCase__	,	strict=UpperCamelCase__						)
  if len(UpperCamelCase__						) > 0 and not set(UpperCamelCase__						) <= {
      "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__     : Optional[Any]  =make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    a__     : Tuple  =proj_out_weights

  model.save_pretrained(UpperCamelCase__						)


if __name__ == "__main__":
    UpperCAmelCase     :     str							=	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.""")
    UpperCAmelCase     :     List[Any]							=	parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)













UpperCAmelCase     :     Dict							=	[0, 2, 4, 6, 8]
UpperCAmelCase     :     Tuple							=	[1, 3, 5, 7, 9]
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if remaining_length == 0:
    if digits[0] == 0 or digits[-1] == 0:
      return 0

    for i in range(length // 2 - 1	,	-1	,	-1						):
      remainder += digits[i] + digits[length - i - 1]

      if remainder % 2 == 0:
        return 0

      remainder //= 10

    return 1

  if remaining_length == 1:
    if remainder % 2 == 0:
      return 0

    a__     : str  =0
    for digit in range(10						):
      a__     : int  =digit
      result += reversible_numbers(
          0	,	(remainder + 2 * digit) // 10	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    return result

  a__     : List[str]  =0
  for digita in range(10						):
    a__     : Optional[int]  =digita

    if (remainder + digita) % 2 == 0:
      a__     : Dict  =ODD_DIGITS
    else:
      a__     : Any  =EVEN_DIGITS

    for digita in other_parity_digits:
      a__     : Union[str, Any]  =digita
      result += reversible_numbers(
          remaining_length - 2	,	(remainder + digita + digita) // 10	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	)
  return result
def 					_A	(		SCREAMING_SNAKE_CASE					:       int = 9						):





  """simple docstring"""



  a__     : List[str]  =0
  for length in range(1	,	max_power + 1						):
    result += reversible_numbers(SCREAMING_SNAKE_CASE	,	0	,	[0] * length	,	SCREAMING_SNAKE_CASE						)
  return result


if __name__ == "__main__":
     print(F"""{solution() = }""")






