





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     :     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 					_A	(		SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



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


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					:       str						):





  """simple docstring"""



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

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

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





  """simple docstring"""



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





  """simple docstring"""



  os.makedirs(SCREAMING_SNAKE_CASE	,	exist_ok=SCREAMING_SNAKE_CASE						)
  a__     : int  =os.path.basename(SCREAMING_SNAKE_CASE						)

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

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

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

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

  a__     : Optional[int]  =open(SCREAMING_SNAKE_CASE	,	"rb"						).read()
  if hashlib.shaaaa(SCREAMING_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 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



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

  a__     : Optional[int]  =WhisperConfig(
      vocab_size=dimensions["n_vocab"]	,	encoder_ffn_dim=SCREAMING_SNAKE_CASE	,	decoder_ffn_dim=SCREAMING_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__     : List[str]  =WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE						)
  a__ ,							a__     : Union[str, Any]  =model.model.load_state_dict(SCREAMING_SNAKE_CASE	,	strict=SCREAMING_SNAKE_CASE						)
  if len(SCREAMING_SNAKE_CASE						) > 0 and not set(SCREAMING_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__     : str  =make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    a__     : List[Any]  =proj_out_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE						)


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     :     Optional[int]							=	parser.parse_args()

     convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)













from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,				split=lowerCAmelCase__		,				features=lowerCAmelCase__		,				cache_dir=lowerCAmelCase__		,				keep_in_memory=lowerCAmelCase__		,				streaming=lowerCAmelCase__		,				num_proc=lowerCAmelCase__		,				**lowerCAmelCase__		,				)
        a__     : Tuple  =path_or_paths if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							) else {self.split: path_or_paths}
        a__     : List[str]  =Text(
            cache_dir=lowerCAmelCase__		,				data_files=lowerCAmelCase__		,				features=lowerCAmelCase__		,				**lowerCAmelCase__		,				)







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



        '''simple docstring'''




        if self.streaming:
          a__     : str  =self.builder.as_streaming_dataset(split=self.split							)
        # Build regular (map-style) dataset
        else:
          a__     : Dict  =None
          a__     : Optional[Any]  =None
          a__     : Union[str, Any]  =None
          a__     : Tuple  =None

          self.builder.download_and_prepare(
              download_config=lowerCAmelCase__		,				download_mode=lowerCAmelCase__		,				verification_mode=lowerCAmelCase__		,				base_path=lowerCAmelCase__		,				num_proc=self.num_proc		,				)
          a__     : Tuple  =self.builder.as_dataset(
              split=self.split		,				verification_mode=lowerCAmelCase__		,				in_memory=self.keep_in_memory							)
        return dataset






