
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


lowercase  : Optional[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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       lowercase							:					List[str]	     =				["layers", "blocks"]
       for k in ignore_keys:
              state_dict.pop(_lowercase ,     _lowercase    )


lowercase  : Union[str, 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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					Dict	     =				list(s_dict.keys()    )
       for key in keys:
              lowercase							:					Tuple	     =				key
              for k, v in WHISPER_MAPPING.items():
                     if k in key:
                            lowercase							:					List[Any]	     =				new_key.replace(_lowercase ,     _lowercase    )

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

              lowercase							:					List[Any]	     =				s_dict.pop(_lowercase    )
       return s_dict







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   bytes:
       os.makedirs(_lowercase ,     exist_ok=_lowercase    )
       lowercase							:					Dict	     =				os.path.basename(_lowercase    )

       lowercase							:					List[str]	     =				url.split("""/"""    )[-2]
       lowercase							:					Optional[int]	     =				os.path.join(_lowercase ,     _lowercase    )

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

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

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

       lowercase							:					Optional[Any]	     =				open(_lowercase ,     """rb"""    ).read()
       if hashlib.shaaaa(_lowercase    ).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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:






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

       lowercase							:					Tuple	     =				WhisperConfig(
           vocab_size=dimensions["""n_vocab"""] ,     encoder_ffn_dim=_lowercase ,     decoder_ffn_dim=_lowercase ,     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"""] ,     )

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

       model.save_pretrained(_lowercase    )


if __name__ == "__main__":
  lowercase  : Union[str, Any]						   =							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.""")
  lowercase  : Union[str, Any]						   =							parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)


from collections.abc import Callable

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   np.array:
       lowercase							:					Optional[int]	     =				int(np.ceil((x_end - xa) / step_size    )    )
       lowercase							:					List[Any]	     =				np.zeros((n + 1,)    )
       lowercase							:					Optional[int]	     =				ya
       lowercase							:					Optional[int]	     =				xa

       for k in range(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					str	     =				y[k] + step_size * ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    )
              lowercase							:					Union[str, Any]	     =				y[k] + (
                  (step_size / 2) * (ode_func(SCREAMING_SNAKE_CASE__ ,     y[k]    ) + ode_func(x + step_size ,     SCREAMING_SNAKE_CASE__    ))
              )
              x += step_size

       return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
