



'''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__    :   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_   (		_UpperCAmelCase    :      List[Any]				)    ->  Union[str, Any]:
      __snake_case :   Tuple													=    ['layers', 'blocks']
      for k in ignore_keys:
            state_dict.pop(_UpperCAmelCase ,_UpperCAmelCase				)


A__    :   List[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_   (		_UpperCAmelCase    :      List[Any]				)    ->  List[str]:
      __snake_case :   Optional[int]													=    list(s_dict.keys()				)
      for key in keys:
            __snake_case :   Union[str, Any]													=    key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        __snake_case :   str													=    new_key.replace(_UpperCAmelCase ,_UpperCAmelCase				)

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

            __snake_case :   int													=    s_dict.pop(_UpperCAmelCase				)
      return s_dict





def   a_   (		_UpperCAmelCase    :      Optional[Any]				)    ->  List[Any]:
      __snake_case							,     __snake_case :   Optional[Any]													=    emb.weight.shape
      __snake_case :   List[str]													=    nn.Linear(_UpperCAmelCase ,_UpperCAmelCase ,bias=_UpperCAmelCase				)
      __snake_case :   Optional[Any]													=    emb.weight.data
      return lin_layer





def   a_   (		_UpperCAmelCase    :      str ,_UpperCAmelCase    :      str				)    ->  bytes:
      os.makedirs(_UpperCAmelCase ,exist_ok=_UpperCAmelCase				)
      __snake_case :   str													=    os.path.basename(_UpperCAmelCase				)

      __snake_case :   Optional[int]													=    url.split('/'				)[-2]
      __snake_case :   Union[str, Any]													=    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				):
            __snake_case :   List[Any]													=    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=10_24				) as loop:
                  while True:
                        __snake_case :   List[str]													=    source.read(81_92				)
                        if not buffer:
                              break

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

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

      __snake_case :   Union[str, Any]													=    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'] ,)

      __snake_case :   int													=    WhisperForConditionalGeneration(_UpperCAmelCase				)
      __snake_case							,     __snake_case :   Union[str, Any]													=    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:
            __snake_case :   Union[str, Any]													=    make_linear_from_emb(model.model.decoder.embed_tokens				)
      else:
            __snake_case :   str													=    proj_out_weights

      model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
    A__    :   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.''')
    A__    :   Optional[int]           =					parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''





from ..utils import DummyObject, requires_backends



class  snake_case__						(    metaclass=SCREAMING_SNAKE_CASE_				):
 A__				= ['''note_seq''']
 def __init__(   self						:							Tuple      ,     *__a						:							int      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(self      ,     ['note_seq']  )
 @classmethod
 def A_   (   cls						:							Optional[Any]      ,     *__a						:							Optional[Any]      ,     **__a						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )





 @classmethod
 def A_   (   cls						:							List[Any]      ,     *__a						:							Union[str, Any]      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )



