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


__snake_case						=			{
    '''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_       ( __lowerCAmelCase  )->     List[Any]:






      '''simple docstring'''





      UpperCAmelCase			:       List[str]			     =['''layers''', '''blocks''']
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase						,    _UpperCamelCase  )


__snake_case						=			{
    '''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_       ( __lowerCAmelCase  )->     int:






      '''simple docstring'''





      UpperCAmelCase			:       str			     =list(s_dict.keys()  )
      for key in keys:
            UpperCAmelCase			:       Optional[int]			     =key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        UpperCAmelCase			:       List[str]			     =new_key.replace(_UpperCamelCase						,    _UpperCamelCase  )

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

            UpperCAmelCase			:       Tuple			     =s_dict.pop(_UpperCamelCase  )
      return s_dict





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     int:






      '''simple docstring'''





      UpperCAmelCase			:       Dict			     =emb.weight.shape
      UpperCAmelCase			:       Tuple			     =nn.Linear(_UpperCamelCase						,    _UpperCamelCase						,    bias=_UpperCamelCase  )
      UpperCAmelCase			:       Any			     =emb.weight.data
      return lin_layer





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     bytes:






      '''simple docstring'''





      os.makedirs(_UpperCamelCase						,    exist_ok=_UpperCamelCase  )
      UpperCAmelCase			:       List[Any]			     =os.path.basename(_UpperCamelCase  )

      UpperCAmelCase			:       Any			     =url.split('''/'''  )[-2]
      UpperCAmelCase			:       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  ):
            UpperCAmelCase			:       Union[str, 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:
                        UpperCAmelCase			:       Dict			     =source.read(81_92  )
                        if not buffer:
                              break

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

      UpperCAmelCase			:       Any			     =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      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     int:






      '''simple docstring'''





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

      UpperCAmelCase			:       List[str]			     =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''']						,    )

      UpperCAmelCase			:       Union[str, Any]			     =WhisperForConditionalGeneration(_UpperCamelCase  )
      UpperCAmelCase			:       List[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:
            UpperCAmelCase			:       List[str]			     =make_linear_from_emb(model.model.decoder.embed_tokens  )
      else:
            UpperCAmelCase			:       Any			     =proj_out_weights

      model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
     __snake_case						=			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.''')
     __snake_case						=			parser.parse_args()

     convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
