


"""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


_lowerCAmelCase							:    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        lowerCamelCase_(		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     ["layers", "blocks"]
   for k in ignore_keys:
      state_dict.pop(_lowerCamelCase			,		_lowerCamelCase       )


_lowerCAmelCase							:    int					   =	{
    '''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       )    ->     Any:
   '''simple docstring'''





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

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

      _lowerCamelCase   :				Union[str, Any]							=     s_dict.pop(_lowerCamelCase       )
   return s_dict







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase,					_lowerCamelCase   :				Any							=     emb.weight.shape
   _lowerCamelCase   :				Union[str, Any]							=     nn.Linear(_lowerCamelCase			,		_lowerCamelCase			,		bias=_lowerCamelCase       )
   _lowerCamelCase   :				List[Any]							=     emb.weight.data
   return lin_layer







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     bytes:
   '''simple docstring'''





   os.makedirs(_lowerCamelCase			,		exist_ok=_lowerCamelCase       )
   _lowerCamelCase   :				Optional[Any]							=     os.path.basename(_lowerCamelCase       )

   _lowerCamelCase   :				Union[str, Any]							=     url.split("/"       )[-2]
   _lowerCamelCase   :				Union[str, Any]							=     os.path.join(_lowerCamelCase			,		_lowerCamelCase       )

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

   if os.path.isfile(_lowerCamelCase       ):
      _lowerCamelCase   :				List[Any]							=     open(_lowerCamelCase			,		"rb"       ).read()
      if hashlib.shaaaa(_lowerCamelCase       ).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(_lowerCamelCase       ) as source, open(_lowerCamelCase			,		"wb"       ) as output:
      with tqdm(
          total=int(source.info().get("Content-Length"       )       )			,		ncols=80			,		unit="iB"			,		unit_scale=_lowerCamelCase			,		unit_divisor=1024       ) as loop:
         while True:
            _lowerCamelCase   :				str							=     source.read(8192       )
            if not buffer:
               break

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

   _lowerCamelCase   :				Dict							=     open(_lowerCamelCase			,		"rb"       ).read()
   if hashlib.shaaaa(_lowerCamelCase       ).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       )    ->     str:
   '''simple docstring'''





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

   _lowerCamelCase   :				Dict							=     WhisperConfig(
       vocab_size=dimensions["n_vocab"]			,		encoder_ffn_dim=_lowerCamelCase			,		decoder_ffn_dim=_lowerCamelCase			,		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"]			,		)

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

   model.save_pretrained(_lowerCamelCase       )


if __name__ == "__main__":
  _lowerCAmelCase							:    Dict					   =	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.''')
  _lowerCAmelCase							:    int					   =	parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



from typing import List, Optional

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


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='autoformer'
				snake_case	    ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				    'num_hidden_layers': 'encoder_layers',
				}





				def __init__(				self       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = "student_t"       ,  lowercase_ = "nll"       ,  lowercase_ = 1       ,  lowercase_ = [1, 2, 3, 4, 5, 6, 7]       ,  lowercase_ = True       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = 64       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 32       ,  lowercase_ = 32       ,  lowercase_ = "gelu"       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 100       ,  lowercase_ = 0.02       ,  lowercase_ = True       ,  lowercase_=True       ,  lowercase_ = 10       ,  lowercase_ = 25       ,  lowercase_ = 3       ,  **lowercase_       ,  )  -> Union[str, Any]:
											# time series specific configuration
											a__      =prediction_length
											a__      =context_length if context_length is not None else prediction_length
											a__      =distribution_output
											a__      =loss
											a__      =input_size
											a__      =num_time_features
											a__      =lags_sequence
											a__      =scaling
											a__      =num_dynamic_real_features
											a__      =num_static_real_features
											a__      =num_static_categorical_features
											if cardinality is not None and num_static_categorical_features > 0:
																		if len(lowercase_) != num_static_categorical_features:
																									raise ValueError(
																									    'The cardinality should be a list of the same length as `num_static_categorical_features`')
																		a__      =cardinality
											else:
																		a__      =[0]
											if embedding_dimension is not None and num_static_categorical_features > 0:
																		if len(lowercase_) != num_static_categorical_features:
																									raise ValueError(
																									    'The embedding dimension should be a list of the same length as `num_static_categorical_features`')
																		a__      =embedding_dimension
											else:
																		a__      =[min(50       ,  (cat + 1) // 2) for cat in self.cardinality]
											a__      =num_parallel_samples

											# Transformer architecture configuration
											a__      =input_size * len(self.lags_sequence) + self._number_of_features
											a__      =d_model
											a__      =encoder_attention_heads
											a__      =decoder_attention_heads
											a__      =encoder_ffn_dim
											a__      =decoder_ffn_dim
											a__      =encoder_layers
											a__      =decoder_layers

											a__      =dropout
											a__      =attention_dropout
											a__      =activation_dropout
											a__      =encoder_layerdrop
											a__      =decoder_layerdrop

											a__      =activation_function
											a__      =init_std

											a__      =use_cache

											# Autoformer
											a__      =label_length
											a__      =moving_average
											a__      =autocorrelation_factor

											super().__init__(is_encoder_decoder=lowercase_       ,  **lowercase_)





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return (
											    sum(self.embedding_dimension)
											    + self.num_dynamic_real_features
											    + self.num_time_features
											    + self.num_static_real_features
											    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
											)

