





import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


lowercase :			Optional[Any]     		=    logging.get_logger(__name__)





class 					A__       (				UpperCAmelCase_      ):




   """simple docstring"""



   __A  :							Dict			    =    '''AutoTokenizer'''
   __A  :							Tuple			    =    ['''tokenizer''']

   __A  :							Optional[int]			    =    {
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }

   def __init__(   self   ,    lowercase   ,    lowercase=None)     ->		Tuple:







          '''simple docstring'''

          super().__init__(__a)

          a__		:      Tuple					=							speaker_embeddings

   @classmethod
   def 				__lowercase						(   cls   ,    lowercase   ,    lowercase="speaker_embeddings_path.json"   ,    **lowercase)     ->		Tuple:







          '''simple docstring'''

          if speaker_embeddings_dict_path is not None:
                 a__		:      List[Any]					=							get_file_from_repo(
                     __a   ,    __a   ,    subfolder=kwargs.pop('subfolder'   ,    __a)   ,    cache_dir=kwargs.pop('cache_dir'   ,    __a)   ,    force_download=kwargs.pop('force_download'   ,    __a)   ,    proxies=kwargs.pop('proxies'   ,    __a)   ,    resume_download=kwargs.pop('resume_download'   ,    __a)   ,    local_files_only=kwargs.pop('local_files_only'   ,    __a)   ,    use_auth_token=kwargs.pop('use_auth_token'   ,    __a)   ,    revision=kwargs.pop('revision'   ,    __a)   ,    )
                 if speaker_embeddings_path is None:
                        logger.warning(
                            F'`{os.path.join(__a   ,    __a)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.')
                        a__		:      Any					=							None
                 else:
                        with open(__a) as speaker_embeddings_json:
                               a__		:      Dict					=							json.load(__a)
          else:
                 a__		:      List[Any]					=							None

          a__		:      List[Any]					=							AutoTokenizer.from_pretrained(__a   ,    **__a)

          return cls(tokenizer=__a   ,    speaker_embeddings=__a)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase="speaker_embeddings_path.json"   ,    lowercase="speaker_embeddings"   ,    lowercase = False   ,    **lowercase   ,    )     ->		Any:







          '''simple docstring'''

          if self.speaker_embeddings is not None:
                 os.makedirs(os.path.join(__a   ,    __a   ,    'v2')   ,    exist_ok=__a)

                 a__		:      Any					=							{}

                 a__		:      Dict					=							save_directory

                 for prompt_key in self.speaker_embeddings:
                        if prompt_key != "repo_or_path":
                               a__		:      int					=							self._load_voice_preset(__a)

                               a__		:      str					=							{}
                               for key in self.speaker_embeddings[prompt_key]:
                                      np.save(
                                          os.path.join(
                                              embeddings_dict['repo_or_path']   ,    __a   ,    F'{prompt_key}_{key}')   ,    voice_preset[key]   ,    allow_pickle=__a   ,    )
                                      a__		:      Dict					=							os.path.join(__a   ,    F'{prompt_key}_{key}.npy')

                               a__		:      Union[str, Any]					=							tmp_dict

                 with open(os.path.join(__a   ,    __a)   ,    'w') as fp:
                        json.dump(__a   ,    __a)

          super().save_pretrained(__a   ,    __a   ,    **__a)

   def 				__lowercase						(   self   ,    lowercase = None   ,    **lowercase)     ->		int:







          '''simple docstring'''

          a__		:      Any					=							self.speaker_embeddings[voice_preset]

          a__		:      Dict					=							{}
          for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                 if key not in voice_preset_paths:
                        raise ValueError(
                            F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].')

                 a__		:      Any					=							get_file_from_repo(
                     self.speaker_embeddings.get('repo_or_path'   ,    '/')   ,    voice_preset_paths[key]   ,    subfolder=kwargs.pop('subfolder'   ,    __a)   ,    cache_dir=kwargs.pop('cache_dir'   ,    __a)   ,    force_download=kwargs.pop('force_download'   ,    __a)   ,    proxies=kwargs.pop('proxies'   ,    __a)   ,    resume_download=kwargs.pop('resume_download'   ,    __a)   ,    local_files_only=kwargs.pop('local_files_only'   ,    __a)   ,    use_auth_token=kwargs.pop('use_auth_token'   ,    __a)   ,    revision=kwargs.pop('revision'   ,    __a)   ,    )
                 if path is None:
                        raise ValueError(
                            F'`{os.path.join(self.speaker_embeddings.get("repo_or_path"   ,    "/")   ,    voice_preset_paths[key])}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.')

                 a__		:      List[str]					=							np.load(__a)

          return voice_preset_dict

   def 				__lowercase						(   self   ,    lowercase = None)     ->		List[Any]:







          '''simple docstring'''

          for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                 if key not in voice_preset:
                        raise ValueError(F'Voice preset unrecognized, missing {key} as a key.')

                 if not isinstance(voice_preset[key]   ,    np.ndarray):
                        raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key])}D ndarray.')

                 if len(voice_preset[key].shape) != self.preset_shape[key]:
                        raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key])}D ndarray.')

   def __call__(   self   ,    lowercase=None   ,    lowercase=None   ,    lowercase="pt"   ,    lowercase=256   ,    lowercase=False   ,    lowercase=True   ,    lowercase=False   ,    **lowercase   ,    )     ->		Dict:







          '''simple docstring'''

          if voice_preset is not None and not isinstance(__a   ,    __a):
                 if (
                     isinstance(__a   ,    __a)
                     and self.speaker_embeddings is not None
                     and voice_preset in self.speaker_embeddings
                 ):
                        a__		:      Dict					=							self._load_voice_preset(__a)

                 else:
                        if isinstance(__a   ,    __a) and not voice_preset.endswith('.npz'):
                               a__		:      str					=							voice_preset + '.npz'

                        a__		:      List[Any]					=							np.load(__a)

          if voice_preset is not None:
                 self._validate_voice_preset_dict(__a   ,    **__a)
                 a__		:      Dict					=							BatchFeature(data=__a   ,    tensor_type=__a)

          a__		:      Tuple					=							self.tokenizer(
              __a   ,    return_tensors=__a   ,    padding='max_length'   ,    max_length=__a   ,    return_attention_mask=__a   ,    return_token_type_ids=__a   ,    add_special_tokens=__a   ,    **__a   ,    )

          if voice_preset is not None:
                 a__		:      Union[str, Any]					=							voice_preset

          return encoded_text







import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)