
"""simple docstring"""


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							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[int]      =    '''AutoTokenizer'''
						_lowerCamelCase:    Any      =    ['''tokenizer''']

						_lowerCamelCase:    Any      =    {
						    '''semantic_prompt''': 1,
						    '''coarse_prompt''': 2,
						    '''fine_prompt''': 2,
						}


						def __init__( self     :      str  ,A_     :      List[Any]  ,A_     :      List[str]=None      )     ->   int:
							super().__init__(A_      )

							A						   =   speaker_embeddings


						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      Optional[int]  ,A_     :      int  ,A_     :      Any="speaker_embeddings_path.json"  ,**A_     :      Dict      )     ->   Union[str, Any]:

							if speaker_embeddings_dict_path is not None:
								A						   =   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						   =   None
								else:
									with open(A_      ) as speaker_embeddings_json:
										A						   =   json.load(A_      )
							else:
								A						   =   None

							A						   =   AutoTokenizer.from_pretrained(A_  ,**A_      )

							return cls(tokenizer=A_  ,speaker_embeddings=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Optional[int]  ,A_     :      Dict="speaker_embeddings_path.json"  ,A_     :      Optional[int]="speaker_embeddings"  ,A_     :      bool = False  ,**A_     :      Optional[int]  ,)     ->   int:
							if self.speaker_embeddings is not None:
								os.makedirs(os.path.join(A_  ,A_  ,'v2'      )  ,exist_ok=A_      )

								A						   =   {}

								A						   =   save_directory

								for prompt_key in self.speaker_embeddings:
									if prompt_key != "repo_or_path":
										A						   =   self._load_voice_preset(A_      )

										A						   =   {}
										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						   =   os.path.join(A_  ,F'{prompt_key}_{key}.npy'      )

										A						   =   tmp_dict

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

							super().save_pretrained(A_  ,A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      str = None  ,**A_     :      List[str]      )     ->   List[Any]:
							A						   =   self.speaker_embeddings[voice_preset]

							A						   =   {}
							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						   =   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						   =   np.load(A_      )

							return voice_preset_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[dict] = None      )     ->   Optional[Any]:
							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     :      Any  ,A_     :      Union[str, Any]=None  ,A_     :      Optional[Any]=None  ,A_     :      Optional[int]="pt"  ,A_     :      Optional[int]=256  ,A_     :      str=False  ,A_     :      List[Any]=True  ,A_     :      Tuple=False  ,**A_     :      Union[str, Any]  ,)     ->   Tuple:
							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						   =   self._load_voice_preset(A_      )

								else:
									if isinstance(A_  ,A_      ) and not voice_preset.endswith('.npz'      ):
										A						   =   voice_preset + '.npz'

									A						   =   np.load(A_      )

							if voice_preset is not None:
								self._validate_voice_preset_dict(A_  ,**A_      )
								A						   =   BatchFeature(data=A_  ,tensor_type=A_      )

							A						   =   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						   =   voice_preset

							return encoded_text

"""simple docstring"""


import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowercase							   =				data_utils.TransfoXLTokenizer
_lowercase							   =				data_utils.TransfoXLCorpus
_lowercase							   =				data_utils
_lowercase							   =				data_utils






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Dict	,      snake_case__   :     Tuple	,      snake_case__   :     int     ):
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(snake_case__	,      'rb'     ) as fp:
			A						   =   pickle.load(snake_case__	,      encoding='latin1'     )
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		A						   =   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
		print(F'Save vocabulary to {pytorch_vocab_dump_path}'     )
		A						   =   corpus.vocab.__dict__
		torch.save(snake_case__	,      snake_case__     )

		A						   =   corpus.__dict__
		corpus_dict_no_vocab.pop('vocab'	,      snake_case__     )
		A						   =   pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F'Save dataset to {pytorch_dataset_dump_path}'     )
		torch.save(snake_case__	,      snake_case__     )

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		A						   =   os.path.abspath(snake_case__     )
		A						   =   os.path.abspath(snake_case__     )

		print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'     )
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			A						   =   TransfoXLConfig()
		else:
			A						   =   TransfoXLConfig.from_json_file(snake_case__     )
		print(F'Building PyTorch model from configuration: {config}'     )
		A						   =   TransfoXLLMHeadModel(snake_case__     )

		A						   =   load_tf_weights_in_transfo_xl(snake_case__	,      snake_case__	,      snake_case__     )
		# Save pytorch-model
		A						   =   os.path.join(snake_case__	,      snake_case__     )
		A						   =   os.path.join(snake_case__	,      snake_case__     )
		print(F'Save PyTorch model to {os.path.abspath(snake_case__     )}'     )
		torch.save(model.state_dict()	,      snake_case__     )
		print(F'Save configuration file to {os.path.abspath(snake_case__     )}'     )
		with open(snake_case__	,      'w'	,      encoding='utf-8'     ) as f:
			f.write(config.to_json_string()     )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
		)
		parser.add_argument(
		    '''--tf_checkpoint_path''',
		    default='''''',
		    type=str,
		    help='''An optional path to a TensorFlow checkpoint path to be converted.''',
		)
		parser.add_argument(
		    '''--transfo_xl_config_file''',
		    default='''''',
		    type=str,
		    help=(
		        '''An optional config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--transfo_xl_dataset_file''',
		    default='''''',
		    type=str,
		    help='''An optional dataset file to be converted in a vocabulary.''',
		)
		_lowercase							   =				parser.parse_args()
		convert_transfo_xl_checkpoint_to_pytorch(
		    args.tf_checkpoint_path,
		    args.transfo_xl_config_file,
		    args.pytorch_dump_folder_path,
		    args.transfo_xl_dataset_file,
		)