
"""simple docstring"""


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
		import torch







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    '''char'''
						_lowerCamelCase:    List[Any]      =    '''bpe'''
						_lowerCamelCase:    Optional[Any]      =    '''wp'''

_lowercase							   =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[str]      =    ['''image_processor''', '''char_tokenizer''']
						_lowerCamelCase:    Optional[int]      =    '''ViTImageProcessor'''
						_lowerCamelCase:    Union[str, Any]      =    '''MgpstrTokenizer'''


						def __init__( self     :      List[Any]  ,A_     :      Dict=None  ,A_     :      List[str]=None  ,**A_     :      Union[str, Any]      )     ->   Tuple:
							A						   =   None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
								    ' instead.'  ,A_  ,)
								A						   =   kwargs.pop('feature_extractor'      )

							A						   =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError('You need to specify an `image_processor`.'      )
							if tokenizer is None:
								raise ValueError('You need to specify a `tokenizer`.'      )

							A						   =   tokenizer
							A						   =   AutoTokenizer.from_pretrained('gpt2'      )
							A						   =   AutoTokenizer.from_pretrained('bert-base-uncased'      )

							super().__init__(A_  ,A_      )


						def __call__( self     :      Dict  ,A_     :      str=None  ,A_     :      Tuple=None  ,A_     :      str=None  ,**A_     :      Dict      )     ->   Dict:
							if images is None and text is None:
								raise ValueError('You need to specify either an `images` or `text` input to process.'      )

							if images is not None:
								A						   =   self.image_processor(A_  ,return_tensors=A_  ,**A_      )
							if text is not None:
								A						   =   self.char_tokenizer(A_  ,return_tensors=A_  ,**A_      )

							if text is None:
								return inputs
							elif images is None:
								return encodings
							else:
								A						   =   encodings['input_ids']
								return inputs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Dict      )     ->   List[str]:
							A   ,      A   ,      A						   =   sequences
							A						   =   char_preds.size(0      )

							A   ,      A						   =   self._decode_helper(A_  ,'char'      )
							A   ,      A						   =   self._decode_helper(A_  ,'bpe'      )
							A   ,      A						   =   self._decode_helper(A_  ,'wp'      )

							A						   =   []
							A						   =   []
							for i in range(A_      ):
								A						   =   [char_scores[i], bpe_scores[i], wp_scores[i]]
								A						   =   [char_strs[i], bpe_strs[i], wp_strs[i]]
								A						   =   scores.index(max(A_      )      )
								final_strs.append(strs[max_score_index]      )
								final_scores.append(scores[max_score_index]      )

							A						   =   {}
							A						   =   final_strs
							A						   =   final_scores
							A						   =   char_strs
							A						   =   bpe_strs
							A						   =   wp_strs
							return out


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      str  ,A_     :      int      )     ->   int:
							if format == DecodeType.CHARACTER:
								A						   =   self.char_decode
								A						   =   1
								A						   =   '[s]'
							elif format == DecodeType.BPE:
								A						   =   self.bpe_decode
								A						   =   2
								A						   =   '#'
							elif format == DecodeType.WORDPIECE:
								A						   =   self.wp_decode
								A						   =   102
								A						   =   '[SEP]'
							else:
								raise ValueError(F'Format {format} is not supported.'      )

							A   ,      A						   =   [], []
							A						   =   pred_logits.size(0      )
							A						   =   pred_logits.size(1      )
							A   ,      A						   =   pred_logits.topk(1  ,dim=-1  ,largest=A_  ,sorted=A_      )
							A						   =   preds_index.view(-1  ,A_      )[:, 1:]
							A						   =   decoder(A_      )
							A   ,      A						   =   torch.nn.functional.softmax(A_  ,dim=2      ).max(dim=2      )
							A						   =   preds_max_prob[:, 1:]

							for index in range(A_      ):
								A						   =   preds_str[index].find(A_      )
								A						   =   preds_str[index][:pred_eos]
								A						   =   preds_index[index].cpu().tolist()
								A						   =   pred_index.index(A_      ) if eos_token in pred_index else -1
								A						   =   preds_max_prob[index][: pred_eos_index + 1]
								A						   =   pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
								dec_strs.append(A_      )
								conf_scores.append(A_      )

							return dec_strs, conf_scores


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      str      )     ->   Optional[Any]:
							A						   =   [seq.replace(' '  ,''      ) for seq in self.char_tokenizer.batch_decode(A_      )]
							return decode_strs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      int      )     ->   List[Any]:
							return self.bpe_tokenizer.batch_decode(A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]      )     ->   str:
							A						   =   [seq.replace(' '  ,''      ) for seq in self.wp_tokenizer.batch_decode(A_      )]
							return decode_strs

"""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,
		)