





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
UpperCAmelCase     :     int							=	data_utils.TransfoXLTokenizer
UpperCAmelCase     :     str							=	data_utils.TransfoXLCorpus
UpperCAmelCase     :     int							=	data_utils
UpperCAmelCase     :     List[str]							=	data_utils
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





		"""simple docstring"""



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

				a__     : Any  =corpus.__dict__
				corpus_dict_no_vocab.pop("vocab"	,	a__						)
				a__     : str  =pytorch_dump_folder_path + "/" + CORPUS_NAME
				print(f'''Save dataset to {pytorch_dataset_dump_path}'''						)
				torch.save(a__	,	a__						)

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				a__     : Any  =os.path.abspath(a__						)
				a__     : List[str]  =os.path.abspath(a__						)

				print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''						)
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						a__     : Optional[int]  =TransfoXLConfig()
				else:
						a__     : Optional[int]  =TransfoXLConfig.from_json_file(a__						)
				print(f'''Building PyTorch model from configuration: {config}'''						)
				a__     : Union[str, Any]  =TransfoXLLMHeadModel(a__						)

				a__     : str  =load_tf_weights_in_transfo_xl(a__	,	a__	,	a__						)
				# Save pytorch-model
				a__     : Any  =os.path.join(a__	,	a__						)
				a__     : Union[str, Any]  =os.path.join(a__	,	a__						)
				print(f'''Save PyTorch model to {os.path.abspath(a__						)}'''						)
				torch.save(model.state_dict()	,	a__						)
				print(f'''Save configuration file to {os.path.abspath(a__						)}'''						)
				with open(a__	,	"w"	,	encoding="utf-8"						) as f:
						f.write(config.to_json_string()						)


if __name__ == "__main__":
					UpperCAmelCase     :     Any							=	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.""",
					)
					UpperCAmelCase     :     Union[str, Any]							=	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,
					)













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	(   UpperCamelCase__):
      _lowercase    : Any            =      """char"""
      _lowercase    : str            =      """bpe"""
      _lowercase    : List[Any]            =      """wp"""
UpperCAmelCase     :     List[Any]							=	(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Tuple            =      ["""image_processor""", """char_tokenizer"""]
      _lowercase    : Any            =      """ViTImageProcessor"""
      _lowercase    : Optional[Any]            =      """MgpstrTokenizer"""







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =None
        if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."		,				lowerCAmelCase__		,				)
          a__     : List[str]  =kwargs.pop("feature_extractor"							)

        a__     : List[str]  =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__     : str  =tokenizer
        a__     : List[str]  =AutoTokenizer.from_pretrained("gpt2"							)
        a__     : Optional[int]  =AutoTokenizer.from_pretrained("bert-base-uncased"							)

        super().__init__(lowerCAmelCase__		,				lowerCAmelCase__							)







      def __call__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        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__     : Union[str, Any]  =self.image_processor(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)
        if text is not None:
          a__     : int  =self.char_tokenizer(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)

        if text is None:
          return inputs
        elif images is None:
          return encodings
        else:
          a__     : Tuple  =encodings["input_ids"]
          return inputs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__ ,							a__ ,							a__     : Any  =sequences
        a__     : Union[str, Any]  =char_preds.size(0							)

        a__ ,							a__     : Dict  =self._decode_helper(lowerCAmelCase__		,				"char"							)
        a__ ,							a__     : List[Any]  =self._decode_helper(lowerCAmelCase__		,				"bpe"							)
        a__ ,							a__     : Optional[int]  =self._decode_helper(lowerCAmelCase__		,				"wp"							)

        a__     : List[Any]  =[]
        a__     : Dict  =[]
        for i in range(lowerCAmelCase__							):
          a__     : int  =[char_scores[i], bpe_scores[i], wp_scores[i]]
          a__     : Tuple  =[char_strs[i], bpe_strs[i], wp_strs[i]]
          a__     : Any  =scores.index(max(lowerCAmelCase__							)							)
          final_strs.append(strs[max_score_index]							)
          final_scores.append(scores[max_score_index]							)

        a__     : Dict  ={}
        a__     : str  =final_strs
        a__     : Optional[int]  =final_scores
        a__     : Union[str, Any]  =char_strs
        a__     : List[str]  =bpe_strs
        a__     : Union[str, Any]  =wp_strs
        return out







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        if format == DecodeType.CHARACTER:
          a__     : Optional[Any]  =self.char_decode
          a__     : Dict  =1
          a__     : Tuple  ="[s]"
        elif format == DecodeType.BPE:
          a__     : str  =self.bpe_decode
          a__     : Dict  =2
          a__     : Optional[int]  ="#"
        elif format == DecodeType.WORDPIECE:
          a__     : Union[str, Any]  =self.wp_decode
          a__     : List[Any]  =1_0_2
          a__     : Dict  ="[SEP]"
        else:
          raise ValueError(F'''Format {format} is not supported.'''							)

        a__ ,							a__     : Any  =[], []
        a__     : str  =pred_logits.size(0							)
        a__     : Optional[Any]  =pred_logits.size(1							)
        a__ ,							a__     : Optional[int]  =pred_logits.topk(1		,				dim=-1		,				largest=lowerCAmelCase__		,				sorted=lowerCAmelCase__							)
        a__     : Optional[Any]  =preds_index.view(-1		,				lowerCAmelCase__							)[:, 1:]
        a__     : Dict  =decoder(lowerCAmelCase__							)
        a__ ,							a__     : Any  =torch.nn.functional.softmax(lowerCAmelCase__		,				dim=2							).max(dim=2							)
        a__     : int  =preds_max_prob[:, 1:]

        for index in range(lowerCAmelCase__							):
          a__     : Optional[Any]  =preds_str[index].find(lowerCAmelCase__							)
          a__     : Optional[int]  =preds_str[index][:pred_eos]
          a__     : List[Any]  =preds_index[index].cpu().tolist()
          a__     : List[Any]  =pred_index.index(lowerCAmelCase__							) if eos_token in pred_index else -1
          a__     : Union[str, Any]  =preds_max_prob[index][: pred_eos_index + 1]
          a__     : List[Any]  =pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
          dec_strs.append(lowerCAmelCase__							)
          conf_scores.append(lowerCAmelCase__							)

        return dec_strs, conf_scores







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =[seq.replace(" "		,				""							) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        return self.bpe_tokenizer.batch_decode(lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =[seq.replace(" "		,				""							) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs






