






"""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
lowerCamelCase_							      = data_utils.TransfoXLTokenizer
lowerCamelCase_							      = data_utils.TransfoXLCorpus
lowerCamelCase_							      = data_utils
lowerCamelCase_							      = data_utils

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

						UpperCAmelCase_	:					Union[str, Any]     	=   corpus.__dict__
						corpus_dict_no_vocab.pop("vocab"      ,A__		)
						UpperCAmelCase_	:					List[Any]     	=   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
						UpperCAmelCase_	:					Optional[Any]     	=   os.path.abspath(A__		)
						UpperCAmelCase_	:					List[Any]     	=   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 == "":
									UpperCAmelCase_	:					Optional[Any]     	=   TransfoXLConfig()
						else:
									UpperCAmelCase_	:					Dict     	=   TransfoXLConfig.from_json_file(A__		)
						print(F"""Building PyTorch model from configuration: {config}"""		)
						UpperCAmelCase_	:					Union[str, Any]     	=   TransfoXLLMHeadModel(A__		)

						UpperCAmelCase_	:					Union[str, Any]     	=   load_tf_weights_in_transfo_xl(A__      ,A__      ,A__		)
						# Save pytorch-model
						UpperCAmelCase_	:					Any     	=   os.path.join(A__      ,A__		)
						UpperCAmelCase_	:					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__":
			lowerCamelCase_							      = 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.''',
			)
			lowerCamelCase_							      = 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,
			)











"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = '''T5Config'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig



