






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
A__      :     str       =					data_utils.TransfoXLTokenizer
A__      :     Optional[Any]       =					data_utils.TransfoXLCorpus
A__      :     Tuple       =					data_utils
A__      :     List[Any]       =					data_utils






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






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

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

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

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

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


if __name__ == "__main__":
							A__      :     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.',
							)
							A__      :     Dict       =					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,
							)












from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				@slow
				@require_torch
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''',    '''prajjwal1/bert-tiny'''    )
								lowercase__  					=     BertTokenizer.from_pretrained('''bert-base-uncased'''    )

								lowercase__  					=     bertabert.config.encoder.vocab_size
								lowercase__  					=     tokenizer.sep_token_id
								lowercase__  					=     tokenizer.cls_token_id
								lowercase__  					=     128

								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''train[:1%]'''    )
								lowercase__  					=     datasets.load_dataset('''cnn_dailymail''',    '''3.0.0''',    split='''validation[:1%]'''    )

								lowercase__  					=     train_dataset.select(range(32    )    )
								lowercase__  					=     val_dataset.select(range(16    )    )

								lowercase__  					=     4

								def _map_to_encoder_decoder_inputs(lowerCamelCase		:       str    ):
												# Tokenizer will automatically set [BOS] <text> [EOS]
												lowercase__  					=     tokenizer(batch['''article'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=512    )
												lowercase__  					=     tokenizer(batch['''highlights'''],    padding='''max_length''',    truncation=lowerCamelCase,    max_length=128    )
												lowercase__  					=     inputs.input_ids
												lowercase__  					=     inputs.attention_mask

												lowercase__  					=     outputs.input_ids
												lowercase__  					=     outputs.input_ids.copy()
												lowercase__  					=     [
												    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												lowercase__  					=     outputs.attention_mask

												assert all(len(lowerCamelCase    ) == 512 for x in inputs.input_ids    )
												assert all(len(lowerCamelCase    ) == 128 for x in outputs.input_ids    )

												return batch

								def _compute_metrics(lowerCamelCase		:       Any    ):
												lowercase__  					=     pred.label_ids
												lowercase__  					=     pred.predictions

												# all unnecessary tokens are removed
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )

												lowercase__  					=     sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCamelCase    )    )]    ) / len(lowerCamelCase    )

												return {"accuracy": accuracy}

								# map train dataset
								lowercase__  					=     train_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								train_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								# same for validation dataset
								lowercase__  					=     val_dataset.map(
								    _map_to_encoder_decoder_inputs,    batched=lowerCamelCase,    batch_size=lowerCamelCase,    remove_columns=['''article''', '''highlights'''],    )
								val_dataset.set_format(
								    type='''torch''',    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''],    )

								lowercase__  					=     self.get_auto_remove_tmp_dir()

								lowercase__  					=     SeqaSeqTrainingArguments(
								    output_dir=lowerCamelCase,    per_device_train_batch_size=lowerCamelCase,    per_device_eval_batch_size=lowerCamelCase,    predict_with_generate=lowerCamelCase,    evaluation_strategy='''steps''',    do_train=lowerCamelCase,    do_eval=lowerCamelCase,    warmup_steps=0,    eval_steps=2,    logging_steps=2,    )

								# instantiate trainer
								lowercase__  					=     SeqaSeqTrainer(
								    model=lowerCamelCase,    args=lowerCamelCase,    compute_metrics=_compute_metrics,    train_dataset=lowerCamelCase,    eval_dataset=lowerCamelCase,    tokenizer=lowerCamelCase,    )

								# start training
								trainer.train()




