





"""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
__magic_name__       :					Tuple						=			data_utils.TransfoXLTokenizer
__magic_name__       :					Union[str, Any]						=			data_utils.TransfoXLCorpus
__magic_name__       :					Any						=			data_utils
__magic_name__       :					Optional[int]						=			data_utils

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if transfo_xl_dataset_file:
												# Convert a pre-processed corpus (see original TensorFlow repo)
												with open(SCREAMING_SNAKE_CASE							,     """rb"""     ) as fp:
																		UpperCamelCase				:		Optional[int]				   =			pickle.load(SCREAMING_SNAKE_CASE							,     encoding="""latin1"""     )
												# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
												UpperCamelCase				:		Optional[int]				   =			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(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

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

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

												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				:		str				   =			TransfoXLConfig.from_json_file(SCREAMING_SNAKE_CASE     )
												print(f"""Building PyTorch model from configuration: {config}"""     )
												UpperCamelCase				:		Optional[int]				   =			TransfoXLLMHeadModel(SCREAMING_SNAKE_CASE     )

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


if __name__ == "__main__":
		__magic_name__       :					Optional[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.""",
		)
		__magic_name__       :					Optional[int]						=			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"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



