





"""simple docstring"""


import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.framework.errors_impl import ResourceExhaustedError

		from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
		import pyanvml.pyanvml as nvml

__magic_name__       :					Optional[Any]						=			logging.get_logger(__name__)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						def run_func(SCREAMING_SNAKE_CASE     ):
												@wraps(SCREAMING_SNAKE_CASE     )
												def run_in_eager_mode(*SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE     ):
																		return func(*SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE     )

												@wraps(SCREAMING_SNAKE_CASE     )
												@tf.function(experimental_compile=SCREAMING_SNAKE_CASE     )
												def run_in_graph_mode(*SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE     ):
																		return func(*SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE     )

												if do_eager_mode is True:
																		if use_xla is not False:
																								raise ValueError(
																								    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""     )
																		return run_in_eager_mode
												else:
																		return run_in_graph_mode

						return run_func

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			random.Random()
						UpperCamelCase				:		Tuple				   =			[rng.randint(0							,     vocab_size - 1     ) for i in range(batch_size * sequence_length     )]
						return tf.constant(SCREAMING_SNAKE_CASE							,     shape=(batch_size, sequence_length)							,     dtype=tf.intaa     )






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				TensorFlowBenchmarkArguments
			__lowerCAmelCase     :				PretrainedConfig
			__lowerCAmelCase     :				str										=  "TensorFlow"







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return tf.__version__







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			self.args.strategy
									if strategy is None:
															raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
									UpperCamelCase				:		Optional[int]				   =			self._prepare_inference_func(_A						,   _A						,   _A    )
									return self._measure_speed(_inference    )







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			self.args.strategy
									if strategy is None:
															raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
									UpperCamelCase				:		str				   =			self._prepare_train_func(_A						,   _A						,   _A    )
									return self._measure_speed(_train    )







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									if self.args.is_gpu:
															tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,   _A    )
									UpperCamelCase				:		int				   =			self.args.strategy
									if strategy is None:
															raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )
									UpperCamelCase				:		Dict				   =			self._prepare_inference_func(_A						,   _A						,   _A    )
									return self._measure_memory(_inference    )







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									if self.args.is_gpu:
															tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,   _A    )
									UpperCamelCase				:		List[str]				   =			self.args.strategy
									if strategy is None:
															raise ValueError("""A device strategy has to be initialized before using TensorFlow."""    )

									UpperCamelCase				:		Any				   =			self._prepare_train_func(_A						,   _A						,   _A    )
									return self._measure_memory(_train    )







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			self.config_dict[model_name]

									if self.args.fpaa:
															raise NotImplementedError("""Mixed precision is currently not supported."""    )

									UpperCamelCase				:		Optional[int]				   =			(
									    hasattr(_A						,   """architectures"""    )
									    and isinstance(config.architectures						,   _A    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					UpperCamelCase				:		List[str]				   =			"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																					UpperCamelCase				:		Optional[int]				   =			__import__("""transformers"""						,   fromlist=[model_class]    )
																					UpperCamelCase				:		str				   =			getattr(_A						,   _A    )
																					UpperCamelCase				:		Dict				   =			model_cls(_A    )
															except ImportError:
																					raise ImportError(
																					    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""    )
									else:
															UpperCamelCase				:		Optional[int]				   =			TF_MODEL_MAPPING[config.__class__](_A    )

									# encoder-decoder has vocab size saved differently
									UpperCamelCase				:		Optional[Any]				   =			config.vocab_size if hasattr(_A						,   """vocab_size"""    ) else config.encoder.vocab_size
									UpperCamelCase				:		Dict				   =			random_input_ids(_A						,   _A						,   _A    )

									@run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla    )
									def encoder_decoder_forward():
															return model(_A						,   decoder_input_ids=_A						,   training=_A    )

									@run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla    )
									def encoder_forward():
															return model(_A						,   training=_A    )

									UpperCamelCase				:		str				   =			encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

									return _inference







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			self.config_dict[model_name]

									if self.args.eager_mode is not False:
															raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""    )

									if self.args.fpaa:
															raise NotImplementedError("""Mixed precision is currently not supported."""    )

									UpperCamelCase				:		int				   =			(
									    hasattr(_A						,   """architectures"""    )
									    and isinstance(config.architectures						,   _A    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					UpperCamelCase				:		Optional[int]				   =			"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
																					UpperCamelCase				:		Any				   =			__import__("""transformers"""						,   fromlist=[model_class]    )
																					UpperCamelCase				:		Optional[int]				   =			getattr(_A						,   _A    )
																					UpperCamelCase				:		Union[str, Any]				   =			model_cls(_A    )
															except ImportError:
																					raise ImportError(
																					    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""    )
									else:
															UpperCamelCase				:		str				   =			TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_A    )

									# encoder-decoder has vocab size saved differently
									UpperCamelCase				:		str				   =			config.vocab_size if hasattr(_A						,   """vocab_size"""    ) else config.encoder.vocab_size
									UpperCamelCase				:		Union[str, Any]				   =			random_input_ids(_A						,   _A						,   _A    )

									@run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla    )
									def encoder_decoder_train():
															UpperCamelCase				:		str				   =			model(_A						,   decoder_input_ids=_A						,   labels=_A						,   training=_A    )[0]
															UpperCamelCase				:		Any				   =			tf.gradients(_A						,   model.trainable_variables    )
															return gradients

									@run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla    )
									def encoder_train():
															UpperCamelCase				:		List[str]				   =			model(_A						,   labels=_A						,   training=_A    )[0]
															UpperCamelCase				:		int				   =			tf.gradients(_A						,   model.trainable_variables    )
															return gradients

									UpperCamelCase				:		Tuple				   =			encoder_decoder_train if config.is_encoder_decoder else encoder_train

									return _train







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									with self.args.strategy.scope():
															try:
																					if self.args.is_tpu or self.args.use_xla:
																											# run additional 10 times to stabilize compilation for tpu
																											logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""    )
																											timeit.repeat(_A						,   repeat=1						,   number=5    )

																					# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																					UpperCamelCase				:		int				   =			timeit.repeat(
																					    _A						,   repeat=self.args.repeat						,   number=1_0						,   )

																					return min(_A    ) / 10.0
															except ResourceExhaustedError as e:
																					self.print_fn(f"""Doesn't fit on GPU. {e}"""    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									logger.info(
									    """Note that TensorFlow allocates more memory than """
									    """it might need to speed up computation. """
									    """The memory reported here corresponds to the memory """
									    """reported by `nvidia-smi`, which can vary depending """
									    """on total available memory on the GPU that is used."""    )
									with self.args.strategy.scope():
															try:
																					if self.args.trace_memory_line_by_line:
																											if not self.args.eager_mode:
																																	raise ValueError(
																																	    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																																	    """ consumption line by line."""    )
																											UpperCamelCase				:		List[str]				   =			start_memory_tracing("""transformers"""    )

																					if self.args.is_tpu:
																											# tpu
																											raise NotImplementedError(
																											    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
																											    """ with `args.memory=False`"""    )
																					elif self.args.is_gpu:
																											# gpu
																											if not is_pyanvml_available():
																																	logger.warning(
																																	    """py3nvml not installed, we won't log GPU memory usage. """
																																	    """Install py3nvml (pip install py3nvml) to log information about GPU."""    )
																																	UpperCamelCase				:		str				   =			"""N/A"""
																											else:
																																	logger.info(
																																	    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																																	    """ running on the same GPU."""    )
																																	# init nvml
																																	nvml.nvmlInit()
																																	func()
																																	UpperCamelCase				:		Dict				   =			nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
																																	UpperCamelCase				:		List[str]				   =			nvml.nvmlDeviceGetMemoryInfo(_A    )
																																	UpperCamelCase				:		Union[str, Any]				   =			meminfo.used
																																	UpperCamelCase				:		Tuple				   =			Memory(_A    )
																																	# shutdown nvml
																																	nvml.nvmlShutdown()
																					else:
																											# cpu
																											if self.args.trace_memory_line_by_line:
																																	logger.info(
																																	    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																																	    """ TensorFlow."""    )
																																	UpperCamelCase				:		Dict				   =			None
																											else:
																																	UpperCamelCase				:		List[Any]				   =			measure_peak_memory_cpu(_A    )
																																	UpperCamelCase				:		Dict				   =			Memory(_A    ) if isinstance(_A						,   _A    ) else memory_bytes
																					if self.args.trace_memory_line_by_line:
																											UpperCamelCase				:		List[str]				   =			stop_memory_tracing(_A    )
																											if memory is None:
																																	UpperCamelCase				:		str				   =			summary.total
																					else:
																											UpperCamelCase				:		Tuple				   =			None

																					return memory, summary
															except ResourceExhaustedError as e:
																					self.print_fn(f"""Doesn't fit on GPU. {e}"""    )
																					return "N/A", None










import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCamelCase:	Optional[Any] = logging.getLogger(__name__)


__UpperCamelCase:	int = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase:	Tuple = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class       __lowerCAmelCase					:


	'''simple docstring'''





	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={
	        "help": (
	            "The model checkpoint for weights initialization. Leave None if you want to train a model from"
	            " scratch."
	        )
	    }   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_UpperCamelCase      )}   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,    )


@dataclass
class       __lowerCAmelCase					:


	'''simple docstring'''





	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "The input training data file (a text file)."}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={
	        "help": (
	            "The input training data files (multiple files in glob format). "
	            "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
	        )
	    }   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}   ,    )

	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Train with masked-language modeling loss instead of language modeling."}      )
	_A													=				field(default=_UpperCamelCase   ,    metadata={"help": "Whether ot not to use whole word mask."}      )
	_A													=				field(
	    default=0.15   ,    metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}      )
	_A													=				field(
	    default=1 / 6   ,    metadata={
	        "help": (
	            "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
	            " modeling."
	        )
	    }   ,    )
	_A													=				field(
	    default=5   ,    metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}      )

	_A													=				field(
	    default=-1   ,    metadata={
	        "help": (
	            "Optional input sequence length after tokenization."
	            "The training dataset will be truncated in block of this size for training."
	            "Default to the model max input length for single sentence inputs (take into account special tokens)."
	        )
	    }   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Overwrite the cached training and evaluation sets"}      )


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : DataTrainingArguments   ,    _lowercase     : PreTrainedTokenizer   ,    _lowercase     : bool = False   ,    _lowercase     : Optional[str] = None   ,    )   ->							Tuple:




		'''simple docstring'''
		def _dataset(_lowercase     : Any   ,    _lowercase     : Dict=None			):
				if args.line_by_line:
						if ref_path is not None:
								if not args.whole_word_mask or not args.mlm:
										raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask'			)
								return LineByLineWithRefDataset(
								    tokenizer=_lowercase   ,    file_path=_lowercase   ,    block_size=args.block_size   ,    ref_path=_lowercase   ,    )

						return LineByLineTextDataset(tokenizer=_lowercase   ,    file_path=_lowercase   ,    block_size=args.block_size			)
				else:
						return TextDataset(
						    tokenizer=_lowercase   ,    file_path=_lowercase   ,    block_size=args.block_size   ,    overwrite_cache=args.overwrite_cache   ,    cache_dir=_lowercase   ,    )

		if evaluate:
				return _dataset(args.eval_data_file   ,    args.eval_ref_file			)
		elif args.train_data_files:
				return ConcatDataset([_dataset(_lowercase			) for f in glob(args.train_data_files			)]			)
		else:
				return _dataset(args.train_data_file   ,    args.train_ref_file			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		lowercase__					:      Optional[Any]      =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
		lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		parser.parse_args_into_dataclasses()

		if data_args.eval_data_file is None and training_args.do_eval:
				raise ValueError(
				    'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
				    'or remove the --do_eval argument.'			)
		if (
		    os.path.exists(training_args.output_dir			)
		    and os.listdir(training_args.output_dir			)
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
				    ' --overwrite_output_dir to overcome.'			)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,    datefmt='%m/%d/%Y %H:%M:%S'   ,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,    )
		logger.warning(
		    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'   ,    training_args.local_rank   ,    training_args.device   ,    training_args.n_gpu   ,    bool(training_args.local_rank != -1			)   ,    training_args.fpaa   ,    )
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank			):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('Training/evaluation parameters %s'   ,    _lowercase			)

		# Set seed
		set_seed(training_args.seed			)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		if model_args.config_name:
				lowercase__					:      Union[str, Any]      =		AutoConfig.from_pretrained(model_args.config_name   ,    cache_dir=model_args.cache_dir			)
		elif model_args.model_name_or_path:
				lowercase__					:      Tuple      =		AutoConfig.from_pretrained(model_args.model_name_or_path   ,    cache_dir=model_args.cache_dir			)
		else:
				lowercase__					:      Optional[Any]      =		CONFIG_MAPPING[model_args.model_type]()
				logger.warning('You are instantiating a new config instance from scratch.'			)

		if model_args.tokenizer_name:
				lowercase__					:      Optional[int]      =		AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,    cache_dir=model_args.cache_dir			)
		elif model_args.model_name_or_path:
				lowercase__					:      Tuple      =		AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,    cache_dir=model_args.cache_dir			)
		else:
				raise ValueError(
				    'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
				    ' script, save it,and load it from here, using --tokenizer_name'			)

		if model_args.model_name_or_path:
				lowercase__					:      Tuple      =		AutoModelWithLMHead.from_pretrained(
				    model_args.model_name_or_path   ,    from_tf=bool('.ckpt' in model_args.model_name_or_path			)   ,    config=_lowercase   ,    cache_dir=model_args.cache_dir   ,    )
		else:
				logger.info('Training new model from scratch'			)
				lowercase__					:      Optional[Any]      =		AutoModelWithLMHead.from_config(_lowercase			)

		model.resize_token_embeddings(len(_lowercase			)			)

		if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
				raise ValueError(
				    'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
				    '--mlm flag (masked language modeling).'			)

		if data_args.block_size <= 0:
				lowercase__					:      Optional[int]      =		tokenizer.max_len
				# Our input block size will be the max possible for the model
		else:
				lowercase__					:      Union[str, Any]      =		min(data_args.block_size   ,    tokenizer.max_len			)

		# Get datasets

		lowercase__					:      Optional[Any]      =		(
		    get_dataset(_lowercase   ,    tokenizer=_lowercase   ,    cache_dir=model_args.cache_dir			) if training_args.do_train else None
		)
		lowercase__					:      List[str]      =		(
		    get_dataset(_lowercase   ,    tokenizer=_lowercase   ,    evaluate=_lowercase   ,    cache_dir=model_args.cache_dir			)
		    if training_args.do_eval
		    else None
		)
		if config.model_type == "xlnet":
				lowercase__					:      Optional[Any]      =		DataCollatorForPermutationLanguageModeling(
				    tokenizer=_lowercase   ,    plm_probability=data_args.plm_probability   ,    max_span_length=data_args.max_span_length   ,    )
		else:
				if data_args.mlm and data_args.whole_word_mask:
						lowercase__					:      List[Any]      =		DataCollatorForWholeWordMask(
						    tokenizer=_lowercase   ,    mlm_probability=data_args.mlm_probability			)
				else:
						lowercase__					:      Optional[int]      =		DataCollatorForLanguageModeling(
						    tokenizer=_lowercase   ,    mlm=data_args.mlm   ,    mlm_probability=data_args.mlm_probability			)

    # Initialize our Trainer
		lowercase__					:      Any      =		Trainer(
		    model=_lowercase   ,    args=_lowercase   ,    data_collator=_lowercase   ,    train_dataset=_lowercase   ,    eval_dataset=_lowercase   ,    prediction_loss_only=_lowercase   ,    )

		# Training
		if training_args.do_train:
				lowercase__					:      int      =		(
				    model_args.model_name_or_path
				    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path			)
				    else None
				)
				trainer.train(model_path=_lowercase			)
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
		lowercase__					:      Union[str, Any]      =		{}
		if training_args.do_eval:
				logger.info('*** Evaluate ***'			)

				lowercase__					:      Any      =		trainer.evaluate()

				lowercase__					:      List[Any]      =		math.exp(eval_output['eval_loss']			)
				lowercase__					:      Tuple      =		{'perplexity': perplexity}

				lowercase__					:      List[str]      =		os.path.join(training_args.output_dir   ,    'eval_results_lm.txt'			)
				if trainer.is_world_master():
						with open(_lowercase   ,    'w'			) as writer:
								logger.info('***** Eval results *****'			)
								for key in sorted(result.keys()			):
										logger.info('  %s = %s'   ,    _lowercase   ,    str(result[key]			)			)
										writer.write('%s = %s\n' % (key, str(result[key]			))			)

				results.update(_lowercase			)

		return results


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Dict			)   ->							Union[str, Any]:




		'''simple docstring'''
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()


