





import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
			    CTRLForSequenceClassification,
			    CTRLLMHeadModel,
			    CTRLModel,
			)






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=1_4       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=9_9       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		seq_length
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_token_type_ids
				lowerCamelCase__								=		use_input_mask
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		use_mc_token_ids
				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		type_vocab_size
				lowerCamelCase__								=		type_sequence_label_size
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		num_labels
				lowerCamelCase__								=		num_choices
				lowerCamelCase__								=		scope
				lowerCamelCase__								=		self.vocab_size - 1





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size							)

				lowerCamelCase__								=		None
				if self.use_input_mask:
					lowerCamelCase__								=		random_attention_mask([self.batch_size, self.seq_length]							)

				lowerCamelCase__								=		None
				if self.use_token_type_ids:
					lowerCamelCase__								=		ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size							)

				lowerCamelCase__								=		None
				if self.use_mc_token_ids:
					lowerCamelCase__								=		ids_tensor([self.batch_size, self.num_choices]       ,  self.seq_length							)

				lowerCamelCase__								=		None
				lowerCamelCase__								=		None
				lowerCamelCase__								=		None
				if self.use_labels:
					lowerCamelCase__								=		ids_tensor([self.batch_size]       ,  self.type_sequence_label_size							)
					lowerCamelCase__								=		ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels							)
					lowerCamelCase__								=		ids_tensor([self.batch_size]       ,  self.num_choices							)

				lowerCamelCase__								=		self.get_config()

				lowerCamelCase__								=		ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,  2							)

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				    token_type_ids,
				    mc_token_ids,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return CTRLConfig(
				    vocab_size=self.vocab_size       ,  n_embd=self.hidden_size       ,  n_layer=self.num_hidden_layers       ,  n_head=self.num_attention_heads       ,  n_positions=self.max_position_embeddings       ,  pad_token_id=self.pad_token_id       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  *__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		CTRLModel(config=UpperCamelCase_							)
				model.to(UpperCamelCase_							)
				model.eval()

				model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  head_mask=UpperCamelCase_							)
				model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_							)
				lowerCamelCase__								=		model(UpperCamelCase_							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)							)
				self.parent.assertEqual(len(result.past_key_values							)       ,  config.n_layer							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  *__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		CTRLLMHeadModel(UpperCamelCase_							)
				model.to(UpperCamelCase_							)
				model.eval()

				lowerCamelCase__								=		model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  labels=UpperCamelCase_							)
				self.parent.assertEqual(result.loss.shape       ,  ()							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.prepare_config_and_inputs()

				(
				    (
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,			
				)								=		config_and_inputs

				lowerCamelCase__								=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

				return config, inputs_dict





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  *__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.num_labels
				lowerCamelCase__								=		CTRLForSequenceClassification(UpperCamelCase_							)
				model.to(UpperCamelCase_							)
				model.eval()
				lowerCamelCase__								=		ids_tensor([self.batch_size]       ,  self.type_sequence_label_size							)
				lowerCamelCase__								=		model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  labels=UpperCamelCase_							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)							)






@require_torch
class 					__A    (    UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
			lowerCAmelCase_							     =						(CTRLLMHeadModel,) if is_torch_available() else ()
			lowerCAmelCase_							     =						(
			    {
			        '''feature-extraction''': CTRLModel,
			        '''text-classification''': CTRLForSequenceClassification,
			        '''text-generation''': CTRLLMHeadModel,
			        '''zero-shot''': CTRLForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCAmelCase_							     =						True
			lowerCAmelCase_							     =						False
			lowerCAmelCase_							     =						False





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
					# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
					# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
					# config could not be created.
					return True

				return False





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		CTRLModelTester(self							)
				lowerCamelCase__								=		ConfigTester(self       ,  config_class=UpperCamelCase_       ,  n_embd=3_7							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*UpperCamelCase_							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*UpperCamelCase_							)





			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__								=		CTRLModel.from_pretrained(UpperCamelCase_							)
					self.assertIsNotNone(UpperCamelCase_							)





			@unittest.skip('''The model doesn\'t support left padding'''							)  # and it's not used enough to be worth fixing :)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass






@require_torch
class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		CTRLLMHeadModel.from_pretrained('''ctrl'''							)
				model.to(UpperCamelCase_							)
				lowerCamelCase__								=		torch.tensor(
				    [[1_1_8_5_9, 0, 1_6_1_1, 8]]       ,  dtype=torch.long       ,  device=UpperCamelCase_							)  # Legal the president is
				lowerCamelCase__								=		[
				    1_1_8_5_9,
				    0,
				    1_6_1_1,
				    8,
				    5,
				    1_5_0,
				    2_6_4_4_9,
				    2,
				    1_9,
				    3_4_8,
				    4_6_9,
				    3,
				    2_5_9_5,
				    4_8,
				    2_0_7_4_0,
				    2_4_6_5_3_3,
				    2_4_6_5_3_3,
				    1_9,
				    3_0,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				lowerCamelCase__								=		model.generate(UpperCamelCase_       ,  do_sample=UpperCamelCase_							)
				self.assertListEqual(output_ids[0].tolist()       ,  UpperCamelCase_							)







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


lowerCAmelCase       =							logging.getLogger(__name__)


lowerCAmelCase       =							list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowerCAmelCase       =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class        _a							:
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
							            ''' scratch.'''
							        )
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase__			)}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,	)


@dataclass
class        _a							:
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The input training data file (a text file).'''}			)
							_lowercase	:			Optional[str]        = 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'''
							        )
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''}     ,	)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''}     ,	)

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

							_lowercase	:			int        = 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).'''
							        )
							    }     ,	)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE = False						,				SCREAMING_SNAKE_CASE = None						,				):


		"""simple docstring"""




		def _dataset(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=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=SCREAMING_SNAKE_CASE						,				file_path=SCREAMING_SNAKE_CASE						,				block_size=args.block_size						,				ref_path=SCREAMING_SNAKE_CASE						,				)

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

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


		"""simple docstring"""




		lowercase__            =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
		lowercase__     ,		lowercase__     ,		lowercase__            =      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'''						,				SCREAMING_SNAKE_CASE     )

		# 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__            =      AutoConfig.from_pretrained(model_args.config_name						,				cache_dir=model_args.cache_dir     )
		elif model_args.model_name_or_path:
				lowercase__            =      AutoConfig.from_pretrained(model_args.model_name_or_path						,				cache_dir=model_args.cache_dir     )
		else:
				lowercase__            =      CONFIG_MAPPING[model_args.model_type]()
				logger.warning('''You are instantiating a new config instance from scratch.'''     )

		if model_args.tokenizer_name:
				lowercase__            =      AutoTokenizer.from_pretrained(model_args.tokenizer_name						,				cache_dir=model_args.cache_dir     )
		elif model_args.model_name_or_path:
				lowercase__            =      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__            =      AutoModelWithLMHead.from_pretrained(
				    model_args.model_name_or_path						,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )						,				config=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir						,				)
		else:
				logger.info('''Training new model from scratch'''     )
				lowercase__            =      AutoModelWithLMHead.from_config(SCREAMING_SNAKE_CASE     )

		model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE     )     )

		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__            =      tokenizer.max_len
				# Our input block size will be the max possible for the model
		else:
				lowercase__            =      min(data_args.block_size						,				tokenizer.max_len     )

		# Get datasets

		lowercase__            =      (
		    get_dataset(SCREAMING_SNAKE_CASE						,				tokenizer=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir     ) if training_args.do_train else None
		)
		lowercase__            =      (
		    get_dataset(SCREAMING_SNAKE_CASE						,				tokenizer=SCREAMING_SNAKE_CASE						,				evaluate=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir     )
		    if training_args.do_eval
		    else None
		)
		if config.model_type == "xlnet":
				lowercase__            =      DataCollatorForPermutationLanguageModeling(
				    tokenizer=SCREAMING_SNAKE_CASE						,				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__            =      DataCollatorForWholeWordMask(
						    tokenizer=SCREAMING_SNAKE_CASE						,				mlm_probability=data_args.mlm_probability     )
				else:
						lowercase__            =      DataCollatorForLanguageModeling(
						    tokenizer=SCREAMING_SNAKE_CASE						,				mlm=data_args.mlm						,				mlm_probability=data_args.mlm_probability     )

    # Initialize our Trainer
		lowercase__            =      Trainer(
		    model=SCREAMING_SNAKE_CASE						,				args=SCREAMING_SNAKE_CASE						,				data_collator=SCREAMING_SNAKE_CASE						,				train_dataset=SCREAMING_SNAKE_CASE						,				eval_dataset=SCREAMING_SNAKE_CASE						,				prediction_loss_only=SCREAMING_SNAKE_CASE						,				)

		# Training
		if training_args.do_train:
				lowercase__            =      (
				    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=SCREAMING_SNAKE_CASE     )
				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__            =      {}
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''     )

				lowercase__            =      trainer.evaluate()

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

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

				results.update(SCREAMING_SNAKE_CASE     )

		return results
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		main()


if __name__ == "__main__":
			main()


