






import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFEfficientFormerForImageClassification,
						    TFEfficientFormerForImageClassificationWithTeacher,
						    TFEfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_tf_efficientformer import (
						    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							int = 13       ,				_lowerCAmelCase      :							int = 64       ,				_lowerCAmelCase      :							int = 2       ,				_lowerCAmelCase      :							int = 3       ,				_lowerCAmelCase      :							int = 3       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							int = 128       ,				_lowerCAmelCase      :							Optional[int]=[16, 32, 64, 128]       ,				_lowerCAmelCase      :							int = 7       ,				_lowerCAmelCase      :							int = 4       ,				_lowerCAmelCase      :							int = 37       ,				_lowerCAmelCase      :							str = "gelu"       ,				_lowerCAmelCase      :							float = 0.1       ,				_lowerCAmelCase      :							float = 0.1       ,				_lowerCAmelCase      :							int = 10       ,				_lowerCAmelCase      :							float = 0.02       ,				_lowerCAmelCase      :							int = 2       ,				_lowerCAmelCase      :							int = 1       ,				_lowerCAmelCase      :							int = 128       ,				_lowerCAmelCase      :							List[int] = [2, 2, 2, 2]       ,				_lowerCAmelCase      :							int = 2       ,				_lowerCAmelCase      :							int = 2       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  image_size
		__lowercase             =  patch_size
		__lowercase             =  num_channels
		__lowercase             =  is_training
		__lowercase             =  use_labels
		__lowercase             =  hidden_size
		__lowercase             =  num_hidden_layers
		__lowercase             =  num_attention_heads
		__lowercase             =  intermediate_size
		__lowercase             =  hidden_act
		__lowercase             =  hidden_dropout_prob
		__lowercase             =  attention_probs_dropout_prob
		__lowercase             =  type_sequence_label_size
		__lowercase             =  initializer_range
		__lowercase             =  encoder_stride
		__lowercase             =  num_attention_outputs
		__lowercase             =  embed_dim
		__lowercase             =  embed_dim + 1
		__lowercase             =  resolution
		__lowercase             =  depths
		__lowercase             =  hidden_sizes
		__lowercase             =  dim
		__lowercase             =  mlp_expansion_ratio







	def  _a   (  self      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

		__lowercase             =  None
		if self.use_labels:
			__lowercase             =  ids_tensor([self.batch_size]       ,				self.type_sequence_label_size			)

		__lowercase             =  self.get_config()

		return config, pixel_values, labels







	def  _a   (  self      :							Optional[Any]			)		-> str:
		"""simple docstring"""







		return EfficientFormerConfig(
		    image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				is_decoder=_lowerCAmelCase       ,				initializer_range=self.initializer_range       ,				encoder_stride=self.encoder_stride       ,				resolution=self.resolution       ,				depths=self.depths       ,				hidden_sizes=self.hidden_sizes       ,				dim=self.dim       ,				mlp_expansion_ratio=self.mlp_expansion_ratio       ,				)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  TFEfficientFormerModel(config=_lowerCAmelCase			)
		__lowercase             =  model(_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)			)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  self.type_sequence_label_size
		__lowercase             =  TFEfficientFormerForImageClassification(_lowerCAmelCase			)
		__lowercase             =  model(_lowerCAmelCase       ,				labels=_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)			)

		# test greyscale images
		__lowercase             =  1
		__lowercase             =  TFEfficientFormerForImageClassification(_lowerCAmelCase			)

		__lowercase             =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
		__lowercase             =  model(_lowerCAmelCase       ,				labels=_lowerCAmelCase			)
		self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)			)







	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		__lowercase       ,						__lowercase       ,						__lowercase             =  config_and_inputs
		__lowercase             =  {"""pixel_values""": pixel_values}
		return config, inputs_dict


@require_tf
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       (
	    (
	        TFEfficientFormerModel,
	        TFEfficientFormerForImageClassificationWithTeacher,
	        TFEfficientFormerForImageClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	__snake_case	:Any   =       (
	    {
	        'feature-extraction': TFEfficientFormerModel,
	        'image-classification': (
	            TFEfficientFormerForImageClassification,
	            TFEfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_tf_available()
	    else {}
	)

	__snake_case	:int   =       False

	__snake_case	:Optional[int]   =       False
	__snake_case	:int   =       False
	__snake_case	:Any   =       False
	__snake_case	:Any   =       False







	def  _a   (  self      :							Tuple			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  TFEfficientFormerModelTester(self			)
		__lowercase             =  ConfigTester(
		    self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase       ,				hidden_size=37			)







	def  _a   (  self      :							Optional[int]			)		-> int:
		"""simple docstring"""







		self.config_tester.run_common_tests()







	@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""			)
	def  _a   (  self      :							Optional[int]			)		-> Any:
		"""simple docstring"""







		pass







	@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""			)
	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Union[str, Any]			)		-> List[str]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			__lowercase             =  inspect.signature(model.call			)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			__lowercase             =  [*signature.parameters.keys()]

			__lowercase             =  ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		def check_hidden_states_output(_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							List[Any]			):
			__lowercase             =  model_class(_lowerCAmelCase			)

			__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				training=_lowerCAmelCase			)
			__lowercase             =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			__lowercase             =  getattr(
			    self.model_tester       ,				"""expected_num_hidden_layers"""       ,				self.model_tester.num_hidden_layers + 1			)
			self.assertEqual(len(_lowerCAmelCase			)       ,				_lowerCAmelCase			)

			if hasattr(self.model_tester       ,				"""encoder_seq_length"""			):
				__lowercase             =  self.model_tester.encoder_seq_length
				if hasattr(self.model_tester       ,				"""chunk_length"""			) and self.model_tester.chunk_length > 1:
					__lowercase             =  seq_length * self.model_tester.chunk_length
			else:
				__lowercase             =  self.model_tester.seq_length

			self.assertListEqual(
			    list(hidden_states[-1].shape[-2:]			)       ,				[seq_length, self.model_tester.hidden_size]       ,				)

			if config.is_encoder_decoder:
				__lowercase             =  outputs.decoder_hidden_states

				self.asseretIsInstance(_lowerCAmelCase       ,				(list, tuple)			)
				self.assertEqual(len(_lowerCAmelCase			)       ,				_lowerCAmelCase			)
				__lowercase             =  getattr(self.model_tester       ,				"""seq_length"""       ,				_lowerCAmelCase			)
				__lowercase             =  getattr(self.model_tester       ,				"""decoder_seq_length"""       ,				_lowerCAmelCase			)

				self.assertListEqual(
				    list(hidden_states[-1].shape[-2:]			)       ,				[decoder_seq_length, self.model_tester.hidden_size]       ,				)

		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  True
			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			__lowercase             =  True

			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[Any]=False			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  super()._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase       ,				return_labels=_lowerCAmelCase			)

		if return_labels:
			if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
				del inputs_dict["labels"]

		return inputs_dict







	def  _a   (  self      :							int			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_lowerCAmelCase			)







	@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""			)
	def  _a   (  self      :							Union[str, Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__lowercase             =  TFEfficientFormerModel.from_pretrained(_lowerCAmelCase			)
			self.assertIsNotNone(_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> List[str]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()
		__lowercase             =  True

		__lowercase             =  getattr(self.model_tester       ,				"""seq_length"""       ,				_lowerCAmelCase			)
		__lowercase             =  getattr(self.model_tester       ,				"""encoder_seq_length"""       ,				_lowerCAmelCase			)
		__lowercase             =  getattr(self.model_tester       ,				"""key_length"""       ,				_lowerCAmelCase			)
		__lowercase             =  getattr(self.model_tester       ,				"""chunk_length"""       ,				_lowerCAmelCase			)

		if chunk_length is not None and hasattr(self.model_tester       ,				"""num_hashes"""			):
			__lowercase             =  encoder_seq_length * self.model_tester.num_hashes

		for model_class in self.all_model_classes:
			__lowercase             =  True
			__lowercase             =  False
			__lowercase             =  True
			__lowercase             =  model_class(_lowerCAmelCase			)

			__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				training=_lowerCAmelCase			)
			__lowercase             =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(_lowerCAmelCase			)       ,				self.model_tester.num_attention_outputs			)

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			__lowercase             =  True
			__lowercase             =  model_class(_lowerCAmelCase			)
			__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				training=_lowerCAmelCase			)

			__lowercase             =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(_lowerCAmelCase			)       ,				self.model_tester.num_attention_outputs			)

			if chunk_length is not None:
				self.assertListEqual(
				    list(attentions[0].shape[-4:]			)       ,				[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,				)
			else:
				self.assertListEqual(
				    list(attentions[0].shape[-3:]			)       ,				[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,				)







	def  _a   (  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			# Prepare our model
			__lowercase             =  model_class(_lowerCAmelCase			)
			# These are maximally general inputs for the model, with multiple None dimensions
			# Hopefully this will catch any conditionals that fail for flexible shapes
			__lowercase             =  {
			    key: tf.keras.Input(shape=val.shape[1:]       ,				dtype=val.dtype       ,				name=_lowerCAmelCase			)
			    for key, val in model.input_signature.items()
			    if key in model.dummy_inputs
			}
			__lowercase             =  model(_lowerCAmelCase			)
			self.assertTrue(outputs_dict is not None			)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image




@require_tf
@require_vision
class        __UpperCamelCase    (     unittest.TestCase   ):







	@cached_property
	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







		return (
		    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""			)
		    if is_vision_available()
		    else None
		)







	@slow
	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""			)
		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""tf"""			)
		# forward pass
		__lowercase             =  model(**_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		# verify the logits
		__lowercase             =  tf.TensorShape((1, 1000)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)
		__lowercase             =  tf.constant([-0.0_555, 0.4_825, -0.0_852]			)
		self.assertTrue(np.allclose(outputs.logits[0, :3]       ,				_lowerCAmelCase       ,				atol=1e-4			)			)







	@slow
	def  _a   (  self      :							Optional[Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
		    """snap-research/efficientformer-l1-300"""			)
		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""tf"""			)
		# forward pass
		__lowercase             =  model(**_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		# verify the logits
		__lowercase             =  tf.TensorShape((1, 1000)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)
		__lowercase             =  tf.constant([-0.1_312, 0.4_353, -1.0_499]			)
		self.assertTrue(np.allclose(outputs.logits[0, :3]       ,				_lowerCAmelCase       ,				atol=1e-4			)			)









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      :      List[Any]     						=						logging.getLogger(__name__)


__UpperCamelCase      :      Optional[Any]     						=						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__UpperCamelCase      :      List[str]     						=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


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


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

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

	__snake_case	: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).'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = False				,  lowerCamelCase = None				,  ):




	'''simple docstring'''







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

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

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





def 				snake_case  (     ):




	'''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"""				,  lowerCamelCase					)

	# 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=lowerCamelCase				,  cache_dir=model_args.cache_dir				,  )
	else:
		logger.info("""Training new model from scratch"""					)
		__lowercase             =  AutoModelWithLMHead.from_config(lowerCamelCase					)

	model.resize_token_embeddings(len(lowerCamelCase					)					)

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

    # Initialize our Trainer
	__lowercase             =  Trainer(
	    model=lowerCamelCase				,  args=lowerCamelCase				,  data_collator=lowerCamelCase				,  train_dataset=lowerCamelCase				,  eval_dataset=lowerCamelCase				,  prediction_loss_only=lowerCamelCase				,  )

	# 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=lowerCamelCase					)
		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(lowerCamelCase				,  """w"""					) as writer:
				logger.info("""***** Eval results *****"""					)
				for key in sorted(result.keys()					):
					logger.info("""  %s = %s"""				,  lowerCamelCase				,  str(result[key]					)					)
					writer.write("""%s = %s\n""" % (key, str(result[key]					))					)

		results.update(lowerCamelCase					)

	return results





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	main()


if __name__ == "__main__":
						main()

