
"""simple docstring"""


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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowercase							   =				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')







@dataclass
class 				lowerCAmelCase_				:



						'''simple docstring'''


						_lowerCamelCase:    Optional[str]      =    field(
						    default='''cifar10'''		,					metadata={'''help''': '''Name of a dataset from the datasets package'''}    )
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}    )
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={'''help''': '''The column name of the images in the files.'''}    )
						_lowerCamelCase:    Optional[str]      =    field(default=_lowercase		,					metadata={'''help''': '''A folder containing the training data.'''}    )
						_lowerCamelCase:    Optional[str]      =    field(default=_lowercase		,					metadata={'''help''': '''A folder containing the validation data.'''}    )
						_lowerCamelCase:    Optional[float]      =    field(
						    default=0.15		,					metadata={'''help''': '''Percent to split off of train for validation.'''}    )
						_lowerCamelCase:    Optional[int]      =    field(
						    default=_lowercase		,					metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
						            '''value if set.'''
						        )
						    }		,					)
						_lowerCamelCase:    Optional[int]      =    field(
						    default=_lowercase		,					metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
						            '''value if set.'''
						        )
						    }		,					)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
							A						   =   {}
							if self.train_dir is not None:
								A						   =   self.train_dir
							if self.validation_dir is not None:
								A						   =   self.validation_dir
							A						   =   data_files if data_files else None







@dataclass
class 				lowerCAmelCase_				:



						'''simple docstring'''


						_lowerCamelCase:    str      =    field(
						    default=_lowercase		,					metadata={
						        '''help''': (
						            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
						        )
						    }		,					)
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}    )
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={
						        '''help''': (
						            '''Override some existing default config settings when a model is trained from scratch. Example: '''
						            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
						        )
						    }		,					)
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}    )
						_lowerCamelCase:    str      =    field(
						    default='''main'''		,					metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}		,					)
						_lowerCamelCase:    str      =    field(default=_lowercase		,					metadata={'''help''': '''Name or path of preprocessor config.'''}    )
						_lowerCamelCase:    bool      =    field(
						    default=_lowercase		,					metadata={
						        '''help''': (
						            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
						            '''with private models).'''
						        )
						    }		,					)
						_lowerCamelCase:    float      =    field(
						    default=0.75		,					metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}    )
						_lowerCamelCase:    bool      =    field(
						    default=_lowercase		,					metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}    )







@dataclass
class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    float      =    field(
						    default=1E-3		,					metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}    )






def       _snake_case				(		snake_case__   :     Union[str, Any]     ):
	A						   =   torch.stack([example['pixel_values'] for example in examples]     )
	return {"pixel_values": pixel_values}






def       _snake_case				(		):
	# 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.

	A						   =   HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)     )
	if len(sys.argv     ) == 2 and sys.argv[1].endswith('.json'     ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		A   ,      A   ,      A						   =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
	else:
		A   ,      A   ,      A						   =   parser.parse_args_into_dataclasses()

	# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
	# information sent is the one passed as arguments along with your Python/PyTorch versions.
	send_example_telemetry('run_mae'	,      snake_case__	,      snake_case__     )

	# Setup logging
	logging.basicConfig(
	    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,      datefmt='%m/%d/%Y %H:%M:%S'	,      handlers=[logging.StreamHandler(sys.stdout     )]	,      )

	if training_args.should_log:
		# The default of training_args.log_level is passive, so we set log level at info here to have that default.
		transformers.utils.logging.set_verbosity_info()

	A						   =   training_args.get_process_log_level()
	logger.setLevel(snake_case__     )
	transformers.utils.logging.set_verbosity(snake_case__     )
	transformers.utils.logging.enable_default_handler()
	transformers.utils.logging.enable_explicit_format()

	# Log on each process the small summary:
	logger.warning(
	    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
	    + F'distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'     )
	logger.info(F'Training/evaluation parameters {training_args}'     )

	# Detecting last checkpoint.
	A						   =   None
	if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
		A						   =   get_last_checkpoint(training_args.output_dir     )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
			raise ValueError(
			    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
			    'Use --overwrite_output_dir to overcome.'     )
		elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
			logger.info(
			    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
			    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'     )

    # Initialize our dataset.
	A						   =   load_dataset(
	    data_args.dataset_name	,      data_args.dataset_config_name	,      data_files=data_args.data_files	,      cache_dir=model_args.cache_dir	,      use_auth_token=True if model_args.use_auth_token else None	,      )

	# If we don't have a validation split, split off a percentage of train as validation.
	A						   =   None if 'validation' in ds.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split	,      snake_case__     ) and data_args.train_val_split > 0.0:
		A						   =   ds['train'].train_test_split(data_args.train_val_split     )
		A						   =   split['train']
		A						   =   split['test']

	# Load pretrained model and image processor
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	A						   =   {
	    'cache_dir': model_args.cache_dir,
	    'revision': model_args.model_revision,
	    'use_auth_token': True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		A						   =   ViTMAEConfig.from_pretrained(model_args.config_name	,      **snake_case__     )
	elif model_args.model_name_or_path:
		A						   =   ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,      **snake_case__     )
	else:
		A						   =   ViTMAEConfig()
		logger.warning('You are instantiating a new config instance from scratch.'     )
		if model_args.config_overrides is not None:
			logger.info(F'Overriding config: {model_args.config_overrides}'     )
			config.update_from_string(model_args.config_overrides     )
			logger.info(F'New config: {config}'     )

    # adapt config
	config.update(
	    {
	        'mask_ratio': model_args.mask_ratio,
	        'norm_pix_loss': model_args.norm_pix_loss,
	    }     )

	# create image processor
	if model_args.image_processor_name:
		A						   =   ViTImageProcessor.from_pretrained(model_args.image_processor_name	,      **snake_case__     )
	elif model_args.model_name_or_path:
		A						   =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,      **snake_case__     )
	else:
		A						   =   ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		A						   =   ViTMAEForPreTraining.from_pretrained(
		    model_args.model_name_or_path	,      from_tf=bool('.ckpt' in model_args.model_name_or_path     )	,      config=snake_case__	,      cache_dir=model_args.cache_dir	,      revision=model_args.model_revision	,      use_auth_token=True if model_args.use_auth_token else None	,      )
	else:
		logger.info('Training new model from scratch'     )
		A						   =   ViTMAEForPreTraining(snake_case__     )

	if training_args.do_train:
		A						   =   ds['train'].column_names
	else:
		A						   =   ds['validation'].column_names

	if data_args.image_column_name is not None:
		A						   =   data_args.image_column_name
	elif "image" in column_names:
		A						   =   'image'
	elif "img" in column_names:
		A						   =   'img'
	else:
		A						   =   column_names[0]

	# transformations as done in original MAE paper
	# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
	if "shortest_edge" in image_processor.size:
		A						   =   image_processor.size['shortest_edge']
	else:
		A						   =   (image_processor.size['height'], image_processor.size['width'])
	A						   =   Compose(
	    [
	        Lambda(lambda snake_case__     : img.convert('RGB'     ) if img.mode != "RGB" else img     ),
	        RandomResizedCrop(snake_case__	,      scale=(0.2, 1.0)	,      interpolation=InterpolationMode.BICUBIC     ),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        Normalize(mean=image_processor.image_mean	,      std=image_processor.image_std     ),
	    ]     )

	def preprocess_images(snake_case__   :     Dict     ):
		A						   =   [transforms(snake_case__     ) for image in examples[image_column_name]]
		return examples

	if training_args.do_train:
		if "train" not in ds:
			raise ValueError('--do_train requires a train dataset'     )
		if data_args.max_train_samples is not None:
			A						   =   ds['train'].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
		# Set the training transforms
		ds["train"].set_transform(snake_case__     )

	if training_args.do_eval:
		if "validation" not in ds:
			raise ValueError('--do_eval requires a validation dataset'     )
		if data_args.max_eval_samples is not None:
			A						   =   (
			    ds['validation'].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
			)
		# Set the validation transforms
		ds["validation"].set_transform(snake_case__     )

	# Compute absolute learning rate
	A						   =   (
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		A						   =   training_args.base_learning_rate * total_train_batch_size / 256

	# Initialize our trainer
	A						   =   Trainer(
	    model=snake_case__	,      args=snake_case__	,      train_dataset=ds['train'] if training_args.do_train else None	,      eval_dataset=ds['validation'] if training_args.do_eval else None	,      tokenizer=snake_case__	,      data_collator=snake_case__	,      )

	# Training
	if training_args.do_train:
		A						   =   None
		if training_args.resume_from_checkpoint is not None:
			A						   =   training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			A						   =   last_checkpoint
		A						   =   trainer.train(resume_from_checkpoint=snake_case__     )
		trainer.save_model()
		trainer.log_metrics('train'	,      train_result.metrics     )
		trainer.save_metrics('train'	,      train_result.metrics     )
		trainer.save_state()

	# Evaluation
	if training_args.do_eval:
		A						   =   trainer.evaluate()
		trainer.log_metrics('eval'	,      snake_case__     )
		trainer.save_metrics('eval'	,      snake_case__     )

	# Write model card and (optionally) push to hub
	A						   =   {
	    'tasks': 'masked-auto-encoding',
	    'dataset': data_args.dataset_name,
	    'tags': ['masked-auto-encoding'],
	}
	if training_args.push_to_hub:
		trainer.push_to_hub(**snake_case__     )
	else:
		trainer.create_model_card(**snake_case__     )






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()

"""simple docstring"""


import unittest

from transformers import XLMConfig, 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 (
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMWithLMHeadModel,
		)
		from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      Tuple=13  ,A_     :      Optional[Any]=7  ,A_     :      Dict=True  ,A_     :      Optional[Any]=True  ,A_     :      str=True  ,A_     :      Union[str, Any]=True  ,A_     :      Optional[Any]=True  ,A_     :      Tuple=False  ,A_     :      Optional[int]=False  ,A_     :      str=False  ,A_     :      int=2  ,A_     :      Union[str, Any]=99  ,A_     :      int=0  ,A_     :      Dict=32  ,A_     :      List[str]=5  ,A_     :      Any=4  ,A_     :      str=0.1  ,A_     :      Any=0.1  ,A_     :      int=512  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.02  ,A_     :      Optional[Any]=2  ,A_     :      List[str]=4  ,A_     :      Optional[int]="last"  ,A_     :      str=True  ,A_     :      List[str]=None  ,A_     :      List[Any]=0  ,)     ->   int:
							A						   =   parent
							A						   =   batch_size
							A						   =   seq_length
							A						   =   is_training
							A						   =   use_input_lengths
							A						   =   use_token_type_ids
							A						   =   use_labels
							A						   =   gelu_activation
							A						   =   sinusoidal_embeddings
							A						   =   causal
							A						   =   asm
							A						   =   n_langs
							A						   =   vocab_size
							A						   =   n_special
							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   max_position_embeddings
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   num_labels
							A						   =   num_choices
							A						   =   summary_type
							A						   =   use_proj
							A						   =   scope
							A						   =   bos_token_id


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Dict:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )
							A						   =   random_attention_mask([self.batch_size, self.seq_length]      )

							A						   =   None
							if self.use_input_lengths:
								A						   =   (
								    ids_tensor([self.batch_size]  ,vocab_size=2      ) + self.seq_length - 2
								)  # small variation of seq_length

							A						   =   None
							if self.use_token_type_ids:
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.n_langs      )

							A						   =   None
							A						   =   None
							A						   =   None
							if self.use_labels:
								A						   =   ids_tensor([self.batch_size]  ,self.type_sequence_label_size      )
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels      )
								A						   =   ids_tensor([self.batch_size]  ,2      ).float()
								A						   =   ids_tensor([self.batch_size]  ,self.num_choices      )

							A						   =   self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							return XLMConfig(
							    vocab_size=self.vocab_size  ,n_special=self.n_special  ,emb_dim=self.hidden_size  ,n_layers=self.num_hidden_layers  ,n_heads=self.num_attention_heads  ,dropout=self.hidden_dropout_prob  ,attention_dropout=self.attention_probs_dropout_prob  ,gelu_activation=self.gelu_activation  ,sinusoidal_embeddings=self.sinusoidal_embeddings  ,asm=self.asm  ,causal=self.causal  ,n_langs=self.n_langs  ,max_position_embeddings=self.max_position_embeddings  ,initializer_range=self.initializer_range  ,summary_type=self.summary_type  ,use_proj=self.use_proj  ,num_labels=self.num_labels  ,bos_token_id=self.bos_token_id  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      int  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      List[str]  ,A_     :      Optional[int]  ,)     ->   Tuple:
							A						   =   XLMModel(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   model(A_  ,lengths=A_  ,langs=A_      )
							A						   =   model(A_  ,langs=A_      )
							A						   =   model(A_      )
							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      str  ,A_     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      List[str]  ,)     ->   Union[str, Any]:
							A						   =   XLMWithLMHeadModel(A_      )
							model.to(A_      )
							model.eval()

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      Tuple  ,A_     :      str  ,A_     :      int  ,A_     :      str  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Dict  ,)     ->   List[str]:
							A						   =   XLMForQuestionAnsweringSimple(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )
							A						   =   outputs
							self.parent.assertEqual(result.start_logits.shape  ,(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape  ,(self.batch_size, self.seq_length)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[int]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Any  ,)     ->   Optional[int]:
							A						   =   XLMForQuestionAnswering(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,p_mask=A_  ,)

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,)

							((A)   ,      )						   =   result_with_labels.to_tuple()

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )

							((A)   ,      )						   =   result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape  ,()      )
							self.parent.assertEqual(result.start_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(result.start_top_index.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(
							    result.end_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(
							    result.end_top_index.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(result.cls_logits.shape  ,(self.batch_size,)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Tuple  ,A_     :      Union[str, Any]  ,A_     :      str  ,)     ->   List[Any]:
							A						   =   XLMForSequenceClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )
							A						   =   model(A_  ,labels=A_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Tuple  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      Dict  ,A_     :      Any  ,)     ->   Any:
							A						   =   self.num_labels
							A						   =   XLMForTokenClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_  ,attention_mask=A_  ,labels=A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      int  ,A_     :      str  ,A_     :      int  ,A_     :      Tuple  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      int  ,)     ->   Tuple:
							A						   =   self.num_choices
							A						   =   XLMForMultipleChoice(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   input_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   token_type_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   input_mask.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   model(
							    A_  ,attention_mask=A_  ,token_type_ids=A_  ,labels=A_  ,)
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_choices)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    (
						    (
						        XLMModel,
						        XLMWithLMHeadModel,
						        XLMForQuestionAnswering,
						        XLMForSequenceClassification,
						        XLMForQuestionAnsweringSimple,
						        XLMForTokenClassification,
						        XLMForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCamelCase:    Dict      =    (
						    (XLMWithLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						_lowerCamelCase:    Optional[Any]      =    (
						    {
						        '''feature-extraction''': XLMModel,
						        '''fill-mask''': XLMWithLMHeadModel,
						        '''question-answering''': XLMForQuestionAnsweringSimple,
						        '''text-classification''': XLMForSequenceClassification,
						        '''text-generation''': XLMWithLMHeadModel,
						        '''token-classification''': XLMForTokenClassification,
						        '''zero-shot''': XLMForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      Any  ,A_     :      str      )     ->   Tuple:
							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith('Fast'      )
							):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

							return False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      str=False      )     ->   Dict:
							A						   =   super()._prepare_for_class(A_  ,A_  ,return_labels=A_      )

							if return_labels:
								if model_class.__name__ == "XLMForQuestionAnswering":
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )

							return inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							A						   =   XLMModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,emb_dim=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   str:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_simple_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_token_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Dict  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      List[Any]=False  ,A_     :      Tuple=1      )     ->   List[Any]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_attentions in attentions]  ,[True] * len(A_      )      )
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_attentions in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   min_length + idx + 1

								A						   =   (
								    batch_size * num_beam_groups,
								    config.num_attention_heads,
								    tgt_len,
								    src_len,
								)
								# check attn size
								self.assertListEqual(
								    [layer_attention.shape for layer_attention in iter_attentions]  ,[expected_shape] * len(A_      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      Tuple=False  ,A_     :      Optional[Any]=1      )     ->   List[str]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_hidden_states in hidden_states]  ,[True] * len(A_      )  ,)
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_hidden_states in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
								# check hidden size
								self.assertListEqual(
								    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]  ,[expected_shape] * len(A_      )  ,)
							pass




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[str]:
							for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   XLMModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )







@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'      )
							model.to(A_      )
							A						   =   torch.tensor([[14, 447]]  ,dtype=torch.long  ,device=A_      )  # the president
							A						   =   [
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							A						   =   model.generate(A_  ,do_sample=A_      )
							self.assertListEqual(output_ids[0].cpu().numpy().tolist()  ,A_      )