
"""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        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Optional[Any]=13				,     UpperCAmelCase_	:			Dict=7				,     UpperCAmelCase_	:			Any=True				,     UpperCAmelCase_	:			List[Any]=True				,     UpperCAmelCase_	:			List[Any]=True				,     UpperCAmelCase_	:			Any=True				,     UpperCAmelCase_	:			List[Any]=True				,     UpperCAmelCase_	:			Any=False				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			Optional[Any]=2				,     UpperCAmelCase_	:			int=99				,     UpperCAmelCase_	:			str=0				,     UpperCAmelCase_	:			Union[str, Any]=32				,     UpperCAmelCase_	:			Dict=5				,     UpperCAmelCase_	:			Optional[Any]=4				,     UpperCAmelCase_	:			Optional[int]=0.1				,     UpperCAmelCase_	:			str=0.1				,     UpperCAmelCase_	:			List[str]=512				,     UpperCAmelCase_	:			Tuple=2				,     UpperCAmelCase_	:			Optional[int]=0.02				,     UpperCAmelCase_	:			List[str]=2				,     UpperCAmelCase_	:			List[Any]=4				,     UpperCAmelCase_	:			Tuple="last"				,     UpperCAmelCase_	:			Optional[int]=True				,     UpperCAmelCase_	:			List[Any]=None				,     UpperCAmelCase_	:			Optional[int]=0				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			seq_length
											_lowerCAmelCase  	=			is_training
											_lowerCAmelCase  	=			use_input_lengths
											_lowerCAmelCase  	=			use_token_type_ids
											_lowerCAmelCase  	=			use_labels
											_lowerCAmelCase  	=			gelu_activation
											_lowerCAmelCase  	=			sinusoidal_embeddings
											_lowerCAmelCase  	=			causal
											_lowerCAmelCase  	=			asm
											_lowerCAmelCase  	=			n_langs
											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			n_special
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_hidden_layers
											_lowerCAmelCase  	=			num_attention_heads
											_lowerCAmelCase  	=			hidden_dropout_prob
											_lowerCAmelCase  	=			attention_probs_dropout_prob
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			type_sequence_label_size
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			num_labels
											_lowerCAmelCase  	=			num_choices
											_lowerCAmelCase  	=			summary_type
											_lowerCAmelCase  	=			use_proj
											_lowerCAmelCase  	=			scope
											_lowerCAmelCase  	=			bos_token_id


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  List[str]:







											"""simple docstring"""




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

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

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

											_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]				,     2  ).float()
																		_lowerCAmelCase  	=			ids_tensor([self.batch_size]				,     self.num_choices  )

											_lowerCAmelCase  	=			self.get_config()

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


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Union[str, Any]:







											"""simple docstring"""




											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 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple				,     )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLMModel(config=UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()
											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     lengths=UpperCAmelCase_				,     langs=UpperCAmelCase_  )
											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     langs=UpperCAmelCase_  )
											_lowerCAmelCase  	=			model(UpperCAmelCase_  )
											self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)  )


				def 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			str				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLMWithLMHeadModel(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	:			List[Any]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Tuple				,     )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLMForQuestionAnsweringSimple(UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()

											_lowerCAmelCase  	=			model(UpperCAmelCase_  )

											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     start_positions=UpperCAmelCase_				,     end_positions=UpperCAmelCase_  )
											_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]				,     )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLMForQuestionAnswering(UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()

											_lowerCAmelCase  	=			model(UpperCAmelCase_  )

											_lowerCAmelCase  	=			model(
											    UpperCAmelCase_				,     start_positions=UpperCAmelCase_				,     end_positions=UpperCAmelCase_				,     cls_index=UpperCAmelCase_				,     is_impossible=UpperCAmelCase_				,     p_mask=UpperCAmelCase_				,     )

											_lowerCAmelCase  	=			model(
											    UpperCAmelCase_				,     start_positions=UpperCAmelCase_				,     end_positions=UpperCAmelCase_				,     cls_index=UpperCAmelCase_				,     is_impossible=UpperCAmelCase_				,     )

											(_lowerCAmelCase						)  	=			result_with_labels.to_tuple()

											_lowerCAmelCase  	=			model(UpperCAmelCase_				,     start_positions=UpperCAmelCase_				,     end_positions=UpperCAmelCase_  )

											(_lowerCAmelCase						)  	=			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 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			List[str]				,     )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLMForSequenceClassification(UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()

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


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Any				,     )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.num_labels
											_lowerCAmelCase  	=			XLMForTokenClassification(UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()

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


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			str				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.num_choices
											_lowerCAmelCase  	=			XLMForMultipleChoice(config=UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )
											model.eval()
											_lowerCAmelCase  	=			input_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
											_lowerCAmelCase  	=			token_type_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
											_lowerCAmelCase  	=			input_mask.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
											_lowerCAmelCase  	=			model(
											    UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_				,     labels=UpperCAmelCase_				,     )
											self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)  )







				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.prepare_config_and_inputs()
											(
											    _lowerCAmelCase						
											)  	=			config_and_inputs
											_lowerCAmelCase  	=			{"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
											return config, inputs_dict






@require_torch
class        _SCREAMING_SNAKE_CASE  (  _A      ,				_A      ,				_A      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					(
				    (
				        XLMModel,
				        XLMWithLMHeadModel,
				        XLMForQuestionAnswering,
				        XLMForSequenceClassification,
				        XLMForQuestionAnsweringSimple,
				        XLMForTokenClassification,
				        XLMForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					(
				    (XLMWithLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				SCREAMING_SNAKE_CASE_:							Tuple							=					(
				    {
				        "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 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			List[str]  )		->  int:







											"""simple docstring"""




											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 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Tuple=False  )		->  int:







											"""simple docstring"""




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

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

											return inputs_dict


				def 				__lowerCamelCase	(					self	:			str  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLMModelTester(self  )
											_lowerCAmelCase  	=			ConfigTester(self				,     config_class=UpperCAmelCase_				,     emb_dim=37  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Tuple:







											"""simple docstring"""




											self.config_tester.run_common_tests()


				def 				__lowerCamelCase	(					self	:			str  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_model(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Dict=False				,     UpperCAmelCase_	:			Optional[Any]=1  )		->  Tuple:







											"""simple docstring"""




											self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_  )
											self.assertListEqual(
											    [isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) for iter_attentions in attentions]				,     [True] * len(UpperCAmelCase_  )  )
											self.assertEqual(len(UpperCAmelCase_  )				,     (max_length - min_length) * num_beam_groups  )

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

																		_lowerCAmelCase  	=			(
																		    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(UpperCAmelCase_  )  )


				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			Dict=1  )		->  Any:







											"""simple docstring"""




											self.assertIsInstance(UpperCAmelCase_				,     UpperCAmelCase_  )
											self.assertListEqual(
											    [isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) for iter_hidden_states in hidden_states]				,     [True] * len(UpperCAmelCase_  )				,     )
											self.assertEqual(len(UpperCAmelCase_  )				,     (max_length - min_length) * num_beam_groups  )

											for idx, iter_hidden_states in enumerate(UpperCAmelCase_  ):
																		# adds PAD dummy token
																		_lowerCAmelCase  	=			min_length + idx + 1
																		_lowerCAmelCase  	=			(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(UpperCAmelCase_  )				,     )
											pass







				@slow
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  str:







											"""simple docstring"""




											for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase  	=			XLMModel.from_pretrained(UpperCAmelCase_  )
																		self.assertIsNotNone(UpperCAmelCase_  )






@require_torch
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				@slow
				def 				__lowerCamelCase	(					self	:			Dict  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'  )
											model.to(UpperCAmelCase_  )
											_lowerCAmelCase  	=			torch.tensor([[14, 447]]				,     dtype=torch.long				,     device=UpperCAmelCase_  )  # the president
											_lowerCAmelCase  	=			[
											    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
											_lowerCAmelCase  	=			model.generate(UpperCAmelCase_				,     do_sample=UpperCAmelCase_  )
											self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,     UpperCAmelCase_  )








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0

							# if input_string is "aba" than new_input_string become "a|b|a"
							_lowerCAmelCase  	=			''
							_lowerCAmelCase  	=			''

							# append each character + "|" in new_string for range(0, length-1)
							for i in input_string[: len(SCREAMING_SNAKE_CASE						) - 1]:
														new_input_string += i + "|"
							# append last character
							new_input_string += input_string[-1]

							# we will store the starting and ending of previous furthest ending palindromic
							# substring
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							# length[i] shows the length of palindromic substring with center i
							_lowerCAmelCase  	=			[1 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# for each character in new_string find corresponding palindromic string
							_lowerCAmelCase  	=			0
							for j in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			1 if j > r else min(length[l + r - j] // 2     ,  r - j + 1						)
														while (
														    j - k >= 0
														    and j + k < len(SCREAMING_SNAKE_CASE						)
														    and new_input_string[k + j] == new_input_string[j - k]
														):
																					k += 1

														_lowerCAmelCase  	=			2 * k - 1

														# does this string is ending after the previously explored end (that is r) ?
														# if yes the update the new r to the last index of this
														if j + k - 1 > r:
																					_lowerCAmelCase  	=			j - k + 1  # noqa: E741
																					_lowerCAmelCase  	=			j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					_lowerCAmelCase  	=			length[j]
																					_lowerCAmelCase  	=			j

    # create that string
							_lowerCAmelCase  	=			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
							for i in s:
														if i != "|":
																					output_string += i

							return output_string


if __name__ == "__main__":
	import doctest

	doctest.testmod()






