


"""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__    :







						def __init__(				self :     str      ,      _lowercase :     Tuple      ,      _lowercase :     Optional[Any]=13      ,      _lowercase :     Optional[Any]=7      ,      _lowercase :     Tuple=True      ,      _lowercase :     List[str]=True      ,      _lowercase :     Union[str, Any]=True      ,      _lowercase :     Tuple=True      ,      _lowercase :     Union[str, Any]=True      ,      _lowercase :     Tuple=False      ,      _lowercase :     List[str]=False      ,      _lowercase :     Dict=False      ,      _lowercase :     List[Any]=2      ,      _lowercase :     Dict=99      ,      _lowercase :     Optional[Any]=0      ,      _lowercase :     Any=32      ,      _lowercase :     Tuple=5      ,      _lowercase :     List[str]=4      ,      _lowercase :     Dict=0.1      ,      _lowercase :     Optional[int]=0.1      ,      _lowercase :     str=512      ,      _lowercase :     str=2      ,      _lowercase :     Optional[Any]=0.0_2      ,      _lowercase :     Dict=2      ,      _lowercase :     str=4      ,      _lowercase :     str="last"      ,      _lowercase :     Tuple=True      ,      _lowercase :     str=None      ,      _lowercase :     Any=0      ,      ):
								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 __a    (				self :     Optional[Any]		):
								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 __a    (				self :     Dict		):
								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 __a    (				self :     int      ,      _lowercase :     Any      ,      _lowercase :     Optional[Any]      ,      _lowercase :     Dict      ,      _lowercase :     Dict      ,      _lowercase :     List[Any]      ,      _lowercase :     Tuple      ,      _lowercase :     Optional[Any]      ,      _lowercase :     int      ,      _lowercase :     Optional[Any]      ,      ):
								A       							=			XLMModel(config=lowerCAmelCase__		)
								model.to(lowerCAmelCase__		)
								model.eval()
								A       							=			model(lowerCAmelCase__      ,      lengths=lowerCAmelCase__      ,      langs=lowerCAmelCase__		)
								A       							=			model(lowerCAmelCase__      ,      langs=lowerCAmelCase__		)
								A       							=			model(lowerCAmelCase__		)
								self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)		)







						def __a    (				self :     Any      ,      _lowercase :     List[Any]      ,      _lowercase :     Union[str, Any]      ,      _lowercase :     List[str]      ,      _lowercase :     str      ,      _lowercase :     Optional[int]      ,      _lowercase :     Optional[Any]      ,      _lowercase :     Optional[int]      ,      _lowercase :     Optional[int]      ,      _lowercase :     Optional[Any]      ,      ):
								A       							=			XLMWithLMHeadModel(lowerCAmelCase__		)
								model.to(lowerCAmelCase__		)
								model.eval()

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







						def __a    (				self :     str      ,      _lowercase :     Tuple      ,      _lowercase :     Optional[int]      ,      _lowercase :     List[Any]      ,      _lowercase :     List[str]      ,      _lowercase :     Any      ,      _lowercase :     Tuple      ,      _lowercase :     List[Any]      ,      _lowercase :     Optional[int]      ,      _lowercase :     Optional[int]      ,      ):
								A       							=			XLMForQuestionAnsweringSimple(lowerCAmelCase__		)
								model.to(lowerCAmelCase__		)
								model.eval()

								A       							=			model(lowerCAmelCase__		)

								A       							=			model(lowerCAmelCase__      ,      start_positions=lowerCAmelCase__      ,      end_positions=lowerCAmelCase__		)
								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 __a    (				self :     Any      ,      _lowercase :     Optional[Any]      ,      _lowercase :     Optional[Any]      ,      _lowercase :     Dict      ,      _lowercase :     Any      ,      _lowercase :     str      ,      _lowercase :     Any      ,      _lowercase :     Optional[Any]      ,      _lowercase :     int      ,      _lowercase :     Dict      ,      ):
								A       							=			XLMForQuestionAnswering(lowerCAmelCase__		)
								model.to(lowerCAmelCase__		)
								model.eval()

								A       							=			model(lowerCAmelCase__		)

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

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

								(A			)       							=			result_with_labels.to_tuple()

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

								(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 __a    (				self :     Union[str, Any]      ,      _lowercase :     Optional[int]      ,      _lowercase :     Optional[int]      ,      _lowercase :     str      ,      _lowercase :     int      ,      _lowercase :     Optional[Any]      ,      _lowercase :     List[Any]      ,      _lowercase :     List[str]      ,      _lowercase :     Any      ,      _lowercase :     Union[str, Any]      ,      ):
								A       							=			XLMForSequenceClassification(lowerCAmelCase__		)
								model.to(lowerCAmelCase__		)
								model.eval()

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







						def __a    (				self :     Tuple      ,      _lowercase :     Tuple      ,      _lowercase :     Tuple      ,      _lowercase :     Union[str, Any]      ,      _lowercase :     str      ,      _lowercase :     Tuple      ,      _lowercase :     Optional[int]      ,      _lowercase :     Optional[Any]      ,      _lowercase :     Optional[int]      ,      _lowercase :     Dict      ,      ):
								A       							=			self.num_labels
								A       							=			XLMForTokenClassification(lowerCAmelCase__		)
								model.to(lowerCAmelCase__		)
								model.eval()

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







						def __a    (				self :     Optional[int]      ,      _lowercase :     List[str]      ,      _lowercase :     Optional[Any]      ,      _lowercase :     str      ,      _lowercase :     int      ,      _lowercase :     int      ,      _lowercase :     int      ,      _lowercase :     str      ,      _lowercase :     str      ,      _lowercase :     str      ,      ):
								A       							=			self.num_choices
								A       							=			XLMForMultipleChoice(config=lowerCAmelCase__		)
								model.to(lowerCAmelCase__		)
								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(
								    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      labels=lowerCAmelCase__      ,      )
								self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_choices)		)







						def __a    (				self :     List[str]		):
								A       							=			self.prepare_config_and_inputs()
								(
								    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__    ( a__				,     a__				,     a__				,     unittest.TestCase      ):
						lowerCAmelCase  	=  (
						    (
						        XLMModel,
						        XLMWithLMHeadModel,
						        XLMForQuestionAnswering,
						        XLMForSequenceClassification,
						        XLMForQuestionAnsweringSimple,
						        XLMForTokenClassification,
						        XLMForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase  	=  (
						    (XLMWithLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						lowerCAmelCase  	=  (
						    {
						        """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 __a    (				self :     Dict      ,      _lowercase :     Tuple      ,      _lowercase :     List[str]      ,      _lowercase :     Union[str, Any]      ,      _lowercase :     List[str]      ,      _lowercase :     Union[str, Any]		):
								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 __a    (				self :     List[str]      ,      _lowercase :     Optional[Any]      ,      _lowercase :     List[Any]      ,      _lowercase :     List[str]=False		):
								A       							=			super()._prepare_for_class(lowerCAmelCase__      ,      lowerCAmelCase__      ,      return_labels=lowerCAmelCase__		)

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

								return inputs_dict







						def __a    (				self :     Optional[int]		):
								A       							=			XLMModelTester(self		)
								A       							=			ConfigTester(self      ,      config_class=lowerCAmelCase__      ,      emb_dim=37		)







						def __a    (				self :     Dict		):
								self.config_tester.run_common_tests()







						def __a    (				self :     List[Any]		):
								A       							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_xlm_model(*lowerCAmelCase__		)







						def __a    (				self :     Optional[int]		):
								A       							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_xlm_lm_head(*lowerCAmelCase__		)







						def __a    (				self :     Optional[Any]		):
								A       							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_xlm_simple_qa(*lowerCAmelCase__		)







						def __a    (				self :     Union[str, Any]		):
								A       							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_xlm_qa(*lowerCAmelCase__		)







						def __a    (				self :     List[Any]		):
								A       							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_xlm_sequence_classif(*lowerCAmelCase__		)







						def __a    (				self :     Tuple		):
								A       							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_xlm_token_classif(*lowerCAmelCase__		)







						def __a    (				self :     str		):
								A       							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCAmelCase__		)







						def __a    (				self :     List[Any]      ,      _lowercase :     List[Any]      ,      _lowercase :     str      ,      _lowercase :     int      ,      _lowercase :     int      ,      _lowercase :     str      ,      _lowercase :     Optional[int]=False      ,      _lowercase :     Optional[int]=1		):
								self.assertIsInstance(lowerCAmelCase__      ,      lowerCAmelCase__		)
								self.assertListEqual(
								    [isinstance(lowerCAmelCase__      ,      lowerCAmelCase__		) for iter_attentions in attentions]      ,      [True] * len(lowerCAmelCase__		)		)
								self.assertEqual(len(lowerCAmelCase__		)      ,      (max_length - min_length) * num_beam_groups		)

								for idx, iter_attentions in enumerate(lowerCAmelCase__		):
										# 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(lowerCAmelCase__		)		)







						def __a    (				self :     Dict      ,      _lowercase :     int      ,      _lowercase :     List[Any]      ,      _lowercase :     Optional[int]      ,      _lowercase :     List[Any]      ,      _lowercase :     Optional[Any]      ,      _lowercase :     Union[str, Any]=False      ,      _lowercase :     Any=1		):
								self.assertIsInstance(lowerCAmelCase__      ,      lowerCAmelCase__		)
								self.assertListEqual(
								    [isinstance(lowerCAmelCase__      ,      lowerCAmelCase__		) for iter_hidden_states in hidden_states]      ,      [True] * len(lowerCAmelCase__		)      ,      )
								self.assertEqual(len(lowerCAmelCase__		)      ,      (max_length - min_length) * num_beam_groups		)

								for idx, iter_hidden_states in enumerate(lowerCAmelCase__		):
										# 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(lowerCAmelCase__		)      ,      )
								pass







						@slow
						def __a    (				self :     Optional[Any]		):
								for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										A       							=			XLMModel.from_pretrained(lowerCAmelCase__		)
										self.assertIsNotNone(lowerCAmelCase__		)



@require_torch
class       lowerCamelCase__    ( unittest.TestCase      ):







						@slow
						def __a    (				self :     Optional[int]		):
								A       							=			XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'		)
								model.to(lowerCAmelCase__		)
								A       							=			torch.tensor([[14, 447]]      ,      dtype=torch.long      ,      device=lowerCAmelCase__		)  # 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(lowerCAmelCase__      ,      do_sample=lowerCAmelCase__		)
								self.assertListEqual(output_ids[0].cpu().numpy().tolist()      ,      lowerCAmelCase__		)












"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Optional[Any]:






	'''simple docstring'''






	a :						Any      =							[]
	a :						List[str]      =							set({"(", "[", "{"}   )
	a :						int      =							set({")", "]", "}"}   )
	a :						int      =							{"{": "}", "[": "]", "(": ")"}

	for i in range(len(_lowercase   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(_lowercase   ) == 0 or (len(_lowercase   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(_lowercase   ) == 0






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






	a :						Any      =							input("Enter sequence of brackets: "   )
	if is_balanced(_lowercase   ):
		print(_lowercase       ,			"is balanced"   )
	else:
		print(_lowercase       ,			"is not balanced"   )


if __name__ == "__main__":
		main()


