import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertWithLMHeadModel,
						)
						from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class       snake_case					(      UpperCamelCase_						):




		def __init__(      self     :	int     , a_     :	List[Any]     , a_     :	Tuple=13     , a_     :	List[str]=7     , a_     :	Dict=True     , a_     :	str=True     , a_     :	List[str]=True     , a_     :	Optional[Any]=True     , a_     :	List[str]=True     , a_     :	Tuple=False     , a_     :	Optional[int]=False     , a_     :	Union[str, Any]=False     , a_     :	List[Any]=2     , a_     :	Optional[int]=99     , a_     :	Optional[int]=0     , a_     :	str=32     , a_     :	Optional[Any]=5     , a_     :	Union[str, Any]=4     , a_     :	Any=0.1     , a_     :	Tuple=0.1     , a_     :	Union[str, Any]=512     , a_     :	str=12     , a_     :	Union[str, Any]=2     , a_     :	Optional[int]=0.02     , a_     :	int=3     , a_     :	Optional[Any]=4     , a_     :	int="last"     , a_     :	List[Any]=None     , a_     :	Optional[Any]=None     , )->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		parent
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		batch_size
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		seq_length
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		is_training
							SCREAMING_SNAKE_CASE__				:				Any			=		use_input_lengths
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		use_token_type_ids
							SCREAMING_SNAKE_CASE__				:				List[str]			=		use_labels
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		gelu_activation
							SCREAMING_SNAKE_CASE__				:				Tuple			=		sinusoidal_embeddings
							SCREAMING_SNAKE_CASE__				:				str			=		causal
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		asm
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		n_langs
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		vocab_size
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		n_special
							SCREAMING_SNAKE_CASE__				:				List[str]			=		hidden_size
							SCREAMING_SNAKE_CASE__				:				str			=		num_hidden_layers
							SCREAMING_SNAKE_CASE__				:				Dict			=		num_attention_heads
							SCREAMING_SNAKE_CASE__				:				Dict			=		hidden_dropout_prob
							SCREAMING_SNAKE_CASE__				:				Any			=		attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE__				:				Dict			=		max_position_embeddings
							SCREAMING_SNAKE_CASE__				:				str			=		type_vocab_size
							SCREAMING_SNAKE_CASE__				:				Dict			=		type_sequence_label_size
							SCREAMING_SNAKE_CASE__				:				Tuple			=		initializer_range
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		num_labels
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		num_choices
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		summary_type
							SCREAMING_SNAKE_CASE__				:				Dict			=		use_proj
							SCREAMING_SNAKE_CASE__				:				Dict			=		scope




		def 	__lowercase(      self     :	Any				)->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				str			=		ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size				)
							SCREAMING_SNAKE_CASE__				:				str			=		random_attention_mask([self.batch_size, self.seq_length]				)

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

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
							if self.use_token_type_ids:
												SCREAMING_SNAKE_CASE__				:				int			=		ids_tensor([self.batch_size, self.seq_length]     , self.n_langs				)

							SCREAMING_SNAKE_CASE__				:				str			=		None
							SCREAMING_SNAKE_CASE__				:				int			=		None
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
							if self.use_labels:
												SCREAMING_SNAKE_CASE__				:				str			=		ids_tensor([self.batch_size]     , self.type_sequence_label_size				)
												SCREAMING_SNAKE_CASE__				:				List[Any]			=		ids_tensor([self.batch_size, self.seq_length]     , self.num_labels				)
												SCREAMING_SNAKE_CASE__				:				Any			=		ids_tensor([self.batch_size]     , 2				).float()
												SCREAMING_SNAKE_CASE__				:				Any			=		ids_tensor([self.batch_size]     , self.num_choices				)

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_config()

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




		def 	__lowercase(      self     :	Any				)->       Tuple:




							"""simple docstring"""
							return FlaubertConfig(
							    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     , )




		def 	__lowercase(      self     :	Any     , a_     :	Any     , a_     :	Dict     , a_     :	Dict     , a_     :	str     , a_     :	str     , a_     :	Any     , a_     :	Tuple     , a_     :	List[Any]     , a_     :	Optional[Any]     , )->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		FlaubertModel(config=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							SCREAMING_SNAKE_CASE__				:				int			=		model(UpperCamelCase_     , lengths=UpperCamelCase_     , langs=UpperCamelCase_				)
							SCREAMING_SNAKE_CASE__				:				int			=		model(UpperCamelCase_     , langs=UpperCamelCase_				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(UpperCamelCase_				)
							self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)				)




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		FlaubertWithLMHeadModel(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							SCREAMING_SNAKE_CASE__				:				Tuple			=		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 	__lowercase(      self     :	Any     , a_     :	Union[str, Any]     , a_     :	Any     , a_     :	str     , a_     :	Optional[int]     , a_     :	Optional[Any]     , a_     :	List[str]     , a_     :	Any     , a_     :	Dict     , a_     :	Union[str, Any]     , )->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		FlaubertForQuestionAnsweringSimple(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							SCREAMING_SNAKE_CASE__				:				int			=		model(UpperCamelCase_				)

							SCREAMING_SNAKE_CASE__				:				Dict			=		model(UpperCamelCase_     , start_positions=UpperCamelCase_     , end_positions=UpperCamelCase_				)
							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 	__lowercase(      self     :	Optional[Any]     , a_     :	Dict     , a_     :	Any     , a_     :	List[Any]     , a_     :	Union[str, Any]     , a_     :	Dict     , a_     :	Any     , a_     :	Optional[Any]     , a_     :	Optional[int]     , a_     :	Optional[int]     , )->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		FlaubertForQuestionAnswering(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							SCREAMING_SNAKE_CASE__				:				Dict			=		model(UpperCamelCase_				)

							SCREAMING_SNAKE_CASE__				:				str			=		model(
							    UpperCamelCase_     , start_positions=UpperCamelCase_     , end_positions=UpperCamelCase_     , cls_index=UpperCamelCase_     , is_impossible=UpperCamelCase_     , p_mask=UpperCamelCase_     , )

							SCREAMING_SNAKE_CASE__				:				Dict			=		model(
							    UpperCamelCase_     , start_positions=UpperCamelCase_     , end_positions=UpperCamelCase_     , cls_index=UpperCamelCase_     , is_impossible=UpperCamelCase_     , )

							(SCREAMING_SNAKE_CASE__ )				:				str			=		result_with_labels.to_tuple()

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(UpperCamelCase_     , start_positions=UpperCamelCase_     , end_positions=UpperCamelCase_				)

							(SCREAMING_SNAKE_CASE__ )				:				Tuple			=		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 	__lowercase(      self     :	Optional[Any]     , a_     :	List[str]     , a_     :	Tuple     , a_     :	List[Any]     , a_     :	Optional[Any]     , a_     :	Any     , a_     :	Any     , a_     :	int     , a_     :	Union[str, Any]     , a_     :	Any     , )->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		FlaubertForSequenceClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							SCREAMING_SNAKE_CASE__				:				Any			=		model(UpperCamelCase_				)
							SCREAMING_SNAKE_CASE__				:				int			=		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 	__lowercase(      self     :	Any     , a_     :	Any     , a_     :	Optional[Any]     , a_     :	int     , a_     :	List[Any]     , a_     :	Tuple     , a_     :	Optional[int]     , a_     :	int     , a_     :	Any     , a_     :	str     , )->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				int			=		self.num_labels
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		FlaubertForTokenClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							SCREAMING_SNAKE_CASE__				:				str			=		model(UpperCamelCase_     , attention_mask=UpperCamelCase_     , labels=UpperCamelCase_				)
							self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)				)




		def 	__lowercase(      self     :	Optional[Any]     , a_     :	List[str]     , a_     :	Dict     , a_     :	Dict     , a_     :	Dict     , a_     :	str     , a_     :	Tuple     , a_     :	Tuple     , a_     :	List[str]     , a_     :	Any     , )->       List[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.num_choices
							SCREAMING_SNAKE_CASE__				:				Dict			=		FlaubertForMultipleChoice(config=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							SCREAMING_SNAKE_CASE__				:				str			=		input_ids.unsqueeze(1				).expand(-1     , self.num_choices     , -1				).contiguous()
							SCREAMING_SNAKE_CASE__				:				Any			=		token_type_ids.unsqueeze(1				).expand(-1     , self.num_choices     , -1				).contiguous()
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		input_mask.unsqueeze(1				).expand(-1     , self.num_choices     , -1				).contiguous()
							SCREAMING_SNAKE_CASE__				:				str			=		model(
							    UpperCamelCase_     , attention_mask=UpperCamelCase_     , token_type_ids=UpperCamelCase_     , labels=UpperCamelCase_     , )
							self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_choices)				)




		def 	__lowercase(      self     :	Any				)->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.prepare_config_and_inputs()
							(
							    SCREAMING_SNAKE_CASE__ 
							)				:				Dict			=		config_and_inputs
							SCREAMING_SNAKE_CASE__				:				str			=		{
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "lengths": input_lengths,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict




@require_torch
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
		lowercase_			=     (
		    (
		        FlaubertModel,
		        FlaubertWithLMHeadModel,
		        FlaubertForQuestionAnswering,
		        FlaubertForQuestionAnsweringSimple,
		        FlaubertForSequenceClassification,
		        FlaubertForTokenClassification,
		        FlaubertForMultipleChoice,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase_			=     (
		    {
		        "feature-extraction": FlaubertModel,
		        "fill-mask": FlaubertWithLMHeadModel,
		        "question-answering": FlaubertForQuestionAnsweringSimple,
		        "text-classification": FlaubertForSequenceClassification,
		        "token-classification": FlaubertForTokenClassification,
		        "zero-shot": FlaubertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)




		def 	__lowercase(      self     :	Union[str, Any]     , a_     :	int     , a_     :	Any     , a_     :	List[Any]     , a_     :	Any     , a_     :	Any				)->       Any:




							"""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 	__lowercase(      self     :	Any     , a_     :	Tuple     , a_     :	List[Any]     , a_     :	int=False				)->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		super()._prepare_for_class(UpperCamelCase_     , UpperCamelCase_     , return_labels=UpperCamelCase_				)

							if return_labels:
												if model_class.__name__ == "FlaubertForQuestionAnswering":
																	SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.zeros(
																	    self.model_tester.batch_size     , dtype=torch.long     , device=UpperCamelCase_				)
																	SCREAMING_SNAKE_CASE__				:				Tuple			=		torch.zeros(
																	    self.model_tester.batch_size     , dtype=torch.long     , device=UpperCamelCase_				)

							return inputs_dict




		def 	__lowercase(      self     :	Dict				)->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		FlaubertModelTester(self				)
							SCREAMING_SNAKE_CASE__				:				int			=		ConfigTester(self     , config_class=UpperCamelCase_     , emb_dim=37				)




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




							"""simple docstring"""
							self.config_tester.run_common_tests()




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_model(*UpperCamelCase_				)




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_lm_head(*UpperCamelCase_				)




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				str			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_simple_qa(*UpperCamelCase_				)




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_qa(*UpperCamelCase_				)




		def 	__lowercase(      self     :	str				)->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCamelCase_				)




		def 	__lowercase(      self     :	Tuple				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_token_classif(*UpperCamelCase_				)




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_multiple_choice(*UpperCamelCase_				)




		@slow
		def 	__lowercase(      self     :	List[str]				)->       List[str]:




							"""simple docstring"""
							for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												SCREAMING_SNAKE_CASE__				:				List[str]			=		FlaubertModel.from_pretrained(UpperCamelCase_				)
												self.assertIsNotNone(UpperCamelCase_				)




		@slow
		@require_torch_gpu
		def 	__lowercase(      self     :	Tuple				)->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
												# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
												if model_class == FlaubertForMultipleChoice:
																	return

												SCREAMING_SNAKE_CASE__				:				Optional[int]			=		True
												SCREAMING_SNAKE_CASE__				:				Tuple			=		model_class(config=UpperCamelCase_				)

												SCREAMING_SNAKE_CASE__				:				Dict			=		self._prepare_for_class(UpperCamelCase_     , UpperCamelCase_				)
												SCREAMING_SNAKE_CASE__				:				Any			=		torch.jit.trace(
												    UpperCamelCase_     , (inputs_dict['input_ids'].to('cpu'				), inputs_dict['attention_mask'].to('cpu'				))				)

												with tempfile.TemporaryDirectory() as tmp:
																	torch.jit.save(UpperCamelCase_     , os.path.join(UpperCamelCase_     , 'traced_model.pt'				)				)
																	SCREAMING_SNAKE_CASE__				:				Dict			=		torch.jit.load(os.path.join(UpperCamelCase_     , 'traced_model.pt'				)     , map_location=UpperCamelCase_				)
																	loaded(inputs_dict['input_ids'].to(UpperCamelCase_				)     , inputs_dict['attention_mask'].to(UpperCamelCase_				)				)




@require_torch
class       snake_case					(      unittest.TestCase						):




		@slow
		def 	__lowercase(      self     :	str				)->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		FlaubertModel.from_pretrained('flaubert/flaubert_base_cased'				)
							SCREAMING_SNAKE_CASE__				:				int			=		torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
							with torch.no_grad():
												SCREAMING_SNAKE_CASE__				:				Any			=		model(UpperCamelCase_				)[0]
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.Size((1, 11, 768)				)
							self.assertEqual(output.shape     , UpperCamelCase_				)
							SCREAMING_SNAKE_CASE__				:				int			=		torch.tensor(
							    [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]				)

							self.assertTrue(torch.allclose(output[:, :3, :3]     , UpperCamelCase_     , atol=1e-4				)				)




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     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     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



