




import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax

						from transformers.models.big_bird.modeling_flax_big_bird import (
						    FlaxBigBirdForCausalLM,
						    FlaxBigBirdForMaskedLM,
						    FlaxBigBirdForMultipleChoice,
						    FlaxBigBirdForPreTraining,
						    FlaxBigBirdForQuestionAnswering,
						    FlaxBigBirdForSequenceClassification,
						    FlaxBigBirdForTokenClassification,
						    FlaxBigBirdModel,
						)







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def __init__(			self      ,snake_case__      ,snake_case__=2      ,snake_case__=56      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=99      ,snake_case__=32      ,snake_case__=2      ,snake_case__=2      ,snake_case__=7      ,snake_case__="gelu_new"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=16      ,snake_case__=2      ,snake_case__=0.02      ,snake_case__=4      ,snake_case__="block_sparse"      ,snake_case__=True      ,snake_case__=False      ,snake_case__=2      ,snake_case__=3      ,):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      parent
							SCREAMING_SNAKE_CASE_   : int					  =      batch_size
							SCREAMING_SNAKE_CASE_   : str					  =      seq_length
							SCREAMING_SNAKE_CASE_   : Tuple					  =      is_training
							SCREAMING_SNAKE_CASE_   : int					  =      use_attention_mask
							SCREAMING_SNAKE_CASE_   : int					  =      use_token_type_ids
							SCREAMING_SNAKE_CASE_   : int					  =      use_labels
							SCREAMING_SNAKE_CASE_   : str					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      hidden_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      intermediate_size
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_   : str					  =      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : int					  =      type_vocab_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      type_sequence_label_size
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      num_choices

							SCREAMING_SNAKE_CASE_   : Any					  =      rescale_embeddings
							SCREAMING_SNAKE_CASE_   : int					  =      attention_type
							SCREAMING_SNAKE_CASE_   : Any					  =      use_bias
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      block_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      num_random_blocks






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size )

							SCREAMING_SNAKE_CASE_   : int					  =      None
							if self.use_attention_mask:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      random_attention_mask([self.batch_size, self.seq_length] )

							SCREAMING_SNAKE_CASE_   : Any					  =      None
							if self.use_token_type_ids:
										SCREAMING_SNAKE_CASE_   : Dict					  =      ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size )

							SCREAMING_SNAKE_CASE_   : int					  =      BigBirdConfig(
							    vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,is_decoder=snake_case__      ,initializer_range=self.initializer_range      ,attention_type=self.attention_type      ,block_size=self.block_size      ,num_random_blocks=self.num_random_blocks      ,use_bias=self.use_bias      ,rescale_embeddings=self.rescale_embeddings      ,)

							return config, input_ids, token_type_ids, attention_mask






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      config_and_inputs
							SCREAMING_SNAKE_CASE_   : int					  =      {
							    'input_ids': input_ids,
							    'token_type_ids': token_type_ids,
							    'attention_mask': attention_mask,
							}
							return config, inputs_dict







@require_flax
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):
				__a     :       int						=     (
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				__a     :       List[Any]						=     False
				__a     :       Any						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      FlaxBigBirdModelTester(self )






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_from_pretrained_save_pretrained()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_from_pretrained_with_no_automatic_init()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_no_automatic_init()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_hidden_states_output()






				@slow
				def 		snake_case	(			self ):
							for model_class_name in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   : Dict					  =      model_class_name.from_pretrained('google/bigbird-roberta-base' )
										self.assertIsNotNone(snake_case__ )






				def 		snake_case	(			self ):
							if self.test_attn_probs:
										super().test_attention_outputs()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__ ):
													SCREAMING_SNAKE_CASE_   : str					  =      self._prepare_for_class(snake_case__      ,snake_case__ )
													SCREAMING_SNAKE_CASE_   : Dict					  =      model_class(snake_case__ )

													@jax.jit
													def model_jitted(snake_case__      ,snake_case__=None      ,**snake_case__ ):
																return model(input_ids=snake_case__      ,attention_mask=snake_case__      ,**snake_case__ )

													with self.subTest('JIT Enabled' ):
																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model_jitted(**snake_case__ ).to_tuple()

													with self.subTest('JIT Disabled' ):
																with jax.disable_jit():
																			SCREAMING_SNAKE_CASE_   : Any					  =      model_jitted(**snake_case__ ).to_tuple()

													self.assertEqual(len(snake_case__ )      ,len(snake_case__ ) )
													for jitted_output, output in zip(snake_case__      ,snake_case__ ):
																self.assertEqual(jitted_output.shape      ,output.shape )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=1E-5      ,snake_case__="outputs"      ,snake_case__=None ):
							# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
							# an effort was done to return `attention_probs` (yet to be verified).
							if name.startswith('outputs.attentions' ):
										return
							else:
										super().check_pt_flax_outputs(snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ )











from collections import defaultdict



def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> bool:
				_A	:				Union[str, Any]   =      first_str.lower().strip()
				_A	:				int   =      second_str.lower().strip()

				# Remove whitespace
				_A	:				int   =      first_str.replace(''' '''					,  ''''''   )
				_A	:				Optional[Any]   =      second_str.replace(''' '''					,  ''''''   )

				# Strings of different lengths are not anagrams
				if len(__lowercase   ) != len(__lowercase   ):
								return False

				# Default values for count should be 0
				_A	:				defaultdict[str, int]   =      defaultdict(__lowercase   )

				# For each character in input strings,
				# increment count in the corresponding
				for i in range(len(__lowercase   )   ):
								count[first_str[i]] += 1
								count[second_str[i]] -= 1

				return all(_count == 0 for _count in count.values()   )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	_UpperCamelCase			:	Dict								=input('Enter the first string ').strip()
	_UpperCamelCase			:	Union[str, Any]								=input('Enter the second string ').strip()

	_UpperCamelCase			:	Dict								=check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')



