



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  __magic_name__   (							unittest.TestCase						):






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Dict=2				,		UpperCamelCase__      :     Tuple=56				,		UpperCamelCase__      :     Optional[int]=True				,		UpperCamelCase__      :     Optional[int]=True				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     Optional[int]=True				,		UpperCamelCase__      :     List[Any]=99				,		UpperCamelCase__      :     Union[str, Any]=32				,		UpperCamelCase__      :     Optional[Any]=2				,		UpperCamelCase__      :     int=2				,		UpperCamelCase__      :     str=7				,		UpperCamelCase__      :     Tuple="gelu_new"				,		UpperCamelCase__      :     Optional[int]=0.1				,		UpperCamelCase__      :     Optional[int]=0.1				,		UpperCamelCase__      :     Dict=5_12				,		UpperCamelCase__      :     Tuple=16				,		UpperCamelCase__      :     Any=2				,		UpperCamelCase__      :     Optional[Any]=0.02				,		UpperCamelCase__      :     Union[str, Any]=4				,		UpperCamelCase__      :     List[Any]="block_sparse"				,		UpperCamelCase__      :     Union[str, Any]=True				,		UpperCamelCase__      :     str=False				,		UpperCamelCase__      :     Optional[int]=2				,		UpperCamelCase__      :     int=3				,		)				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     seq_length
								UpperCAmelCase         =     is_training
								UpperCAmelCase         =     use_attention_mask
								UpperCAmelCase         =     use_token_type_ids
								UpperCAmelCase         =     use_labels
								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     hidden_size
								UpperCAmelCase         =     num_hidden_layers
								UpperCAmelCase         =     num_attention_heads
								UpperCAmelCase         =     intermediate_size
								UpperCAmelCase         =     hidden_act
								UpperCAmelCase         =     hidden_dropout_prob
								UpperCAmelCase         =     attention_probs_dropout_prob
								UpperCAmelCase         =     max_position_embeddings
								UpperCAmelCase         =     type_vocab_size
								UpperCAmelCase         =     type_sequence_label_size
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     num_choices

								UpperCAmelCase         =     rescale_embeddings
								UpperCAmelCase         =     attention_type
								UpperCAmelCase         =     use_bias
								UpperCAmelCase         =     block_size
								UpperCAmelCase         =     num_random_blocks






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







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

								UpperCAmelCase         =     None
								if self.use_attention_mask:
														UpperCAmelCase         =     random_attention_mask([self.batch_size, self.seq_length]      )

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

								UpperCAmelCase         =     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=UpperCamelCase__				,		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 SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     config_and_inputs
								UpperCAmelCase         =     {
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "attention_mask": attention_mask,
								}
								return config, inputs_dict





@require_flax
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Dict          =(
		    (
		        FlaxBigBirdForCausalLM,
		        FlaxBigBirdModel,
		        FlaxBigBirdForPreTraining,
		        FlaxBigBirdForMaskedLM,
		        FlaxBigBirdForMultipleChoice,
		        FlaxBigBirdForQuestionAnswering,
		        FlaxBigBirdForSequenceClassification,
		        FlaxBigBirdForTokenClassification,
		    )
		    if is_flax_available()
		    else ()
		)

		lowercase      :       Any          =False
		lowercase      :       Tuple          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     FlaxBigBirdModelTester(self      )






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
								'''simple docstring'''







								super().test_from_pretrained_save_pretrained()






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
								'''simple docstring'''







								super().test_from_pretrained_with_no_automatic_init()






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					str:
								'''simple docstring'''







								super().test_no_automatic_init()






		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								super().test_hidden_states_output()






		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					str:
								'''simple docstring'''







								for model_class_name in self.all_model_classes:
														UpperCAmelCase         =     model_class_name.from_pretrained("google/bigbird-roberta-base"      )
														self.assertIsNotNone(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__      ):
																				UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__      )
																				UpperCAmelCase         =     model_class(UpperCamelCase__      )

																				@jax.jit
																				def model_jitted(UpperCamelCase__      :     str				,		UpperCamelCase__      :     Tuple=None				,		**UpperCamelCase__      :     List[str]      ):
																										return model(input_ids=UpperCamelCase__				,		attention_mask=UpperCamelCase__				,		**UpperCamelCase__      )

																				with self.subTest("JIT Enabled"      ):
																										UpperCAmelCase         =     model_jitted(**UpperCamelCase__      ).to_tuple()

																				with self.subTest("JIT Disabled"      ):
																										with jax.disable_jit():
																																UpperCAmelCase         =     model_jitted(**UpperCamelCase__      ).to_tuple()

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







		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[str]=1e-5				,		UpperCamelCase__      :     Tuple="outputs"				,		UpperCamelCase__      :     Optional[Any]=None      )				->					Optional[int]:
								'''simple docstring'''







								if name.startswith("outputs.attentions"      ):
														return
								else:
														super().check_pt_flax_outputs(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )








def    lowerCamelCase_(lowerCamelCase_ = 600_851_475_143    ) ->       int:

						try:
												UpperCAmelCase         =     int(lowerCamelCase_    )
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."    )
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."    )
						UpperCAmelCase         =     1
						UpperCAmelCase         =     2
						while i * i <= n:
												while n % i == 0:
																		UpperCAmelCase         =     i
																		n //= i
												i += 1
						if n > 1:
												UpperCAmelCase         =     n
						return int(lowerCamelCase_    )


if __name__ == "__main__":
		print(F'''{solution() = }''')



