





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					,							__magic_name__					,							__magic_name__=2					,							__magic_name__=56					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=99					,							__magic_name__=32					,							__magic_name__=2					,							__magic_name__=2					,							__magic_name__=7					,							__magic_name__="gelu_new"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=16					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=4					,							__magic_name__="block_sparse"					,							__magic_name__=True					,							__magic_name__=False					,							__magic_name__=2					,							__magic_name__=3					,							)     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  parent
							snake_case_						:    str				=  batch_size
							snake_case_						:    str				=  seq_length
							snake_case_						:    str				=  is_training
							snake_case_						:    List[str]				=  use_attention_mask
							snake_case_						:    List[Any]				=  use_token_type_ids
							snake_case_						:    Optional[int]				=  use_labels
							snake_case_						:    List[str]				=  vocab_size
							snake_case_						:    str				=  hidden_size
							snake_case_						:    int				=  num_hidden_layers
							snake_case_						:    List[str]				=  num_attention_heads
							snake_case_						:    Any				=  intermediate_size
							snake_case_						:    Optional[Any]				=  hidden_act
							snake_case_						:    Union[str, Any]				=  hidden_dropout_prob
							snake_case_						:    Dict				=  attention_probs_dropout_prob
							snake_case_						:    Optional[Any]				=  max_position_embeddings
							snake_case_						:    Union[str, Any]				=  type_vocab_size
							snake_case_						:    Union[str, Any]				=  type_sequence_label_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    Union[str, Any]				=  num_choices

							snake_case_						:    Tuple				=  rescale_embeddings
							snake_case_						:    Dict				=  attention_type
							snake_case_						:    str				=  use_bias
							snake_case_						:    Any				=  block_size
							snake_case_						:    Tuple				=  num_random_blocks






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    Any				=  ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size    )

							snake_case_						:    int				=  None
							if self.use_attention_mask:
											snake_case_						:    Optional[int]				=  random_attention_mask([self.batch_size, self.seq_length]    )

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

							snake_case_						:    Union[str, Any]				=  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=__magic_name__					,							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 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.prepare_config_and_inputs()
							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    Dict				=  config_and_inputs
							snake_case_						:    Tuple				=  {
							    '''input_ids''': input_ids,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': attention_mask,
							}
							return config, inputs_dict




@require_flax
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     str       			=      (
			    (
			        FlaxBigBirdForCausalLM,
			        FlaxBigBirdModel,
			        FlaxBigBirdForPreTraining,
			        FlaxBigBirdForMaskedLM,
			        FlaxBigBirdForMultipleChoice,
			        FlaxBigBirdForQuestionAnswering,
			        FlaxBigBirdForSequenceClassification,
			        FlaxBigBirdForTokenClassification,
			    )
			    if is_flax_available()
			    else ()
			)

			lowerCamelCase_     :     Any       			=      False
			lowerCamelCase_     :     List[str]       			=      False






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


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  FlaxBigBirdModelTester(self    )






			@slow
			# copied from `test_modeling_flax_common` because it takes much longer than other models
			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							super().test_from_pretrained_save_pretrained()






			@slow
			# copied from `test_modeling_flax_common` because it takes much longer than other models
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''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 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							super().test_no_automatic_init()






			@slow
			# copied from `test_modeling_flax_common` because it takes much longer than other models
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							super().test_hidden_states_output()






			@slow
			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							for model_class_name in self.all_model_classes:
											snake_case_						:    Optional[int]				=  model_class_name.from_pretrained('''google/bigbird-roberta-base'''    )
											self.assertIsNotNone(__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   int:


							'''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 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_			,							snake_case_						:    str				=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__    ):
															snake_case_						:    str				=  self._prepare_for_class(__magic_name__					,							__magic_name__    )
															snake_case_						:    Union[str, Any]				=  model_class(__magic_name__    )

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

															with self.subTest('''JIT Enabled'''    ):
																			snake_case_						:    List[Any]				=  model_jitted(**__magic_name__    ).to_tuple()

															with self.subTest('''JIT Disabled'''    ):
																			with jax.disable_jit():
																							snake_case_						:    List[str]				=  model_jitted(**__magic_name__    ).to_tuple()

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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=1e-5					,							__magic_name__="outputs"					,							__magic_name__=None    )     ->   List[str]:


							'''simple docstring'''



							if name.startswith('''outputs.attentions'''    ):
											return
							else:
											super().check_pt_flax_outputs(__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__    )







from math import factorial

lowerCAmelCase_    =				{str(digit): factorial(digit) for digit in range(1_0)}






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameter number must be int''' )

				if number < 0:
								raise ValueError('''Parameter number must be greater than or equal to 0''' )

				# Converts number in string to iterate on its digits and adds its factorial.
				return sum(DIGIT_FACTORIAL[digit] for digit in str(_UpperCamelCase ) )






def      lowerCamelCase_						(				_UpperCamelCase = 60 ,				_UpperCamelCase = 1_000_000 )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ) or not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameters chain_length and number_limit must be int''' )

				if chain_length <= 0 or number_limit <= 0:
								raise ValueError(
								    '''Parameters chain_length and number_limit must be greater than 0''' )

				# the counter for the chains with the exact desired length
				snake_case_						:    Optional[Any]				=  0
				# the cached sizes of the previous chains
				snake_case_						:    dict[int, int]				=  {}

				for start_chain_element in range(1 ,				_UpperCamelCase ):
								# The temporary set will contain the elements of the chain
								snake_case_						:    List[str]				=  set()
								snake_case_						:    List[Any]				=  0

								# Stop computing the chain when you find a cached size, a repeating item or the
								# length is greater then the desired one.
								snake_case_						:    Any				=  start_chain_element
								while (
								    chain_element not in chain_sets_lengths
								    and chain_element not in chain_set
								    and chain_set_length <= chain_length
								):
												chain_set.add(_UpperCamelCase )
												chain_set_length += 1
												snake_case_						:    List[Any]				=  digit_factorial_sum(_UpperCamelCase )

								if chain_element in chain_sets_lengths:
												chain_set_length += chain_sets_lengths[chain_element]

								snake_case_						:    List[str]				=  chain_set_length

								# If chain contains the exact amount of elements increase the counter
								if chain_set_length == chain_length:
												chains_counter += 1

				return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
