
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 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def __init__(			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=2			,				SCREAMING_SNAKE_CASE_    :     List[Any]=5_6			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=True			,				SCREAMING_SNAKE_CASE_    :     Dict=9_9			,				SCREAMING_SNAKE_CASE_    :     Any=3_2			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     Dict=2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=7			,				SCREAMING_SNAKE_CASE_    :     str="gelu_new"			,				SCREAMING_SNAKE_CASE_    :     List[str]=0.1			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     str=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Tuple=1_6			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.02			,				SCREAMING_SNAKE_CASE_    :     Any=4			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="block_sparse"			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     int=2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=3			,				)			->	Tuple:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   seq_length
					lowercase_	    =   is_training
					lowercase_	    =   use_attention_mask
					lowercase_	    =   use_token_type_ids
					lowercase_	    =   use_labels
					lowercase_	    =   vocab_size
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_attention_heads
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   hidden_dropout_prob
					lowercase_	    =   attention_probs_dropout_prob
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   type_vocab_size
					lowercase_	    =   type_sequence_label_size
					lowercase_	    =   initializer_range
					lowercase_	    =   num_choices

					lowercase_	    =   rescale_embeddings
					lowercase_	    =   attention_type
					lowercase_	    =   use_bias
					lowercase_	    =   block_size
					lowercase_	    =   num_random_blocks
	def 		_lowercase    (			self    :     Tuple			)			->	Union[str, Any]:
					lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

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

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

					lowercase_	    =   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=_SCREAMING_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 		_lowercase    (			self    :     List[str]			)			->	Optional[Any]:
					lowercase_	    =   self.prepare_config_and_inputs()
					lowercase_	    =   config_and_inputs
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': attention_mask,
					}
					return config, inputs_dict

@require_flax
class 					lowercase__(							_a					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      (
	    (
	        FlaxBigBirdForCausalLM,
	        FlaxBigBirdModel,
	        FlaxBigBirdForPreTraining,
	        FlaxBigBirdForMaskedLM,
	        FlaxBigBirdForMultipleChoice,
	        FlaxBigBirdForQuestionAnswering,
	        FlaxBigBirdForSequenceClassification,
	        FlaxBigBirdForTokenClassification,
	    )
	    if is_flax_available()
	    else ()
	)

	a     :List[Any]       	=      False
	a     :Tuple       	=      False
	def 		_lowercase    (			self    :     Optional[Any]			)			->	int:
					lowercase_	    =   FlaxBigBirdModelTester(self			)
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 		_lowercase    (			self    :     Optional[int]			)			->	Optional[int]:
					super().test_from_pretrained_save_pretrained()
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 		_lowercase    (			self    :     Tuple			)			->	Optional[Any]:
					super().test_from_pretrained_with_no_automatic_init()
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 		_lowercase    (			self    :     List[Any]			)			->	Tuple:
					super().test_no_automatic_init()
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 		_lowercase    (			self    :     List[Any]			)			->	int:
					super().test_hidden_states_output()
	@slow
	def 		_lowercase    (			self    :     Optional[Any]			)			->	str:
					for model_class_name in self.all_model_classes:
									lowercase_	    =   model_class_name.from_pretrained('''google/bigbird-roberta-base'''			)
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)
	def 		_lowercase    (			self    :     Optional[int]			)			->	Any:
					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 		_lowercase    (			self    :     Union[str, Any]			)			->	List[str]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__			):
													lowercase_	    =   self._prepare_for_class(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			)
													lowercase_	    =   model_class(_SCREAMING_SNAKE_CASE			)

													@jax.jit
													def model_jitted(SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     int=None			,				**SCREAMING_SNAKE_CASE_    :     int			):
																	return model(input_ids=_SCREAMING_SNAKE_CASE			,				attention_mask=_SCREAMING_SNAKE_CASE			,				**_SCREAMING_SNAKE_CASE			)

													with self.subTest('''JIT Enabled'''			):
																	lowercase_	    =   model_jitted(**_SCREAMING_SNAKE_CASE			).to_tuple()

													with self.subTest('''JIT Disabled'''			):
																	with jax.disable_jit():
																					lowercase_	    =   model_jitted(**_SCREAMING_SNAKE_CASE			).to_tuple()

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




	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1e-5			,				SCREAMING_SNAKE_CASE_    :     Any="outputs"			,				SCREAMING_SNAKE_CASE_    :     int=None			)			->	Any:
					# `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(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			)










import unittest

import numpy as np







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase = None					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     np.shape(_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     np.shape(_lowercase )
     UpperCAmelCase_     :				Tuple  =     np.shape(_lowercase )

     if shape_a[0] != shape_b[0]:
          UpperCAmelCase_     :				Tuple  =     (
              '''Expected the same number of rows for A and B. '''
              f'''Instead found A of size {shape_a} and B of size {shape_b}'''
          )
          raise ValueError(_lowercase )

     if shape_b[1] != shape_c[1]:
          UpperCAmelCase_     :				List[Any]  =     (
              '''Expected the same number of columns for B and C. '''
              f'''Instead found B of size {shape_b} and C of size {shape_c}'''
          )
          raise ValueError(_lowercase )

     UpperCAmelCase_     :				Dict  =     pseudo_inv
     if a_inv is None:
          try:
               UpperCAmelCase_     :				Any  =     np.linalg.inv(_lowercase )
          except np.linalg.LinAlgError:
               raise ValueError(
                   '''Input matrix A is not invertible. Cannot compute Schur complement.''' )

     return mat_c - mat_b.T @ a_inv @ mat_b

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				str  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Any  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				List[str]  =     np.array([[2, 1], [6, 3]]    )

         UpperCAmelCase_     :				Tuple  =     schur_complement(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     np.block([[a, b], [b.T, c]]    )

         UpperCAmelCase_     :				List[Any]  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )

         self.assertAlmostEqual(_SCREAMING_SNAKE_CASE  ,det_a * det_s    )





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				str  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Optional[int]  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				Optional[int]  =     np.array([[2, 1], [6, 3]]    )

         with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
              schur_complement(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				Optional[int]  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Optional[Any]  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				int  =     np.array([[2, 1, 3], [6, 3, 5]]    )

         with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
              schur_complement(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     unittest.main()