


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 		__a				(    unittest.TestCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=56      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE="gelu_new"      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=512      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE="block_sparse"      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=3      ,   )			->	str:




									"""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      UpperCAmelCase__    (      self						)			->	Any:




									"""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=__lowerCAmelCase      ,   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      UpperCAmelCase__    (      self						)			->	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 		__a				(    _a   ,				unittest.TestCase      ):
		_a     :  List[Any]           =     (
		    (
		        FlaxBigBirdForCausalLM,
		        FlaxBigBirdModel,
		        FlaxBigBirdForPreTraining,
		        FlaxBigBirdForMaskedLM,
		        FlaxBigBirdForMultipleChoice,
		        FlaxBigBirdForQuestionAnswering,
		        FlaxBigBirdForSequenceClassification,
		        FlaxBigBirdForTokenClassification,
		    )
		    if is_flax_available()
		    else ()
		)

		_a     :  List[Any]           =     False
		_a     :  Any           =     False
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      FlaxBigBirdModelTester(self						)
		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def      UpperCAmelCase__    (      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      UpperCAmelCase__    (      self						)			->	Tuple:




									"""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      UpperCAmelCase__    (      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      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									super().test_hidden_states_output()
		@slow
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									for model_class_name in self.all_model_classes:
																_UpperCAmelCase					=      model_class_name.from_pretrained('google/bigbird-roberta-base'						)
																self.assertIsNotNone(__lowerCAmelCase						)
		def      UpperCAmelCase__    (      self						)			->	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      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""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(__lowerCAmelCase      ,   __lowerCAmelCase						)
																							_UpperCAmelCase					=      model_class(__lowerCAmelCase						)

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

																							with self.subTest('JIT Enabled'						):
																														_UpperCAmelCase					=      model_jitted(**__lowerCAmelCase						).to_tuple()

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

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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=1e-5      ,   _SCREAMING_SNAKE_CASE="outputs"      ,   _SCREAMING_SNAKE_CASE=None						)			->	Optional[int]:




									"""simple docstring"""





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




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
