


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				, lowerCAmelCase__				, lowerCAmelCase__=2				, lowerCAmelCase__=56				, lowerCAmelCase__=True				, lowerCAmelCase__=True				, lowerCAmelCase__=True				, lowerCAmelCase__=True				, lowerCAmelCase__=99				, lowerCAmelCase__=32				, lowerCAmelCase__=2				, lowerCAmelCase__=2				, lowerCAmelCase__=7				, lowerCAmelCase__="gelu_new"				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=512				, lowerCAmelCase__=16				, lowerCAmelCase__=2				, lowerCAmelCase__=0.0_2				, lowerCAmelCase__=4				, lowerCAmelCase__="block_sparse"				, lowerCAmelCase__=True				, lowerCAmelCase__=False				, lowerCAmelCase__=2				, lowerCAmelCase__=3				, )					->				str:




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

        lowercase__:   Tuple  			=   rescale_embeddings
        lowercase__:   Dict  			=   attention_type
        lowercase__:   Dict  			=   use_bias
        lowercase__:   List[Any]  			=   block_size
        lowercase__:   Dict  			=   num_random_blocks
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   List[str]  			=   ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size )

        lowercase__:   List[Any]  			=   None
        if self.use_attention_mask:
               lowercase__:   Union[str, Any]  			=   random_attention_mask([self.batch_size, self.seq_length] )

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

        lowercase__:   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=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 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        lowercase__:   str  			=   self.prepare_config_and_inputs()
        lowercase__			,						lowercase__			,						lowercase__			,						lowercase__:   List[str]  			=   config_and_inputs
        lowercase__:   Union[str, Any]  			=   {
            'input_ids': input_ids,
            'token_type_ids': token_type_ids,
            'attention_mask': attention_mask,
        }
        return config, inputs_dict





@require_flax
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							Optional[Any]        = (
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 __lowercase :							Any        = False
 __lowercase :							str        = False
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




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




        '''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 )					->				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 		SCREAMING_SNAKE_CASE__   (   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 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        super().test_hidden_states_output()
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        for model_class_name in self.all_model_classes:
               lowercase__:   Union[str, Any]  			=   model_class_name.from_pretrained('google/bigbird-roberta-base' )
               self.assertIsNotNone(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   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 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__			,						lowercase__:   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__ ):
                      lowercase__:   Optional[int]  			=   self._prepare_for_class(lowerCAmelCase__				, lowerCAmelCase__ )
                      lowercase__:   Any  			=   model_class(lowerCAmelCase__ )

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

                      with self.subTest('JIT Enabled' ):
                             lowercase__:   str  			=   model_jitted(**lowerCAmelCase__ ).to_tuple()

                      with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                    lowercase__:   Dict  			=   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__=1E-5				, lowerCAmelCase__="outputs"				, lowerCAmelCase__=None )					->				Optional[int]:




        '''simple docstring'''
        # `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(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase		       =							list[list[float | int]]




def    snake_case_     (  snake_case			,							snake_case     )  ->				Matrix:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(size + 1     )] for _ in range(snake_case     )]
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   float

       for row in range(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[Any]  			=   matrix[row][col]

              lowercase__:   Optional[int]  			=   vector[row][0]

       lowercase__:   str  			=   0
       lowercase__:   Any  			=   0
       while row < size and col < size:
              # pivoting
              lowercase__:   List[Any]  			=   max((abs(augmented[rowa][col]     ), rowa) for rowa in range(snake_case			,							snake_case     )     )[
                  1
              ]
              if augmented[pivot_row][col] == 0:
                     col += 1
                     continue
              else:
                     lowercase__			,						lowercase__:   Optional[int]  			=   augmented[pivot_row], augmented[row]

              for rowa in range(row + 1			,							snake_case     ):
                     lowercase__:   Any  			=   augmented[rowa][col] / augmented[row][col]
                     lowercase__:   int  			=   0
                     for cola in range(col + 1			,							size + 1     ):
                            augmented[rowa][cola] -= augmented[row][cola] * ratio

              row += 1
              col += 1

       # back substitution
       for col in range(1			,							snake_case     ):
              for row in range(snake_case     ):
                     lowercase__:   Union[str, Any]  			=   augmented[row][col] / augmented[col][col]
                     for cola in range(snake_case			,							size + 1     ):
                            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
       return [
           [round(augmented[row][size] / augmented[row][row]			,							10     )] for row in range(snake_case     )
       ]




def    snake_case_     (  snake_case     )  ->				Callable[[int], int]:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(snake_case     )] for _ in range(snake_case     )]
       lowercase__:   Matrix  			=   [[0] for _ in range(snake_case     )]
       lowercase__:   Matrix
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int

       for x_val, y_val in enumerate(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[str]  			=   (x_val + 1) ** (size - col - 1)
              lowercase__:   str  			=   y_val

       lowercase__:   Optional[int]  			=   solve(snake_case			,							snake_case     )

       def interpolated_func(snake_case     ) -> int:
              return sum(
                  round(coeffs[x_val][0]     ) * (var ** (size - x_val - 1))
                  for x_val in range(snake_case     )     )

       return interpolated_func




def    snake_case_     (  snake_case     )  ->				int:
       return (
           1
           - variable
           + variable**2
           - variable**3
           + variable**4
           - variable**5
           + variable**6
           - variable**7
           + variable**8
           - variable**9
           + variable**10
       )




def    snake_case_     (  snake_case = question_function			,							snake_case = 10     )  ->				int:
       lowercase__:   list[int]  			=   [func(snake_case     ) for x_val in range(1			,							order + 1     )]

       lowercase__:   list[Callable[[int], int]]  			=   [
           interpolate(data_points[:max_coeff]     ) for max_coeff in range(1			,							order + 1     )
       ]

       lowercase__:   int  			=   0
       lowercase__:   Callable[[int], int]
       lowercase__:   int

       for poly in polynomials:
              lowercase__:   List[str]  			=   1
              while func(snake_case     ) == poly(snake_case     ):
                     x_val += 1

              ret += poly(snake_case     )

       return ret


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


