

from ..utils import DummyObject, requires_backends



class SCREAMING_SNAKE_CASE     (  metaclass=a_			):
  """simple docstring"""






  lowerCamelCase   :      Optional[Any]										=["speech"]







  def __init__(      self			:   List[Any]	,							*lowerCAmelCase			:   Any	,							**lowerCAmelCase			:   Optional[int]   )							->     int:






     """simple docstring"""







     requires_backends(self	,							["""speech"""]   )






class SCREAMING_SNAKE_CASE     (  metaclass=a_			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=["speech"]







  def __init__(      self			:   Union[str, Any]	,							*lowerCAmelCase			:   List[Any]	,							**lowerCAmelCase			:   Tuple   )							->     str:






     """simple docstring"""







     requires_backends(self	,							["""speech"""]   )





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 SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






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






     """simple docstring"""







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

     __lowerCAmelCase  :      Any    =						rescale_embeddings
     __lowerCAmelCase  :      str    =						attention_type
     __lowerCAmelCase  :      List[Any]    =						use_bias
     __lowerCAmelCase  :      List[str]    =						block_size
     __lowerCAmelCase  :      Union[str, Any]    =						num_random_blocks







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

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

     __lowerCAmelCase  :      Any    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      Optional[int]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[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=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			:   Tuple   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						config_and_inputs
     __lowerCAmelCase  :      Union[str, Any]    =						{
         """input_ids""": input_ids,
         """token_type_ids""": token_type_ids,
         """attention_mask""": attention_mask,
     }
     return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






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

  lowerCamelCase   :      List[str]										=False
  lowerCamelCase   :      Union[str, Any]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						FlaxBigBirdModelTester(self   )







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






     """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			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     super().test_hidden_states_output()







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     for model_class_name in self.all_model_classes:
        __lowerCAmelCase  :      List[str]    =						model_class_name.from_pretrained("""google/bigbird-roberta-base"""   )
        self.assertIsNotNone(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[str]:






     """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]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
           __lowerCAmelCase  :      List[str]    =						self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )
           __lowerCAmelCase  :      Dict    =						model_class(lowerCAmelCase   )

           @jax.jit
           def model_jitted(lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]=None	,							**lowerCAmelCase			:   Union[str, Any]   ):
              return model(input_ids=lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							**lowerCAmelCase   )

           with self.subTest("""JIT Enabled"""   ):
              __lowerCAmelCase  :      str    =						model_jitted(**lowerCAmelCase   ).to_tuple()

           with self.subTest("""JIT Disabled"""   ):
              with jax.disable_jit():
                 __lowerCAmelCase  :      List[Any]    =						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			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]=1e-5	,							lowerCAmelCase			:   Union[str, Any]="outputs"	,							lowerCAmelCase			:   Union[str, Any]=None   )							->     Optional[int]:






     """simple docstring"""







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


