


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						):






    '''simple docstring'''





    def __init__(	self				:   Optional[Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   List[Any]=2       ,   __snake_case				:   str=56       ,   __snake_case				:   Union[str, Any]=True       ,   __snake_case				:   Optional[int]=True       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   Any=99       ,   __snake_case				:   List[str]=32       ,   __snake_case				:   Union[str, Any]=2       ,   __snake_case				:   Tuple=2       ,   __snake_case				:   List[Any]=7       ,   __snake_case				:   List[Any]="gelu_new"       ,   __snake_case				:   Tuple=0.1       ,   __snake_case				:   Any=0.1       ,   __snake_case				:   int=512       ,   __snake_case				:   Union[str, Any]=16       ,   __snake_case				:   int=2       ,   __snake_case				:   Tuple=0.02       ,   __snake_case				:   List[str]=4       ,   __snake_case				:   Union[str, Any]="block_sparse"       ,   __snake_case				:   Optional[int]=True       ,   __snake_case				:   int=False       ,   __snake_case				:   int=2       ,   __snake_case				:   List[Any]=3       ,   ):





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

       UpperCAmelCase_							:      Tuple						=   rescale_embeddings
       UpperCAmelCase_							:      Union[str, Any]						=   attention_type
       UpperCAmelCase_							:      Optional[Any]						=   use_bias
       UpperCAmelCase_							:      str						=   block_size
       UpperCAmelCase_							:      Tuple						=   num_random_blocks





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size	)

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

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

       UpperCAmelCase_							:      List[str]						=   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=__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 					_lowerCamelCase (	self				:   Any	):





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





@require_flax
class  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Union[str, Any]			= (
        (
            FlaxBigBirdForCausalLM,
            FlaxBigBirdModel,
            FlaxBigBirdForPreTraining,
            FlaxBigBirdForMaskedLM,
            FlaxBigBirdForMultipleChoice,
            FlaxBigBirdForQuestionAnswering,
            FlaxBigBirdForSequenceClassification,
            FlaxBigBirdForTokenClassification,
        )
        if is_flax_available()
        else ()
    )

    A_  :     str			= False
    A_  :     Dict			= False





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   FlaxBigBirdModelTester(self	)





    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''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				:   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				:   Union[str, Any]	):





       '''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				:   Union[str, Any]	):





       '''simple docstring'''
       super().test_hidden_states_output()





    @slow
    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       for model_class_name in self.all_model_classes:
          UpperCAmelCase_							:      Tuple						=   model_class_name.from_pretrained('''google/bigbird-roberta-base'''	)
          self.assertIsNotNone(__snake_case	)





    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				:   int	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__	):
             UpperCAmelCase_							:      List[Any]						=   self._prepare_for_class(__snake_case       ,   __snake_case	)
             UpperCAmelCase_							:      Optional[Any]						=   model_class(__snake_case	)

             @jax.jit
             def model_jitted(__snake_case				:   Dict       ,   __snake_case				:   int=None       ,   **__snake_case				:   List[str]	):
                return model(input_ids=__snake_case       ,   attention_mask=__snake_case       ,   **__snake_case	)

             with self.subTest('''JIT Enabled'''	):
                UpperCAmelCase_							:      Optional[Any]						=   model_jitted(**__snake_case	).to_tuple()

             with self.subTest('''JIT Disabled'''	):
                with jax.disable_jit():
                   UpperCAmelCase_							:      Any						=   model_jitted(**__snake_case	).to_tuple()

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





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   Any       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   str=1E-5       ,   __snake_case				:   Tuple="outputs"       ,   __snake_case				:   str=None	):





       '''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(__snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case	)



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def      snake_case_						( ):
   UpperCAmelCase_							:      str						=   HfArgumentParser(__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   parser.parse_args_into_dataclasses()[0]
   UpperCAmelCase_							:      Optional[int]						=   TensorFlowBenchmark(args=__lowercase       )
   try:
      UpperCAmelCase_							:      List[Any]						=   parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      UpperCAmelCase_							:      List[Any]						=   '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
      UpperCAmelCase_							:      List[str]						=   ''' '''.join(str(__lowercase       ).split(''' '''       )[:-1]       )
      UpperCAmelCase_							:      Optional[int]						=   ''''''
      UpperCAmelCase_							:      Dict						=   eval(str(__lowercase       ).split(''' '''       )[-1]       )
      UpperCAmelCase_							:      int						=   []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]       )
         else:
            wrong_args.append(__lowercase       )
      if len(__lowercase       ) > 0:
         UpperCAmelCase_							:      Tuple						=   full_error_msg + begin_error_msg + str(__lowercase       )
      raise ValueError(__lowercase       )
   benchmark.run()


if __name__ == "__main__":
       main()