




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 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






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





           '''simple docstring'''

           A__       =    parent
           A__       =    batch_size
           A__       =    seq_length
           A__       =    is_training
           A__       =    use_attention_mask
           A__       =    use_token_type_ids
           A__       =    use_labels
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    intermediate_size
           A__       =    hidden_act
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    type_vocab_size
           A__       =    type_sequence_label_size
           A__       =    initializer_range
           A__       =    num_choices

           A__       =    rescale_embeddings
           A__       =    attention_type
           A__       =    use_bias
           A__       =    block_size
           A__       =    num_random_blocks


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





           '''simple docstring'''

           A__       =    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size)

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

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

           A__       =    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=UpperCAmelCase__					, 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      :	Union[str, Any])						->Optional[int]:





           '''simple docstring'''

           A__       =    self.prepare_config_and_inputs()
           A__ ,							A__ ,							A__ ,							A__       =    config_and_inputs
           A__       =    {
               '''input_ids''': input_ids,
               '''token_type_ids''': token_type_ids,
               '''attention_mask''': attention_mask,
           }
           return config, inputs_dict


@require_flax
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (
        (
            FlaxBigBirdForCausalLM,
            FlaxBigBirdModel,
            FlaxBigBirdForPreTraining,
            FlaxBigBirdForMaskedLM,
            FlaxBigBirdForMultipleChoice,
            FlaxBigBirdForQuestionAnswering,
            FlaxBigBirdForSequenceClassification,
            FlaxBigBirdForTokenClassification,
        )
        if is_flax_available()
        else ()
    )

    UpperCAmelCase__							  =      False
    UpperCAmelCase__							  =      False


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





           '''simple docstring'''

           A__       =    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])						->str:





           '''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      :	Any)						->int:





           '''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      :	Dict)						->Dict:





           '''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)						->Dict:





           '''simple docstring'''

           super().test_hidden_states_output()


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->str:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__       =    model_class_name.from_pretrained('''google/bigbird-roberta-base''')
                  self.assertIsNotNone(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Tuple:





           '''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      :	str)						->str:





           '''simple docstring'''

           A__ ,							A__       =    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         A__       =    self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__)
                         A__       =    model_class(UpperCAmelCase__)

                         @jax.jit
                         def model_jitted(UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Any=None					, **UpperCAmelCase__      :	Tuple):
                                return model(input_ids=UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, **UpperCAmelCase__)

                         with self.subTest('''JIT Enabled'''):
                                A__       =    model_jitted(**UpperCAmelCase__).to_tuple()

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

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





    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Dict=1e-5					, UpperCAmelCase__      :	Union[str, Any]="outputs"					, UpperCAmelCase__      :	Optional[int]=None)						->List[str]:





           '''simple docstring'''

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








_lowerCamelCase	:		Optional[int]							       =   65521







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    1
       A__       =    0
       for plain_chr in plain_text:
              A__       =    (a + ord(lowercase_						)) % MOD_ADLER
              A__       =    (b + a) % MOD_ADLER
       return (b << 16) | a


