






"""simple docstring"""



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   ):
   def __init__(		self      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	Tuple=2							,			snake_case__      :	Any=5_6							,			snake_case__      :	Union[str, Any]=True							,			snake_case__      :	Dict=True							,			snake_case__      :	Tuple=True							,			snake_case__      :	Dict=True							,			snake_case__      :	Optional[int]=9_9							,			snake_case__      :	Optional[int]=3_2							,			snake_case__      :	Optional[Any]=2							,			snake_case__      :	Optional[Any]=2							,			snake_case__      :	Dict=7							,			snake_case__      :	Tuple="gelu_new"							,			snake_case__      :	Any=0.1							,			snake_case__      :	int=0.1							,			snake_case__      :	List[str]=5_1_2							,			snake_case__      :	Tuple=1_6							,			snake_case__      :	int=2							,			snake_case__      :	Optional[int]=0.02							,			snake_case__      :	int=4							,			snake_case__      :	Dict="block_sparse"							,			snake_case__      :	Dict=True							,			snake_case__      :	Union[str, Any]=False							,			snake_case__      :	Tuple=2							,			snake_case__      :	Optional[int]=3							,			):


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

        UpperCAmelCase__						:    Any							=							rescale_embeddings
        UpperCAmelCase__						:    int							=							attention_type
        UpperCAmelCase__						:    Any							=							use_bias
        UpperCAmelCase__						:    Optional[int]							=							block_size
        UpperCAmelCase__						:    Dict							=							num_random_blocks
   def       __a							(		self      :	List[str]							):


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

        UpperCAmelCase__						:    int							=							None
        if self.use_attention_mask:
             UpperCAmelCase__						:    List[Any]							=							random_attention_mask([self.batch_size, self.seq_length]							)

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

        UpperCAmelCase__						:    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=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       __a							(		self      :	int							):


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




@require_flax
class 			lowerCAmelCase__			(    lowerCamelCase_							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =(
       (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdModel,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
       )
       if is_flax_available()
       else ()
   )

   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							FlaxBigBirdModelTester(self							)
   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __a							(		self      :	List[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       __a							(		self      :	Dict							):


        '''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       __a							(		self      :	Any							):


        '''simple docstring'''
        super().test_no_automatic_init()
   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        super().test_hidden_states_output()
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        for model_class_name in self.all_model_classes:
             UpperCAmelCase__						:    Dict							=							model_class_name.from_pretrained("google/bigbird-roberta-base"							)
             self.assertIsNotNone(snake_case__							)
   def       __a							(		self      :	Optional[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       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    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__							):
                  UpperCAmelCase__						:    str							=							self._prepare_for_class(snake_case__							,			snake_case__							)
                  UpperCAmelCase__						:    Dict							=							model_class(snake_case__							)

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

                  with self.subTest("JIT Enabled"							):
                       UpperCAmelCase__						:    Optional[int]							=							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       __a							(		self      :	Optional[Any]							,			snake_case__      :	Dict							,			snake_case__      :	List[Any]							,			snake_case__      :	List[Any]							,			snake_case__      :	str=1e-5							,			snake_case__      :	Optional[int]="outputs"							,			snake_case__      :	int=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__							)








import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__ :   str						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use PoolFormerImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



