





"""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 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 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   ,		):



     '''simple docstring'''




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

     __A       :		Any							=	rescale_embeddings
     __A       :		List[str]							=	attention_type
     __A       :		str							=	use_bias
     __A       :		Dict							=	block_size
     __A       :		Optional[int]							=	num_random_blocks

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size    )

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

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

     __A       :		Optional[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    UpperCamelCase__(       self    ):



     '''simple docstring'''




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






@require_flax
class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


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

 _lowerCamelCase								=	False
 _lowerCamelCase								=	False

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	FlaxBigBirdModelTester(self    )

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().test_from_pretrained_save_pretrained()

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def    UpperCamelCase__(       self    ):



     '''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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().test_no_automatic_init()

 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().test_hidden_states_output()

 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for model_class_name in self.all_model_classes:
         __A       :		Dict							=	model_class_name.from_pretrained('''google/bigbird-roberta-base'''    )
         self.assertIsNotNone(__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''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    UpperCamelCase__(       self    ):



     '''simple docstring'''




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

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__    ):
             __A       :		Any							=	self._prepare_for_class(__lowerCamelCase   ,		__lowerCamelCase    )
             __A       :		List[str]							=	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'''    ):
                 __A       :		Optional[int]							=	model_jitted(**__lowerCamelCase    ).to_tuple()

             with self.subTest('''JIT Disabled'''    ):
                 with jax.disable_jit():
                     __A       :		Union[str, 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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=1e-5   ,		__lowerCamelCase="outputs"   ,		__lowerCamelCase=None    ):



     '''simple docstring'''




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











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->list:



    '''simple docstring'''







    for i in range(len(snake_case_		) - 1							,0							,-1		):
        __A       :		Union[str, Any]							=	False

        for j in range(snake_case_							,0							,-1		):
            if unsorted[j] < unsorted[j - 1]:
                __A					,			__A       :		Union[str, Any]							=	unsorted[j - 1], unsorted[j]
                __A       :		Optional[int]							=	True

        for j in range(snake_case_		):
            if unsorted[j] > unsorted[j + 1]:
                __A					,			__A       :		Optional[Any]							=	unsorted[j + 1], unsorted[j]
                __A       :		Union[str, Any]							=	True

        if not swapped:
            break
    return unsorted


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       a_					=     input("""Enter numbers separated by a comma:\n""").strip()
       a_					=     [int(item) for item in user_input.split(""",""")]
       print(f'''{cocktail_shaker_sort(unsorted) = }''')




