'''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       UpperCAmelCase  (						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     ,       )  ->  Optional[int]:
        lowercase__    :   Optional[Any]						= parent
        lowercase__    :   int						= batch_size
        lowercase__    :   Tuple						= seq_length
        lowercase__    :   int						= is_training
        lowercase__    :   Tuple						= use_attention_mask
        lowercase__    :   int						= use_token_type_ids
        lowercase__    :   Optional[int]						= use_labels
        lowercase__    :   str						= vocab_size
        lowercase__    :   Union[str, Any]						= hidden_size
        lowercase__    :   Union[str, Any]						= num_hidden_layers
        lowercase__    :   Dict						= num_attention_heads
        lowercase__    :   int						= intermediate_size
        lowercase__    :   Optional[int]						= hidden_act
        lowercase__    :   Optional[int]						= hidden_dropout_prob
        lowercase__    :   Tuple						= attention_probs_dropout_prob
        lowercase__    :   int						= max_position_embeddings
        lowercase__    :   Optional[Any]						= type_vocab_size
        lowercase__    :   Optional[Any]						= type_sequence_label_size
        lowercase__    :   List[str]						= initializer_range
        lowercase__    :   str						= num_choices

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

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[Any]						= ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size			)

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

        lowercase__    :   Union[str, Any]						= None
        if self.use_token_type_ids:
         lowercase__    :   Optional[Any]						= ids_tensor([self.batch_size, self.seq_length]     ,       self.type_vocab_size			)

        lowercase__    :   Union[str, 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 _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Optional[int]						= self.prepare_config_and_inputs()
        lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Tuple						= config_and_inputs
        lowercase__    :   Union[str, Any]						= {
            '''input_ids''': input_ids,
            '''token_type_ids''': token_type_ids,
            '''attention_mask''': attention_mask,
        }
        return config, inputs_dict



@require_flax
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       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 _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   Optional[int]						= FlaxBigBirdModelTester(self			)

       @slow
       # copied from `test_modeling_flax_common` because it takes much longer than other models
       def _lowerCAmelCase(						self			)  ->  int:
        super().test_from_pretrained_save_pretrained()

       @slow
       # copied from `test_modeling_flax_common` because it takes much longer than other models
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        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			)  ->  int:
        super().test_no_automatic_init()

       @slow
       # copied from `test_modeling_flax_common` because it takes much longer than other models
       def _lowerCAmelCase(						self			)  ->  Any:
        super().test_hidden_states_output()

       @slow
       def _lowerCAmelCase(						self			)  ->  List[str]:
        for model_class_name in self.all_model_classes:
         lowercase__    :   Union[str, Any]						= model_class_name.from_pretrained('''google/bigbird-roberta-base'''			)
         self.assertIsNotNone(__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        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			)  ->  Optional[int]:
        lowercase__			,							lowercase__    :   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__			):
          lowercase__    :   str						= self._prepare_for_class(__lowerCAmelCase     ,       __lowerCAmelCase			)
          lowercase__    :   Dict						= 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'''			):
           lowercase__    :   Tuple						= model_jitted(**__lowerCAmelCase			).to_tuple()

          with self.subTest('''JIT Disabled'''			):
           with jax.disable_jit():
            lowercase__    :   int						= 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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=1E-5     ,       __lowerCAmelCase="outputs"     ,       __lowerCAmelCase=None			)  ->  List[Any]:
        # `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(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase					):

 def merge(UpperCAmelCase		,					UpperCAmelCase					) -> list:

  def _merge():
   while left and right:
    yield (left if left[0] <= right[0] else right).pop(0					)
   yield from left
   yield from right

  return list(_merge()					)

 if len(UpperCAmelCase					) <= 1:
  return collection
 lowercase__    :   int						= len(UpperCAmelCase					) // 2
 return merge(merge_sort(collection[:mid]					)		,					merge_sort(collection[mid:]					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __a:			str	       =       input("""Enter numbers separated by a comma:\n""").strip()
  __a:			Optional[int]	       =       [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")

