


'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
  )






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Any       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    Optional[Any]=13       ,		lowerCamelCase       :    Optional[Any]=7       ,		lowerCamelCase       :    List[str]=True       ,		lowerCamelCase       :    Union[str, Any]=True       ,		lowerCamelCase       :    Dict=True       ,		lowerCamelCase       :    int=True       ,		lowerCamelCase       :    List[Any]=99       ,		lowerCamelCase       :    int=32       ,		lowerCamelCase       :    Dict=2       ,		lowerCamelCase       :    str=4       ,		lowerCamelCase       :    List[str]=37       ,		lowerCamelCase       :    str="gelu"       ,		lowerCamelCase       :    List[Any]=0.1       ,		lowerCamelCase       :    Dict=0.1       ,		lowerCamelCase       :    Optional[Any]=5_12       ,		lowerCamelCase       :    Any=16       ,		lowerCamelCase       :    Tuple=2       ,		lowerCamelCase       :    Dict=0.02       ,		lowerCamelCase       :    int=False       ,		lowerCamelCase       :    Dict=True       ,		lowerCamelCase       :    List[str]="None"       ,		lowerCamelCase       :    Optional[int]=3       ,		lowerCamelCase       :    List[str]=4       ,		lowerCamelCase       :    List[str]=None       ,		)  ->    int:
      lowerCAmelCase_     : List[str]     =  parent
      lowerCAmelCase_     : Union[str, Any]     =  batch_size
      lowerCAmelCase_     : Optional[Any]     =  seq_length
      lowerCAmelCase_     : List[Any]     =  is_training
      lowerCAmelCase_     : Dict     =  use_input_mask
      lowerCAmelCase_     : Optional[int]     =  use_token_type_ids
      lowerCAmelCase_     : Union[str, Any]     =  use_labels
      lowerCAmelCase_     : Optional[int]     =  vocab_size
      lowerCAmelCase_     : Union[str, Any]     =  hidden_size
      lowerCAmelCase_     : str     =  num_hidden_layers
      lowerCAmelCase_     : Optional[int]     =  num_attention_heads
      lowerCAmelCase_     : int     =  intermediate_size
      lowerCAmelCase_     : Any     =  hidden_act
      lowerCAmelCase_     : Any     =  hidden_dropout_prob
      lowerCAmelCase_     : Any     =  attention_probs_dropout_prob
      lowerCAmelCase_     : str     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : str     =  type_sequence_label_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : int     =  num_labels
      lowerCAmelCase_     : Tuple     =  num_choices
      lowerCAmelCase_     : Union[str, Any]     =  relative_attention
      lowerCAmelCase_     : Optional[int]     =  position_biased_input
      lowerCAmelCase_     : Tuple     =  pos_att_type
      lowerCAmelCase_     : Optional[Any]     =  scope

    def __lowercase  (  self       :    List[Any]						)  ->    Optional[Any]:
      lowerCAmelCase_     : Dict     =  ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size						)

      lowerCAmelCase_     : Optional[int]     =  None
      if self.use_input_mask:
        lowerCAmelCase_     : Dict     =  random_attention_mask([self.batch_size, self.seq_length]						)

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

      lowerCAmelCase_     : Any     =  None
      lowerCAmelCase_     : List[str]     =  None
      lowerCAmelCase_     : Any     =  None
      if self.use_labels:
        lowerCAmelCase_     : Union[str, Any]     =  ids_tensor([self.batch_size]       ,		self.type_sequence_label_size						)
        lowerCAmelCase_     : int     =  ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels						)

      lowerCAmelCase_     : str     =  DebertaVaConfig(
          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       ,		relative_attention=self.relative_attention       ,		position_biased_input=self.position_biased_input       ,		initializer_range=self.initializer_range       ,		return_dict=lowerCamelCase       ,		)

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def __lowercase  (  self       :    Dict       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Optional[Any]       ,		lowerCamelCase       :    List[str]						)  ->    List[str]:
      lowerCAmelCase_     : Optional[Any]     =  TFDebertaVaModel(config=lowerCamelCase						)
      lowerCAmelCase_     : List[str]     =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

      lowerCAmelCase_     : str     =  [input_ids, input_mask]
      lowerCAmelCase_     : Tuple     =  model(lowerCamelCase						)

      lowerCAmelCase_     : Union[str, Any]     =  model(lowerCamelCase						)

      self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)						)

    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    Union[str, Any]						)  ->    Dict:
      lowerCAmelCase_     : List[str]     =  TFDebertaVaForMaskedLM(config=lowerCamelCase						)
      lowerCAmelCase_     : str     =  {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      lowerCAmelCase_     : Union[str, Any]     =  model(lowerCamelCase						)
      self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)						)

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Tuple						)  ->    Any:
      lowerCAmelCase_     : List[Any]     =  self.num_labels
      lowerCAmelCase_     : List[Any]     =  TFDebertaVaForSequenceClassification(config=lowerCamelCase						)
      lowerCAmelCase_     : Dict     =  {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      lowerCAmelCase_     : List[Any]     =  model(lowerCamelCase						)
      self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)						)

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    Tuple						)  ->    Union[str, Any]:
      lowerCAmelCase_     : Optional[int]     =  self.num_labels
      lowerCAmelCase_     : Any     =  TFDebertaVaForTokenClassification(config=lowerCamelCase						)
      lowerCAmelCase_     : Dict     =  {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      lowerCAmelCase_     : int     =  model(lowerCamelCase						)
      self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)						)

    def __lowercase  (  self       :    str       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    List[Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Optional[int]       ,		lowerCamelCase       :    int						)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[int]     =  TFDebertaVaForQuestionAnswering(config=lowerCamelCase						)
      lowerCAmelCase_     : Union[str, Any]     =  {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      lowerCAmelCase_     : Optional[int]     =  model(lowerCamelCase						)
      self.parent.assertEqual(result.start_logits.shape       ,		(self.batch_size, self.seq_length)						)
      self.parent.assertEqual(result.end_logits.shape       ,		(self.batch_size, self.seq_length)						)





    def __lowercase  (  self       :    Any						)  ->    int:
      lowerCAmelCase_     : Dict     =  self.prepare_config_and_inputs()
      (
          (
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				(
          lowerCAmelCase_
      ),				
      )     : str     =  config_and_inputs
      lowerCAmelCase_     : Tuple     =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_tf
class   __snake_case			(  _SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



    lowercase  					=   (
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowercase  					=   (
        {
            'feature-extraction': TFDebertaVaModel,
            'fill-mask': TFDebertaVaForMaskedLM,
            'question-answering': TFDebertaVaForQuestionAnswering,
            'text-classification': TFDebertaVaForSequenceClassification,
            'token-classification': TFDebertaVaForTokenClassification,
            'zero-shot': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    lowercase  					=   False
    lowercase  					=   False

    def __lowercase  (  self       :    Optional[Any]						)  ->    Optional[int]:
      lowerCAmelCase_     : List[Any]     =  TFDebertaVaModelTester(self						)
      lowerCAmelCase_     : Optional[int]     =  ConfigTester(self       ,		config_class=lowerCamelCase       ,		hidden_size=37						)

    def __lowercase  (  self       :    List[str]						)  ->    Optional[int]:
      self.config_tester.run_common_tests()

    def __lowercase  (  self       :    Optional[Any]						)  ->    Tuple:
      lowerCAmelCase_     : str     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]						)  ->    Optional[Any]:
      lowerCAmelCase_     : List[str]     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]						)  ->    Any:
      lowerCAmelCase_     : Any     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase						)

    def __lowercase  (  self       :    str						)  ->    int:
      lowerCAmelCase_     : Optional[int]     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]						)  ->    Union[str, Any]:
      lowerCAmelCase_     : List[Any]     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase						)





    @slow
    def __lowercase  (  self       :    int						)  ->    Tuple:
      lowerCAmelCase_     : Dict     =  TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""						)
      self.assertIsNotNone(lowerCamelCase						)







@require_tf
class   __snake_case			(  unittest.TestCase):







    """simple docstring"""



    @unittest.skip(reason="""Model not available yet"""						)
    def __lowercase  (  self       :    Any						)  ->    List[str]:
      pass





    @slow
    def __lowercase  (  self       :    str						)  ->    int:
      lowerCAmelCase_     : Dict     =  TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""						)
      lowerCAmelCase_     : List[Any]     =  tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]						)
      lowerCAmelCase_     : Any     =  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
      lowerCAmelCase_     : List[Any]     =  model(lowerCamelCase       ,		attention_mask=lowerCamelCase						)[0]

      lowerCAmelCase_     : Union[str, Any]     =  tf.constant(
          [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]						)
      tf.debugging.assert_near(output[:, 1:4, 1:4]       ,		lowerCamelCase       ,		atol=1E-4						)







'''simple docstring'''






from __future__ import annotations







def 							UpperCamelCase_     (       A__   :  list				,       A__   :  int				,       A__   :  int				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0   )   )
  lowerCAmelCase_     : Dict     =  result + left + right
  return input_list







def 							UpperCamelCase_     (       A__   :  list   ):
  '''simple docstring'''





  if len(A__   ) <= 1:
    return input_list
  lowerCAmelCase_     : str     =  list(A__   )

  # iteration for two-way merging
  lowerCAmelCase_     : Dict     =  2
  while p <= len(A__   ):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0				,       len(A__   )				,       A__   ):
      lowerCAmelCase_     : Tuple     =  i
      lowerCAmelCase_     : List[str]     =  i + p - 1
      lowerCAmelCase_     : Optional[Any]     =  (low + high + 1) // 2
      lowerCAmelCase_     : List[str]     =  merge(A__				,       A__				,       A__				,       A__   )
    # final merge of last two parts
    if p * 2 >= len(A__   ):
      lowerCAmelCase_     : Union[str, Any]     =  i
      lowerCAmelCase_     : Dict     =  merge(A__				,       0				,       A__				,       len(A__   ) - 1   )
      break
    p *= 2

  return input_list


if __name__ == "__main__":
  __A   :    int											= input("Enter numbers separated by a comma:\n").strip()
  if user_input == "":
    __A   :    str											= []
  else:
    __A   :    Optional[Any]											= [int(item.strip()) for item in user_input.split(",")]
  print(iter_merge_sort(unsorted))



