code: 
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import DebertaConfig, 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 (
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
        )
  
  
  class       a_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=13				,	A=7				,	A=True				,	A=True				,	A=True				,	A=True				,	A=99				,	A=32				,	A=2				,	A=4				,	A=37				,	A="gelu"				,	A=0.1				,	A=0.1				,	A=512				,	A=16				,	A=2				,	A=0.02				,	A=3				,	A=4				,	A=None				,	)	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    parent
          _SCREAMING_SNAKE_CASE             =    13
          _SCREAMING_SNAKE_CASE             =    7
          _SCREAMING_SNAKE_CASE             =    True
          _SCREAMING_SNAKE_CASE             =    True
          _SCREAMING_SNAKE_CASE             =    True
          _SCREAMING_SNAKE_CASE             =    True
          _SCREAMING_SNAKE_CASE             =    99
          _SCREAMING_SNAKE_CASE             =    32
          _SCREAMING_SNAKE_CASE             =    2
          _SCREAMING_SNAKE_CASE             =    4
          _SCREAMING_SNAKE_CASE             =    37
          _SCREAMING_SNAKE_CASE             =    """gelu"""
          _SCREAMING_SNAKE_CASE             =    0.1
          _SCREAMING_SNAKE_CASE             =    0.1
          _SCREAMING_SNAKE_CASE             =    512
          _SCREAMING_SNAKE_CASE             =    16
          _SCREAMING_SNAKE_CASE             =    False
          _SCREAMING_SNAKE_CASE             =    -1
          _SCREAMING_SNAKE_CASE             =    True
          _SCREAMING_SNAKE_CASE             =    2
          _SCREAMING_SNAKE_CASE             =    0.02
          _SCREAMING_SNAKE_CASE             =    3
          _SCREAMING_SNAKE_CASE             =    4
          _SCREAMING_SNAKE_CASE             =    None
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size )
  
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_input_mask:
              _SCREAMING_SNAKE_CASE             =    random_attention_mask([self.batch_size, self.seq_length] )
  
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_token_type_ids:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.type_vocab_size )
  
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_labels:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size]				,	self.type_sequence_label_size )
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels )
  
          _SCREAMING_SNAKE_CASE             =    DebertaConfig(
              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				,	max_relative_positions=self.max_relative_positions				,	position_biased_input=self.position_biased_input				,	initializer_range=self.initializer_range				,	return_dict=A				,	)
  
          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     Any:
          _SCREAMING_SNAKE_CASE             =    TFDebertaModel(config=A )
          _SCREAMING_SNAKE_CASE             =    {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  
          _SCREAMING_SNAKE_CASE             =    [input_ids, input_mask]
          _SCREAMING_SNAKE_CASE             =    model(A )
  
          _SCREAMING_SNAKE_CASE             =    model(A )
  
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    TFDebertaForMaskedLM(config=A )
          _SCREAMING_SNAKE_CASE             =    {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    TFDebertaForSequenceClassification(config=A )
          _SCREAMING_SNAKE_CASE             =    {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
  
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    TFDebertaForTokenClassification(config=A )
          _SCREAMING_SNAKE_CASE             =    {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    TFDebertaForQuestionAnswering(config=A )
          _SCREAMING_SNAKE_CASE             =    {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
  
          _SCREAMING_SNAKE_CASE             =    model(A )
          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     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
          (
              (
              _SCREAMING_SNAKE_CASE 
          )       , (
              _SCREAMING_SNAKE_CASE 
          )       , (
              _SCREAMING_SNAKE_CASE 
          )       , (
              _SCREAMING_SNAKE_CASE 
          )       , (
              _SCREAMING_SNAKE_CASE 
          )       , (
              _SCREAMING_SNAKE_CASE 
          )       , (
              _SCREAMING_SNAKE_CASE 
          )       ,  
          )             =    config_and_inputs
          _SCREAMING_SNAKE_CASE             =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict
  
  
  
  
  @require_tf
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				(
          (
              TFDebertaModel,
              TFDebertaForMaskedLM,
              TFDebertaForQuestionAnswering,
              TFDebertaForSequenceClassification,
              TFDebertaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase									=				(
          {
              '''feature-extraction''': TFDebertaModel,
              '''fill-mask''': TFDebertaForMaskedLM,
              '''question-answering''': TFDebertaForQuestionAnswering,
              '''text-classification''': TFDebertaForSequenceClassification,
              '''token-classification''': TFDebertaForTokenClassification,
              '''zero-shot''': TFDebertaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
  
      UpperCamelCase									=				False
      UpperCamelCase									=				False
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    TFDebertaModelTester(self )
          _SCREAMING_SNAKE_CASE             =    ConfigTester(self				,	config_class=A				,	hidden_size=37 )
  
  
      def     snake_case_(	self )	->     Optional[int]:
          self.config_tester.run_common_tests()
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A )
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A )
  
  
  
  
  
      @slow
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""" )
          self.assertIsNotNone(A )
  
  
  
  
  @require_tf
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      @unittest.skip(reason="""Model not available yet""" )
      def     snake_case_(	self )	->     List[str]:
          pass
  
  
  
  
  
      @slow
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    TFDebertaModel.from_pretrained("""kamalkraj/deberta-base""" )
          _SCREAMING_SNAKE_CASE             =    tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
          _SCREAMING_SNAKE_CASE             =    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A )[0]
  
          _SCREAMING_SNAKE_CASE             =    tf.constant(
              [
                  [
                      [-0.5985_5896, -0.8055_2566, -0.846_2135],
                      [1.448_4025, -0.9348_3794, -0.8059_3085],
                      [0.312_2741, 0.0031_6059, -1.413_1377],
                  ]
              ] )
          tf.debugging.assert_near(output[:, 1:4, 1:4]				,	A				,	atol=1e-4 )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import math
  from timeit import timeit
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    abs(__lowerCamelCase					)
      while True:
          _SCREAMING_SNAKE_CASE             =    n // 10
          digits += 1
          if n == 0:
              break
      return digits
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase					)      , 10					) + 1					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->int:
      return len(str(abs(__lowerCamelCase					)					)					)
  
  
  
  
  
  def lowerCamelCase		(  )		->None:
      from collections.abc import Callable
  
      def benchmark_a_function(__lowerCamelCase				:					Callable      , __lowerCamelCase				:					int					) -> None:
          _SCREAMING_SNAKE_CASE             =    F'{func.__name__}({value})'
          _SCREAMING_SNAKE_CASE             =    timeit(F'__main__.{call}'      , setup="""import __main__"""					)
          print(F'{call}: {func(__lowerCamelCase					)} -- {timing} seconds'					)
  
      for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
          for func in (num_digits, num_digits_fast, num_digits_faster):
              benchmark_a_function(__lowerCamelCase      , __lowerCamelCase					)
          print()
  
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
        benchmark()
  
  
  
  