
'''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 UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict							,_a      :     List[Any]							,_a      :     Tuple=13							,_a      :     List[str]=7							,_a      :     Any=True							,_a      :     Optional[Any]=True							,_a      :     Optional[Any]=True							,_a      :     List[Any]=True							,_a      :     str=99							,_a      :     List[str]=32							,_a      :     List[Any]=2							,_a      :     List[str]=4							,_a      :     str=37							,_a      :     Any="gelu"							,_a      :     str=0.1							,_a      :     Any=0.1							,_a      :     Tuple=512							,_a      :     Optional[int]=16							,_a      :     Tuple=2							,_a      :     Optional[int]=0.02							,_a      :     List[Any]=False							,_a      :     Optional[Any]=True							,_a      :     Union[str, Any]="None"							,_a      :     int=3							,_a      :     Union[str, Any]=4							,_a      :     Optional[int]=None							,):


     '''simple docstring'''






     _a				:	Optional[Any]			=   parent
     _a				:	Any			=   batch_size
     _a				:	Optional[int]			=   seq_length
     _a				:	List[str]			=   is_training
     _a				:	List[str]			=   use_input_mask
     _a				:	List[Any]			=   use_token_type_ids
     _a				:	Any			=   use_labels
     _a				:	Union[str, Any]			=   vocab_size
     _a				:	str			=   hidden_size
     _a				:	Tuple			=   num_hidden_layers
     _a				:	str			=   num_attention_heads
     _a				:	Optional[Any]			=   intermediate_size
     _a				:	Tuple			=   hidden_act
     _a				:	List[str]			=   hidden_dropout_prob
     _a				:	Any			=   attention_probs_dropout_prob
     _a				:	Any			=   max_position_embeddings
     _a				:	Any			=   type_vocab_size
     _a				:	str			=   type_sequence_label_size
     _a				:	List[str]			=   initializer_range
     _a				:	Optional[Any]			=   num_labels
     _a				:	int			=   num_choices
     _a				:	int			=   relative_attention
     _a				:	Dict			=   position_biased_input
     _a				:	Any			=   pos_att_type
     _a				:	List[Any]			=   scope


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	str			=   ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size       )

     _a				:	Optional[int]			=   None
     if self.use_input_mask:
        _a				:	Union[str, Any]			=   random_attention_mask([self.batch_size, self.seq_length]       )

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

     _a				:	str			=   None
     _a				:	List[str]			=   None
     _a				:	List[Any]			=   None
     if self.use_labels:
        _a				:	Dict			=   ids_tensor([self.batch_size]							,self.type_sequence_label_size       )
        _a				:	Tuple			=   ids_tensor([self.batch_size, self.seq_length]							,self.num_labels       )

     _a				:	Dict			=   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      :     int							,_a      :     Tuple							,_a      :     Dict							,_a      :     Dict							,_a      :     Any							,_a      :     Optional[int]							,_a      :     Union[str, Any]							,_a      :     int       ):


     '''simple docstring'''






     _a				:	str			=   TFDebertaVaModel(config=lowerCamelCase__       )
     _a				:	Optional[Any]			=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

     _a				:	Union[str, Any]			=   [input_ids, input_mask]
     _a				:	Optional[int]			=   model(lowerCamelCase__       )

     _a				:	int			=   model(lowerCamelCase__       )

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


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     str							,_a      :     str							,_a      :     Tuple							,_a      :     List[str]							,_a      :     List[str]							,_a      :     List[str]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	List[str]			=   TFDebertaVaForMaskedLM(config=lowerCamelCase__       )
     _a				:	Any			=   {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }
     _a				:	Dict			=   model(lowerCamelCase__       )
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.vocab_size)       )


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     Dict							,_a      :     Optional[Any]							,_a      :     Optional[int]							,_a      :     Union[str, Any]							,_a      :     List[Any]							,_a      :     Any							,_a      :     Dict       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.num_labels
     _a				:	List[str]			=   TFDebertaVaForSequenceClassification(config=lowerCamelCase__       )
     _a				:	Optional[int]			=   {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }

     _a				:	Optional[Any]			=   model(lowerCamelCase__       )
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)       )


  def   __lowercase		(		self      :     Any							,_a      :     List[str]							,_a      :     Dict							,_a      :     List[Any]							,_a      :     int							,_a      :     List[Any]							,_a      :     Optional[Any]							,_a      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	int			=   self.num_labels
     _a				:	Tuple			=   TFDebertaVaForTokenClassification(config=lowerCamelCase__       )
     _a				:	int			=   {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }
     _a				:	Union[str, Any]			=   model(lowerCamelCase__       )
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.seq_length, self.num_labels)       )


  def   __lowercase		(		self      :     List[Any]							,_a      :     int							,_a      :     Any							,_a      :     List[str]							,_a      :     Optional[Any]							,_a      :     Dict							,_a      :     Optional[int]							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	Tuple			=   TFDebertaVaForQuestionAnswering(config=lowerCamelCase__       )
     _a				:	Optional[Any]			=   {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }

     _a				:	Optional[Any]			=   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      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Any			=   self.prepare_config_and_inputs()
     (
         _a							
     )				:	List[Any]			=   config_and_inputs
     _a				:	str			=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
     return config, inputs_dict







@require_tf
class UpperCAmelCase__	( SCREAMING_SNAKE_CASE__    ,				SCREAMING_SNAKE_CASE__    ,				unittest.TestCase	):





  """simple docstring"""

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

  __UpperCAmelCase				:				Tuple   =					False
  __UpperCAmelCase				:				str   =					False


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	List[Any]			=   TFDebertaVaModelTester(self       )
     _a				:	Optional[Any]			=   ConfigTester(self							,config_class=lowerCamelCase__							,hidden_size=37       )


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     self.config_tester.run_common_tests()


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCamelCase__       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Any			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	str			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )


  @slow
  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Tuple			=   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'       )
     self.assertIsNotNone(lowerCamelCase__       )







@require_tf
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @unittest.skip(reason='Model not available yet'       )
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     pass


  @slow
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'       )
     _a				:	Dict			=   tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       )
     _a				:	Tuple			=   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
     _a				:	Optional[Any]			=   model(lowerCamelCase__							,attention_mask=lowerCamelCase__       )[0]

     _a				:	List[str]			=   tf.constant(
         [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]       )
     tf.debugging.assert_near(output[:, 1:4, 1:4]							,lowerCamelCase__							,atol=1E-4       )








def 	__UpperCamelCase	(  _lowerCAmelCase = 100_0000		)       ->  int:



    """simple docstring"""






    A :       str          =   limit + 1
    A :       Tuple          =   [0] * limit
    for first_term in range(1      ,     _lowerCAmelCase		):
        for n in range(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		):
            A :       Any          =   first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    A :       Optional[int]          =   sum(1 for x in frequency[1:limit] if x == 10		)

    return count


if __name__ == "__main__":
     print(F"""{solution() = }""")


