





"""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 			_UpperCAmelCase  :



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_=1_3 ,				snake_case_=7 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=9_9 ,				snake_case_=3_2 ,				snake_case_=2 ,				snake_case_=4 ,				snake_case_=3_7 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=5_1_2 ,				snake_case_=1_6 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=3 ,				snake_case_=4 ,				snake_case_=None ,				):

        """simple docstring"""

        A_  :	Any    						=  parent
        A_  :	Union[str, Any]    						=  1_3
        A_  :	List[str]    						=  7
        A_  :	Optional[int]    						=  True
        A_  :	str    						=  True
        A_  :	List[str]    						=  True
        A_  :	Tuple    						=  True
        A_  :	List[str]    						=  9_9
        A_  :	List[Any]    						=  3_2
        A_  :	Tuple    						=  2
        A_  :	Dict    						=  4
        A_  :	str    						=  3_7
        A_  :	List[str]    						=  'gelu'
        A_  :	Tuple    						=  0.1
        A_  :	List[Any]    						=  0.1
        A_  :	Dict    						=  5_1_2
        A_  :	Any    						=  1_6
        A_  :	Dict    						=  False
        A_  :	str    						=  -1
        A_  :	str    						=  True
        A_  :	List[str]    						=  2
        A_  :	Dict    						=  0.02
        A_  :	List[str]    						=  3
        A_  :	int    						=  4
        A_  :	Any    						=  None

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

        A_  :	Optional[Any]    						=  None
        if self.use_input_mask:
          A_  :	List[str]    						=  random_attention_mask([self.batch_size, self.seq_length]  )

        A_  :	Optional[int]    						=  None
        if self.use_token_type_ids:
          A_  :	List[str]    						=  ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size  )

        A_  :	str    						=  None
        A_  :	Any    						=  None
        A_  :	Any    						=  None
        if self.use_labels:
          A_  :	Optional[int]    						=  ids_tensor([self.batch_size] ,				self.type_sequence_label_size  )
          A_  :	Dict    						=  ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels  )

        A_  :	Union[str, Any]    						=  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=snake_case_ ,				)

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

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  TFDebertaModel(config=snake_case_  )
        A_  :	str    						=  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        A_  :	Union[str, Any]    						=  [input_ids, input_mask]
        A_  :	Dict    						=  model(snake_case_  )

        A_  :	str    						=  model(snake_case_  )

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

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  TFDebertaForMaskedLM(config=snake_case_  )
        A_  :	Dict    						=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        A_  :	Union[str, Any]    						=  model(snake_case_  )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Tuple    						=  self.num_labels
        A_  :	int    						=  TFDebertaForSequenceClassification(config=snake_case_  )
        A_  :	List[Any]    						=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        A_  :	List[str]    						=  model(snake_case_  )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	List[Any]    						=  self.num_labels
        A_  :	List[Any]    						=  TFDebertaForTokenClassification(config=snake_case_  )
        A_  :	Any    						=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        A_  :	Tuple    						=  model(snake_case_  )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  TFDebertaForQuestionAnswering(config=snake_case_  )
        A_  :	str    						=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        A_  :	Optional[Any]    						=  model(snake_case_  )
        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  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  self.prepare_config_and_inputs()
        (
            (
            A_
        )						,	(
            A_
        )						,	(
            A_
        )						,	(
            A_
        )						,	(
            A_
        )						,	(
            A_
        )						,	(
            A_
        )						,	
        )  :	int    						=  config_and_inputs
        A_  :	Dict    						=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict


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



      '''simple docstring'''
      lowercase_				:			int        =       (
          (
              TFDebertaModel,
              TFDebertaForMaskedLM,
              TFDebertaForQuestionAnswering,
              TFDebertaForSequenceClassification,
              TFDebertaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      lowercase_				:			Any        =       (
          {
              """feature-extraction""": TFDebertaModel,
              """fill-mask""": TFDebertaForMaskedLM,
              """question-answering""": TFDebertaForQuestionAnswering,
              """text-classification""": TFDebertaForSequenceClassification,
              """token-classification""": TFDebertaForTokenClassification,
              """zero-shot""": TFDebertaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      lowercase_				:			List[Any]        =       False
      lowercase_				:			Union[str, Any]        =       False

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  TFDebertaModelTester(self  )
        A_  :	int    						=  ConfigTester(self ,				config_class=snake_case_ ,				hidden_size=3_7  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	str    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[str]    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	str    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[str]    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*snake_case_  )

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  TFDebertaModel.from_pretrained('kamalkraj/deberta-base'  )
        self.assertIsNotNone(snake_case_  )


@require_tf
class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''

      @unittest.skip(reason='Model not available yet'  )
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        pass

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  TFDebertaModel.from_pretrained('kamalkraj/deberta-base'  )
        A_  :	List[Any]    						=  tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  )
        A_  :	Optional[Any]    						=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
        A_  :	str    						=  model(snake_case_ ,				attention_mask=snake_case_  )[0]

        A_  :	Optional[Any]    						=  tf.constant(
            [
                [
                    [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                    [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                    [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
                ]
            ]  )
        tf.debugging.assert_near(output[:, 1:4, 1:4] ,				snake_case_ ,				atol=1E-4  )






"""simple docstring"""




import math as m





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=0.001 ):






  """simple docstring"""





  return (f(a + h ) - f(a - h )) / (2 * h)





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=0			, _UpperCAmelCase=100			, _UpperCAmelCase=0.0_001			, _UpperCAmelCase=1E-6			, _UpperCAmelCase=False ):






  """simple docstring"""





  A_  :	Optional[int]    						=  xa  # set the initial guess
  A_  :	Any    						=  [a]
  A_  :	str    						=  abs(f(_UpperCAmelCase ) )
  A_  :	str    						=  lambda _UpperCAmelCase : calc_derivative(_UpperCAmelCase			, _UpperCAmelCase			, h=_UpperCAmelCase )  # noqa: E731  Derivative of f(x)
  for _ in range(_UpperCAmelCase ):
    if fa(_UpperCAmelCase ) == 0:
      raise ValueError('No converging solution found' )
    A_  :	Union[str, Any]    						=  a - f(_UpperCAmelCase ) / fa(_UpperCAmelCase )  # Calculate the next estimate
    if logsteps:
      steps.append(_UpperCAmelCase )
    if error < maxerror:
      break
  else:
    raise ValueError('Iteration limit reached, no converging solution found' )
  if logsteps:
    # If logstep is true, then log intermediate steps
    return a, error, steps
  return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   lowerCamelCase_					:						str      = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_					:						int      = newton_raphson(
       f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"solution = {{{solution:f}}}, error = {{{error:f}}}")