
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   __lowerCAmelCase		:



      """simple docstring"""







      def __init__( self						:    Dict			,    _snake_case						:    Union[str, Any]			,    _snake_case						:    Tuple=13			,    _snake_case						:    Union[str, Any]=7			,    _snake_case						:    List[str]=True			,    _snake_case						:    Any=True			,    _snake_case						:    str=True			,    _snake_case						:    Any=True			,    _snake_case						:    Union[str, Any]=99			,    _snake_case						:    List[Any]=32			,    _snake_case						:    int=2			,    _snake_case						:    Optional[int]=4			,    _snake_case						:    Dict=37			,    _snake_case						:    str="gelu"			,    _snake_case						:    Tuple=0.1			,    _snake_case						:    Any=0.1			,    _snake_case						:    Any=512			,    _snake_case						:    Optional[int]=16			,    _snake_case						:    Optional[int]=2			,    _snake_case						:    int=0.02			,    _snake_case						:    Optional[int]=3			,    _snake_case						:    Tuple=4			,    _snake_case						:    str=None			,    ):
          __lowercase						:     Optional[Any]		      =    parent
          __lowercase						:     List[str]		      =    13
          __lowercase						:     str		      =    7
          __lowercase						:     List[str]		      =    True
          __lowercase						:     Tuple		      =    True
          __lowercase						:     str		      =    True
          __lowercase						:     Optional[int]		      =    True
          __lowercase						:     str		      =    99
          __lowercase						:     Dict		      =    32
          __lowercase						:     List[str]		      =    2
          __lowercase						:     Optional[Any]		      =    4
          __lowercase						:     List[str]		      =    37
          __lowercase						:     int		      =    '''gelu'''
          __lowercase						:     Optional[int]		      =    0.1
          __lowercase						:     Any		      =    0.1
          __lowercase						:     List[Any]		      =    512
          __lowercase						:     Union[str, Any]		      =    16
          __lowercase						:     Optional[int]		      =    False
          __lowercase						:     Any		      =    -1
          __lowercase						:     Any		      =    True
          __lowercase						:     Tuple		      =    2
          __lowercase						:     str		      =    0.02
          __lowercase						:     Any		      =    3
          __lowercase						:     str		      =    4
          __lowercase						:     List[Any]		      =    None




      def 						snake_case_     ( self						:    str			):
          __lowercase						:     Any		      =    ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size			)

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

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

          __lowercase						:     Tuple		      =    None
          __lowercase						:     Optional[Any]		      =    None
          __lowercase						:     Tuple		      =    None
          if self.use_labels:
              __lowercase						:     Union[str, Any]		      =    ids_tensor([self.batch_size]			,    self.type_sequence_label_size			)
              __lowercase						:     Any		      =    ids_tensor([self.batch_size, self.seq_length]			,    self.num_labels			)

          __lowercase						:     Tuple		      =    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 						snake_case_     ( self						:    Optional[int]			,    _snake_case						:    Dict			,    _snake_case						:    Optional[int]			,    _snake_case						:    str			,    _snake_case						:    Dict			,    _snake_case						:    str			,    _snake_case						:    Any			,    _snake_case						:    Optional[int]			):
          __lowercase						:     Any		      =    TFDebertaModel(config=_snake_case			)
          __lowercase						:     int		      =    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

          __lowercase						:     Tuple		      =    [input_ids, input_mask]
          __lowercase						:     str		      =    model(_snake_case			)

          __lowercase						:     Optional[int]		      =    model(_snake_case			)

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




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




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

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




      def 						snake_case_     ( self						:    Optional[Any]			,    _snake_case						:    int			,    _snake_case						:    str			,    _snake_case						:    Optional[Any]			,    _snake_case						:    Any			,    _snake_case						:    Optional[Any]			,    _snake_case						:    List[Any]			,    _snake_case						:    List[str]			):
          __lowercase						:     Dict		      =    self.num_labels
          __lowercase						:     Any		      =    TFDebertaForTokenClassification(config=_snake_case			)
          __lowercase						:     List[Any]		      =    {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }
          __lowercase						:     int		      =    model(_snake_case			)
          self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels)			)




      def 						snake_case_     ( self						:    str			,    _snake_case						:    List[Any]			,    _snake_case						:    str			,    _snake_case						:    List[str]			,    _snake_case						:    Dict			,    _snake_case						:    Any			,    _snake_case						:    Optional[Any]			,    _snake_case						:    Optional[int]			):
          __lowercase						:     Dict		      =    TFDebertaForQuestionAnswering(config=_snake_case			)
          __lowercase						:     int		      =    {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''token_type_ids''': token_type_ids,
          }

          __lowercase						:     Tuple		      =    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 						snake_case_     ( self						:    Union[str, Any]			):
          __lowercase						:     List[str]		      =    self.prepare_config_and_inputs()
          (
              __lowercase 
          )						:     Any		      =    config_and_inputs
          __lowercase						:     int		      =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict






@require_tf
class   __lowerCAmelCase		(		lowerCAmelCase_	,	lowerCAmelCase_	,	unittest.TestCase ):



      """simple docstring"""







      A__		:     str       				=      (
          (
              TFDebertaModel,
              TFDebertaForMaskedLM,
              TFDebertaForQuestionAnswering,
              TFDebertaForSequenceClassification,
              TFDebertaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      A__		:     Dict       				=      (
          {
              '''feature-extraction''': TFDebertaModel,
              '''fill-mask''': TFDebertaForMaskedLM,
              '''question-answering''': TFDebertaForQuestionAnswering,
              '''text-classification''': TFDebertaForSequenceClassification,
              '''token-classification''': TFDebertaForTokenClassification,
              '''zero-shot''': TFDebertaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      A__		:     str       				=      False
      A__		:     List[Any]       				=      False




      def 						snake_case_     ( self						:    List[str]			):
          __lowercase						:     Optional[int]		      =    TFDebertaModelTester(self			)
          __lowercase						:     Any		      =    ConfigTester(self			,    config_class=_snake_case			,    hidden_size=37			)




      def 						snake_case_     ( self						:    Tuple			):
          self.config_tester.run_common_tests()




      def 						snake_case_     ( self						:    Optional[Any]			):
          __lowercase						:     Optional[Any]		      =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_snake_case			)




      def 						snake_case_     ( self						:    Optional[int]			):
          __lowercase						:     Any		      =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_snake_case			)




      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     Any		      =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_snake_case			)




      def 						snake_case_     ( self						:    Dict			):
          __lowercase						:     Optional[int]		      =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_snake_case			)




      def 						snake_case_     ( self						:    List[str]			):
          __lowercase						:     Dict		      =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_snake_case			)




      @slow
      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     List[Any]		      =    TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''			)
          self.assertIsNotNone(_snake_case			)






@require_tf
class   __lowerCAmelCase		(		unittest.TestCase ):



      """simple docstring"""







      @unittest.skip(reason='''Model not available yet'''			)
      def 						snake_case_     ( self						:    List[str]			):
          pass




      @slow
      def 						snake_case_     ( self						:    Optional[int]			):
          __lowercase						:     Optional[int]		      =    TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''			)
          __lowercase						:     Union[str, Any]		      =    tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]			)
          __lowercase						:     Tuple		      =    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
          __lowercase						:     List[Any]		      =    model(_snake_case			,    attention_mask=_snake_case			)[0]

          __lowercase						:     List[str]		      =    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			)




from __future__ import annotations

from math import pi, pow


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  float:
    if side_length < 0:
        raise ValueError('''vol_cube() only accepts non-negative values'''		)
    return pow(__lowerCAmelCase		,   3		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or radius < 0:
        raise ValueError('''vol_spherical_cap() only accepts non-negative values'''		)
    # Volume is 1/3 pi * height squared * (3 * radius - height)
    return 1 / 3 * pi * pow(__lowerCAmelCase		,   2		) * (3 * radius - height)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if radius_a < 0 or radius_a < 0 or centers_distance < 0:
        raise ValueError('''vol_spheres_intersect() only accepts non-negative values'''		)
    if centers_distance == 0:
        return vol_sphere(min(__lowerCAmelCase		,   __lowerCAmelCase		)		)

    __lowercase						:     Dict		      =    (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )
    __lowercase						:     Optional[int]		      =    (
        (radius_a - radius_a + centers_distance)
        * (radius_a + radius_a - centers_distance)
        / (2 * centers_distance)
    )

    return vol_spherical_cap(__lowerCAmelCase		,   __lowerCAmelCase		) + vol_spherical_cap(__lowerCAmelCase		,   __lowerCAmelCase		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:

    if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
        raise ValueError(
            '''vol_spheres_union() only accepts non-negative values, non-zero radius'''		)

    if centers_distance == 0:
        return vol_sphere(max(__lowerCAmelCase		,   __lowerCAmelCase		)		)

    return (
        vol_sphere(__lowerCAmelCase		)
        + vol_sphere(__lowerCAmelCase		)
        - vol_spheres_intersect(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)
    )


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if width < 0 or height < 0 or length < 0:
        raise ValueError('''vol_cuboid() only accepts non-negative values'''		)
    return float(width * height * length		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or area_of_base < 0:
        raise ValueError('''vol_cone() only accepts non-negative values'''		)
    return area_of_base * height / 3.0


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or radius < 0:
        raise ValueError('''vol_right_circ_cone() only accepts non-negative values'''		)
    return pi * pow(__lowerCAmelCase		,   2		) * height / 3.0


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or area_of_base < 0:
        raise ValueError('''vol_prism() only accepts non-negative values'''		)
    return float(area_of_base * height		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or area_of_base < 0:
        raise ValueError('''vol_pyramid() only accepts non-negative values'''		)
    return area_of_base * height / 3.0


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  float:
    if radius < 0:
        raise ValueError('''vol_sphere() only accepts non-negative values'''		)
    # Volume is 4/3 * pi * radius cubed
    return 4 / 3 * pi * pow(__lowerCAmelCase		,   3		)


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  float:
    if radius < 0:
        raise ValueError('''vol_hemisphere() only accepts non-negative values'''		)
    # Volume is radius cubed * pi * 2/3
    return pow(__lowerCAmelCase		,   3		) * pi * 2 / 3


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if height < 0 or radius < 0:
        raise ValueError('''vol_circular_cylinder() only accepts non-negative values'''		)
    # Volume is radius squared * height * pi
    return pow(__lowerCAmelCase		,   2		) * height * pi


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    # Volume - (outer_radius squared - inner_radius squared) * pi * height
    if inner_radius < 0 or outer_radius < 0 or height < 0:
        raise ValueError(
            '''vol_hollow_circular_cylinder() only accepts non-negative values'''		)
    if outer_radius <= inner_radius:
        raise ValueError('''outer_radius must be greater than inner_radius'''		)
    return pi * (pow(__lowerCAmelCase		,   2		) - pow(__lowerCAmelCase		,   2		)) * height


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    # Volume is 1/3 * pi * height *
    #           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
    if radius_a < 0 or radius_a < 0 or height < 0:
        raise ValueError('''vol_conical_frustum() only accepts non-negative values'''		)
    return (
        1
        / 3
        * pi
        * height
        * (pow(__lowerCAmelCase		,   2		) + pow(__lowerCAmelCase		,   2		) + radius_a * radius_a)
    )


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  float:
    if torus_radius < 0 or tube_radius < 0:
        raise ValueError('''vol_torus() only accepts non-negative values'''		)
    return 2 * pow(__lowerCAmelCase		,   2		) * torus_radius * pow(__lowerCAmelCase		,   2		)


def       UpperCAmelCase_					(  )	->  Optional[Any]:
    print('''Volumes:'''		)
    print(F'Cube: {vol_cube(2		) = }'		)  # = 8
    print(F'Cuboid: {vol_cuboid(2		,   2		,   2		) = }'		)  # = 8
    print(F'Cone: {vol_cone(2		,   2		) = }'		)  # ~= 1.33
    print(F'Right Circular Cone: {vol_right_circ_cone(2		,   2		) = }'		)  # ~= 8.38
    print(F'Prism: {vol_prism(2		,   2		) = }'		)  # = 4
    print(F'Pyramid: {vol_pyramid(2		,   2		) = }'		)  # ~= 1.33
    print(F'Sphere: {vol_sphere(2		) = }'		)  # ~= 33.5
    print(F'Hemisphere: {vol_hemisphere(2		) = }'		)  # ~= 16.75
    print(F'Circular Cylinder: {vol_circular_cylinder(2		,   2		) = }'		)  # ~= 25.1
    print(F'Torus: {vol_torus(2		,   2		) = }'		)  # ~= 157.9
    print(F'Conical Frustum: {vol_conical_frustum(2		,   2		,   4		) = }'		)  # ~= 58.6
    print(F'Spherical cap: {vol_spherical_cap(1		,   2		) = }'		)  # ~= 5.24
    print(F'Spheres intersetion: {vol_spheres_intersect(2		,   2		,   1		) = }'		)  # ~= 21.21
    print(F'Spheres union: {vol_spheres_union(2		,   2		,   1		) = }'		)  # ~= 45.81
    print(
        F'Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1		,   2		,   3		) = }'		)  # ~= 28.3


if __name__ == "__main__":
 main()


