



'''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       lowerCamelCase_					:
  """simple docstring"""




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




  def 		_lowercase  (				self   :			str     )      ->      Dict:
    __lowerCamelCase : str					 =				ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size     )

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

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

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

    __lowerCamelCase : Optional[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=_a    ,			)

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




  def 		_lowercase  (				self   :			Optional[Any]    ,			_a   :			List[str]    ,			_a   :			List[Any]    ,			_a   :			List[Any]    ,			_a   :			List[str]    ,			_a   :			str    ,			_a   :			Union[str, Any]    ,			_a   :			Optional[int]     )      ->      Union[str, Any]:
    __lowerCamelCase : Union[str, Any]					 =				TFDebertaModel(config=_a     )
    __lowerCamelCase : Any					 =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

    __lowerCamelCase : Tuple					 =				[input_ids, input_mask]
    __lowerCamelCase : Tuple					 =				model(_a     )

    __lowerCamelCase : List[str]					 =				model(_a     )

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




  def 		_lowercase  (				self   :			str    ,			_a   :			List[str]    ,			_a   :			Optional[int]    ,			_a   :			Optional[Any]    ,			_a   :			Any    ,			_a   :			Optional[int]    ,			_a   :			List[str]    ,			_a   :			Tuple     )      ->      Optional[Any]:
    __lowerCamelCase : Dict					 =				TFDebertaForMaskedLM(config=_a     )
    __lowerCamelCase : Union[str, Any]					 =				{
        'input_ids': input_ids,
        'attention_mask': input_mask,
        'token_type_ids': token_type_ids,
    }
    __lowerCamelCase : Dict					 =				model(_a     )
    self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size)     )




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int    ,			_a   :			str    ,			_a   :			Union[str, Any]    ,			_a   :			Optional[int]    ,			_a   :			Any    ,			_a   :			Tuple    ,			_a   :			Tuple     )      ->      int:
    __lowerCamelCase : List[Any]					 =				self.num_labels
    __lowerCamelCase : int					 =				TFDebertaForSequenceClassification(config=_a     )
    __lowerCamelCase : Optional[Any]					 =				{
        'input_ids': input_ids,
        'attention_mask': input_mask,
        'token_type_ids': token_type_ids,
    }

    __lowerCamelCase : Any					 =				model(_a     )
    self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)     )




  def 		_lowercase  (				self   :			Any    ,			_a   :			List[str]    ,			_a   :			Tuple    ,			_a   :			Optional[int]    ,			_a   :			Tuple    ,			_a   :			List[str]    ,			_a   :			Union[str, Any]    ,			_a   :			Optional[Any]     )      ->      Tuple:
    __lowerCamelCase : str					 =				self.num_labels
    __lowerCamelCase : Optional[int]					 =				TFDebertaForTokenClassification(config=_a     )
    __lowerCamelCase : Optional[Any]					 =				{
        'input_ids': input_ids,
        'attention_mask': input_mask,
        'token_type_ids': token_type_ids,
    }
    __lowerCamelCase : Union[str, Any]					 =				model(_a     )
    self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.num_labels)     )




  def 		_lowercase  (				self   :			List[str]    ,			_a   :			Optional[Any]    ,			_a   :			Union[str, Any]    ,			_a   :			Optional[Any]    ,			_a   :			Optional[int]    ,			_a   :			Tuple    ,			_a   :			Any    ,			_a   :			Dict     )      ->      Optional[int]:
    __lowerCamelCase : Optional[int]					 =				TFDebertaForQuestionAnswering(config=_a     )
    __lowerCamelCase : Tuple					 =				{
        'input_ids': input_ids,
        'attention_mask': input_mask,
        'token_type_ids': token_type_ids,
    }

    __lowerCamelCase : str					 =				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 		_lowercase  (				self   :			List[str]     )      ->      str:
    __lowerCamelCase : List[str]					 =				self.prepare_config_and_inputs()
    (
        (
        __lowerCamelCase
    ) ,(
        __lowerCamelCase
    ) ,(
        __lowerCamelCase
    ) ,(
        __lowerCamelCase
    ) ,(
        __lowerCamelCase
    ) ,(
        __lowerCamelCase
    ) ,(
        __lowerCamelCase
    ) ,
    ) : Any					 =				config_and_inputs
    __lowerCamelCase : List[Any]					 =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
    return config, inputs_dict






@require_tf
class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_     ,     SCREAMING_SNAKE_CASE_     ,     unittest.TestCase  ):
  """simple docstring"""




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

  a_							      =False
  a_							      =False




  def 		_lowercase  (				self   :			str     )      ->      Optional[int]:
    __lowerCamelCase : int					 =				TFDebertaModelTester(self     )
    __lowerCamelCase : List[Any]					 =				ConfigTester(self    ,			config_class=_a    ,			hidden_size=37     )




  def 		_lowercase  (				self   :			Any     )      ->      Optional[int]:
    self.config_tester.run_common_tests()




  def 		_lowercase  (				self   :			str     )      ->      str:
    __lowerCamelCase : List[str]					 =				self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a     )




  def 		_lowercase  (				self   :			str     )      ->      Dict:
    __lowerCamelCase : List[str]					 =				self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*_a     )




  def 		_lowercase  (				self   :			Tuple     )      ->      Optional[int]:
    __lowerCamelCase : Optional[Any]					 =				self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*_a     )




  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      Any:
    __lowerCamelCase : Optional[Any]					 =				self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*_a     )




  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      Union[str, Any]:
    __lowerCamelCase : Optional[int]					 =				self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*_a     )




  @slow
  def 		_lowercase  (				self   :			Tuple     )      ->      List[Any]:
    __lowerCamelCase : Union[str, Any]					 =				TFDebertaModel.from_pretrained('kamalkraj/deberta-base'     )
    self.assertIsNotNone(_a     )






@require_tf
class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  @unittest.skip(reason='Model not available yet'     )
  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      List[str]:
    pass




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

    __lowerCamelCase : List[str]					 =				tf.constant(
        [
            [
                [-0.59855896, -0.80552566, -0.8462135],
                [1.4484025, -0.93483794, -0.80593085],
                [0.3122741, 0.00316059, -1.4131377],
            ]
        ]     )
    tf.debugging.assert_near(output[:, 1:4, 1:4]    ,			_a    ,			atol=1e-4     )











'''simple docstring'''





from __future__ import annotations

# Define the Gravitational Constant G and the function
_UpperCamelCase							       =		6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   dict[str, float]:
  __lowerCamelCase : Dict					 =				mass_a * mass_a

  if (force, mass_a, mass_a, distance).count(0 ) != 1:
    raise ValueError('One and only one argument must be 0' )
  if force < 0:
    raise ValueError('Gravitational force can not be negative' )
  if distance < 0:
    raise ValueError('Distance can not be negative' )
  if mass_a < 0 or mass_a < 0:
    raise ValueError('Mass can not be negative' )
  if force == 0:
    __lowerCamelCase : Optional[Any]					 =				GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
    return {"force": force}
  elif mass_a == 0:
    __lowerCamelCase : Tuple					 =				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_1": mass_a}
  elif mass_a == 0:
    __lowerCamelCase : str					 =				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_2": mass_a}
  elif distance == 0:
    __lowerCamelCase : int					 =				(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
    return {"distance": distance}
  raise ValueError('One and only one argument must be 0' )


# Run doctest
if __name__ == "__main__":
   import doctest

   doctest.testmod()






