

import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
    )


class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase=13      ,    _UpperCamelCase=7      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=False      ,    _UpperCamelCase=True      ,    _UpperCamelCase=99      ,    _UpperCamelCase=32      ,    _UpperCamelCase=5      ,    _UpperCamelCase=4      ,    _UpperCamelCase=64      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=16      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=3      ,    _UpperCamelCase=4      ,    _UpperCamelCase=None      ,    _UpperCamelCase=2      ,    _UpperCamelCase=2      ,    _UpperCamelCase=2      ,    _UpperCamelCase=2      ,    _UpperCamelCase=4      ,    _UpperCamelCase=1      ,    ):





      """simple docstring"""





      _lowercase      :				Dict      					=       parent
      _lowercase      :				List[Any]      					=       batch_size
      _lowercase      :				Union[str, Any]      					=       seq_length
      _lowercase      :				Dict      					=       is_training
      _lowercase      :				Union[str, Any]      					=       use_input_mask
      _lowercase      :				Optional[int]      					=       use_token_type_ids
      _lowercase      :				Optional[Any]      					=       use_labels
      _lowercase      :				Union[str, Any]      					=       vocab_size
      _lowercase      :				Optional[Any]      					=       hidden_size
      _lowercase      :				Optional[int]      					=       num_hidden_layers
      _lowercase      :				Any      					=       num_attention_heads
      _lowercase      :				List[Any]      					=       intermediate_size
      _lowercase      :				Optional[int]      					=       hidden_act
      _lowercase      :				Any      					=       hidden_dropout_prob
      _lowercase      :				Optional[int]      					=       attention_probs_dropout_prob
      _lowercase      :				int      					=       max_position_embeddings
      _lowercase      :				Union[str, Any]      					=       type_vocab_size
      _lowercase      :				Any      					=       type_sequence_label_size
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				Any      					=       num_labels
      _lowercase      :				Union[str, Any]      					=       num_choices
      _lowercase      :				Tuple      					=       scope
      _lowercase      :				int      					=       q_groups
      _lowercase      :				List[str]      					=       k_groups
      _lowercase      :				List[Any]      					=       v_groups
      _lowercase      :				List[Any]      					=       post_attention_groups
      _lowercase      :				Tuple      					=       intermediate_groups
      _lowercase      :				str      					=       output_groups


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size  )

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

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

      _lowercase      :				Dict      					=       self.get_config()

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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return SqueezeBertConfig(
          embedding_size=self.hidden_size      ,    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      ,    attention_probs_dropout_prob=self.hidden_dropout_prob      ,    attention_dropout=self.attention_probs_dropout_prob      ,    max_position_embeddings=self.max_position_embeddings      ,    initializer_range=self.initializer_range      ,    q_groups=self.q_groups      ,    k_groups=self.k_groups      ,    v_groups=self.v_groups      ,    post_attention_groups=self.post_attention_groups      ,    intermediate_groups=self.intermediate_groups      ,    output_groups=self.output_groups      ,    )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       SqueezeBertModel(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Dict      					=       model(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       model(_UpperCamelCase  )
      self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size)  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       SqueezeBertForMaskedLM(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Optional[Any]      					=       model(_UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    labels=_UpperCamelCase  )
      self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size)  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       SqueezeBertForQuestionAnswering(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Union[str, Any]      					=       model(
          _UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    start_positions=_UpperCamelCase      ,    end_positions=_UpperCamelCase  )
      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      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.num_labels
      _lowercase      :				List[Any]      					=       SqueezeBertForSequenceClassification(_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				str      					=       model(_UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    labels=_UpperCamelCase  )
      self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels)  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.num_labels
      _lowercase      :				str      					=       SqueezeBertForTokenClassification(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()

      _lowercase      :				List[Any]      					=       model(_UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    labels=_UpperCamelCase  )
      self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels)  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       self.num_choices
      _lowercase      :				Any      					=       SqueezeBertForMultipleChoice(config=_UpperCamelCase  )
      model.to(_UpperCamelCase  )
      model.eval()
      _lowercase      :				Union[str, Any]      					=       input_ids.unsqueeze(1  ).expand(-1      ,    self.num_choices      ,    -1  ).contiguous()
      _lowercase      :				Tuple      					=       input_mask.unsqueeze(1  ).expand(-1      ,    self.num_choices      ,    -1  ).contiguous()
      _lowercase      :				List[str]      					=       model(
          _UpperCamelCase      ,    attention_mask=_UpperCamelCase      ,    labels=_UpperCamelCase      ,    )
      self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_choices)  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       self.prepare_config_and_inputs()
      (_lowercase)      :				int      					=       config_and_inputs
      _lowercase      :				Tuple      					=       {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict




@require_torch
class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					Dict     	=							(
       (
           SqueezeBertModel,
           SqueezeBertForMaskedLM,
           SqueezeBertForMultipleChoice,
           SqueezeBertForQuestionAnswering,
           SqueezeBertForSequenceClassification,
           SqueezeBertForTokenClassification,
       )
       if is_torch_available()
       else None
   )
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							(
       {
           'feature-extraction': SqueezeBertModel,
           'fill-mask': SqueezeBertForMaskedLM,
           'question-answering': SqueezeBertForQuestionAnswering,
           'text-classification': SqueezeBertForSequenceClassification,
           'token-classification': SqueezeBertForTokenClassification,
           'zero-shot': SqueezeBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							False
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							True
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							False


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       SqueezeBertModelTester(self  )
      _lowercase      :				List[Any]      					=       ConfigTester(self      ,    config_class=_UpperCamelCase      ,    dim=37  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_model(*_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCamelCase  )






   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowercase      :				Optional[int]      					=       SqueezeBertModel.from_pretrained(_UpperCamelCase  )
         self.assertIsNotNone(_UpperCamelCase  )




@require_sentencepiece
@require_tokenizers
@require_torch
class        a__  (							unittest.TestCase  ):


   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"  )

      _lowercase      :				Tuple      					=       torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]  )
      _lowercase      :				int      					=       model(_UpperCamelCase  )[0]
      _lowercase      :				Optional[Any]      					=       torch.Size((1, 3)  )
      self.assertEqual(output.shape      ,    _UpperCamelCase  )
      _lowercase      :				List[str]      					=       torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]  )
      self.assertTrue(torch.allclose(_UpperCamelCase      ,    _UpperCamelCase      ,    atol=1E-4  )  )









'''simple docstring'''
from math import pi, sqrt, tan







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("surface_area_cube() only accepts non-negative values"					)
   return 6 * side_length**2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if length < 0 or breadth < 0 or height < 0:
      raise ValueError("surface_area_cuboid() only accepts non-negative values"					)
   return 2 * ((length * breadth) + (breadth * height) + (length * height))







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_sphere() only accepts non-negative values"					)
   return 4 * pi * radius**2







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_hemisphere() only accepts non-negative values"					)
   return 3 * pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius < 0 or height < 0:
      raise ValueError("surface_area_cone() only accepts non-negative values"					)
   return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          "surface_area_conical_frustum() only accepts non-negative values"					)
   _lowercase      :				Union[str, Any]      					=       (height**2 + (radius_a - radius_a) ** 2) ** 0.5
   return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius < 0 or height < 0:
      raise ValueError("surface_area_cylinder() only accepts non-negative values"					)
   return 2 * pi * radius * (height + radius)







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if torus_radius < 0 or tube_radius < 0:
      raise ValueError("surface_area_torus() only accepts non-negative values"					)
   if torus_radius < tube_radius:
      raise ValueError(
          "surface_area_torus() does not support spindle or self intersecting tori"					)
   return 4 * pow(snake_case		,							2					) * torus_radius * tube_radius







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if length < 0 or width < 0:
      raise ValueError("area_rectangle() only accepts non-negative values"					)
   return length * width







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("area_square() only accepts non-negative values"					)
   return side_length**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_triangle() only accepts non-negative values"					)
   return (base * height) / 2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if sidea < 0 or sidea < 0 or sidea < 0:
      raise ValueError("area_triangle_three_sides() only accepts non-negative values"					)
   elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
      raise ValueError("Given three sides do not form a triangle"					)
   _lowercase      :				Any      					=       (sidea + sidea + sidea) / 2
   _lowercase      :				List[str]      					=       sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)					)
   return area







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_parallelogram() only accepts non-negative values"					)
   return base * height







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if basea < 0 or basea < 0 or height < 0:
      raise ValueError("area_trapezium() only accepts non-negative values"					)
   return 1 / 2 * (basea + basea) * height







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("area_circle() only accepts non-negative values"					)
   return pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if radius_x < 0 or radius_y < 0:
      raise ValueError("area_ellipse() only accepts non-negative values"					)
   return pi * radius_x * radius_y







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if diagonal_a < 0 or diagonal_a < 0:
      raise ValueError("area_rhombus() only accepts non-negative values"					)
   return 1 / 2 * diagonal_a * diagonal_a







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if not isinstance(snake_case		,							snake_case					) or sides < 3:
      raise ValueError(
          "area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"					)
   elif length < 0:
      raise ValueError(
          "area_reg_polygon() only accepts non-negative values as \
length of a side"					)
   return (sides * length**2) / (4 * tan(pi / sides					))
   return (sides * length**2) / (4 * tan(pi / sides					))


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

    print('[DEMO] Areas of various geometric shapes: \n')
    print(F'''Rectangle: {area_rectangle(10, 20) = }''')
    print(F'''Square: {area_square(10) = }''')
    print(F'''Triangle: {area_triangle(10, 10) = }''')
    print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
    print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
    print(F'''Rhombus: {area_rhombus(10, 20) = }''')
    print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
    print(F'''Circle: {area_circle(20) = }''')
    print(F'''Ellipse: {area_ellipse(10, 20) = }''')
    print('\nSurface Areas of various geometric shapes: \n')
    print(F'''Cube: {surface_area_cube(20) = }''')
    print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
    print(F'''Sphere: {surface_area_sphere(20) = }''')
    print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
    print(F'''Cone: {surface_area_cone(10, 20) = }''')
    print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
    print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
    print(F'''Torus: {surface_area_torus(20, 10) = }''')
    print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
    print(F'''Square: {area_reg_polygon(4, 10) = }''')
    print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')






