






"""simple docstring"""


import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import 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 (
       ErnieMForInformationExtraction,
       ErnieMForMultipleChoice,
       ErnieMForQuestionAnswering,
       ErnieMForSequenceClassification,
       ErnieMForTokenClassification,
       ErnieMModel,
   )
   from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST



class        lowercase_			:

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=13				,   __SCREAMING_SNAKE_CASE=7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=99				,   __SCREAMING_SNAKE_CASE=32				,   __SCREAMING_SNAKE_CASE=5				,   __SCREAMING_SNAKE_CASE=4				,   __SCREAMING_SNAKE_CASE=37				,   __SCREAMING_SNAKE_CASE="gelu"				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=512				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=0.02				,   __SCREAMING_SNAKE_CASE=3				,   __SCREAMING_SNAKE_CASE=4				,   __SCREAMING_SNAKE_CASE=None				,   ):






        '''simple docstring'''

        snake_case__      							=			parent
        snake_case__      							=			batch_size
        snake_case__      							=			seq_length
        snake_case__      							=			is_training
        snake_case__      							=			use_input_mask
        snake_case__      							=			use_labels
        snake_case__      							=			vocab_size
        snake_case__      							=			hidden_size
        snake_case__      							=			num_hidden_layers
        snake_case__      							=			num_attention_heads
        snake_case__      							=			intermediate_size
        snake_case__      							=			hidden_act
        snake_case__      							=			hidden_dropout_prob
        snake_case__      							=			attention_probs_dropout_prob
        snake_case__      							=			max_position_embeddings
        snake_case__      							=			type_vocab_size
        snake_case__      							=			type_sequence_label_size
        snake_case__      							=			initializer_range
        snake_case__      							=			num_labels
        snake_case__      							=			num_choices
        snake_case__      							=			scope

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size       )

        snake_case__      							=			None
        if self.use_input_mask:
         snake_case__      							=			random_attention_mask([self.batch_size, self.seq_length]       )

        snake_case__      							=			None
        snake_case__      							=			None
        snake_case__      							=			None
        if self.use_labels:
         snake_case__      							=			ids_tensor([self.batch_size]				,   self.type_sequence_label_size       )
         snake_case__      							=			ids_tensor([self.batch_size, self.seq_length]				,   self.num_labels       )
         snake_case__      							=			ids_tensor([self.batch_size]				,   self.num_choices       )

        snake_case__      							=			self.get_config()

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size       )

        snake_case__      							=			None
        if self.use_input_mask:
         snake_case__      							=			random_attention_mask([self.batch_size, self.seq_length]       )
        snake_case__      							=			self.get_config()

        return config, input_ids, input_mask

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return ErnieMConfig(
            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				,   initializer_range=self.initializer_range				,   )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			ErnieMModel(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   return_dict=__SCREAMING_SNAKE_CASE       )
        self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			model(
            __SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE				,   start_positions=__SCREAMING_SNAKE_CASE				,   end_positions=__SCREAMING_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 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			torch.ones_like(__SCREAMING_SNAKE_CASE				,   dtype=torch.floataa       )
        snake_case__      							=			model(
            __SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE				,   start_positions=__SCREAMING_SNAKE_CASE				,   end_positions=__SCREAMING_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 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.num_labels
        snake_case__      							=			ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE				,   labels=__SCREAMING_SNAKE_CASE       )
        self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.num_labels
        snake_case__      							=			ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        input_ids.to(__SCREAMING_SNAKE_CASE       )
        input_mask.to(__SCREAMING_SNAKE_CASE       )
        token_labels.to(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE				,   labels=__SCREAMING_SNAKE_CASE       )

        self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.num_labels)       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.num_choices
        snake_case__      							=			ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			input_ids.unsqueeze(1       ).expand(-1				,   self.num_choices				,   -1       ).contiguous()
        snake_case__      							=			input_mask.unsqueeze(1       ).expand(-1				,   self.num_choices				,   -1       ).contiguous()
        snake_case__      							=			model(
            __SCREAMING_SNAKE_CASE				,   attention_mask=__SCREAMING_SNAKE_CASE				,   labels=__SCREAMING_SNAKE_CASE				,   )
        self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_choices)       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.prepare_config_and_inputs()
        (
            (
            snake_case__						
        )							,				(
            snake_case__						
        )							,				(
            snake_case__						
        )							,				(
            snake_case__						
        )							,				(
            snake_case__						
        )							,				(
            snake_case__						
        )							,										
        )      							=			config_and_inputs
        snake_case__      							=			{"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict



@require_torch
class        lowercase_			(       A__			,A__			,unittest.TestCase							):
       snake_case__			:							List[Any]										=       (
           (
               ErnieMModel,
               ErnieMForMultipleChoice,
               ErnieMForQuestionAnswering,
               ErnieMForSequenceClassification,
               ErnieMForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       snake_case__			:							Tuple										=       ()
       snake_case__			:							str										=       (
           {
               """feature-extraction""": ErnieMModel,
               """question-answering""": ErnieMForQuestionAnswering,
               """text-classification""": ErnieMForSequenceClassification,
               """token-classification""": ErnieMForTokenClassification,
               """zero-shot""": ErnieMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       snake_case__			:							int										=       False

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        if pipeline_test_casse_name == "QAPipelineTests":
         return True

        return False

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			ErnieMModelTester(self       )
        snake_case__      							=			ConfigTester(self				,   config_class=__SCREAMING_SNAKE_CASE				,   hidden_size=37       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
         snake_case__      							=			type
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE       )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case__      							=			ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
         self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



@require_torch
class        lowercase_			(       unittest.TestCase							):

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			ErnieMModel.from_pretrained("susnato/ernie-m-base_pytorch"       )
        model.eval()
        snake_case__      							=			torch.tensor([[0, 1, 2, 3, 4, 5]]       )
        snake_case__      							=			model(__SCREAMING_SNAKE_CASE       )[0]

        # TODO Replace vocab size
        snake_case__      							=			768

        snake_case__      							=			torch.Size((1, 6, hidden_size)       )
        self.assertEqual(output.shape				,   __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			torch.tensor(
            [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]       )

        self.assertTrue(torch.allclose(output[:, :3, :3]				,   __SCREAMING_SNAKE_CASE				,   atol=1e-3       )       )








"""simple docstring"""


from __future__ import annotations

from math import pi, pow





def 							a__     (					_UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if side_length < 0:
  raise ValueError("vol_cube() only accepts non-negative values"		)
 return pow(_UpperCAmelCase		,       3		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 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(_UpperCAmelCase		,       2		) * (3 * radius - height)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 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(_UpperCAmelCase		,       _UpperCAmelCase		)		)

 snake_case__      							=			(
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 snake_case__      							=			(
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )

 return vol_spherical_cap(_UpperCAmelCase		,       _UpperCAmelCase		) + vol_spherical_cap(_UpperCAmelCase		,       _UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 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(_UpperCAmelCase		,       _UpperCAmelCase		)		)

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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if width < 0 or height < 0 or length < 0:
  raise ValueError("vol_cuboid() only accepts non-negative values"		)
 return float(width * height * length		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 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 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if height < 0 or radius < 0:
  raise ValueError("vol_right_circ_cone() only accepts non-negative values"		)
 return pi * pow(_UpperCAmelCase		,       2		) * height / 3.0





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if height < 0 or area_of_base < 0:
  raise ValueError("vol_prism() only accepts non-negative values"		)
 return float(area_of_base * height		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 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 							a__     (					_UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if radius < 0:
  raise ValueError("vol_sphere() only accepts non-negative values"		)
 # Volume is 4/3 * pi * radius cubed
 return 4 / 3 * pi * pow(_UpperCAmelCase		,       3		)





def 							a__     (					_UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if radius < 0:
  raise ValueError("vol_hemisphere() only accepts non-negative values"		)
 # Volume is radius cubed * pi * 2/3
 return pow(_UpperCAmelCase		,       3		) * pi * 2 / 3





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if height < 0 or radius < 0:
  raise ValueError("vol_circular_cylinder() only accepts non-negative values"		)
 # Volume is radius squared * height * pi
 return pow(_UpperCAmelCase		,       2		) * height * pi





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 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(_UpperCAmelCase		,       2		) - pow(_UpperCAmelCase		,       2		)) * height





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 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(_UpperCAmelCase		,       2		) + pow(_UpperCAmelCase		,       2		) + radius_a * radius_a)
 )





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					float:

 '''simple docstring'''






 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("vol_torus() only accepts non-negative values"		)
 return 2 * pow(_UpperCAmelCase		,       2		) * torus_radius * pow(_UpperCAmelCase		,       2		)





def 							a__     (					)      ->					Union[str, Any]:

 '''simple docstring'''






 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()

