





"""simple docstring"""


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
   from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from transformers import VivitImageProcessor



class        lowercase_			:

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=10				,   __SCREAMING_SNAKE_CASE=10				,   __SCREAMING_SNAKE_CASE=8				,   __SCREAMING_SNAKE_CASE=[2, 4, 4]				,   __SCREAMING_SNAKE_CASE=3				,   __SCREAMING_SNAKE_CASE=768				,   __SCREAMING_SNAKE_CASE=5				,   __SCREAMING_SNAKE_CASE=4				,   __SCREAMING_SNAKE_CASE=37				,   __SCREAMING_SNAKE_CASE="gelu_fast"				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.02				,   __SCREAMING_SNAKE_CASE=1e-06				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=None				,   ):






        '''simple docstring'''

        snake_case__      							=			parent
        snake_case__      							=			batch_size
        snake_case__      							=			is_training
        snake_case__      							=			use_labels
        snake_case__      							=			num_labels
        snake_case__      							=			image_size
        snake_case__      							=			num_frames
        snake_case__      							=			tubelet_size
        snake_case__      							=			num_channels
        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__      							=			initializer_range
        snake_case__      							=			layer_norm_eps
        snake_case__      							=			qkv_bias
        snake_case__      							=			scope

        snake_case__      							=			(
            (self.image_size // self.tubelet_size[2])
            * (self.image_size // self.tubelet_size[1])
            * (self.num_frames // self.tubelet_size[0])
        ) + 1  # CLS token

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]       )

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

        snake_case__      							=			self.get_config()

        return config, pixel_values, labels

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			VivitConfig(
            num_frames=self.num_frames				,   image_size=self.image_size				,   tubelet_size=self.tubelet_size				,   num_channels=self.num_channels				,   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				,   initializer_range=self.initializer_range				,   layer_norm_eps=self.layer_norm_eps				,   qkv_bias=self.qkv_bias				,   )
        snake_case__      							=			self.num_labels
        return config

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






        '''simple docstring'''

        snake_case__      							=			VivitModel(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			model(__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       ):






        '''simple docstring'''

        snake_case__      							=			VivitForVideoClassification(__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()

        snake_case__      							=			model(__SCREAMING_SNAKE_CASE       )

        # verify the logits shape
        snake_case__      							=			torch.Size((self.batch_size, self.num_labels)       )
        self.parent.assertEqual(result.logits.shape				,   __SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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



@require_torch
class        lowercase_			(       __lowerCAmelCase			,unittest.TestCase							):
       snake_case__			:							Tuple										=       (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

       snake_case__			:							Dict										=       False
       snake_case__			:							Optional[int]										=       False
       snake_case__			:							str										=       False
       snake_case__			:							str										=       False

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			VivitModelTester(self       )
        snake_case__      							=			ConfigTester(self				,   config_class=__SCREAMING_SNAKE_CASE				,   has_text_modality=__SCREAMING_SNAKE_CASE				,   hidden_size=37       )

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






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(__SCREAMING_SNAKE_CASE       )

        if return_labels:
         if model_class in get_values(__SCREAMING_SNAKE_CASE       ):
          snake_case__      							=			torch.zeros(
              self.model_tester.batch_size				,   dtype=torch.long				,   device=__SCREAMING_SNAKE_CASE       )

        return inputs_dict

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

       @unittest.skip(reason="Vivit does not use inputs_embeds"       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        pass

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         self.assertIsInstance(model.get_input_embeddings()				,   (nn.Module)       )
         snake_case__      							=			model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE				,   nn.Linear       )       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         snake_case__      							=			inspect.signature(model.forward       )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case__      							=			[*signature.parameters.keys()]

         snake_case__      							=			["pixel_values", "head_mask"]
         self.assertListEqual(arg_names[:2]				,   __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_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_video_classification(*__SCREAMING_SNAKE_CASE       )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()
        snake_case__      							=			True

        for model_class in self.all_model_classes:
         snake_case__      							=			self.model_tester.seq_length

         snake_case__      							=			True
         snake_case__      							=			False
         snake_case__      							=			True
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()
         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )
         snake_case__      							=			outputs.attentions
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   self.model_tester.num_hidden_layers       )

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         snake_case__      							=			True
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()
         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )
         snake_case__      							=			outputs.attentions
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   self.model_tester.num_hidden_layers       )

         self.assertListEqual(
             list(attentions[0].shape[-3:]       )				,   [self.model_tester.num_attention_heads, seq_len, seq_len]				,   )
         snake_case__      							=			len(__SCREAMING_SNAKE_CASE       )

         # Check attention is always last and order is fine
         snake_case__      							=			True
         snake_case__      							=			True
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()
         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )

         self.assertEqual(out_len + 1				,   len(__SCREAMING_SNAKE_CASE       )       )

         snake_case__      							=			outputs.attentions

         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   self.model_tester.num_hidden_layers       )

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]       )				,   [self.model_tester.num_attention_heads, seq_len, seq_len]				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        def check_hidden_states_output(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()

         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )

         snake_case__      							=			outputs.hidden_states
         snake_case__      							=			self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]       )				,   [seq_length, self.model_tester.hidden_size]				,   )

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         snake_case__      							=			True
         check_hidden_states_output(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case__      							=			True

         check_hidden_states_output(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        pass





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

 '''simple docstring'''






 snake_case__      							=			hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"		,       filename="eating_spaghetti_32_frames.npy"		,       repo_type="dataset"		)
 snake_case__      							=			np.load(_UpperCAmelCase		)
 return list(_UpperCAmelCase		)



@require_torch
@require_vision
class        lowercase_			(       unittest.TestCase							):

       @cached_property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return VivitImageProcessor() if is_vision_available() else None

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400"       ).to(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.default_image_processor
        snake_case__      							=			prepare_video()
        snake_case__      							=			image_processor(__SCREAMING_SNAKE_CASE				,   return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

        # forward pass
        with torch.no_grad():
         snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       )

        # verify the logits
        snake_case__      							=			torch.Size((1, 400)       )
        self.assertEqual(outputs.logits.shape				,   __SCREAMING_SNAKE_CASE       )

        # taken from original model
        snake_case__      							=			torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]       ).to(__SCREAMING_SNAKE_CASE       )

        self.assertTrue(torch.allclose(outputs.logits[0, :5]				,   __SCREAMING_SNAKE_CASE				,   atol=1e-4       )       )








"""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()

