


import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class  UpperCamelCase__ ( lowerCAmelCase_							,						unittest.TestCase			):
						a__   :     List[Any]								=						KandinskyVaaPriorEmbaEmbPipeline
						a__   :     str								=						["""prompt""", """image"""]
						a__   :     List[str]								=						["""prompt""", """image"""]
						a__   :     Any								=						[
						    """num_images_per_prompt""",
						    """strength""",
						    """generator""",
						    """num_inference_steps""",
						    """latents""",
						    """negative_prompt""",
						    """guidance_scale""",
						    """output_type""",
						    """return_dict""",
						]
						a__   :     Dict								=						False






						@property
						def 	__lowercase(     self					:   Optional[int]   )  -> List[str]:
											return 32






						@property
						def 	__lowercase(     self					:   Dict   )  -> Dict:
											return 32






						@property
						def 	__lowercase(     self					:   Tuple   )  -> Any:
											return self.time_input_dim






						@property
						def 	__lowercase(     self					:   str   )  -> Optional[Any]:
											return self.time_input_dim * 4






						@property
						def 	__lowercase(     self					:   Optional[int]   )  -> Dict:
											return 1_00






						@property
						def 	__lowercase(     self					:   str   )  -> List[Any]:
											UpperCamelCase__							:  Tuple								=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
											return tokenizer






						@property
						def 	__lowercase(     self					:   Tuple   )  -> str:
											torch.manual_seed(0   )
											UpperCamelCase__							:  List[str]								=     CLIPTextConfig(
											    bos_token_id=0,		eos_token_id=2,		hidden_size=self.text_embedder_hidden_size,		projection_dim=self.text_embedder_hidden_size,		intermediate_size=37,		layer_norm_eps=1e-05,		num_attention_heads=4,		num_hidden_layers=5,		pad_token_id=1,		vocab_size=10_00,		)
											return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE   )






						@property
						def 	__lowercase(     self					:   Any   )  -> Dict:
											torch.manual_seed(0   )

											UpperCamelCase__							:  Tuple								=     {
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 12,
											    '''embedding_dim''': self.text_embedder_hidden_size,
											    '''num_layers''': 1,
											}

											UpperCamelCase__							:  Tuple								=     PriorTransformer(**__SCREAMING_SNAKE_CASE   )
											# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
											UpperCamelCase__							:  List[Any]								=     nn.Parameter(torch.ones(model.clip_std.shape   )   )
											return model






						@property
						def 	__lowercase(     self					:   List[Any]   )  -> Tuple:
											torch.manual_seed(0   )
											UpperCamelCase__							:  Dict								=     CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size,		image_size=2_24,		projection_dim=self.text_embedder_hidden_size,		intermediate_size=37,		num_attention_heads=4,		num_channels=3,		num_hidden_layers=5,		patch_size=14,		)

											UpperCamelCase__							:  str								=     CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE   )
											return model






						@property
						def 	__lowercase(     self					:   int   )  -> Dict:
											UpperCamelCase__							:  Any								=     CLIPImageProcessor(
											    crop_size=2_24,		do_center_crop=__SCREAMING_SNAKE_CASE,		do_normalize=__SCREAMING_SNAKE_CASE,		do_resize=__SCREAMING_SNAKE_CASE,		image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073],		image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711],		resample=3,		size=2_24,		)

											return image_processor






						def 	__lowercase(     self					:   Optional[int]   )  -> Optional[Any]:
											UpperCamelCase__							:  List[str]								=     self.dummy_prior
											UpperCamelCase__							:  Tuple								=     self.dummy_image_encoder
											UpperCamelCase__							:  Dict								=     self.dummy_text_encoder
											UpperCamelCase__							:  Any								=     self.dummy_tokenizer
											UpperCamelCase__							:  Dict								=     self.dummy_image_processor

											UpperCamelCase__							:  List[str]								=     UnCLIPScheduler(
											    variance_type='''fixed_small_log''',		prediction_type='''sample''',		num_train_timesteps=10_00,		clip_sample=__SCREAMING_SNAKE_CASE,		clip_sample_range=10.0,		)

											UpperCamelCase__							:  int								=     {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''scheduler''': scheduler,
											    '''image_processor''': image_processor,
											}

											return components






						def 	__lowercase(     self					:   Optional[int],		__lowerCamelCase					:   int,		__lowerCamelCase					:   int=0   )  -> Union[str, Any]:

											if str(__SCREAMING_SNAKE_CASE   ).startswith('''mps'''   ):
																UpperCamelCase__							:  List[str]								=     torch.manual_seed(__SCREAMING_SNAKE_CASE   )
											else:
																UpperCamelCase__							:  str								=     torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )

											UpperCamelCase__							:  int								=     floats_tensor((1, 3, 64, 64),		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
											UpperCamelCase__							:  Dict								=     image.cpu().permute(0,		2,		3,		1   )[0]
											UpperCamelCase__							:  Tuple								=     Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

											UpperCamelCase__							:  Tuple								=     {
											    '''prompt''': '''horse''',
											    '''image''': init_image,
											    '''strength''': 0.5,
											    '''generator''': generator,
											    '''guidance_scale''': 4.0,
											    '''num_inference_steps''': 2,
											    '''output_type''': '''np''',
											}
											return inputs






						def 	__lowercase(     self					:   List[str]   )  -> List[Any]:
											UpperCamelCase__							:  Union[str, Any]								=     '''cpu'''

											UpperCamelCase__							:  Any								=     self.get_dummy_components()

											UpperCamelCase__							:  Optional[Any]								=     self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
											UpperCamelCase__							:  Union[str, Any]								=     pipe.to(__SCREAMING_SNAKE_CASE   )

											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

											UpperCamelCase__							:  Dict								=     pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
											UpperCamelCase__							:  Optional[int]								=     output.image_embeds

											UpperCamelCase__							:  Tuple								=     pipe(
											    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   ),		return_dict=__SCREAMING_SNAKE_CASE,		)[0]

											UpperCamelCase__							:  Union[str, Any]								=     image[0, -10:]
											UpperCamelCase__							:  List[str]								=     image_from_tuple[0, -10:]

											assert image.shape == (1, 32)

											UpperCamelCase__							:  Dict								=     np.array(
											    [
											        0.107_1284,
											        1.333_0271,
											        0.6126_0223,
											        -0.669_1065,
											        -0.384_6852,
											        -1.030_3661,
											        0.2271_6111,
											        0.0334_8901,
											        0.3004_0675,
											        -0.2480_5029,
											    ]   )

											assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2






						@skip_mps
						def 	__lowercase(     self					:   Tuple   )  -> int:
											UpperCamelCase__							:  Optional[Any]								=     torch_device == '''cpu'''
											UpperCamelCase__							:  Dict								=     True
											UpperCamelCase__							:  Union[str, Any]								=     False

											self._test_inference_batch_single_identical(
											    test_max_difference=__SCREAMING_SNAKE_CASE,		relax_max_difference=__SCREAMING_SNAKE_CASE,		test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,		)






						@skip_mps
						def 	__lowercase(     self					:   int   )  -> List[Any]:
											UpperCamelCase__							:  Union[str, Any]								=     torch_device == '''cpu'''
											UpperCamelCase__							:  Union[str, Any]								=     False

											self._test_attention_slicing_forward_pass(
											    test_max_difference=__SCREAMING_SNAKE_CASE,		test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,		)








from __future__ import annotations

from math import pi, pow







def 						_lowercase       (     __lowerCamelCase			:					int | float			)  ->   float:





     '''simple docstring'''





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







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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(__lowerCamelCase		,2			) * (3 * radius - height)







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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(__lowerCamelCase		,__lowerCamelCase			)			)

     UpperCamelCase__							:  Dict								=     (
         (radius_a - radius_a + centers_distance)
         * (radius_a + radius_a - centers_distance)
         / (2 * centers_distance)
     )
     UpperCamelCase__							:  Tuple								=     (
         (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 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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(__lowerCamelCase		,__lowerCamelCase			)			)

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







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     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 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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 						_lowercase       (     __lowerCamelCase			:					float			)  ->   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(__lowerCamelCase		,3			)







def 						_lowercase       (     __lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     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 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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(__lowerCamelCase		,2			) * height * pi







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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(__lowerCamelCase		,2			) - pow(__lowerCamelCase		,2			)) * height







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   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(__lowerCamelCase		,2			) + pow(__lowerCamelCase		,2			) + radius_a * radius_a)
     )







def 						_lowercase       (     __lowerCamelCase			:					float		,__lowerCamelCase			:					float			)  ->   float:





     '''simple docstring'''





     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 						_lowercase       (     )  ->   Tuple:





     '''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()




