




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class       _UpperCamelCase   (					lowerCAmelCase						,							lowerCAmelCase						,							unittest.TestCase		):
      UpperCAmelCase_          =		StableDiffusionXLPipeline
      UpperCAmelCase_          =		TEXT_TO_IMAGE_PARAMS
      UpperCAmelCase_          =		TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCAmelCase_          =		TEXT_TO_IMAGE_IMAGE_PARAMS
      UpperCAmelCase_          =		TEXT_TO_IMAGE_IMAGE_PARAMS
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      int:
       torch.manual_seed(0   )
       UpperCAmelCase__							=    UNetaDConditionModel(
           block_out_channels=(32, 64)			,   layers_per_block=2			,   sample_size=32			,   in_channels=4			,   out_channels=4			,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,   attention_head_dim=(2, 4)			,   use_linear_projection=lowerCamelCase			,   addition_embed_type="text_time"			,   addition_time_embed_dim=8			,   transformer_layers_per_block=(1, 2)			,   projection_class_embeddings_input_dim=80			,   cross_attention_dim=64			,   )
       UpperCAmelCase__							=    EulerDiscreteScheduler(
           beta_start=0.0_00_85			,   beta_end=0.0_12			,   steps_offset=1			,   beta_schedule="scaled_linear"			,   timestep_spacing="leading"			,   )
       torch.manual_seed(0   )
       UpperCAmelCase__							=    AutoencoderKL(
           block_out_channels=[32, 64]			,   in_channels=3			,   out_channels=3			,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,   latent_channels=4			,   sample_size=128			,   )
       torch.manual_seed(0   )
       UpperCAmelCase__							=    CLIPTextConfig(
           bos_token_id=0			,   eos_token_id=2			,   hidden_size=32			,   intermediate_size=37			,   layer_norm_eps=1e-05			,   num_attention_heads=4			,   num_hidden_layers=5			,   pad_token_id=1			,   vocab_size=1000			,   hidden_act="gelu"			,   projection_dim=32			,   )
       UpperCAmelCase__							=    CLIPTextModel(lowerCamelCase   )
       UpperCAmelCase__							=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,   local_files_only=lowerCamelCase   )

       UpperCAmelCase__							=    CLIPTextModelWithProjection(lowerCamelCase   )
       UpperCAmelCase__							=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,   local_files_only=lowerCamelCase   )

       UpperCAmelCase__							=    {
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "text_encoder_2": text_encoder_a,
           "tokenizer_2": tokenizer_a,
           # "safety_checker": None,
           # "feature_extractor": None,
       }
       return components
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Any			,   lowerCamelCase :Any=0   )  ->      Dict:
       if str(lowerCamelCase   ).startswith("mps"   ):
        UpperCAmelCase__							=    torch.manual_seed(lowerCamelCase   )
       else:
        UpperCAmelCase__							=    torch.Generator(device=lowerCamelCase   ).manual_seed(lowerCamelCase   )
       UpperCAmelCase__							=    {
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 5.0,
           "output_type": "numpy",
       }
       return inputs
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      Optional[int]:
       UpperCAmelCase__							=    "cpu"  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase__							=    self.get_dummy_components()
       UpperCAmelCase__							=    StableDiffusionXLPipeline(**lowerCamelCase   )
       UpperCAmelCase__							=    sd_pipe.to(lowerCamelCase   )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase   )

       UpperCAmelCase__							=    self.get_dummy_inputs(lowerCamelCase   )
       UpperCAmelCase__							=    sd_pipe(**lowerCamelCase   ).images
       UpperCAmelCase__							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase__							=    np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      Optional[int]:
       UpperCAmelCase__							=    self.get_dummy_components()
       UpperCAmelCase__							=    StableDiffusionXLPipeline(**lowerCamelCase   )
       UpperCAmelCase__							=    sd_pipe.to(lowerCamelCase   )
       UpperCAmelCase__							=    sd_pipe.to(lowerCamelCase   )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase   )

       # forward without prompt embeds
       UpperCAmelCase__							=    self.get_dummy_inputs(lowerCamelCase   )
       UpperCAmelCase__							=    3 * ["this is a negative prompt"]
       UpperCAmelCase__							=    negative_prompt
       UpperCAmelCase__							=    3 * [inputs["prompt"]]

       UpperCAmelCase__							=    sd_pipe(**lowerCamelCase   )
       UpperCAmelCase__							=    output.images[0, -3:, -3:, -1]

       # forward with prompt embeds
       UpperCAmelCase__							=    self.get_dummy_inputs(lowerCamelCase   )
       UpperCAmelCase__							=    3 * ["this is a negative prompt"]
       UpperCAmelCase__							=    3 * [inputs.pop("prompt"   )]

       (
           (
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			
       )							=    sd_pipe.encode_prompt(lowerCamelCase			,   negative_prompt=lowerCamelCase   )

       UpperCAmelCase__							=    sd_pipe(
           **lowerCamelCase			,   prompt_embeds=lowerCamelCase			,   negative_prompt_embeds=lowerCamelCase			,   pooled_prompt_embeds=lowerCamelCase			,   negative_pooled_prompt_embeds=lowerCamelCase			,   )
       UpperCAmelCase__							=    output.images[0, -3:, -3:, -1]

       # make sure that it's equal
       assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4
      def 						UpperCAmelCase_    (      self :List[str]   )  ->      Optional[Any]:
       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )







      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      int:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



@slow
@require_torch_gpu
class       _UpperCamelCase   (					unittest.TestCase		):
      def 						UpperCAmelCase_    (      self :str   )  ->      List[str]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :int			,   lowerCamelCase :Optional[Any]="cpu"			,   lowerCamelCase :Any=torch.floataa			,   lowerCamelCase :Tuple=0   )  ->      List[Any]:
       UpperCAmelCase__							=    torch.Generator(device=lowerCamelCase   ).manual_seed(lowerCamelCase   )
       UpperCAmelCase__							=    np.random.RandomState(lowerCamelCase   ).standard_normal((1, 4, 64, 64)   )
       UpperCAmelCase__							=    torch.from_numpy(lowerCamelCase   ).to(device=lowerCamelCase			,   dtype=lowerCamelCase   )
       UpperCAmelCase__							=    {
           "prompt": "a photograph of an astronaut riding a horse",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs







      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"   )
       pipe.to(lowerCamelCase   )
       pipe.set_progress_bar_config(disable=lowerCamelCase   )

       UpperCAmelCase__							=    self.get_inputs(lowerCamelCase   )
       UpperCAmelCase__							=    pipe(**lowerCamelCase   ).images
       UpperCAmelCase__							=    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase__							=    np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]   )
       assert np.abs(image_slice - expected_slice   ).max() < 7e-3











from __future__ import annotations

from math import pi, pow



def     lowerCAmelCase		(		_lowerCAmelCase				: int | float						):




 """simple docstring"""







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



def     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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__							=    (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 UpperCAmelCase__							=    (
     (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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		_lowerCAmelCase				: float    ,				_lowerCAmelCase				: 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     lowerCAmelCase		(		):




 """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()





