

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


SCREAMING_SNAKE_CASE_        =      False

class 				a  (	unittest.TestCase				):
						pass







@nightly
@require_torch_gpu
class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"						,					torch_dtype=torch.floataa	)
										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		List[str] 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"	)

										_UpperCAmelCase       :		Any 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe.dual_guided(
										    prompt="first prompt"						,					image=A_						,					text_to_image_strength=0.75						,					generator=A_						,					guidance_scale=7.5						,					num_inference_steps=2						,					output_type="numpy"						,					).images

										with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(A_	)
														_UpperCAmelCase       :		Tuple 		=    VersatileDiffusionPipeline.from_pretrained(A_						,					torch_dtype=torch.floataa	)
										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Optional[int] 		=    generator.manual_seed(0	)
										_UpperCAmelCase       :		str 		=    pipe.dual_guided(
										    prompt="first prompt"						,					image=A_						,					text_to_image_strength=0.75						,					generator=A_						,					guidance_scale=7.5						,					num_inference_steps=2						,					output_type="numpy"						,					).images

										assert np.abs(image - new_image	).sum() < 1e-5, "Models don't have the same forward pass"





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"						,					torch_dtype=torch.floataa	)
										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "cyberpunk 2077"
										_UpperCAmelCase       :		List[str] 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"	)
										_UpperCAmelCase       :		Optional[int] 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe.dual_guided(
										    prompt=A_						,					image=A_						,					text_to_image_strength=0.75						,					generator=A_						,					guidance_scale=7.5						,					num_inference_steps=50						,					output_type="numpy"						,					).images

										_UpperCAmelCase       :		int 		=    image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										_UpperCAmelCase       :		Union[str, Any] 		=    np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]	)

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

										_UpperCAmelCase       :		Tuple 		=    "A painting of a squirrel eating a burger "
										_UpperCAmelCase       :		Any 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[int] 		=    pipe.text_to_image(
										    prompt=A_						,					generator=A_						,					guidance_scale=7.5						,					num_inference_steps=50						,					output_type="numpy"	).images

										_UpperCAmelCase       :		List[str] 		=    image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										_UpperCAmelCase       :		Any 		=    np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]	)

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

										_UpperCAmelCase       :		Tuple 		=    pipe.image_variation(A_						,					generator=A_						,					output_type="numpy"	).images

										_UpperCAmelCase       :		Any 		=    image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										_UpperCAmelCase       :		int 		=    np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]	)

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





from __future__ import annotations

from collections.abc import Callable

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Callable[[int | float], int | float]  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int = 100  ,      )   ->       float:
				_UpperCAmelCase       :		str 		=    x_start
				_UpperCAmelCase       :		Union[str, Any] 		=    fnc(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    0.0
				for _ in range(lowerCAmelCase ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_UpperCAmelCase       :		Union[str, Any] 		=    (x_end - x_start) / steps + xa
								_UpperCAmelCase       :		Optional[Any] 		=    fnc(lowerCAmelCase )
								area += abs(fxa + fxa ) * (xa - xa) / 2
								# Increment step
								_UpperCAmelCase       :		Any 		=    xa
								_UpperCAmelCase       :		Optional[Any] 		=    fxa
				return area


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       List[Any]:
									return x**3 + x**2

					print('f(x) = x^3 + x^2')
					print('The area between the curve, x = -5, x = 5 and the x axis is:')
					SCREAMING_SNAKE_CASE_        =      10
					while i <= 100000:
										print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
										i *= 10


