






'''simple docstring'''

import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				""""""
            __lowerCamelCase						:       List[str]				   =				""""""
            __lowerCamelCase						:       List[str]				   =				[]
            __lowerCamelCase						:       int				   =				0
            __lowerCamelCase						:       List[Any]				   =				256
            __lowerCamelCase						:       Tuple				   =				0
            __lowerCamelCase						:       Union[str, Any]				   =				0
            __lowerCamelCase						:       List[str]				   =				0
            __lowerCamelCase						:       int				   =				0




       def 					a_					(					self      : Tuple	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				cva.imread(_lowerCAmelCase	,    0					)
            __lowerCamelCase						:       List[str]				   =				copy.deepcopy(self.img					)
            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Optional[Any]				   =				plt.hist(self.img.ravel()	,    256	,    [0, 256]	,    label="""x"""					)
            __lowerCamelCase						:       Optional[Any]				   =				np.sum(_lowerCAmelCase					)
            for i in range(len(_lowerCAmelCase					)					):
                 __lowerCamelCase						:       List[Any]				   =				x[i] / self.k
                 self.sk += prk
                 __lowerCamelCase						:       Tuple				   =				(self.L - 1) * self.sk
                 if self.rem != 0:
                      __lowerCamelCase						:       int				   =				int(last % last					)
                 __lowerCamelCase						:       Dict				   =				int(last + 1 if self.rem >= 0.5 else last					)
                 self.last_list.append(_lowerCAmelCase					)
                 __lowerCamelCase						:       List[str]				   =				int(np.ma.count(self.img					) / self.img[1].size					)
                 __lowerCamelCase						:       Optional[Any]				   =				self.img[1].size
            for i in range(self.number_of_cols					):
                 for j in range(self.number_of_rows					):
                      __lowerCamelCase						:       List[str]				   =				self.img[j][i]
                      if num != self.last_list[num]:
                           __lowerCamelCase						:       int				   =				self.last_list[num]
            cva.imwrite("""output_data/output.jpg"""	,    self.img					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            plt.hist(self.img.ravel()	,    256	,    [0, 256]					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            cva.imshow("""Output-Image"""	,    self.img					)
            cva.imshow("""Input-Image"""	,    self.original_image					)
            cva.waitKey(5000					)
            cva.destroyAllWindows()


if __name__ == "__main__":
   UpperCAmelCase__						:int          =			os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
   UpperCAmelCase__						:List[Any]          =			ConstantStretch()
   stretcher.stretch(file_path)
   stretcher.plot_histogram()
   stretcher.show_image()











'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""					)
            __lowerCamelCase						:       Tuple				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       Any				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       List[Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       Tuple				   =				output.images

            __lowerCamelCase						:       int				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       List[str]				   =				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

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




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       int				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       List[Any]				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Union[str, Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       int				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       List[str]				   =				output.images

            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       Optional[int]				   =				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-1




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       List[str]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_dpmpp_2m"""					)

            __lowerCamelCase						:       int				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Tuple				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=7.5	,    num_inference_steps=15	,    output_type="""np"""	,    use_karras_sigmas=A__	,    )

            __lowerCamelCase						:       int				   =				output.images

            __lowerCamelCase						:       Any				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       int				   =				np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

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



