


'''simple docstring'''
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt
class      lowercase			:

		"""simple docstring"""

		def __init__(	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				''''''
							UpperCamelCase__     :str							      =				''''''
							UpperCamelCase__     :Tuple							      =				[]
							UpperCamelCase__     :Any							      =				0
							UpperCamelCase__     :Tuple							      =				256
							UpperCamelCase__     :Optional[Any]							      =				0
							UpperCamelCase__     :str							      =				0
							UpperCamelCase__     :Union[str, Any]							      =				0
							UpperCamelCase__     :Union[str, Any]							      =				0




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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


if __name__ == "__main__":
		__snake_case							   =  os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
		__snake_case							   =  ConstantStretch()
		stretcher.stretch(file_path)
		stretcher.plot_histogram()
		stretcher.show_image()




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps