
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCAmelCase					:    Union[str, Any]           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""linear"""
				lowerCAmelCase_     			=							"""cosine"""
				lowerCAmelCase_     			=							"""cosine_with_restarts"""
				lowerCAmelCase_     			=							"""polynomial"""
				lowerCAmelCase_     			=							"""constant"""
				lowerCAmelCase_     			=							"""constant_with_warmup"""
				lowerCAmelCase_     			=							"""piecewise_constant"""







def 		A_(     A						:		Optimizer		,     A						:		int = -1):
							return LambdaLR(A		,     lambda A: 1		,     last_epoch=A)







def 		A_(     A						:		Optimizer		,     A						:		int		,     A						:		int = -1):

							def lr_lambda(A						:		int):
														if current_step < num_warmup_steps:
																					return float(A) / float(max(1.0		,     A))
														return 1.0

							return LambdaLR(A		,     A		,     last_epoch=A)







def 		A_(     A						:		Optimizer		,     A						:		str		,     A						:		int = -1):
							UpperCamelCase   			=     {}
							UpperCamelCase   			=     step_rules.split(',')
							for rule_str in rule_list[:-1]:
														UpperCamelCase			,	UpperCamelCase   			=     rule_str.split(':')
														UpperCamelCase   			=     int(A)
														UpperCamelCase   			=     float(A)
														UpperCamelCase   			=     value
							UpperCamelCase   			=     float(rule_list[-1])

							def create_rules_function(A						:		List[Any]		,     A						:		int):
														def rule_func(A						:		int) -> float:
																					UpperCamelCase   			=     sorted(rules_dict.keys())
																					for i, sorted_step in enumerate(A):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							UpperCamelCase   			=     create_rules_function(A		,     A)

							return LambdaLR(A		,     A		,     last_epoch=A)







def 		A_(     A						:		int		,     A						:		str		,     A						:		Optional[int]		,     A						:		Optional[int]=-1):

							def lr_lambda(A						:		int):
														if current_step < num_warmup_steps:
																					return float(A) / float(max(1		,     A))
														return max(
														    0.0		,     float(num_training_steps - current_step) / float(max(1		,     num_training_steps - num_warmup_steps)))

							return LambdaLR(A		,     A		,     A)







def 		A_(     A						:		Optimizer		,     A						:		int		,     A						:		int		,     A						:		float = 0.5		,     A						:		int = -1):

							def lr_lambda(A						:		Any):
														if current_step < num_warmup_steps:
																					return float(A) / float(max(1		,     A))
														UpperCamelCase   			=     float(current_step - num_warmup_steps) / float(max(1		,     num_training_steps - num_warmup_steps))
														return max(0.0		,     0.5 * (1.0 + math.cos(math.pi * float(A) * 2.0 * progress)))

							return LambdaLR(A		,     A		,     A)







def 		A_(     A						:		Optimizer		,     A						:		int		,     A						:		int		,     A						:		int = 1		,     A						:		int = -1):

							def lr_lambda(A						:		Tuple):
														if current_step < num_warmup_steps:
																					return float(A) / float(max(1		,     A))
														UpperCamelCase   			=     float(current_step - num_warmup_steps) / float(max(1		,     num_training_steps - num_warmup_steps))
														if progress >= 1.0:
																					return 0.0
														return max(0.0		,     0.5 * (1.0 + math.cos(math.pi * ((float(A) * progress) % 1.0))))

							return LambdaLR(A		,     A		,     A)







def 		A_(     A						:		Tuple		,     A						:		Dict		,     A						:		List[Any]		,     A						:		Optional[Any]=1E-7		,     A						:		Tuple=1.0		,     A						:		Any=-1):
							UpperCamelCase   			=     optimizer.defaults['lr']
							if not (lr_init > lr_end):
														raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''')

							def lr_lambda(A						:		int):
														if current_step < num_warmup_steps:
																					return float(A) / float(max(1		,     A))
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					UpperCamelCase   			=     lr_init - lr_end
																					UpperCamelCase   			=     num_training_steps - num_warmup_steps
																					UpperCamelCase   			=     1 - (current_step - num_warmup_steps) / decay_steps
																					UpperCamelCase   			=     lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(A		,     A		,     A)


lowerCAmelCase					:    Dict           = {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 		A_(     A						:		Union[str, SchedulerType]		,     A						:		Optimizer		,     A						:		Optional[str] = None		,     A						:		Optional[int] = None		,     A						:		Optional[int] = None		,     A						:		int = 1		,     A						:		float = 1.0		,     A						:		int = -1		,     ):
							UpperCamelCase   			=     SchedulerType(A)
							UpperCamelCase   			=     TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(A		,     last_epoch=A)

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(A		,     step_rules=A		,     last_epoch=A)

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.''')

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(A		,     num_warmup_steps=A		,     last_epoch=A)

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.''')

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    A		,     num_warmup_steps=A		,     num_training_steps=A		,     num_cycles=A		,     last_epoch=A		,     )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    A		,     num_warmup_steps=A		,     num_training_steps=A		,     power=A		,     last_epoch=A		,     )

							return schedule_func(
							    A		,     num_warmup_steps=A		,     num_training_steps=A		,     last_epoch=A)






'''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     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






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


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




