


'''simple docstring'''
import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
		from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
		from tensorflow.keras.optimizers import Adam
class    lowercase	(							tf.keras.optimizers.schedules.LearningRateSchedule					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_ = 1.0    ,   UpperCamelCase_ = None    ,   ):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__   :Dict												= initial_learning_rate
							UpperCamelCase__   :Optional[int]												= warmup_steps
							UpperCamelCase__   :str												= power
							UpperCamelCase__   :Dict												= decay_schedule_fn
							UpperCamelCase__   :List[Any]												= name




		def __call__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							with tf.name_scope(self.name or '''WarmUp'''							) as name:
												# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
												# learning rate will be `global_step/num_warmup_steps * init_lr`.
												UpperCamelCase__   :int												= tf.cast(UpperCamelCase_    ,   tf.floataa							)
												UpperCamelCase__   :int												= tf.cast(self.warmup_steps    ,   tf.floataa							)
												UpperCamelCase__   :Any												= global_step_float / warmup_steps_float
												UpperCamelCase__   :Union[str, Any]												= self.initial_learning_rate * tf.math.pow(UpperCamelCase_    ,   self.power							)
												return tf.cond(
												    global_step_float < warmup_steps_float    ,   lambda: warmup_learning_rate    ,   lambda: self.decay_schedule_fn(step - self.warmup_steps							)    ,   name=UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return {
							    "initial_learning_rate": self.initial_learning_rate,
							    "decay_schedule_fn": self.decay_schedule_fn,
							    "warmup_steps": self.warmup_steps,
							    "power": self.power,
							    "name": self.name,
							}



def 			a      (			__a ,					__a ,					__a ,					__a = 0.0 ,					__a = 0.9 ,					__a = 0.9_9_9 ,					__a = 1e-8 ,					__a = None ,					__a = None ,					__a = 0.0 ,					__a = 1.0 ,					__a = None ,					)  ->	Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__   :Union[str, Any]												= tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=__a ,					decay_steps=num_train_steps - num_warmup_steps ,					end_learning_rate=init_lr * min_lr_ratio ,					power=__a ,					)
					if num_warmup_steps:
										UpperCamelCase__   :int												= WarmUp(
										    initial_learning_rate=__a ,					decay_schedule_fn=__a ,					warmup_steps=__a ,					)
					if weight_decay_rate > 0.0:
										UpperCamelCase__   :int												= AdamWeightDecay(
										    learning_rate=__a ,					weight_decay_rate=__a ,					beta_a=__a ,					beta_a=__a ,					epsilon=__a ,					clipnorm=__a ,					global_clipnorm=__a ,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''] ,					include_in_weight_decay=__a ,					)
					else:
										UpperCamelCase__   :Optional[int]												= tf.keras.optimizers.Adam(
										    learning_rate=__a ,					beta_a=__a ,					beta_a=__a ,					epsilon=__a ,					clipnorm=__a ,					global_clipnorm=__a ,					)
					# We return the optimizer and the LR scheduler in order to better track the
					# evolution of the LR independently of the optimizer.
					return optimizer, lr_schedule
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_ = 0.001    ,   UpperCamelCase_ = 0.9    ,   UpperCamelCase_ = 0.999    ,   UpperCamelCase_ = 1e-7    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = 0.0    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = "AdamWeightDecay"    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							)
							UpperCamelCase__   :Any												= weight_decay_rate
							UpperCamelCase__   :Union[str, Any]												= include_in_weight_decay
							UpperCamelCase__   :Optional[int]												= exclude_from_weight_decay




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= {'''WarmUp''': WarmUp}
							return super(UpperCamelCase_    ,   cls							).from_config(UpperCamelCase_    ,   custom_objects=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							super(UpperCamelCase_    ,   self							)._prepare_local(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= tf.constant(
							    self.weight_decay_rate    ,   name='''adam_weight_decay_rate'''							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self._do_use_weight_decay(var.name							)
							if do_decay:
												return var.assign_sub(
												    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']    ,   use_locking=self._use_locking    ,   )
							return tf.no_op()




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

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :List[str]												= list(zip(*UpperCamelCase_							)							)
							return super(UpperCamelCase_    ,   self							).apply_gradients(zip(UpperCamelCase_    ,   UpperCamelCase_							)    ,   name=UpperCamelCase_    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							if apply_state is None:
												return self._decayed_lr_t[var_dtype], {}

							UpperCamelCase__   :Dict												= apply_state or {}
							UpperCamelCase__   :str												= apply_state.get((var_device, var_dtype)							)
							if coefficients is None:
												UpperCamelCase__   :List[str]												= self._fallback_apply_state(UpperCamelCase_    ,   UpperCamelCase_							)
												UpperCamelCase__   :List[Any]												= coefficients

							return coefficients["lr_t"], {"apply_state": apply_state}




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

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self._get_lr(var.device    ,   var.dtype.base_dtype    ,   UpperCamelCase_							)
							UpperCamelCase__   :Dict												= self._decay_weights_op(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							with tf.control_dependencies([decay]							):
												return super(UpperCamelCase_    ,   self							)._resource_apply_dense(UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							)




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

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self._get_lr(var.device    ,   var.dtype.base_dtype    ,   UpperCamelCase_							)
							UpperCamelCase__   :Dict												= self._decay_weights_op(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							with tf.control_dependencies([decay]							):
												return super(UpperCamelCase_    ,   self							)._resource_apply_sparse(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= super().get_config()
							config.update({'''weight_decay_rate''': self.weight_decay_rate}							)
							return config




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if self.weight_decay_rate == 0:
												return False

							if self._include_in_weight_decay:
												for r in self._include_in_weight_decay:
																	if re.search(UpperCamelCase_    ,   UpperCamelCase_							) is not None:
																						return True

							if self._exclude_from_weight_decay:
												for r in self._exclude_from_weight_decay:
																	if re.search(UpperCamelCase_    ,   UpperCamelCase_							) is not None:
																						return False
							return True



class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= []
							UpperCamelCase__   :Tuple												= None




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self._accum_steps is None:
												UpperCamelCase__   :List[str]												= tf.Variable(
												    tf.constant(0    ,   dtype=tf.intaa							)    ,   trainable=UpperCamelCase_    ,   synchronization=tf.VariableSynchronization.ON_READ    ,   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,   )

							return self._accum_steps.value()




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if not self._gradients:
												raise ValueError('''The accumulator should be called first to initialize the gradients'''							)
							return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]




		def __call__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if not self._gradients:
												UpperCamelCase__   :Tuple												= self.step  # Create the step variable.
												self._gradients.extend(
												    [
												        tf.Variable(
												            tf.zeros_like(UpperCamelCase_							)    ,   trainable=UpperCamelCase_    ,   synchronization=tf.VariableSynchronization.ON_READ    ,   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,   )
												        if gradient is not None
												        else gradient
												        for gradient in gradients
												    ]							)
							if len(UpperCamelCase_							) != len(self._gradients							):
												raise ValueError(F'''Expected {len(self._gradients							)} gradients, but got {len(UpperCamelCase_							)}'''							)

							for accum_gradient, gradient in zip(self._gradients    ,   UpperCamelCase_							):
												if accum_gradient is not None and gradient is not None:
																	accum_gradient.assign_add(UpperCamelCase_							)

							self._accum_steps.assign_add(1							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if not self._gradients:
												return
							self._accum_steps.assign(0							)
							for gradient in self._gradients:
												if gradient is not None:
																	gradient.assign(tf.zeros_like(UpperCamelCase_							)							)



'''simple docstring'''
from PIL import Image


def 			a      (			__a ,					__a   )  ->	Image:



					'''simple docstring'''






					def brightness(__a   ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
					return img.point(__a   )


if __name__ == "__main__":
		# Load image
		with Image.open('''image_data/lena.jpg''') as img:
				# Change brightness to 100
				__snake_case							      =     change_brightness(img, 100)
				brigt_img.save('''image_data/lena_brightness.png''', format='''png''')