






"""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 					_UpperCAmelCase       ( tf.keras.optimizers.schedules.LearningRateSchedule							):




					def __init__(				self     :						List[Any]    ,						A     :						float    ,						A     :						Callable    ,						A     :						int    ,						A     :						float = 1.0    ,						A     :						str = None    ,						)  ->  Tuple:
							super().__init__()
							lowercase_ : Dict							       =   initial_learning_rate
							lowercase_ : Union[str, Any]							       =   warmup_steps
							lowercase_ : Union[str, Any]							       =   power
							lowercase_ : int							       =   decay_schedule_fn
							lowercase_ : Optional[int]							       =   name




					def __call__(				self     :						List[str]    ,						A     :						List[Any]   )  ->  Any:
							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`.
									lowercase_ : Optional[Any]							       =   tf.cast(UpperCAmelCase__    ,						tf.floataa   )
									lowercase_ : Optional[int]							       =   tf.cast(self.warmup_steps    ,						tf.floataa   )
									lowercase_ : Tuple							       =   global_step_float / warmup_steps_float
									lowercase_ : 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   A  (				self     :						Tuple   )  ->  Optional[int]:
							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 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Optional[int]		,					__snake_case				: str		,					__snake_case				: int = 0.0		,					__snake_case				: Tuple = 0.9		,					__snake_case				: List[Any] = 0.999		,					__snake_case				: Optional[Any] = 1e-8		,					__snake_case				: str = None		,					__snake_case				: Tuple = None		,					__snake_case				: Optional[int] = 0.0		,					__snake_case				: List[str] = 1.0		,					__snake_case				: Dict = None		,					):
		lowercase_ : List[Any]							       =   tf.keras.optimizers.schedules.PolynomialDecay(
		    initial_learning_rate=lowerCAmelCase_		,					decay_steps=num_train_steps - num_warmup_steps		,					end_learning_rate=init_lr * min_lr_ratio		,					power=lowerCAmelCase_		,					)
		if num_warmup_steps:
				lowercase_ : Dict							       =   WarmUp(
				    initial_learning_rate=lowerCAmelCase_		,					decay_schedule_fn=lowerCAmelCase_		,					warmup_steps=lowerCAmelCase_		,					)
		if weight_decay_rate > 0.0:
				lowercase_ : Optional[int]							       =   AdamWeightDecay(
				    learning_rate=lowerCAmelCase_		,					weight_decay_rate=lowerCAmelCase_		,					beta_a=lowerCAmelCase_		,					beta_a=lowerCAmelCase_		,					epsilon=lowerCAmelCase_		,					clipnorm=lowerCAmelCase_		,					global_clipnorm=lowerCAmelCase_		,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']		,					include_in_weight_decay=lowerCAmelCase_		,					)
		else:
				lowercase_ : Optional[Any]							       =   tf.keras.optimizers.Adam(
				    learning_rate=lowerCAmelCase_		,					beta_a=lowerCAmelCase_		,					beta_a=lowerCAmelCase_		,					epsilon=lowerCAmelCase_		,					clipnorm=lowerCAmelCase_		,					global_clipnorm=lowerCAmelCase_		,					)
		# 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 					_UpperCAmelCase       ( _A							):




					def __init__(				self     :						Tuple    ,						A     :						Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001    ,						A     :						float = 0.9    ,						A     :						float = 0.999    ,						A     :						float = 1e-7    ,						A     :						bool = False    ,						A     :						float = 0.0    ,						A     :						Optional[List[str]] = None    ,						A     :						Optional[List[str]] = None    ,						A     :						str = "AdamWeightDecay"    ,						**A     :						Optional[Any]    ,						)  ->  Tuple:
							super().__init__(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__    ,						**UpperCAmelCase__   )
							lowercase_ : Optional[int]							       =   weight_decay_rate
							lowercase_ : int							       =   include_in_weight_decay
							lowercase_ : List[str]							       =   exclude_from_weight_decay




					@classmethod
					def   A  (				cls     :						Optional[int]    ,						A     :						Union[str, Any]   )  ->  Optional[Any]:
							lowercase_ : str							       =   {'''WarmUp''': WarmUp}
							return super(UpperCAmelCase__    ,						cls   ).from_config(UpperCAmelCase__    ,						custom_objects=UpperCAmelCase__   )




					def   A  (				self     :						Optional[int]    ,						A     :						Union[str, Any]    ,						A     :						Dict    ,						A     :						List[str]   )  ->  Dict:
							super(UpperCAmelCase__    ,						self   )._prepare_local(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__   )
							lowercase_ : Optional[Any]							       =   tf.constant(
							    self.weight_decay_rate    ,						name='''adam_weight_decay_rate'''   )




					def   A  (				self     :						Any    ,						A     :						Optional[int]    ,						A     :						Dict    ,						A     :						Optional[Any]   )  ->  List[Any]:
							lowercase_ : Tuple							       =   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   A  (				self     :						Tuple    ,						A     :						Tuple    ,						A     :						int=None    ,						**A     :						Any   )  ->  Optional[int]:
							lowercase_       ,					lowercase_ : List[str]							       =   list(zip(*UpperCAmelCase__   )   )
							return super(UpperCAmelCase__    ,						self   ).apply_gradients(zip(UpperCAmelCase__    ,						UpperCAmelCase__   )    ,						name=UpperCAmelCase__    ,						**UpperCAmelCase__   )




					def   A  (				self     :						Dict    ,						A     :						Dict    ,						A     :						List[str]    ,						A     :						List[Any]   )  ->  Tuple:
							if apply_state is None:
									return self._decayed_lr_t[var_dtype], {}

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

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




					def   A  (				self     :						Union[str, Any]    ,						A     :						Union[str, Any]    ,						A     :						List[Any]    ,						A     :						List[Any]=None   )  ->  Tuple:
							lowercase_       ,					lowercase_ : Tuple							       =   self._get_lr(var.device    ,						var.dtype.base_dtype    ,						UpperCAmelCase__   )
							lowercase_ : Dict							       =   self._decay_weights_op(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__   )
							with tf.control_dependencies([decay]   ):
									return super(UpperCAmelCase__    ,						self   )._resource_apply_dense(UpperCAmelCase__    ,						UpperCAmelCase__    ,						**UpperCAmelCase__   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[Any]    ,						A     :						List[Any]    ,						A     :						str    ,						A     :						Optional[int]=None   )  ->  Dict:
							lowercase_       ,					lowercase_ : int							       =   self._get_lr(var.device    ,						var.dtype.base_dtype    ,						UpperCAmelCase__   )
							lowercase_ : Optional[int]							       =   self._decay_weights_op(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__   )
							with tf.control_dependencies([decay]   ):
									return super(UpperCAmelCase__    ,						self   )._resource_apply_sparse(UpperCAmelCase__    ,						UpperCAmelCase__    ,						UpperCAmelCase__    ,						**UpperCAmelCase__   )




					def   A  (				self     :						Any   )  ->  Dict:
							lowercase_ : Tuple							       =   super().get_config()
							config.update({'''weight_decay_rate''': self.weight_decay_rate}   )
							return config






					def   A  (				self     :						Optional[Any]    ,						A     :						List[Any]   )  ->  List[Any]:
							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 					_UpperCAmelCase       ( _A							):




					def __init__(				self     :						Optional[Any]   )  ->  str:
							lowercase_ : Tuple							       =   []
							lowercase_ : int							       =   None




					@property
					def   A  (				self     :						Optional[Any]   )  ->  Optional[Any]:
							if self._accum_steps is None:
									lowercase_ : 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   A  (				self     :						Any   )  ->  Any:
							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     :						Dict    ,						A     :						Optional[int]   )  ->  Union[str, Any]:
							if not self._gradients:
									lowercase_ : str							       =   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   A  (				self     :						Tuple   )  ->  Optional[int]:
							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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				image.size
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				image.load()
					for i in range(lowerCAmelCase_   ):
										for j in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				pixels[j, i]
															mean += pixel
					mean //= width * height

					for j in range(lowerCAmelCase_   ):
										for i in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				255 if pixels[i, j] > mean else 0
					return image


if __name__ == "__main__":
		a__				:							List[str]						=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
		image.save('''output_image_path''')
