


import pickle

import numpy as np
from matplotlib import pyplot as plt







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase=0.2   ,						__lowercase=0.2							)					->    str:
										lowerCAmelCase_ :      Union[str, Any]    				=				bp_numa
										lowerCAmelCase_ :      Any    				=				bp_numa
										lowerCAmelCase_ :      Dict    				=				bp_numa
										lowerCAmelCase_ :      Optional[int]    				=				conva_get[:2]
										lowerCAmelCase_ :      Any    				=				conva_get[2]
										lowerCAmelCase_ :      Optional[int]    				=				size_pa
										lowerCAmelCase_ :      Any    				=				rate_w
										lowerCAmelCase_ :      Union[str, Any]    				=				rate_t
										lowerCAmelCase_ :      int    				=				[
										    np.mat(-1 * np.random.rand(self.conva[0]   ,						self.conva[0]							) + 0.5							)
										    for i in range(self.conva[1]							)
										]
										lowerCAmelCase_ :      Optional[Any]    				=				np.mat(-1 * np.random.rand(self.num_bpa   ,						self.num_bpa							) + 0.5							)
										lowerCAmelCase_ :      Tuple    				=				np.mat(-1 * np.random.rand(self.num_bpa   ,						self.num_bpa							) + 0.5							)
										lowerCAmelCase_ :      Union[str, Any]    				=				-2 * np.random.rand(self.conva[1]							) + 1
										lowerCAmelCase_ :      str    				=				-2 * np.random.rand(self.num_bpa							) + 1
										lowerCAmelCase_ :      Tuple    				=				-2 * np.random.rand(self.num_bpa							) + 1




						def 						lowercase_ (				self   ,						__lowercase							)					->    Dict:
										# save model dict with pickle
										lowerCAmelCase_ :      str    				=				{
										    '''num_bp1''': self.num_bpa,
										    '''num_bp2''': self.num_bpa,
										    '''num_bp3''': self.num_bpa,
										    '''conv1''': self.conva,
										    '''step_conv1''': self.step_conva,
										    '''size_pooling1''': self.size_poolinga,
										    '''rate_weight''': self.rate_weight,
										    '''rate_thre''': self.rate_thre,
										    '''w_conv1''': self.w_conva,
										    '''wkj''': self.wkj,
										    '''vji''': self.vji,
										    '''thre_conv1''': self.thre_conva,
										    '''thre_bp2''': self.thre_bpa,
										    '''thre_bp3''': self.thre_bpa,
										}
										with open(__lowercase   ,						'''wb'''							) as f:
														pickle.dump(__lowercase   ,						__lowercase							)

										print(f"""Model saved： {save_path}"""							)




						@classmethod
						def 						lowercase_ (				cls   ,						__lowercase							)					->    List[Any]:
										# read saved model
										with open(__lowercase   ,						'''rb'''							) as f:
														lowerCAmelCase_ :      int    				=				pickle.load(__lowercase							)  # noqa: S301

										lowerCAmelCase_ :      List[Any]    				=				model_dic.get('''conv1'''							)
										conv_get.append(model_dic.get('''step_conv1'''							)							)
										lowerCAmelCase_ :      Optional[Any]    				=				model_dic.get('''size_pooling1'''							)
										lowerCAmelCase_ :      Tuple    				=				model_dic.get('''num_bp1'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				model_dic.get('''num_bp2'''							)
										lowerCAmelCase_ :      Dict    				=				model_dic.get('''num_bp3'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				model_dic.get('''rate_weight'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				model_dic.get('''rate_thre'''							)
										# create model instance
										lowerCAmelCase_ :      Any    				=				CNN(__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)
										# modify model parameter
										lowerCAmelCase_ :      Any    				=				model_dic.get('''w_conv1'''							)
										lowerCAmelCase_ :      Dict    				=				model_dic.get('''wkj'''							)
										lowerCAmelCase_ :      str    				=				model_dic.get('''vji'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				model_dic.get('''thre_conv1'''							)
										lowerCAmelCase_ :      Any    				=				model_dic.get('''thre_bp2'''							)
										lowerCAmelCase_ :      List[str]    				=				model_dic.get('''thre_bp3'''							)
										return conv_ins




						def 						lowercase_ (				self   ,						__lowercase							)					->    Optional[Any]:
										return 1 / (1 + np.exp(-1 * x							))




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										return round(__lowercase   ,						3							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    str:
										# convolution process
										lowerCAmelCase_ :      int    				=				convs[0]
										lowerCAmelCase_ :      Optional[Any]    				=				convs[1]
										lowerCAmelCase_ :      List[Any]    				=				np.shape(__lowercase							)[0]
										# get the data slice of original image data, data_focus
										lowerCAmelCase_ :      Optional[Any]    				=				[]
										for i_focus in range(0   ,						size_data - size_conv + 1   ,						__lowercase							):
														for j_focus in range(0   ,						size_data - size_conv + 1   ,						__lowercase							):
																		lowerCAmelCase_ :      List[Any]    				=				data[
																		    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																		]
																		data_focus.append(__lowercase							)
        # calculate the feature map of every single kernel, and saved as list of matrix
										lowerCAmelCase_ :      Dict    				=				[]
										lowerCAmelCase_ :      List[Any]    				=				int((size_data - size_conv) / conv_step + 1							)
										for i_map in range(__lowercase							):
														lowerCAmelCase_ :      List[Any]    				=				[]
														for i_focus in range(len(__lowercase							)							):
																		lowerCAmelCase_ :      Dict    				=				(
																		    np.sum(np.multiply(data_focus[i_focus]   ,						w_convs[i_map]							)							)
																		    - thre_convs[i_map]
																		)
																		featuremap.append(self.sig(__lowercase							)							)
														lowerCAmelCase_ :      Union[str, Any]    				=				np.asmatrix(__lowercase							).reshape(
														    __lowercase   ,						__lowercase							)
														data_featuremap.append(__lowercase							)

										# expanding the data slice to One dimenssion
										lowerCAmelCase_ :      int    				=				[]
										for each_focus in data_focus:
														focusa_list.extend(self.Expand_Mat(__lowercase							)							)
										lowerCAmelCase_ :      Tuple    				=				np.asarray(__lowercase							)
										return focus_list, data_featuremap




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase="average_pool"							)					->    int:
										# pooling process
										lowerCAmelCase_ :      Any    				=				len(featuremaps[0]							)
										lowerCAmelCase_ :      List[Any]    				=				int(size_map / size_pooling							)
										lowerCAmelCase_ :      Any    				=				[]
										for i_map in range(len(__lowercase							)							):
														lowerCAmelCase_ :      str    				=				featuremaps[i_map]
														lowerCAmelCase_ :      List[str]    				=				[]
														for i_focus in range(0   ,						__lowercase   ,						__lowercase							):
																		for j_focus in range(0   ,						__lowercase   ,						__lowercase							):
																						lowerCAmelCase_ :      Tuple    				=				feature_map[
																						    i_focus : i_focus + size_pooling,
																						    j_focus : j_focus + size_pooling,
																						]
																						if pooling_type == "average_pool":
																										# average pooling
																										map_pooled.append(np.average(__lowercase							)							)
																						elif pooling_type == "max_pooling":
																										# max pooling
																										map_pooled.append(np.max(__lowercase							)							)
														lowerCAmelCase_ :      Any    				=				np.asmatrix(__lowercase							).reshape(__lowercase   ,						__lowercase							)
														featuremap_pooled.append(__lowercase							)
										return featuremap_pooled




						def 						lowercase_ (				self   ,						__lowercase							)					->    List[str]:
										# expanding three dimension data to one dimension list
										lowerCAmelCase_ :      Dict    				=				[]
										for i in range(len(__lowercase							)							):
														lowerCAmelCase_ :      List[str]    				=				np.shape(data[i]							)
														lowerCAmelCase_ :      Optional[int]    				=				data[i].reshape(1   ,						shapes[0] * shapes[1]							)
														lowerCAmelCase_ :      Dict    				=				data_listed.getA().tolist()[0]
														data_expanded.extend(__lowercase							)
										lowerCAmelCase_ :      Dict    				=				np.asarray(__lowercase							)
										return data_expanded




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										# expanding matrix to one dimension list
										lowerCAmelCase_ :      str    				=				np.asarray(__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				np.shape(__lowercase							)
										lowerCAmelCase_ :      Any    				=				data_mat.reshape(1   ,						shapes[0] * shapes[1]							)
										return data_expanded




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Optional[Any]:
										lowerCAmelCase_ :      Optional[int]    				=				[]
										lowerCAmelCase_ :      Any    				=				0
										for i_map in range(__lowercase							):
														lowerCAmelCase_ :      int    				=				np.ones((size_map, size_map)							)
														for i in range(0   ,						__lowercase   ,						__lowercase							):
																		for j in range(0   ,						__lowercase   ,						__lowercase							):
																						lowerCAmelCase_ :      Optional[Any]    				=				pd_pool[
																						    i_pool
																						]
																						lowerCAmelCase_ :      Union[str, Any]    				=				i_pool + 1
														lowerCAmelCase_ :      Optional[int]    				=				np.multiply(
														    __lowercase   ,						np.multiply(out_map[i_map]   ,						(1 - out_map[i_map])							)							)
														pd_all.append(__lowercase							)
										return pd_all




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase=bool							)					->    Optional[Any]:
										# model traning
										print('''----------------------Start Training-------------------------'''							)
										print((''' - - Shape: Train_Data  ''', np.shape(__lowercase							))							)
										print((''' - - Shape: Teach_Data  ''', np.shape(__lowercase							))							)
										lowerCAmelCase_ :      int    				=				0
										lowerCAmelCase_ :      Optional[Any]    				=				[]
										lowerCAmelCase_ :      Dict    				=				1_0_0_0_0
										while rp < n_repeat and mse >= error_accuracy:
														lowerCAmelCase_ :      Tuple    				=				0
														print(f"""-------------Learning Time {rp}--------------"""							)
														for p in range(len(__lowercase							)							):
																		# print('------------Learning Image: %d--------------'%p)
																		lowerCAmelCase_ :      str    				=				np.asmatrix(datas_train[p]							)
																		lowerCAmelCase_ :      List[Any]    				=				np.asarray(datas_teach[p]							)
																		lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				self.convolute(
																		    __lowercase   ,						self.conva   ,						self.w_conva   ,						self.thre_conva   ,						conv_step=self.step_conva   ,						)
																		lowerCAmelCase_ :      Union[str, Any]    				=				self.pooling(__lowercase   ,						self.size_poolinga							)
																		lowerCAmelCase_ :      List[str]    				=				np.shape(__lowercase							)
																		lowerCAmelCase_ :      List[Any]    				=				self._expand(__lowercase							)
																		lowerCAmelCase_ :      Optional[int]    				=				data_bp_input

																		lowerCAmelCase_ :      Dict    				=				np.dot(__lowercase   ,						self.vji.T							) - self.thre_bpa
																		lowerCAmelCase_ :      Union[str, Any]    				=				self.sig(__lowercase							)
																		lowerCAmelCase_ :      int    				=				np.dot(__lowercase   ,						self.wkj.T							) - self.thre_bpa
																		lowerCAmelCase_ :      int    				=				self.sig(__lowercase							)

																		# --------------Model Leaning ------------------------
																		# calculate error and gradient---------------
																		lowerCAmelCase_ :      Union[str, Any]    				=				np.multiply(
																		    (data_teach - bp_outa)   ,						np.multiply(__lowercase   ,						(1 - bp_outa)							)							)
																		lowerCAmelCase_ :      Tuple    				=				np.multiply(
																		    np.dot(__lowercase   ,						self.wkj							)   ,						np.multiply(__lowercase   ,						(1 - bp_outa)							)							)
																		lowerCAmelCase_ :      Any    				=				np.dot(__lowercase   ,						self.vji							)

																		lowerCAmelCase_ :      Tuple    				=				pd_i_all / (self.size_poolinga * self.size_poolinga)
																		lowerCAmelCase_ :      List[Any]    				=				pd_conva_pooled.T.getA().tolist()
																		lowerCAmelCase_ :      List[str]    				=				self._calculate_gradient_from_pool(
																		    __lowercase   ,						__lowercase   ,						shape_featuremapa[0]   ,						shape_featuremapa[1]   ,						self.size_poolinga   ,						)
																		# weight and threshold learning process---------
																		# convolution layer
																		for k_conv in range(self.conva[1]							):
																						lowerCAmelCase_ :      int    				=				self._expand_mat(pd_conva_all[k_conv]							)
																						lowerCAmelCase_ :      int    				=				self.rate_weight * np.dot(__lowercase   ,						__lowercase							)

																						lowerCAmelCase_ :      Optional[Any]    				=				self.w_conva[k_conv] + delta_w.reshape(
																						    (self.conva[0], self.conva[0])							)

																						lowerCAmelCase_ :      List[str]    				=				(
																						    self.thre_conva[k_conv]
																						    - np.sum(pd_conva_all[k_conv]							) * self.rate_thre
																						)
																		# all connected layer
																		lowerCAmelCase_ :      Union[str, Any]    				=				self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																		lowerCAmelCase_ :      Dict    				=				self.vji + pd_j_all.T * bp_outa * self.rate_weight
																		lowerCAmelCase_ :      List[Any]    				=				self.thre_bpa - pd_k_all * self.rate_thre
																		lowerCAmelCase_ :      Optional[Any]    				=				self.thre_bpa - pd_j_all * self.rate_thre
																		# calculate the sum error of all single image
																		lowerCAmelCase_ :      Optional[int]    				=				np.sum(abs(data_teach - bp_outa							)							)
																		error_count += errors
																		# print('   ----Teach      ',data_teach)
																		# print('   ----BP_output  ',bp_out3)
														lowerCAmelCase_ :      int    				=				rp + 1
														lowerCAmelCase_ :      Dict    				=				error_count / patterns
														all_mse.append(__lowercase							)

										def draw_error():
														lowerCAmelCase_ :      str    				=				[error_accuracy for i in range(int(n_repeat * 1.2							)							)]
														plt.plot(__lowercase   ,						'''+-'''							)
														plt.plot(__lowercase   ,						'''r--'''							)
														plt.xlabel('''Learning Times'''							)
														plt.ylabel('''All_mse'''							)
														plt.grid(__lowercase   ,						alpha=0.5							)
														plt.show()

										print('''------------------Training Complished---------------------'''							)
										print((''' - - Training epoch: ''', rp, f"""     - - Mse: {mse:.6f}""")							)
										if draw_e:
														draw_error()
										return mse




						def 						lowercase_ (				self   ,						__lowercase							)					->    Optional[int]:
										# model predict
										lowerCAmelCase_ :      Optional[int]    				=				[]
										print('''-------------------Start Testing-------------------------'''							)
										print((''' - - Shape: Test_Data  ''', np.shape(__lowercase							))							)
										for p in range(len(__lowercase							)							):
														lowerCAmelCase_ :      str    				=				np.asmatrix(datas_test[p]							)
														lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[int]    				=				self.convolute(
														    __lowercase   ,						self.conva   ,						self.w_conva   ,						self.thre_conva   ,						conv_step=self.step_conva   ,						)
														lowerCAmelCase_ :      Dict    				=				self.pooling(__lowercase   ,						self.size_poolinga							)
														lowerCAmelCase_ :      Optional[int]    				=				self._expand(__lowercase							)

														lowerCAmelCase_ :      Tuple    				=				data_bp_input
														lowerCAmelCase_ :      Tuple    				=				bp_outa * self.vji.T - self.thre_bpa
														lowerCAmelCase_ :      int    				=				self.sig(__lowercase							)
														lowerCAmelCase_ :      List[Any]    				=				bp_outa * self.wkj.T - self.thre_bpa
														lowerCAmelCase_ :      Union[str, Any]    				=				self.sig(__lowercase							)
														produce_out.extend(bp_outa.getA().tolist()							)
										lowerCAmelCase_ :      Optional[Any]    				=				[list(map(self.do_round   ,						__lowercase							)							) for each in produce_out]
										return np.asarray(__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										# return the data of image after convoluting process so we can check it out
										lowerCAmelCase_ :      Optional[Any]    				=				np.asmatrix(__lowercase							)
										lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[int]    				=				self.convolute(
										    __lowercase   ,						self.conva   ,						self.w_conva   ,						self.thre_conva   ,						conv_step=self.step_conva   ,						)
										lowerCAmelCase_ :      str    				=				self.pooling(__lowercase   ,						self.size_poolinga							)

										return data_conveda, data_pooleda


if __name__ == "__main__":
						pass



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


_UpperCAmelCase			:  Tuple     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[str]					=							"""linear"""
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							"""cosine"""
						SCREAMING_SNAKE_CASE__      :  Dict					=							"""cosine_with_restarts"""
						SCREAMING_SNAKE_CASE__      :  List[str]					=							"""polynomial"""
						SCREAMING_SNAKE_CASE__      :  Dict					=							"""constant"""
						SCREAMING_SNAKE_CASE__      :  List[str]					=							"""constant_with_warmup"""
						SCREAMING_SNAKE_CASE__      :  str					=							"""piecewise_constant"""






def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ = -1 )-> Tuple:
				return LambdaLR(lowerCAmelCase_ ,   lambda lowerCAmelCase_ : 1 ,   last_epoch=lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = -1 )-> List[Any]:

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

				return LambdaLR(lowerCAmelCase_ ,   lowerCAmelCase_ ,   last_epoch=lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = -1 )-> int:
				lowerCAmelCase_ :      Optional[int]    				=				{}
				lowerCAmelCase_ :      Union[str, Any]    				=				step_rules.split(''',''' )
				for rule_str in rule_list[:-1]:
								lowerCAmelCase_				,  lowerCAmelCase_ :      str    				=				rule_str.split(''':''' )
								lowerCAmelCase_ :      int    				=				int(lowerCAmelCase_ )
								lowerCAmelCase_ :      str    				=				float(lowerCAmelCase_ )
								lowerCAmelCase_ :      List[Any]    				=				value
				lowerCAmelCase_ :      int    				=				float(rule_list[-1] )

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

								return rule_func

				lowerCAmelCase_ :      Tuple    				=				create_rules_function(lowerCAmelCase_ ,   lowerCAmelCase_ )

				return LambdaLR(lowerCAmelCase_ ,   lowerCAmelCase_ ,   last_epoch=lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=-1 )-> Optional[int]:

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

				return LambdaLR(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = 0.5 ,   lowerCAmelCase_ = -1 )-> List[Any]:

				def lr_lambda(lowerCAmelCase_ ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase_ ) / float(max(1 ,   lowerCAmelCase_ ) )
								lowerCAmelCase_ :      Tuple    				=				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(lowerCAmelCase_ ) * 2.0 * progress )) )

				return LambdaLR(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = 1 ,   lowerCAmelCase_ = -1 )-> Dict:

				def lr_lambda(lowerCAmelCase_ ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase_ ) / float(max(1 ,   lowerCAmelCase_ ) )
								lowerCAmelCase_ :      List[Any]    				=				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(lowerCAmelCase_ ) * progress) % 1.0) )) )

				return LambdaLR(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=1e-7 ,   lowerCAmelCase_=1.0 ,   lowerCAmelCase_=-1 )-> Any:
				lowerCAmelCase_ :      Dict    				=				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(lowerCAmelCase_ ):
								if current_step < num_warmup_steps:
												return float(lowerCAmelCase_ ) / float(max(1 ,   lowerCAmelCase_ ) )
								elif current_step > num_training_steps:
												return lr_end / lr_init  # as LambdaLR multiplies by lr_init
								else:
												lowerCAmelCase_ :      List[Any]    				=				lr_init - lr_end
												lowerCAmelCase_ :      Optional[Any]    				=				num_training_steps - num_warmup_steps
												lowerCAmelCase_ :      Any    				=				1 - (current_step - num_warmup_steps) / decay_steps
												lowerCAmelCase_ :      List[Any]    				=				lr_range * pct_remaining**power + lr_end
												return decay / lr_init  # as LambdaLR multiplies by lr_init

				return LambdaLR(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )


_UpperCAmelCase			:  Union[str, Any]     ={
    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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = None ,   lowerCAmelCase_ = None ,   lowerCAmelCase_ = None ,   lowerCAmelCase_ = 1 ,   lowerCAmelCase_ = 1.0 ,   lowerCAmelCase_ = -1 ,   )-> Optional[int]:
				lowerCAmelCase_ :      Union[str, Any]    				=				SchedulerType(lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[int]    				=				TYPE_TO_SCHEDULER_FUNCTION[name]
				if name == SchedulerType.CONSTANT:
								return schedule_func(lowerCAmelCase_ ,   last_epoch=lowerCAmelCase_ )

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

				# 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(lowerCAmelCase_ ,   num_warmup_steps=lowerCAmelCase_ ,   last_epoch=lowerCAmelCase_ )

				# 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(
								    lowerCAmelCase_ ,   num_warmup_steps=lowerCAmelCase_ ,   num_training_steps=lowerCAmelCase_ ,   num_cycles=lowerCAmelCase_ ,   last_epoch=lowerCAmelCase_ ,   )

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

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