






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


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Dict   =       'linear'
	__snake_case	:str   =       'cosine'
	__snake_case	:int   =       'cosine_with_restarts'
	__snake_case	:List[str]   =       'polynomial'
	__snake_case	:List[Any]   =       'constant'
	__snake_case	:Optional[Any]   =       'constant_with_warmup'
	__snake_case	:Optional[int]   =       'piecewise_constant'





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = -1					):




	'''simple docstring'''







	return LambdaLR(lowerCamelCase				,  lambda lowerCamelCase					: 1				,  last_epoch=lowerCamelCase					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = -1					):




	'''simple docstring'''







	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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = -1					):




	'''simple docstring'''







	__lowercase             =  {}
	__lowercase             =  step_rules.split(""","""					)
	for rule_str in rule_list[:-1]:
		__lowercase       ,						__lowercase             =  rule_str.split(""":"""					)
		__lowercase             =  int(lowerCamelCase					)
		__lowercase             =  float(lowerCamelCase					)
		__lowercase             =  value
	__lowercase             =  float(rule_list[-1]					)

	def create_rules_function(lowerCamelCase				,  lowerCamelCase					):
		def rule_func(lowerCamelCase					) -> float:
			__lowercase             =  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

	__lowercase             =  create_rules_function(lowerCamelCase				,  lowerCamelCase					)

	return LambdaLR(lowerCamelCase				,  lowerCamelCase				,  last_epoch=lowerCamelCase					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=-1					):




	'''simple docstring'''







	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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = 0.5				,  lowerCamelCase = -1					):




	'''simple docstring'''







	def lr_lambda(lowerCamelCase					):
		if current_step < num_warmup_steps:
			return float(lowerCamelCase					) / float(max(1				,  lowerCamelCase					)					)
		__lowercase             =  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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = 1				,  lowerCamelCase = -1					):




	'''simple docstring'''







	def lr_lambda(lowerCamelCase					):
		if current_step < num_warmup_steps:
			return float(lowerCamelCase					) / float(max(1				,  lowerCamelCase					)					)
		__lowercase             =  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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=1e-7				,  lowerCamelCase=1.0				,  lowerCamelCase=-1					):




	'''simple docstring'''







	__lowercase             =  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:
			__lowercase             =  lr_init - lr_end
			__lowercase             =  num_training_steps - num_warmup_steps
			__lowercase             =  1 - (current_step - num_warmup_steps) / decay_steps
			__lowercase             =  lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)


__UpperCamelCase      :      Optional[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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = None				,  lowerCamelCase = None				,  lowerCamelCase = None				,  lowerCamelCase = 1				,  lowerCamelCase = 1.0				,  lowerCamelCase = -1				,  ):




	'''simple docstring'''







	__lowercase             =  SchedulerType(lowerCamelCase					)
	__lowercase             =  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					)









def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  """"""
	for ch in key:
		if ch == " " or ch not in key_no_dups and ch.isalpha():
			key_no_dups += ch
	return key_no_dups





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  [chr(i + 65					) for i in range(26					)]
	# Remove duplicate characters from key
	__lowercase             =  remove_duplicates(key.upper()					)
	__lowercase             =  len(lowerCamelCase					)
	# First fill cipher with key characters
	__lowercase             =  {alphabet[i]: char for i, char in enumerate(lowerCamelCase					)}
	# Then map remaining characters in alphabet to
	# the alphabet from the beginning
	for i in range(len(lowerCamelCase					)				,  26					):
		__lowercase             =  alphabet[i - offset]
		# Ensure we are not mapping letters to letters previously mapped
		while char in key:
			offset -= 1
			__lowercase             =  alphabet[i - offset]
		__lowercase             =  char
	return cipher_alphabet





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return "".join(cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {v: k for k, v in cipher_map.items()}
	return "".join(rev_cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  input("""Enter message to encode or decode: """					).strip()
	__lowercase             =  input("""Enter keyword: """					).strip()
	__lowercase             =  input("""Encipher or decipher? E/D:"""					).strip()[0].lower()
	try:
		__lowercase             =  {"""e""": encipher, """d""": decipher}[option]
	except KeyError:
		raise KeyError("""invalid input option"""					)
	__lowercase             =  create_cipher_map(lowerCamelCase					)
	print(func(lowerCamelCase				,  lowerCamelCase					)					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

