


'''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__    	=     logging.get_logger(__name__)






class 	lowerCAmelCase__			(			_snake_case	):
			lowerCAmelCase :	int          =     'linear'
			lowerCAmelCase :	Tuple          =     'cosine'
			lowerCAmelCase :	Tuple          =     'cosine_with_restarts'
			lowerCAmelCase :	List[Any]          =     'polynomial'
			lowerCAmelCase :	Optional[Any]          =     'constant'
			lowerCAmelCase :	Optional[int]          =     'constant_with_warmup'
			lowerCAmelCase :	List[Any]          =     'piecewise_constant'






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = -1   ):
		return LambdaLR(__UpperCAmelCase      , lambda __lowerCAmelCase   : 1      , last_epoch=__UpperCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = -1   ):







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

		return LambdaLR(__UpperCAmelCase      , __UpperCAmelCase      , last_epoch=__UpperCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = -1   ):
		_UpperCAmelCase		:       List[Any]								=      {}
		_UpperCAmelCase		:       List[Any]								=      step_rules.split(","   )
		for rule_str in rule_list[:-1]:
				_UpperCAmelCase					,    _UpperCAmelCase		:       str								=      rule_str.split(":"   )
				_UpperCAmelCase		:       List[str]								=      int(__UpperCAmelCase   )
				_UpperCAmelCase		:       Optional[int]								=      float(__UpperCAmelCase   )
				_UpperCAmelCase		:       List[Any]								=      value
		_UpperCAmelCase		:       Union[str, Any]								=      float(rule_list[-1]   )

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

				return rule_func

		_UpperCAmelCase		:       List[str]								=      create_rules_function(__UpperCAmelCase      , __UpperCAmelCase   )

		return LambdaLR(__UpperCAmelCase      , __UpperCAmelCase      , last_epoch=__UpperCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=-1   ):







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

		return LambdaLR(__UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = 0.5      , __lowerCAmelCase = -1   ):







		def lr_lambda(__lowerCAmelCase   ):
				if current_step < num_warmup_steps:
						return float(__UpperCAmelCase   ) / float(max(1      , __UpperCAmelCase   )   )
				_UpperCAmelCase		:       int								=      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(__UpperCAmelCase   ) * 2.0 * progress   ))   )

		return LambdaLR(__UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = 1      , __lowerCAmelCase = -1   ):







		def lr_lambda(__lowerCAmelCase   ):
				if current_step < num_warmup_steps:
						return float(__UpperCAmelCase   ) / float(max(1      , __UpperCAmelCase   )   )
				_UpperCAmelCase		:       List[str]								=      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(__UpperCAmelCase   ) * progress) % 1.0)   ))   )

		return LambdaLR(__UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase   )






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

		return LambdaLR(__UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase   )


lowerCamelCase__    	=     {
    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      , ):
		_UpperCAmelCase		:       List[Any]								=      SchedulerType(__UpperCAmelCase   )
		_UpperCAmelCase		:       Tuple								=      TYPE_TO_SCHEDULER_FUNCTION[name]
		if name == SchedulerType.CONSTANT:
				return schedule_func(__UpperCAmelCase      , last_epoch=__UpperCAmelCase   )

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

		# 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(__UpperCAmelCase      , num_warmup_steps=__UpperCAmelCase      , last_epoch=__UpperCAmelCase   )

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

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

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







'''simple docstring'''


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return math.sqrt(sum(pow(a - b      , 2   ) for a, b in zip(__lowerCAmelCase      , __lowerCAmelCase   )   )   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):

		if dataset.ndim != value_array.ndim:
				_UpperCAmelCase		:       Optional[Any]								=      (
				    "Wrong input data's dimensions... "
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(__lowerCAmelCase   )

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_UpperCAmelCase		:       Optional[int]								=      (
						    "Wrong input data's shape... "
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(__lowerCAmelCase   )
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError("Wrong shape"   )

		if dataset.dtype != value_array.dtype:
				_UpperCAmelCase		:       Union[str, Any]								=      (
				    "Input data have different datatype... "
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(__lowerCAmelCase   )

		_UpperCAmelCase		:       Optional[int]								=      []

		for value in value_array:
				_UpperCAmelCase		:       List[str]								=      euclidean(__lowerCAmelCase      , dataset[0]   )
				_UpperCAmelCase		:       Dict								=      dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_UpperCAmelCase		:       int								=      euclidean(__lowerCAmelCase      , __lowerCAmelCase   )

						if dist > temp_dist:
								_UpperCAmelCase		:       Tuple								=      temp_dist
								_UpperCAmelCase		:       Dict								=      dataset_value.tolist()

				answer.append([vector, dist]   )

		return answer






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return np.dot(__lowerCAmelCase      , __lowerCAmelCase   ) / (norm(__lowerCAmelCase   ) * norm(__lowerCAmelCase   ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



