


import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


UpperCAmelCase_      : List[str]  					=       'scheduler_config.json'
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[Any]        =						1
      snake_case__				:     Tuple        =						2
      snake_case__				:     List[str]        =						3
      snake_case__				:     int        =						4
      snake_case__				:     str        =						5
      snake_case__				:     Tuple        =						6
      snake_case__				:     Optional[Any]        =						7
      snake_case__				:     Optional[Any]        =						8
      snake_case__				:     Optional[Any]        =						9
      snake_case__				:     Tuple        =						10
      snake_case__				:     Union[str, Any]        =						11
      snake_case__				:     List[Any]        =						12
      snake_case__				:     Optional[int]        =						13
      snake_case__				:     List[str]        =						14
@dataclass
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     torch.FloatTensor
class  SCREAMING_SNAKE_CASE__  :
      snake_case__				:     Optional[Any]        =						SCHEDULER_CONFIG_NAME
      snake_case__				:     Union[str, Any]        =						[]
      snake_case__				:     str        =						True
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, Any] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[str] = None     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=False     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							)		->			Optional[int]:
             a_							,		a_							,		a_					:		Optional[Any]							=  cls.load_config(
                 pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__     ,							subfolder=SCREAMING_SNAKE_CASE__     ,							return_unused_kwargs=SCREAMING_SNAKE_CASE__     ,							return_commit_hash=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)
             return cls.from_config(SCREAMING_SNAKE_CASE__     ,							return_unused_kwargs=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Union[str, os.PathLike]     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Optional[Any]:
             self.save_config(save_directory=SCREAMING_SNAKE_CASE__     ,							push_to_hub=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[str]:
             return self._get_compatibles()

      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       Dict     )		->			Optional[Any]:
             a_					:		int							=  list(set([cls.__name__] + cls._compatibles     )     )
             a_					:		Tuple							=  importlib.import_module(__name__.split('.'     )[0]     )
             a_					:		List[str]							=  [
                 getattr(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             ]
             return compatible_classes








"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







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







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







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


if __name__ == "__main__":
			import doctest

			doctest.testmod()