from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase       =		{"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase       =		[
					    "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FocalNetForImageClassification",
					    "FocalNetForMaskedImageModeling",
					    "FocalNetBackbone",
					    "FocalNetModel",
					    "FocalNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_focalnet import (
										    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FocalNetBackbone,
										    FocalNetForImageClassification,
										    FocalNetForMaskedImageModeling,
										    FocalNetModel,
										    FocalNetPreTrainedModel,
										)

else:
					import sys

					_lowerCAmelCase       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

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

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
