


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	:  Union[str, Any]            =     {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:  Optional[int]            =     [
	    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TimesformerModel",
	    "TimesformerForVideoClassification",
	    "TimesformerPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	A	:  List[str]            =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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

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

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

			__lowerCAmelCase							       =     []

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

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

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

						answer.append([vector, dist] )

			return answer


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return np.dot(_UpperCamelCase       ,					_UpperCamelCase ) / (norm(_UpperCamelCase ) * norm(_UpperCamelCase ))


if __name__ == "__main__":
	import doctest

	doctest.testmod()




