






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray     ,					lowercase__						:       np.ndarray       )		->    float:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray     ,					lowercase__						:       np.ndarray       )		->    list[list[list[float] | float]]:


				'''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(lowercase__       )

				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(lowercase__       )
				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(lowercase__       )

				lowerCAmelCase__      			=						[]

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

								for dataset_value in dataset[1:]:
												lowerCAmelCase__      			=						euclidean(lowercase__     ,					lowercase__       )

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

								answer.append([vector, dist]       )

				return answer





def 	lowerCAmelCase_		(lowercase__						:       np.ndarray     ,					lowercase__						:       np.ndarray       )		->    float:


				'''simple docstring'''




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


if __name__ == "__main__":
			import doctest

			doctest.testmod()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase			:		int   				=    {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GitForCausalLM",
			    "GitModel",
			    "GitPreTrainedModel",
			    "GitVisionModel",
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Optional[int]   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





