





from argparse import ArgumentParser

from .env import EnvironmentCommand


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						ArgumentParser('''Diffusers CLI tool'''    ,       usage='''diffusers-cli <command> [<args>]'''					)
       _UpperCamelCase              =						parser.add_subparsers(help='''diffusers-cli command helpers'''					)

       # Register commands
       EnvironmentCommand.register_subcommand(lowerCAmelCase					)

       # Let's go
       _UpperCamelCase              =						parser.parse_args()

       if not hasattr(lowerCAmelCase    ,       '''func'''					):
              parser.print_help()
              exit(1					)

       # Run
       _UpperCamelCase              =						args.func(lowerCAmelCase					)
       service.run()


if __name__ == "__main__":
       main()













from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


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


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):

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

       _UpperCamelCase              =						[]

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

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

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

              answer.append([vector, dist]					)

       return answer


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


if __name__ == "__main__":
       import doctest

       doctest.testmod()






