



import os
import sys
import unittest


_lowerCamelCase    :		str		      =    os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_lowerCamelCase    :		Optional[Any]		      =    os.path.join(git_repo_path, """src""", """diffusers""")


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				find_backend('    if not is_torch_available():'	)
											self.assertEqual(snake_case       ,	'torch'	)

											# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
											# self.assertEqual(backend_with_underscore, "tensorflow_text")

											SCREAMING_SNAKE_CASE							:     int						=				find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
											self.assertEqual(snake_case       ,	'torch_and_transformers'	)

											# double_backend_with_underscore = find_backend(
											#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
											# )
											# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

											SCREAMING_SNAKE_CASE							:     List[str]						=				find_backend(
											    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'	)
											self.assertEqual(snake_case       ,	'torch_and_transformers_and_onnx'	)







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				read_init()
											# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
											self.assertIn('torch'       ,	snake_case	)
											self.assertIn('torch_and_transformers'       ,	snake_case	)
											self.assertIn('flax_and_transformers'       ,	snake_case	)
											self.assertIn('torch_and_transformers_and_onnx'       ,	snake_case	)

											# Likewise, we can't assert on the exact content of a key
											self.assertIn('UNet2DModel'       ,	objects['torch']	)
											self.assertIn('FlaxUNet2DConditionModel'       ,	objects['flax']	)
											self.assertIn('StableDiffusionPipeline'       ,	objects['torch_and_transformers']	)
											self.assertIn('FlaxStableDiffusionPipeline'       ,	objects['flax_and_transformers']	)
											self.assertIn('LMSDiscreteScheduler'       ,	objects['torch_and_scipy']	)
											self.assertIn('OnnxStableDiffusionPipeline'       ,	objects['torch_and_transformers_and_onnx']	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				create_dummy_object('CONSTANT'       ,	'\'torch\''	)
											self.assertEqual(snake_case       ,	'\nCONSTANT = None\n'	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				create_dummy_object('function'       ,	'\'torch\''	)
											self.assertEqual(
											    snake_case       ,	'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
											SCREAMING_SNAKE_CASE							:     List[str]						=				create_dummy_object('FakeClass'       ,	'\'torch\''	)
											self.assertEqual(snake_case       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
											SCREAMING_SNAKE_CASE							:     List[Any]						=				create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
											self.assertEqual(dummy_files['torch']       ,	snake_case	)




from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()