


"""simple docstring"""







import os
import sys
import unittest


a__    :							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
a__    :							Any            =					os.path.join(git_repo_path, '''src''', '''diffusers''')




class 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    int       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    find_backend("    if not is_torch_available():"       )
						self.assertEqual(UpperCAmelCase__      ,      "torch"       )

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

						__SCREAMING_SNAKE_CASE						       =    find_backend("    if not (is_torch_available() and is_transformers_available()):"       )
						self.assertEqual(UpperCAmelCase__      ,      "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						       =    find_backend(
						    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"       )
						self.assertEqual(UpperCAmelCase__      ,      "torch_and_transformers_and_onnx"       )




	def   UpperCAmelCase_				(			self      :    int       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn("torch"      ,      UpperCAmelCase__       )
						self.assertIn("torch_and_transformers"      ,      UpperCAmelCase__       )
						self.assertIn("flax_and_transformers"      ,      UpperCAmelCase__       )
						self.assertIn("torch_and_transformers_and_onnx"      ,      UpperCAmelCase__       )

						# 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   UpperCAmelCase_				(			self      :    List[Any]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    create_dummy_object("CONSTANT"      ,      "'torch'"       )
						self.assertEqual(UpperCAmelCase__      ,      "\nCONSTANT = None\n"       )

						__SCREAMING_SNAKE_CASE						       =    create_dummy_object("function"      ,      "'torch'"       )
						self.assertEqual(
						    UpperCAmelCase__      ,      "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"       )

						__SCREAMING_SNAKE_CASE						       =    "\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						       =    create_dummy_object("FakeClass"      ,      "'torch'"       )
						self.assertEqual(UpperCAmelCase__      ,      UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    "# 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						       =    create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
						self.assertEqual(dummy_files["torch"]      ,      UpperCAmelCase__       )




"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "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]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "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:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

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

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

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

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
