






"""simple docstring"""







import os
import sys
import unittest


_UpperCamelCase					      =  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
_UpperCamelCase					      =  os.path.join(git_repo_path, """src""", """diffusers""")







class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	find_backend("""    if not is_torch_available():""" )
          self.assertEqual(A     ,"""torch""" )

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

          UpperCAmelCase          =	find_backend("""    if not (is_torch_available() and is_transformers_available()):""" )
          self.assertEqual(A     ,"""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")

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






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

          # 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 ):
          UpperCAmelCase          =	create_dummy_object("""CONSTANT"""     ,"""'torch'""" )
          self.assertEqual(A     ,"""\nCONSTANT = None\n""" )

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

          UpperCAmelCase          =	"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
          UpperCAmelCase          =	create_dummy_object("""FakeClass"""     ,"""'torch'""" )
          self.assertEqual(A     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
          UpperCAmelCase          =	create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
          self.assertEqual(dummy_files["""torch"""]     ,A )











"""simple docstring"""







def _a			(		_snake_case = 6008_5147_5143							):







      """simple docstring"""






      try:
            UpperCAmelCase          =	int(_snake_case							)
      except (TypeError, ValueError):
            raise TypeError("""Parameter n must be int or castable to int."""							)
      if n <= 0:
            raise ValueError("""Parameter n must be greater than or equal to one."""							)
      UpperCAmelCase          =	1
      UpperCAmelCase          =	2
      while i * i <= n:
            while n % i == 0:
                  UpperCAmelCase          =	i
                  n //= i
            i += 1
      if n > 1:
            UpperCAmelCase          =	n
      return int(_snake_case							)


if __name__ == "__main__":
  print(F"""{solution() = }""")



