




'''simple docstring'''






import unittest
from timeit import timeit




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       lowercase	:		Any    =   first_num if first_num >= second_num else second_num
       lowercase	:		Tuple    =   max_num
       while (common_mult % first_num > 0) or (common_mult % second_num > 0):
              common_mult += max_num
       return common_mult




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       return b if a == 0 else greatest_common_divisor(b % a ,					lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       return first_num // greatest_common_divisor(lowerCamelCase ,					lowerCamelCase   ) * second_num




def 		a_(			)					->int:






       '''simple docstring'''




       lowercase	:		Tuple    =   (
           '''from __main__ import least_common_multiple_slow, least_common_multiple_fast'''
       )
       print(
           '''least_common_multiple_slow():''' ,					timeit('''least_common_multiple_slow(1000, 999)''' ,					setup=lowerCamelCase   ) ,					)
       print(
           '''least_common_multiple_fast():''' ,					timeit('''least_common_multiple_fast(1000, 999)''' ,					setup=lowerCamelCase   ) ,					)

class 	SCREAMING_SNAKE_CASE_		(							unittest.TestCase					):
 __snake_case						:  Dict       =   (
     (1_0, 2_0),
     (1_3, 1_5),
     (4, 3_1),
     (1_0, 4_2),
     (4_3, 3_4),
     (5, 1_2),
     (1_2, 2_5),
     (1_0, 2_5),
     (6, 9),
 )
 __snake_case						:  List[Any]       =   (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        for i, (first_num, second_num) in enumerate(self.test_inputs			):
               lowercase	:		Any    =   least_common_multiple_slow(A ,		A			)
               lowercase	:		Dict    =   least_common_multiple_fast(A ,		A			)
               with self.subTest(i=A			):
                      self.assertEqual(A ,		self.expected_results[i]			)
                      self.assertEqual(A ,		self.expected_results[i]			)


if __name__ == "__main__":
      benchmark()
      unittest.main()










'''simple docstring'''






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
      from .testing_utils import (
          floats_tensor,
          load_hf_numpy,
          load_image,
          load_numpy,
          load_pt,
          nightly,
          parse_flag_from_env,
          print_tensor_test,
          require_torch_a,
          require_torch_gpu,
          skip_mps,
          slow,
          torch_all_close,
          torch_device,
      )
      from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		List[str]    =   f"""This example requires a minimum version of {min_version},"""
              error_message += f""" but the version found is {__version__}.\n"""
              raise ImportError(lowerCamelCase   )




