






'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""					)
            __lowerCamelCase						:       Tuple				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       Any				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       List[Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Dict				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       Tuple				   =				output.images

            __lowerCamelCase						:       int				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       List[str]				   =				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       int				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_euler"""					)

            __lowerCamelCase						:       List[Any]				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Union[str, Any]				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       int				   =				sd_pipe([prompt]	,    generator=A__	,    guidance_scale=9.0	,    num_inference_steps=20	,    output_type="""np"""					)

            __lowerCamelCase						:       List[str]				   =				output.images

            __lowerCamelCase						:       Optional[int]				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       Optional[int]				   =				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-1




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
            __lowerCamelCase						:       List[str]				   =				sd_pipe.to(A__					)
            sd_pipe.set_progress_bar_config(disable=A__					)

            sd_pipe.set_scheduler("""sample_dpmpp_2m"""					)

            __lowerCamelCase						:       int				   =				"""A painting of a squirrel eating a burger"""
            __lowerCamelCase						:       Tuple				   =				torch.manual_seed(0					)
            __lowerCamelCase						:       Union[str, Any]				   =				sd_pipe(
                [prompt]	,    generator=A__	,    guidance_scale=7.5	,    num_inference_steps=15	,    output_type="""np"""	,    use_karras_sigmas=A__	,    )

            __lowerCamelCase						:       int				   =				output.images

            __lowerCamelCase						:       Any				   =				image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowerCamelCase						:       int				   =				np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2











'''simple docstring'''

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				HfArgumentParser(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     __lowerCamelCase						:       Dict				   =				TensorFlowBenchmark(args=_lowercase     )
     try:
          __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          __lowerCamelCase						:       Tuple				   =				"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
          __lowerCamelCase						:       List[Any]				   =				""" """.join(str(_lowercase     ).split(""" """     )[:-1]     )
          __lowerCamelCase						:       Tuple				   =				""""""
          __lowerCamelCase						:       List[str]				   =				eval(str(_lowercase     ).split(""" """     )[-1]     )
          __lowerCamelCase						:       Tuple				   =				[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]     )
               else:
                    wrong_args.append(_lowercase     )
          if len(_lowercase     ) > 0:
               __lowerCamelCase						:       Tuple				   =				full_error_msg + begin_error_msg + str(_lowercase     )
          raise ValueError(_lowercase     )
     benchmark.run()


if __name__ == "__main__":
   main()



