"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCAmelCase					: List[str]        =False





class 				_A		( unittest.TestCase			):
      pass






@nightly
@require_torch_gpu
class 				_A		( unittest.TestCase			):

      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""	, torch_dtype=torch.floataa		)
            pipe.to(__lowerCAmelCase		)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

            lowercase														=			load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""		)

            lowercase														=			torch.manual_seed(0		)
            lowercase														=			pipe.dual_guided(
                prompt="""first prompt"""	, image=__lowerCAmelCase	, text_to_image_strength=0.7_5	, generator=__lowerCAmelCase	, guidance_scale=7.5	, num_inference_steps=2	, output_type="""numpy"""	, ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                  pipe.save_pretrained(__lowerCAmelCase		)
                  lowercase														=			VersatileDiffusionPipeline.from_pretrained(__lowerCAmelCase	, torch_dtype=torch.floataa		)
            pipe.to(__lowerCAmelCase		)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

            lowercase														=			generator.manual_seed(0		)
            lowercase														=			pipe.dual_guided(
                prompt="""first prompt"""	, image=__lowerCAmelCase	, text_to_image_strength=0.7_5	, generator=__lowerCAmelCase	, guidance_scale=7.5	, num_inference_steps=2	, output_type="""numpy"""	, ).images

            assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""	, torch_dtype=torch.floataa		)
            pipe.to(__lowerCAmelCase		)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

            lowercase														=			"""cyberpunk 2077"""
            lowercase														=			load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""		)
            lowercase														=			torch.manual_seed(0		)
            lowercase														=			pipe.dual_guided(
                prompt=__lowerCAmelCase	, image=__lowerCAmelCase	, text_to_image_strength=0.7_5	, generator=__lowerCAmelCase	, guidance_scale=7.5	, num_inference_steps=50	, output_type="""numpy"""	, ).images

            lowercase														=			image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            lowercase														=			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]		)

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

            lowercase														=			"""A painting of a squirrel eating a burger """
            lowercase														=			torch.manual_seed(0		)
            lowercase														=			pipe.text_to_image(
                prompt=__lowerCAmelCase	, generator=__lowerCAmelCase	, guidance_scale=7.5	, num_inference_steps=50	, output_type="""numpy"""		).images

            lowercase														=			image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            lowercase														=			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]		)

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

            lowercase														=			pipe.image_variation(__lowerCAmelCase	, generator=__lowerCAmelCase	, output_type="""numpy"""		).images

            lowercase														=			image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            lowercase														=			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]		)

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




"""simple docstring"""






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase					: Union[str, Any]        =subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowerCAmelCase					: List[str]        =subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()

__lowerCAmelCase					: List[str]        ="""|""".join(sys.argv[1:])
__lowerCAmelCase					: Dict        =re.compile(RF"""^({joined_dirs}).*?\.py$""")

__lowerCAmelCase					: int        =[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")



