



"""simple docstring"""
import argparse
import os
import re


SCREAMING_SNAKE_CASE_				= """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE_				= re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE_				= re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False	)			->      List[Any]:
		with open(SCREAMING_SNAKE_CASE__, "r", encoding="utf-8"	) as f:
				a_     :      Optional[int] 						=     f.read()

		a_     :      Optional[Any] 						=     content.split("\n"	)
		a_     :      Tuple 						=     []
		a_     :      Any 						=     0
		while line_idx < len(SCREAMING_SNAKE_CASE__	):
				if _re_intro_mapping.search(lines[line_idx]	) is not None:
						a_     :      str 						=     len(re.search(r"^(\s*)\S", lines[line_idx]	).groups()[0]	) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(" " * indent + "("	):
								new_lines.append(lines[line_idx]	)
								line_idx += 1

						a_     :      Tuple 						=     []
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										a_     :      Dict 						=     line_idx
										while not lines[line_idx].startswith(" " * indent + ")"	):
												line_idx += 1
										blocks.append("\n".join(lines[start_idx : line_idx + 1]	)	)
								else:
										blocks.append(lines[line_idx]	)
								line_idx += 1

						# Sort blocks by their identifiers
						a_     :      Optional[int] 						=     sorted(SCREAMING_SNAKE_CASE__, key=lambda SCREAMING_SNAKE_CASE__	: _re_identifier.search(SCREAMING_SNAKE_CASE__	).groups()[0]	)
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]	)
						line_idx += 1

		if overwrite:
				with open(SCREAMING_SNAKE_CASE__, "w", encoding="utf-8"	) as f:
						f.write("\n".join(SCREAMING_SNAKE_CASE__	)	)
		elif "\n".join(SCREAMING_SNAKE_CASE__	) != content:
				return True



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = False	)			->      str:
		a_     :      int 						=     [os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) for f in os.listdir(SCREAMING_SNAKE_CASE__	) if f.endswith(".py"	)]
		a_     :      Optional[int] 						=     [sort_auto_mapping(SCREAMING_SNAKE_CASE__, overwrite=SCREAMING_SNAKE_CASE__	) for fname in fnames]

		if not overwrite and any(SCREAMING_SNAKE_CASE__	):
				a_     :      Tuple 						=     [f for f, d in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) if d]
				raise ValueError(
				    F"""The following files have auto mappings that need sorting: {', '.join(SCREAMING_SNAKE_CASE__	)}. Run `make style` to fix"""
				    " this."	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
					SCREAMING_SNAKE_CASE_				= parser.parse_args()

					sort_all_auto_mappings(not args.check_only)




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




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






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

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