



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


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE_				= """src/diffusers"""

# Matches is_xxx_available()
SCREAMING_SNAKE_CASE_				= re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
SCREAMING_SNAKE_CASE_				= re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


SCREAMING_SNAKE_CASE_				= """
{0} = None
"""

SCREAMING_SNAKE_CASE_				= """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


SCREAMING_SNAKE_CASE_				= """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      int:
		a_     :      Any 						=     _re_backend.findall(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) == 0:
				return None

		return "_and_".join(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							)			->      List[Any]:
		with open(os.path.join(SCREAMING_SNAKE_CASE__, "__init__.py"	), "r", encoding="utf-8", newline="\n"	) as f:
				a_     :      Any 						=     f.readlines()

		# Get to the point we do the actual imports for type checking
		a_     :      List[Any] 						=     0
		a_     :      Union[str, Any] 						=     {}
		# Go through the end of the file
		while line_index < len(SCREAMING_SNAKE_CASE__	):
				# If the line contains is_backend_available, we grab all objects associated with the `else` block
				a_     :      Optional[Any] 						=     find_backend(lines[line_index]	)
				if backend is not None:
						while not lines[line_index].startswith("else:"	):
								line_index += 1
						line_index += 1
						a_     :      Optional[Any] 						=     []
						# Until we unindent, add backend objects to the list
						while line_index < len(SCREAMING_SNAKE_CASE__	) and len(lines[line_index]	) > 1:
								a_     :      Dict 						=     lines[line_index]
								a_     :      Any 						=     _re_single_line_import.search(SCREAMING_SNAKE_CASE__	)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "	)	)
								elif line.startswith(" " * 8	):
										objects.append(line[8:-2]	)
								line_index += 1

						if len(SCREAMING_SNAKE_CASE__	) > 0:
								a_     :      str 						=     objects
				else:
						line_index += 1

		return backend_specific_objects



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      str:
		if name.isupper():
				return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__	)
		elif name.islower():
				return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		else:
				return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__=None	)			->      int:
		if backend_specific_objects is None:
				a_     :      int 						=     read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		a_     :      List[Any] 						=     {}

		for backend, objects in backend_specific_objects.items():
				a_     :      Dict 						=     "[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"	)	) + "]"
				a_     :      Tuple 						=     "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) for o in objects]	)
				a_     :      Optional[int] 						=     dummy_file

		return dummy_files



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__=False	)			->      int:
		a_     :      List[Any] 						=     create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		a_     :      List[str] 						=     {"torch": "pt"}

		# Locate actual dummy modules and read their content.
		a_     :      Union[str, Any] 						=     os.path.join(SCREAMING_SNAKE_CASE__, "utils"	)
		a_     :      Optional[int] 						=     {
		    backend: os.path.join(SCREAMING_SNAKE_CASE__, F"""dummy_{short_names.get(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)}_objects.py"""	)
		    for backend in dummy_files.keys()
		}

		a_     :      Dict 						=     {}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(SCREAMING_SNAKE_CASE__	):
						with open(SCREAMING_SNAKE_CASE__, "r", encoding="utf-8", newline="\n"	) as f:
								a_     :      Optional[int] 						=     f.read()
				else:
						a_     :      Tuple 						=     ""

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    F"""Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)}_objects.py as the main """
								    "__init__ has new objects."	)
								with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n"	) as f:
										f.write(dummy_files[backend]	)
						else:
								raise ValueError(
								    "The main __init__ has objects that are not present in "
								    F"""diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)}_objects.py. Run `make fix-copies` """
								    "to fix this."	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					SCREAMING_SNAKE_CASE_				= parser.parse_args()

					check_dummies(args.fix_and_overwrite)




"""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