




'''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							:       Any						=			"src/diffusers"

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


_SCREAMING_SNAKE_CASE							:       int						=			"\n{0} = None\n"

_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


_SCREAMING_SNAKE_CASE							:       int						=			"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"


def 					UpperCamelCase_(     snake_case							:	Tuple					):

       '''simple docstring'''






       snake_case_      =				_re_backend.findall(snake_case					)
       if len(snake_case					) == 0:
              return None

       return "_and_".join(snake_case					)


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       with open(os.path.join(snake_case							,    "__init__.py"					)							,    "r"							,    encoding="utf-8"							,    newline="\n"					) as f:
              snake_case_      =				f.readlines()

       # Get to the point we do the actual imports for type checking
       snake_case_      =				0
       snake_case_      =				{}
       # Go through the end of the file
       while line_index < len(snake_case					):
              # If the line contains is_backend_available, we grab all objects associated with the `else` block
              snake_case_      =				find_backend(lines[line_index]					)
              if backend is not None:
                     while not lines[line_index].startswith("else:"					):
                            line_index += 1
                     line_index += 1
                     snake_case_      =				[]
                     # Until we unindent, add backend objects to the list
                     while line_index < len(snake_case					) and len(lines[line_index]					) > 1:
                            snake_case_      =				lines[line_index]
                            snake_case_      =				_re_single_line_import.search(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(snake_case					) > 0:
                            snake_case_      =				objects
              else:
                     line_index += 1

       return backend_specific_objects


def 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Dict					):

       '''simple docstring'''






       if name.isupper():
              return DUMMY_CONSTANT.format(snake_case					)
       elif name.islower():
              return DUMMY_FUNCTION.format(snake_case							,    snake_case					)
       else:
              return DUMMY_CLASS.format(snake_case							,    snake_case					)


def 					UpperCamelCase_(     snake_case							:	List[str]=None					):

       '''simple docstring'''






       if backend_specific_objects is None:
              snake_case_      =				read_init()
       # For special correspondence backend to module name as used in the function requires_modulename
       snake_case_      =				{}

       for backend, objects in backend_specific_objects.items():
              snake_case_      =				"[" + ", ".join(f'"{b}"' for b in backend.split("_and_"					)					) + "]"
              snake_case_      =				"# 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(snake_case							,    snake_case					) for o in objects]					)
              snake_case_      =				dummy_file

       return dummy_files


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]=False					):

       '''simple docstring'''






       snake_case_      =				create_dummy_files()
       # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
       snake_case_      =				{"torch": "pt"}

       # Locate actual dummy modules and read their content.
       snake_case_      =				os.path.join(snake_case							,    "utils"					)
       snake_case_      =				{
           backend: os.path.join(snake_case							,    f'dummy_{short_names.get(snake_case							,    snake_case					)}_objects.py'					)
           for backend in dummy_files.keys()
       }

       snake_case_      =				{}
       for backend, file_path in dummy_file_paths.items():
              if os.path.isfile(snake_case					):
                     with open(snake_case							,    "r"							,    encoding="utf-8"							,    newline="\n"					) as f:
                            snake_case_      =				f.read()
              else:
                     snake_case_      =				""

       for backend in dummy_files.keys():
              if dummy_files[backend] != actual_dummies[backend]:
                     if overwrite:
                            print(
                                f'Updating diffusers.utils.dummy_{short_names.get(snake_case							,    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(snake_case							,    snake_case					)}_objects.py. Run `make fix-copies` '
                                "to fix this."					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE							:       int						=			argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  _SCREAMING_SNAKE_CASE							:       List[str]						=			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							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




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




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
										snake_case_      =				torch.manual_seed(0							)
										snake_case_      =				pipe(
										    image=a__    ,							generator=a__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type="numpy"    ,							).images

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

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]							)

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




