

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
A__						=							"src/diffusers"

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


A__						=							"\n{0} = None\n"

A__						=							"\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"


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




def 					_lowercase     (				a_				:					int				)				->	int:




					'''simple docstring'''







					__magic_name__							=    _re_backend.findall(a_				)
					if len(a_				) == 0:
										return None

					return "_and_".join(a_				)




def 					_lowercase     (				)				->	Any:




					'''simple docstring'''







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

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

					return backend_specific_objects




def 					_lowercase     (				a_				:					int					,a_				:					Any				)				->	List[Any]:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Any=None				)				->	Dict:




					'''simple docstring'''







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

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

					return dummy_files




def 					_lowercase     (				a_				:					List[Any]=False				)				->	Union[str, Any]:




					'''simple docstring'''







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

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

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

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


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

						check_dummies(args.fix_and_overwrite)







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


A__						=							logging.get_logger(__name__)

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Tuple       				=  ["pixel_values"]







	def __init__(   self: Optional[Any]  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: int = 0.9  , __UpperCamelCase: PILImageResampling = PILImageResampling.BICUBIC  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: Union[int, float] = 1 / 2_55  , __UpperCamelCase: bool = True  , __UpperCamelCase: bool = True  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						super().__init__(**__UpperCamelCase      )
						__magic_name__							=    size if size is not None else {'shortest_edge': 2_24}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    crop_pct
						__magic_name__							=    resample
						__magic_name__							=    do_center_crop
						__magic_name__							=    crop_size
						__magic_name__							=    do_rescale
						__magic_name__							=    rescale_factor
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__magic_name__							=    image_std if image_std is not None else IMAGENET_DEFAULT_STD







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: Optional[float] = None  , __UpperCamelCase: PILImageResampling = PILImageResampling.BICUBIC  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						if "shortest_edge" not in size and ("height" not in size or "width" not in size):
											raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
						if crop_pct is not None:
											if "shortest_edge" in size:
																__magic_name__							=    int(size['shortest_edge'] / crop_pct      )
											elif "height" in size and "width" in size:
																if size["height"] == size["width"]:
																					__magic_name__							=    int(size['height'] / crop_pct      )
																else:
																					__magic_name__							=    (int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
											else:
																raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase      )      )

											__magic_name__							=    get_resize_output_image_size(__UpperCamelCase  , size=__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						else:
											if "shortest_edge" in size:
																__magic_name__							=    get_resize_output_image_size(__UpperCamelCase  , size=size['shortest_edge']  , default_to_square=__UpperCamelCase      )
											elif "height" in size and "width" in size:
																__magic_name__							=    (size['height'], size['width'])
											else:
																raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase      )      )

						return resize(__UpperCamelCase  , size=__UpperCamelCase  , resample=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase      )
						if "height" not in size or "width" not in size:
											raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'      )
						return center_crop(__UpperCamelCase  , size=(size['height'], size['width'])  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[int, float]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						return rescale(__UpperCamelCase  , scale=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[float, List[float]]  , __UpperCamelCase: Union[float, List[float]]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[int]  , ):

						'''simple docstring'''





						return normalize(__UpperCamelCase  , mean=__UpperCamelCase  , std=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: ImageInput  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: int = None  , __UpperCamelCase: PILImageResampling = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: float = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[str, TensorType]] = None  , __UpperCamelCase: ChannelDimension = ChannelDimension.FIRST  , **__UpperCamelCase: Any  , ):

						'''simple docstring'''





						__magic_name__							=    do_resize if do_resize is not None else self.do_resize
						__magic_name__							=    crop_pct if crop_pct is not None else self.crop_pct
						__magic_name__							=    resample if resample is not None else self.resample
						__magic_name__							=    do_center_crop if do_center_crop is not None else self.do_center_crop
						__magic_name__							=    do_rescale if do_rescale is not None else self.do_rescale
						__magic_name__							=    rescale_factor if rescale_factor is not None else self.rescale_factor
						__magic_name__							=    do_normalize if do_normalize is not None else self.do_normalize
						__magic_name__							=    image_mean if image_mean is not None else self.image_mean
						__magic_name__							=    image_std if image_std is not None else self.image_std

						__magic_name__							=    size if size is not None else self.size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else self.crop_size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    make_list_of_images(__UpperCamelCase      )

						if not valid_images(__UpperCamelCase      ):
											raise ValueError(
											    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
											    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

						if do_resize and size is None or resample is None:
											raise ValueError('Size and resample must be specified if do_resize is True.'      )

						if do_center_crop and crop_pct is None:
											raise ValueError('Crop_pct must be specified if do_center_crop is True.'      )

						if do_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

						# All transformations expect numpy arrays.
						__magic_name__							=    [to_numpy_array(__UpperCamelCase      ) for image in images]

						if do_resize:
											__magic_name__							=    [self.resize(image=__UpperCamelCase  , size=__UpperCamelCase  , crop_pct=__UpperCamelCase  , resample=__UpperCamelCase      ) for image in images]

						if do_center_crop:
											__magic_name__							=    [self.center_crop(image=__UpperCamelCase  , size=__UpperCamelCase      ) for image in images]

						if do_rescale:
											__magic_name__							=    [self.rescale(image=__UpperCamelCase  , scale=__UpperCamelCase      ) for image in images]

						if do_normalize:
											__magic_name__							=    [self.normalize(image=__UpperCamelCase  , mean=__UpperCamelCase  , std=__UpperCamelCase      ) for image in images]

						__magic_name__							=    [to_channel_dimension_format(__UpperCamelCase  , __UpperCamelCase      ) for image in images]

						__magic_name__							=    {'pixel_values': images}
						return BatchFeature(data=__UpperCamelCase  , tensor_type=__UpperCamelCase      )




