






"""simple docstring"""





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


A__					: Union[str, Any]=	logging.get_logger(__name__)

A__					: List[str]=	OrderedDict(
    [
        ("""align""", """EfficientNetImageProcessor"""),
        ("""beit""", """BeitImageProcessor"""),
        ("""bit""", """BitImageProcessor"""),
        ("""blip""", """BlipImageProcessor"""),
        ("""blip-2""", """BlipImageProcessor"""),
        ("""bridgetower""", """BridgeTowerImageProcessor"""),
        ("""chinese_clip""", """ChineseCLIPImageProcessor"""),
        ("""clip""", """CLIPImageProcessor"""),
        ("""clipseg""", """ViTImageProcessor"""),
        ("""conditional_detr""", """ConditionalDetrImageProcessor"""),
        ("""convnext""", """ConvNextImageProcessor"""),
        ("""convnextv2""", """ConvNextImageProcessor"""),
        ("""cvt""", """ConvNextImageProcessor"""),
        ("""data2vec-vision""", """BeitImageProcessor"""),
        ("""deformable_detr""", """DeformableDetrImageProcessor"""),
        ("""deit""", """DeiTImageProcessor"""),
        ("""deta""", """DetaImageProcessor"""),
        ("""detr""", """DetrImageProcessor"""),
        ("""dinat""", """ViTImageProcessor"""),
        ("""donut-swin""", """DonutImageProcessor"""),
        ("""dpt""", """DPTImageProcessor"""),
        ("""efficientformer""", """EfficientFormerImageProcessor"""),
        ("""efficientnet""", """EfficientNetImageProcessor"""),
        ("""flava""", """FlavaImageProcessor"""),
        ("""focalnet""", """BitImageProcessor"""),
        ("""git""", """CLIPImageProcessor"""),
        ("""glpn""", """GLPNImageProcessor"""),
        ("""groupvit""", """CLIPImageProcessor"""),
        ("""imagegpt""", """ImageGPTImageProcessor"""),
        ("""instructblip""", """BlipImageProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
        ("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
        ("""levit""", """LevitImageProcessor"""),
        ("""mask2former""", """Mask2FormerImageProcessor"""),
        ("""maskformer""", """MaskFormerImageProcessor"""),
        ("""mgp-str""", """ViTImageProcessor"""),
        ("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
        ("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevitv2""", """MobileViTImageProcessor"""),
        ("""nat""", """ViTImageProcessor"""),
        ("""oneformer""", """OneFormerImageProcessor"""),
        ("""owlvit""", """OwlViTImageProcessor"""),
        ("""perceiver""", """PerceiverImageProcessor"""),
        ("""pix2struct""", """Pix2StructImageProcessor"""),
        ("""poolformer""", """PoolFormerImageProcessor"""),
        ("""regnet""", """ConvNextImageProcessor"""),
        ("""resnet""", """ConvNextImageProcessor"""),
        ("""sam""", """SamImageProcessor"""),
        ("""segformer""", """SegformerImageProcessor"""),
        ("""swiftformer""", """ViTImageProcessor"""),
        ("""swin""", """ViTImageProcessor"""),
        ("""swin2sr""", """Swin2SRImageProcessor"""),
        ("""swinv2""", """ViTImageProcessor"""),
        ("""table-transformer""", """DetrImageProcessor"""),
        ("""timesformer""", """VideoMAEImageProcessor"""),
        ("""tvlt""", """TvltImageProcessor"""),
        ("""upernet""", """SegformerImageProcessor"""),
        ("""van""", """ConvNextImageProcessor"""),
        ("""videomae""", """VideoMAEImageProcessor"""),
        ("""vilt""", """ViltImageProcessor"""),
        ("""vit""", """ViTImageProcessor"""),
        ("""vit_hybrid""", """ViTHybridImageProcessor"""),
        ("""vit_mae""", """ViTImageProcessor"""),
        ("""vit_msn""", """ViTImageProcessor"""),
        ("""xclip""", """CLIPImageProcessor"""),
        ("""yolos""", """YolosImageProcessor"""),
    ]
)

A__					: List[str]=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            UpperCamelCase__         =    model_type_to_module_name(SCREAMING_SNAKE_CASE					)

            UpperCamelCase__         =    importlib.import_module(F'.{module_name}' ,	'transformers.models'					)
            try:
                return getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(SCREAMING_SNAKE_CASE ,	'__name__' ,	SCREAMING_SNAKE_CASE					) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    UpperCamelCase__         =    importlib.import_module('transformers'					)
    if hasattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        return getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    return None







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = False ,	SCREAMING_SNAKE_CASE = False ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = False ,	**SCREAMING_SNAKE_CASE ,	)					->	List[Any]:
    """simple docstring"""

    UpperCamelCase__         =    get_file_from_repo(
        SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	cache_dir=SCREAMING_SNAKE_CASE ,	force_download=SCREAMING_SNAKE_CASE ,	resume_download=SCREAMING_SNAKE_CASE ,	proxies=SCREAMING_SNAKE_CASE ,	use_auth_token=SCREAMING_SNAKE_CASE ,	revision=SCREAMING_SNAKE_CASE ,	local_files_only=SCREAMING_SNAKE_CASE ,	)
    if resolved_config_file is None:
        logger.info(
            'Could not locate the image processor configuration file, will try to use the model config instead.'					)
        return {}

    with open(SCREAMING_SNAKE_CASE ,	encoding='utf-8'					) as reader:
        return json.load(SCREAMING_SNAKE_CASE					)





class    __lowerCamelCase    :

   def __init__(					self      )		->       List[str]:
       raise EnvironmentError(
           'AutoImageProcessor is designed to be instantiated '
           'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'      )

   @classmethod
   @replace_list_option_in_docstrings(snake_case_      )
   def 						SCREAMING_SNAKE_CASE__ (					cls ,				snake_case_ ,				**snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    kwargs.pop('config' ,				snake_case_      )
       UpperCamelCase__         =    kwargs.pop('trust_remote_code' ,				snake_case_      )
       UpperCamelCase__         =    True

       UpperCamelCase__         =    ImageProcessingMixin.get_image_processor_dict(snake_case_ ,				**snake_case_      )
       UpperCamelCase__         =    config_dict.get('image_processor_type' ,				snake_case_      )
       UpperCamelCase__         =    None
       if "AutoImageProcessor" in config_dict.get('auto_map' ,				{}      ):
           UpperCamelCase__         =    config_dict["""auto_map"""]["""AutoImageProcessor"""]

       # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
       # and if so, infer the image processor class from there.
       if image_processor_class is None and image_processor_auto_map is None:
           UpperCamelCase__         =    config_dict.pop('feature_extractor_type' ,				snake_case_      )
           if feature_extractor_class is not None:
               logger.warning(
                   'Could not find image processor class in the image processor config or the model config. Loading'
                   ' based on pattern matching with the model\'s feature extractor configuration.'      )
               UpperCamelCase__         =    feature_extractor_class.replace('FeatureExtractor' ,				'ImageProcessor'      )
           if "AutoFeatureExtractor" in config_dict.get('auto_map' ,				{}      ):
               UpperCamelCase__         =    config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
               UpperCamelCase__         =    feature_extractor_auto_map.replace('FeatureExtractor' ,				'ImageProcessor'      )
               logger.warning(
                   'Could not find image processor auto map in the image processor config or the model config.'
                   ' Loading based on pattern matching with the model\'s feature extractor configuration.'      )

        # If we don't find the image processor class in the image processor config, let's try the model config.
       if image_processor_class is None and image_processor_auto_map is None:
           if not isinstance(snake_case_ ,				snake_case_      ):
               UpperCamelCase__         =    AutoConfig.from_pretrained(snake_case_ ,				**snake_case_      )
           # It could be in `config.image_processor_type``
           UpperCamelCase__         =    getattr(snake_case_ ,				'image_processor_type' ,				snake_case_      )
           if hasattr(snake_case_ ,				'auto_map'      ) and "AutoImageProcessor" in config.auto_map:
               UpperCamelCase__         =    config.auto_map["""AutoImageProcessor"""]

       if image_processor_class is not None:
           UpperCamelCase__         =    image_processor_class_from_name(snake_case_      )

       UpperCamelCase__         =    image_processor_auto_map is not None
       UpperCamelCase__         =    image_processor_class is not None or type(snake_case_      ) in IMAGE_PROCESSOR_MAPPING
       UpperCamelCase__         =    resolve_trust_remote_code(
           snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

       if has_remote_code and trust_remote_code:
           UpperCamelCase__         =    get_class_from_dynamic_module(
               snake_case_ ,				snake_case_ ,				**snake_case_      )
           UpperCamelCase__         =    kwargs.pop('code_revision' ,				snake_case_      )
           if os.path.isdir(snake_case_      ):
               image_processor_class.register_for_auto_class()
           return image_processor_class.from_dict(snake_case_ ,				**snake_case_      )
       elif image_processor_class is not None:
           return image_processor_class.from_dict(snake_case_ ,				**snake_case_      )
       # Last try: we use the IMAGE_PROCESSOR_MAPPING.
       elif type(snake_case_      ) in IMAGE_PROCESSOR_MAPPING:
           UpperCamelCase__         =    IMAGE_PROCESSOR_MAPPING[type(snake_case_      )]
           return image_processor_class.from_dict(snake_case_ ,				**snake_case_      )

       raise ValueError(
           F'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
           F'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
           F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()      )}'      )







   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_      )		->       str:
       IMAGE_PROCESSOR_MAPPING.register(snake_case_ ,				snake_case_      )












"""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
A__					: Any=	"""src/diffusers"""

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


A__					: Optional[Any]=	"""
{0} = None
"""

A__					: List[Any]=	"""
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})
"""


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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    _re_backend.findall(SCREAMING_SNAKE_CASE					)
    if len(SCREAMING_SNAKE_CASE					) == 0:
        return None

    return "_and_".join(SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( )					->	str:
    """simple docstring"""

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

    # Get to the point we do the actual imports for type checking
    UpperCamelCase__         =    0
    UpperCamelCase__         =    {}
    # 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
        UpperCamelCase__         =    find_backend(lines[line_index]					)
        if backend is not None:
            while not lines[line_index].startswith('else:'					):
                line_index += 1
            line_index += 1
            UpperCamelCase__         =    []
            # Until we unindent, add backend objects to the list
            while line_index < len(SCREAMING_SNAKE_CASE					) and len(lines[line_index]					) > 1:
                UpperCamelCase__         =    lines[line_index]
                UpperCamelCase__         =    _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:
                UpperCamelCase__         =    objects
        else:
            line_index += 1

    return backend_specific_objects







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    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:
    """simple docstring"""

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

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

    return dummy_files







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=False					)					->	int:
    """simple docstring"""

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

    # Locate actual dummy modules and read their content.
    UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	'utils'					)
    UpperCamelCase__         =    {
        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()
    }

    UpperCamelCase__         =    {}
    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:
                UpperCamelCase__         =    f.read()
        else:
            UpperCamelCase__         =    ''

    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__":
  A__					: Any=	argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  A__					: Optional[int]=	parser.parse_args()

  check_dummies(args.fix_and_overwrite)




