


"""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,
)


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					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"""),
    ]
)

__snake_case         =					_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def 						__lowerCAmelCase		(		lowercase						:						str				)			->		Optional[int]:


    """simple docstring"""





    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            snake_case					:   Dict         =					model_type_to_module_name(lowercase				)

            snake_case					:   Dict         =					importlib.import_module(F'.{module_name}'		,							"transformers.models"				)
            try:
                return getattr(lowercase		,							lowercase				)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(lowercase		,							"__name__"		,							lowercase				) == 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.
    snake_case					:   Dict         =					importlib.import_module("transformers"				)
    if hasattr(lowercase		,							lowercase				):
        return getattr(lowercase		,							lowercase				)

    return None







def 						__lowerCAmelCase		(		lowercase						:						Union[str, os.PathLike]		,							lowercase						:						Optional[Union[str, os.PathLike]] = None		,							lowercase						:						bool = False		,							lowercase						:						bool = False		,							lowercase						:						Optional[Dict[str, str]] = None		,							lowercase						:						Optional[Union[bool, str]] = None		,							lowercase						:						Optional[str] = None		,							lowercase						:						bool = False		,							**lowercase						:						Union[str, Any]		,							)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					get_file_from_repo(
        lowercase		,							lowercase		,							cache_dir=lowercase		,							force_download=lowercase		,							resume_download=lowercase		,							proxies=lowercase		,							use_auth_token=lowercase		,							revision=lowercase		,							local_files_only=lowercase		,							)
    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(lowercase		,							encoding="utf-8"				) as reader:
        return json.load(lowercase				)







class _lowerCAmelCase       :







   def __init__(			self		)			->	Tuple:

       '''simple docstring'''




       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(UpperCamelCase__		)
   def 				lowerCamelCase	(			cls		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Tuple         =					kwargs.pop("config"		,			UpperCamelCase__		)
       snake_case					:   Dict         =					kwargs.pop("trust_remote_code"		,			UpperCamelCase__		)
       snake_case					:   List[str]         =					True

       snake_case      ,snake_case					:   Dict         =					ImageProcessingMixin.get_image_processor_dict(UpperCamelCase__		,			**UpperCamelCase__		)
       snake_case					:   List[str]         =					config_dict.get("image_processor_type"		,			UpperCamelCase__		)
       snake_case					:   Optional[int]         =					None
       if "AutoImageProcessor" in config_dict.get("auto_map"		,			{}		):
           snake_case					:   str         =					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:
           snake_case					:   int         =					config_dict.pop("feature_extractor_type"		,			UpperCamelCase__		)
           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."		)
               snake_case					:   Any         =					feature_extractor_class.replace("FeatureExtractor"		,			"ImageProcessor"		)
           if "AutoFeatureExtractor" in config_dict.get("auto_map"		,			{}		):
               snake_case					:   Union[str, Any]         =					config_dict["auto_map"]["AutoFeatureExtractor"]
               snake_case					:   Optional[Any]         =					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(UpperCamelCase__		,			UpperCamelCase__		):
               snake_case					:   Union[str, Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		,			**UpperCamelCase__		)
           # It could be in `config.image_processor_type``
           snake_case					:   Any         =					getattr(UpperCamelCase__		,			"image_processor_type"		,			UpperCamelCase__		)
           if hasattr(UpperCamelCase__		,			"auto_map"		) and "AutoImageProcessor" in config.auto_map:
               snake_case					:   int         =					config.auto_map["AutoImageProcessor"]

       if image_processor_class is not None:
           snake_case					:   List[Any]         =					image_processor_class_from_name(UpperCamelCase__		)

       snake_case					:   List[Any]         =					image_processor_auto_map is not None
       snake_case					:   List[Any]         =					image_processor_class is not None or type(UpperCamelCase__		) in IMAGE_PROCESSOR_MAPPING
       snake_case					:   List[Any]         =					resolve_trust_remote_code(
           UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)

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

       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 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase__		,			UpperCamelCase__		)









"""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
__snake_case         =					"""src/diffusers"""

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


__snake_case         =					"""
{0} = None
"""

__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})
"""


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







def 						__lowerCAmelCase		(		lowercase						:						Tuple				)			->		str:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					_re_backend.findall(lowercase				)
    if len(lowercase				) == 0:
        return None

    return "_and_".join(lowercase				)







def 						__lowerCAmelCase		(		)			->		Tuple:


    """simple docstring"""





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

    # Get to the point we do the actual imports for type checking
    snake_case					:   Tuple         =					0
    snake_case					:   Optional[Any]         =					{}
    # Go through the end of the file
    while line_index < len(lowercase				):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        snake_case					:   List[Any]         =					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					:   str         =					[]
            # Until we unindent, add backend objects to the list
            while line_index < len(lowercase				) and len(lines[line_index]				) > 1:
                snake_case					:   List[str]         =					lines[line_index]
                snake_case					:   Any         =					_re_single_line_import.search(lowercase				)
                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(lowercase				) > 0:
                snake_case					:   Any         =					objects
        else:
            line_index += 1

    return backend_specific_objects







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Tuple				)			->		int:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]=None				)			->		Optional[int]:


    """simple docstring"""





    if backend_specific_objects is None:
        snake_case					:   List[str]         =					read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    snake_case					:   Dict         =					{}

    for backend, objects in backend_specific_objects.items():
        snake_case					:   Union[str, Any]         =					"[" + ", ".join(F'"{b}"' for b in backend.split("_and_"				)				) + "]"
        snake_case					:   Union[str, Any]         =					"# 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(lowercase		,							lowercase				) for o in objects]				)
        snake_case					:   Optional[Any]         =					dummy_file

    return dummy_files







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]=False				)			->		Any:


    """simple docstring"""





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

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

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

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


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

  check_dummies(args.fix_and_overwrite)





