




"""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_      =    logging.get_logger(__name__)

A_      =    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_      =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def  UpperCAmelCase__	(snake_case__							: str   ):
  """simple docstring"""

  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _snake_case							:		Optional[int]								=  model_type_to_module_name(snake_case__   )

      _snake_case							:		Any								=  importlib.import_module(F".{module_name}"		,       """transformers.models"""   )
      try:
        return getattr(snake_case__		,       snake_case__   )
      except AttributeError:
        continue

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(snake_case__		,       """__name__"""		,       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.
  _snake_case							:		Tuple								=  importlib.import_module("""transformers"""   )
  if hasattr(snake_case__		,       snake_case__   ):
    return getattr(snake_case__		,       snake_case__   )

  return None


def  UpperCAmelCase__	(snake_case__							: Union[str, os.PathLike]		,       snake_case__							: Optional[Union[str, os.PathLike]] = None		,       snake_case__							: bool = False		,       snake_case__							: bool = False		,       snake_case__							: Optional[Dict[str, str]] = None		,       snake_case__							: Optional[Union[bool, str]] = None		,       snake_case__							: Optional[str] = None		,       snake_case__							: bool = False		,       **snake_case__							: Dict		,       ):
  """simple docstring"""

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






class  lowercase:







 '''simple docstring'''





 def __init__(    self:		List[str]		):




   '''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(a_		)
 def 			UpperCamelCase_       (    cls:		List[str],				a_:		Optional[int],				**a_:		str		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  kwargs.pop("""config""",				a_		)
   _snake_case							:		Any								=  kwargs.pop("""trust_remote_code""",				a_		)
   _snake_case							:		Any								=  True

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

   if image_processor_class is not None:
     _snake_case							:		Optional[Any]								=  image_processor_class_from_name(a_		)

   _snake_case							:		List[str]								=  image_processor_auto_map is not None
   _snake_case							:		Any								=  image_processor_class is not None or type(a_		) in IMAGE_PROCESSOR_MAPPING
   _snake_case							:		Dict								=  resolve_trust_remote_code(
       a_,				a_,				a_,				a_		)

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

   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 			UpperCamelCase_       (    a_:		List[str],				a_:		List[Any]		):




   '''simple docstring'''



   IMAGE_PROCESSOR_MAPPING.register(a_,				a_		)








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


