


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,
)


_lowerCAmelCase:						Tuple   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Union[str, Any]   				=       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'),
    ]
)

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






def   _lowercase(					__a				:						str     ):
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					a__      =model_type_to_module_name(__a     )

																					a__      =importlib.import_module(f""".{module_name}"""    ,     'transformers.models'     )
																					try:
																												return getattr(__a    ,     __a     )
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(__a    ,     '__name__'    ,     __a     ) == 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.
							a__      =importlib.import_module('transformers'     )
							if hasattr(__a    ,     __a     ):
														return getattr(__a    ,     __a     )

							return None






def   _lowercase(					__a				:						Union[str, os.PathLike]    ,     __a				:						Optional[Union[str, os.PathLike]] = None    ,     __a				:						bool = False    ,     __a				:						bool = False    ,     __a				:						Optional[Dict[str, str]] = None    ,     __a				:						Optional[Union[bool, str]] = None    ,     __a				:						Optional[str] = None    ,     __a				:						bool = False    ,     **__a				:						Any    ,     ):
							a__      =get_file_from_repo(
							    __a    ,     __a    ,     cache_dir=__a    ,     force_download=__a    ,     resume_download=__a    ,     proxies=__a    ,     use_auth_token=__a    ,     revision=__a    ,     local_files_only=__a    ,     )
							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(__a    ,     encoding='utf-8'     ) as reader:
														return json.load(__a     )







class       lowercase_					:





				def __init__(				self)  -> 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(lowercase_)
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  **lowercase_)  -> str:
											a__      =kwargs.pop('config'       ,  lowercase_)
											a__      =kwargs.pop('trust_remote_code'       ,  lowercase_)
											a__      =True

											a__    ,     a__      =ImageProcessingMixin.get_image_processor_dict(lowercase_       ,  **lowercase_)
											a__      =config_dict.get('image_processor_type'       ,  lowercase_)
											a__      =None
											if "AutoImageProcessor" in config_dict.get('auto_map'       ,  {}):
																		a__      =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:
																		a__      =config_dict.pop('feature_extractor_type'       ,  lowercase_)
																		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.')
																									a__      =feature_extractor_class.replace('FeatureExtractor'       ,  'ImageProcessor')
																		if "AutoFeatureExtractor" in config_dict.get('auto_map'       ,  {}):
																									a__      =config_dict['auto_map']['AutoFeatureExtractor']
																									a__      =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(lowercase_       ,  lowercase_):
																									a__      =AutoConfig.from_pretrained(lowercase_       ,  **lowercase_)
																		# It could be in `config.image_processor_type``
																		a__      =getattr(lowercase_       ,  'image_processor_type'       ,  lowercase_)
																		if hasattr(lowercase_       ,  'auto_map') and "AutoImageProcessor" in config.auto_map:
																									a__      =config.auto_map['AutoImageProcessor']

											if image_processor_class is not None:
																		a__      =image_processor_class_from_name(lowercase_)

											a__      =image_processor_auto_map is not None
											a__      =image_processor_class is not None or type(lowercase_) in IMAGE_PROCESSOR_MAPPING
											a__      =resolve_trust_remote_code(
											    lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)

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

											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    (				lowercase_       ,  lowercase_)  -> Union[str, Any]:
											IMAGE_PROCESSOR_MAPPING.register(lowercase_       ,  lowercase_)





from importlib import import_module

from .logging import get_logger


_lowerCAmelCase:						str   				=       get_logger(__name__)




class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=None)  -> Tuple:
											a__      =attrs or []
											if module is not None:
																		for key in module.__dict__:
																									if key in attrs or not key.startswith('__'):
																																setattr(self       ,  lowercase_       ,  getattr(lowercase_       ,  lowercase_))
											a__      =module._original_module if isinstance(lowercase_       ,  _PatchedModuleObj) else module







class       lowercase_					:
				snake_case	    =[]





				def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=None)  -> List[str]:
											a__      =obj
											a__      =target
											a__      =new
											a__      =target.split('.')[0]
											a__      ={}
											a__      =attrs or []





				def __enter__(				self)  -> Optional[int]:
											*a__    ,     a__      =self.target.split('.')

											# Patch modules:
											# it's used to patch attributes of submodules like "os.path.join";
											# in this case we need to patch "os" and "os.path"

											for i in range(len(lowercase_)):
																		try:
																									a__      =import_module('.'.join(submodules[: i + 1]))
																		except ModuleNotFoundError:
																									continue
																		# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																		for attr in self.obj.__dir__():
																									a__      =getattr(self.obj       ,  lowercase_)
																									# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																									# This allows to patch renamed modules like "from os import path as ospath".
																									if obj_attr is submodule or (
																									    (isinstance(lowercase_       ,  _PatchedModuleObj) and obj_attr._original_module is submodule)
																									):
																																a__      =obj_attr
																																# patch at top level
																																setattr(self.obj       ,  lowercase_       ,  _PatchedModuleObj(lowercase_       ,  attrs=self.attrs))
																																a__      =getattr(self.obj       ,  lowercase_)
																																# construct lower levels patches
																																for key in submodules[i + 1 :]:
																																							setattr(lowercase_       ,  lowercase_       ,  _PatchedModuleObj(getattr(lowercase_       ,  lowercase_       ,  lowercase_)       ,  attrs=self.attrs))
																																							a__      =getattr(lowercase_       ,  lowercase_)
																																# finally set the target attribute
																																setattr(lowercase_       ,  lowercase_       ,  self.new)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

											if submodules:  # if it's an attribute of a submodule like "os.path.join"
																		try:
																									a__      =getattr(import_module('.'.join(lowercase_))       ,  lowercase_)
																		except (AttributeError, ModuleNotFoundError):
																									return
																		# We iterate over all the globals in self.obj in case we find "os.path.join"
																		for attr in self.obj.__dir__():
																									# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																									# This allows to patch renamed attributes like "from os.path import join as pjoin".
																									if getattr(self.obj       ,  lowercase_) is attr_value:
																																a__      =getattr(self.obj       ,  lowercase_)
																																setattr(self.obj       ,  lowercase_       ,  self.new)
											elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																		a__      =globals()['__builtins__'][target_attr]
																		setattr(self.obj       ,  lowercase_       ,  self.new)
											else:
																		raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""")





				def __exit__(				self       ,  *lowercase_)  -> str:
											for attr in list(self.original):
																		setattr(self.obj       ,  lowercase_       ,  self.original.pop(lowercase_))





				def 						__UpperCamelCase    (				self)  -> Any:
											self.__enter__()
											self._active_patches.append(self)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											try:
																		self._active_patches.remove(self)
											except ValueError:
																		# If the patch hasn't been started this will fail
																		return None

											return self.__exit__()

