
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__          =		logging.get_logger(__name__)

lowerCamelCase__          =		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__          =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Union[str, Any]:
			for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
						if class_name in extractors:
									lowerCAmelCase__      :      Optional[int]         =		model_type_to_module_name(SCREAMING_SNAKE_CASE_     )

									lowerCAmelCase__      :      str         =		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.
			lowerCAmelCase__      :      List[Any]         =		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_       ,							)				->		Union[str, Any]:
			lowerCAmelCase__      :      str         =		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      A__							:






				def __init__(						self					:      List[Any]   ):






							'''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 							_lowerCamelCase		(						cls					:      Tuple    ,     a					:      Tuple    ,     **a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		kwargs.pop('config'    ,     a   )
							lowerCAmelCase__      :      Optional[Any]         =		kwargs.pop('trust_remote_code'    ,     a   )
							lowerCAmelCase__      :      Dict         =		True

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

							if image_processor_class is not None:
										lowerCAmelCase__      :      Any         =		image_processor_class_from_name(a   )

							lowerCAmelCase__      :      int         =		image_processor_auto_map is not None
							lowerCAmelCase__      :      List[Any]         =		image_processor_class is not None or type(a   ) in IMAGE_PROCESSOR_MAPPING
							lowerCAmelCase__      :      Optional[Any]         =		resolve_trust_remote_code(
							    a    ,     a    ,     a    ,     a   )

							if has_remote_code and trust_remote_code:
										lowerCAmelCase__      :      Optional[int]         =		get_class_from_dynamic_module(
										    a    ,     a    ,     **a   )
										lowerCAmelCase__      :      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:
										lowerCAmelCase__      :      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 							_lowerCamelCase		(						a					:      Dict    ,     a					:      List[str]   ):






							'''simple docstring'''







							IMAGE_PROCESSOR_MAPPING.register(a    ,     a   )

class      A__							:






				def __init__(						self					:      Optional[Any]    ,     a					:      list   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		set_counts
							lowerCAmelCase__      :      str         =		max(a   )
							lowerCAmelCase__      :      Any         =		len(a   )
							lowerCAmelCase__      :      List[str]         =		[1] * num_sets
							lowerCAmelCase__      :      Dict         =		list(range(a   )   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.get_parent(a   )
							lowerCAmelCase__      :      Tuple         =		self.get_parent(a   )

							if src_parent == dst_parent:
										return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
										self.set_counts[dst_parent] += self.set_counts[src_parent]
										lowerCAmelCase__      :      Tuple         =		0
										lowerCAmelCase__      :      str         =		dst_parent
										if self.ranks[dst_parent] == self.ranks[src_parent]:
													self.ranks[dst_parent] += 1
										lowerCAmelCase__      :      List[Any]         =		self.set_counts[dst_parent]
							else:
										self.set_counts[src_parent] += self.set_counts[dst_parent]
										lowerCAmelCase__      :      Optional[int]         =		0
										lowerCAmelCase__      :      Tuple         =		src_parent
										lowerCAmelCase__      :      Optional[int]         =		self.set_counts[src_parent]

							lowerCAmelCase__      :      Optional[Any]         =		max(self.max_set    ,     a   )
							return True






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      int   ):






							'''simple docstring'''







							if self.parents[disj_set] == disj_set:
										return disj_set
							lowerCAmelCase__      :      Tuple         =		self.get_parent(self.parents[disj_set]   )
							return self.parents[disj_set]