
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,
)


UpperCamelCase_					:						Dict				= logging.get_logger(__name__)

UpperCamelCase_					:						List[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'''),
    ]
)

UpperCamelCase_					:						Tuple				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 							A_     (__a				):

					'''simple docstring'''





					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 							A_     (__a				,   __a = None				,   __a = False				,   __a = False				,   __a = None				,   __a = None				,   __a = None				,   __a = False				,   **__a				,   ):

					'''simple docstring'''





					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 __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]	)      -> 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(_snake_case	)
			def      lowerCamelCase__     (				cls						:					int       ,   _snake_case						:					Optional[int]       ,   **_snake_case						:					List[str]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						kwargs.pop("config"       ,   _snake_case	)
								A_				 =						kwargs.pop("trust_remote_code"       ,   _snake_case	)
								A_				 =						True

								A_ ,    A_				 =						ImageProcessingMixin.get_image_processor_dict(_snake_case       ,   **_snake_case	)
								A_				 =						config_dict.get("image_processor_type"       ,   _snake_case	)
								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"       ,   _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."	)
																		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(_snake_case       ,   _snake_case	):
																		A_				 =						AutoConfig.from_pretrained(_snake_case       ,   **_snake_case	)
													# It could be in `config.image_processor_type``
													A_				 =						getattr(_snake_case       ,   "image_processor_type"       ,   _snake_case	)
													if hasattr(_snake_case       ,   "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(_snake_case	)

								A_				 =						image_processor_auto_map is not None
								A_				 =						image_processor_class is not None or type(_snake_case	) in IMAGE_PROCESSOR_MAPPING
								A_				 =						resolve_trust_remote_code(
								    _snake_case       ,   _snake_case       ,   _snake_case       ,   _snake_case	)

								if has_remote_code and trust_remote_code:
													A_				 =						get_class_from_dynamic_module(
													    _snake_case       ,   _snake_case       ,   **_snake_case	)
													A_				 =						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:
													A_				 =						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      lowerCamelCase__     (				_snake_case						:					str       ,   _snake_case						:					Dict	)      -> Tuple:







								"""simple docstring"""
								IMAGE_PROCESSOR_MAPPING.register(_snake_case       ,   _snake_case	)






"""simple docstring"""





from __future__ import annotations

from collections import Counter
from random import random



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str	)      -> None:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					float	)      -> None:







								"""simple docstring"""
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								A_				 =						probability




			def      lowerCamelCase__     (				self						:					Tuple	)      -> list[str]:







								"""simple docstring"""
								return list(self.connections	)






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						0
								A_				 =						random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(__a				,   __a				,   __a				)

					A_				 =						Counter(graph.get_nodes()				)
					A_				 =						start

					for _ in range(__a				):
										A_				 =						graph.transition(__a				)
										visited[node] += 1

					return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()




