

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_:							str      )			->      Any:




				'''simple docstring'''





				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												A__        =			model_type_to_module_name(SCREAMING_SNAKE_CASE_      )

												A__        =			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.
				A__        =			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_:							Union[str, os.PathLike]	,       SCREAMING_SNAKE_CASE_:							Optional[Union[str, os.PathLike]] = None	,       SCREAMING_SNAKE_CASE_:							bool = False	,       SCREAMING_SNAKE_CASE_:							bool = False	,       SCREAMING_SNAKE_CASE_:							Optional[Dict[str, str]] = None	,       SCREAMING_SNAKE_CASE_:							Optional[Union[bool, str]] = None	,       SCREAMING_SNAKE_CASE_:							Optional[str] = None	,       SCREAMING_SNAKE_CASE_:							bool = False	,       **SCREAMING_SNAKE_CASE_:							List[Any]	,       )			->      str:




				'''simple docstring'''





				A__        =			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__						:
			"""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(lowercase       )
			def      UpperCamelCase		(    cls      ,      lowercase      ,      **lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							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       )  ->						Any:
							'''simple docstring'''






							IMAGE_PROCESSOR_MAPPING.register(lowercase      ,      lowercase       )




from typing import Union

import fire
import torch
from tqdm import tqdm



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str = "cpu"	,       SCREAMING_SNAKE_CASE_:							Union[str, None] = None      )			->      None:




				'''simple docstring'''





				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location=SCREAMING_SNAKE_CASE_      )
				for k, v in tqdm(state_dict.items()      ):
								if not isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
												raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
								A__        =			v.half()
				if save_path is None:  # overwrite src_path
								A__        =			src_path
				torch.save(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	fire.Fire(convert)

