"""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,
)


__lowerCAmelCase    :       Union[str, Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       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    :       str					     =_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str		) ->    Dict:
      '''simple docstring'''


      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  lowercase														=	model_type_to_module_name(lowerCAmelCase__		)

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

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

      return None


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, os.PathLike]					,		lowerCAmelCase__     :Optional[Union[str, os.PathLike]] = None					,		lowerCAmelCase__     :bool = False					,		lowerCAmelCase__     :bool = False					,		lowerCAmelCase__     :Optional[Dict[str, str]] = None					,		lowerCAmelCase__     :Optional[Union[bool, str]] = None					,		lowerCAmelCase__     :Optional[str] = None					,		lowerCAmelCase__     :bool = False					,		**lowerCAmelCase__     :Optional[Any]					,		) ->    str:
      '''simple docstring'''


      lowercase														=	get_file_from_repo(
          lowerCAmelCase__					,		lowerCAmelCase__					,		cache_dir=lowerCAmelCase__					,		force_download=lowerCAmelCase__					,		resume_download=lowerCAmelCase__					,		proxies=lowerCAmelCase__					,		use_auth_token=lowerCAmelCase__					,		revision=lowerCAmelCase__					,		local_files_only=lowerCAmelCase__					,		)
      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(lowerCAmelCase__					,		encoding="""utf-8"""		) as reader:
            return json.load(lowerCAmelCase__		)






class  _A						:

      def __init__(						self						):







            """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(__lowerCAmelCase						)
      def 				A__       (						cls		,       __lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.pop("""config"""		,       __lowerCAmelCase						)
            lowercase														=	kwargs.pop("""trust_remote_code"""		,       __lowerCAmelCase						)
            lowercase														=	True

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

            if image_processor_class is not None:
                  lowercase														=	image_processor_class_from_name(__lowerCAmelCase						)

            lowercase														=	image_processor_auto_map is not None
            lowercase														=	image_processor_class is not None or type(__lowerCAmelCase						) in IMAGE_PROCESSOR_MAPPING
            lowercase														=	resolve_trust_remote_code(
                __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

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

            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 				A__       (						__lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            IMAGE_PROCESSOR_MAPPING.register(__lowerCAmelCase		,       __lowerCAmelCase						)




"""simple docstring"""






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      output_path.parent.mkdir(parents=lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		use_external_data_format=lowerCAmelCase__					,		enable_onnx_checker=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)
      else:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).to(device=lowerCAmelCase__					,		dtype=lowerCAmelCase__		),
              False,
          )					,		output_path=output_path / """vae_decoder""" / """model.onnx"""					,		ordered_input_names=["""latent_sample""", """return_dict"""]					,		output_names=["""sample"""]					,		dynamic_axes={
              """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
          }					,		opset=lowerCAmelCase__					,		)
      del vae_decoder


if __name__ == "__main__":
       __lowerCAmelCase    :       Tuple					     =argparse.ArgumentParser()

       parser.add_argument(
           """--model_path""",
           type=str,
           required=True,
           help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
       )

       parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
       parser.add_argument(
           """--opset""",
           default=1_4,
           type=int,
           help="""The version of the ONNX operator set to use.""",
       )
       parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

       __lowerCAmelCase    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



