

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_SCREAMING_SNAKE_CASE					:  str             =      {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_SCREAMING_SNAKE_CASE					:  Dict             =      {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 80.0,
}

_SCREAMING_SNAKE_CASE					:  str             =      {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 80.0,
}

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 80.0,
}
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if isinstance(_A				,		_A    ):
          return v
     if v.lower() in ("yes", "true", "t", "y", "1"):
          return True
     elif v.lower() in ("no", "false", "f", "n", "0"):
          return False
     else:
          raise argparse.ArgumentTypeError('''boolean value expected'''    )
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=False    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.2.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.in_layers.2.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.3.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.skip_connection.weight''']
          SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=None    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3				,		dim=0    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3				,		dim=0    )

     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.norm.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.norm.bias''']

     SCREAMING_SNAKE_CASE__   =   weight_q.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   bias_q.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   weight_k.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   bias_k.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   weight_v.squeeze(-1    ).squeeze(-1    )
     SCREAMING_SNAKE_CASE__   =   bias_v.squeeze(-1    ).squeeze(-1    )

     SCREAMING_SNAKE_CASE__   =   (
         checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1    ).squeeze(-1    )
     )
     SCREAMING_SNAKE_CASE__   =   checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1    ).squeeze(-1    )

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   torch.load(_A				,		map_location='''cpu'''    )
     SCREAMING_SNAKE_CASE__   =   {}

     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.2.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''time_embed.2.bias''']

     if unet_config["num_class_embeds"] is not None:
          SCREAMING_SNAKE_CASE__   =   checkpoint['''label_emb.weight''']

     SCREAMING_SNAKE_CASE__   =   checkpoint['''input_blocks.0.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''input_blocks.0.0.bias''']

     SCREAMING_SNAKE_CASE__   =   unet_config['''down_block_types''']
     SCREAMING_SNAKE_CASE__   =   unet_config['''layers_per_block''']
     SCREAMING_SNAKE_CASE__   =   unet_config['''attention_head_dim''']
     SCREAMING_SNAKE_CASE__   =   unet_config['''block_out_channels''']
     SCREAMING_SNAKE_CASE__   =   1
     SCREAMING_SNAKE_CASE__   =   channels_list[0]

     for i, layer_type in enumerate(_A    ):
          SCREAMING_SNAKE_CASE__   =   channels_list[i]
          SCREAMING_SNAKE_CASE__   =   current_channels != prev_channels
          if layer_type == "ResnetDownsampleBlock2D":
               for j in range(_A    ):
                    SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   True if j == 0 and downsample_block_has_skip else False
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    current_layer += 1

          elif layer_type == "AttnDownBlock2D":
               for j in range(_A    ):
                    SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   True if j == 0 and downsample_block_has_skip else False
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.attentions.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.1'''
                    SCREAMING_SNAKE_CASE__   =   convert_attention(
                        _A				,		_A				,		_A				,		_A				,		_A    )
                    current_layer += 1

          if i != len(_A    ) - 1:
               SCREAMING_SNAKE_CASE__   =   F'''down_blocks.{i}.downsamplers.0'''
               SCREAMING_SNAKE_CASE__   =   F'''input_blocks.{current_layer}.0'''
               SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )
               current_layer += 1

          SCREAMING_SNAKE_CASE__   =   current_channels

     # hardcoded the mid-block for now
     SCREAMING_SNAKE_CASE__   =   '''mid_block.resnets.0'''
     SCREAMING_SNAKE_CASE__   =   '''middle_block.0'''
     SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   '''mid_block.attentions.0'''
     SCREAMING_SNAKE_CASE__   =   '''middle_block.1'''
     SCREAMING_SNAKE_CASE__   =   convert_attention(_A				,		_A				,		_A				,		_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   '''mid_block.resnets.1'''
     SCREAMING_SNAKE_CASE__   =   '''middle_block.2'''
     SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   unet_config['''up_block_types''']

     for i, layer_type in enumerate(_A    ):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1    ):
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    current_layer += 1

               if i != len(_A    ) - 1:
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.upsamplers.0'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer-1}.1'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )
          elif layer_type == "AttnUpBlock2D":
               for j in range(layers_per_block + 1    ):
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.resnets.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer}.0'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A				,		has_skip=_A    )
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.attentions.{j}'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer}.1'''
                    SCREAMING_SNAKE_CASE__   =   convert_attention(
                        _A				,		_A				,		_A				,		_A				,		_A    )
                    current_layer += 1

               if i != len(_A    ) - 1:
                    SCREAMING_SNAKE_CASE__   =   F'''up_blocks.{i}.upsamplers.0'''
                    SCREAMING_SNAKE_CASE__   =   F'''output_blocks.{current_layer-1}.2'''
                    SCREAMING_SNAKE_CASE__   =   convert_resnet(_A				,		_A				,		_A				,		_A    )

     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.0.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.0.bias''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.2.weight''']
     SCREAMING_SNAKE_CASE__   =   checkpoint['''out.2.bias''']

     return new_checkpoint


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      argparse.ArgumentParser()

  parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
  parser.add_argument(
      '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
  )
  parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

  _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()
  _SCREAMING_SNAKE_CASE					:  List[str]             =      strabool(args.class_cond)

  _SCREAMING_SNAKE_CASE					:  int             =      os.path.basename(args.unet_path)
  print(F"Checkpoint: {ckpt_name}")

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    _SCREAMING_SNAKE_CASE					:  int             =      LSUN_256_UNET_CONFIG
  elif "test" in ckpt_name:
    _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      TEST_UNET_CONFIG
  else:
    raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

  if not args.class_cond:
    _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      None

  _SCREAMING_SNAKE_CASE					:  int             =      con_pt_to_diffuser(args.unet_path, unet_config)

  _SCREAMING_SNAKE_CASE					:  Optional[int]             =      UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    _SCREAMING_SNAKE_CASE					:  Any             =      CT_IMAGENET_64_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    _SCREAMING_SNAKE_CASE					:  int             =      CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

  _SCREAMING_SNAKE_CASE					:  int             =      CMStochasticIterativeScheduler(**scheduler_config)

  _SCREAMING_SNAKE_CASE					:  str             =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)





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,
)


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      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'''),
    ]
)

_SCREAMING_SNAKE_CASE					:  str             =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               SCREAMING_SNAKE_CASE__   =   model_type_to_module_name(_A    )

               SCREAMING_SNAKE_CASE__   =   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.
     SCREAMING_SNAKE_CASE__   =   importlib.import_module('''transformers'''    )
     if hasattr(_A				,		_A    ):
          return getattr(_A				,		_A    )

     return None
def     UpperCAmelCase_       (						_A				,		_A = None				,		_A = False				,		_A = False				,		_A = None				,		_A = None				,		_A = None				,		_A = False				,		**_A				,		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   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 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		List[Any] )		->							int:
       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 				lowercase_		(							cls						:		Optional[int]     ,       __lowerCamelCase						:		Any     ,       **__lowerCamelCase						:		Tuple )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''config'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''trust_remote_code'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   True

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

       if image_processor_class is not None:
            SCREAMING_SNAKE_CASE__   =   image_processor_class_from_name(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   image_processor_auto_map is not None
       SCREAMING_SNAKE_CASE__   =   image_processor_class is not None or type(__lowerCamelCase ) in IMAGE_PROCESSOR_MAPPING
       SCREAMING_SNAKE_CASE__   =   resolve_trust_remote_code(
           __lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase )

       if has_remote_code and trust_remote_code:
            SCREAMING_SNAKE_CASE__   =   get_class_from_dynamic_module(
                __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   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:
            SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							__lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Optional[int] )		->							str:
       IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase     ,       __lowerCamelCase )


