
import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase						:      Tuple							=							[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Optional[int] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   val







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  int 			=   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase							:  Optional[Any] 			=   key.replace("""backbone.0.body"""      ,			"""backbone.conv_encoder.model"""       )
                     lowercase							:  Optional[int] 			=   value
              else:
                     lowercase							:  Dict 			=   value

       return new_state_dict







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=False       )				->					Optional[Any]:
       lowercase							:  str 			=   """"""
       if is_panoptic:
              lowercase							:  Union[str, Any] 			=   """detr."""

       # first: transformer encoder
       for i in range(6       ):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              lowercase							:  Union[str, Any] 			=   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"       )
              lowercase							:  Any 			=   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) to the state dict
              lowercase							:  Union[str, Any] 			=   in_proj_weight[:256, :]
              lowercase							:  str 			=   in_proj_bias[:256]
              lowercase							:  Union[str, Any] 			=   in_proj_weight[256:512, :]
              lowercase							:  Optional[int] 			=   in_proj_bias[256:512]
              lowercase							:  List[Any] 			=   in_proj_weight[-256:, :]
              lowercase							:  List[Any] 			=   in_proj_bias[-256:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6       ):
              # read in weights + bias of input projection layer of self-attention
              lowercase							:  int 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"       )
              lowercase							:  List[Any] 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) to the state dict
              lowercase							:  Any 			=   in_proj_weight[:256, :]
              lowercase							:  int 			=   in_proj_bias[:256]
              lowercase							:  Tuple 			=   in_proj_weight[256:512, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias[256:512]
              lowercase							:  int 			=   in_proj_weight[-256:, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              lowercase							:  int 			=   state_dict.pop(
                  f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"       )
              lowercase							:  Dict 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              lowercase							:  List[str] 			=   in_proj_weight_cross_attn[:256, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias_cross_attn[:256]
              lowercase							:  int 			=   in_proj_weight_cross_attn[256:512, :]
              lowercase							:  Optional[int] 			=   in_proj_bias_cross_attn[256:512]
              lowercase							:  Union[str, Any] 			=   in_proj_weight_cross_attn[-256:, :]
              lowercase							:  int 			=   in_proj_bias_cross_attn[-256:]







def 		_snake_case(						)				->					str:
       lowercase							:  Tuple 			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:  int 			=   Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,			stream=SCREAMING_SNAKE_CASE__       ).raw       )

       return im







@torch.no_grad()
def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Any:
       lowercase							:  List[Any] 			=   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowercase							:  Union[str, Any] 			=   """resnet101"""
       if "dc5" in model_name:
              lowercase							:  List[str] 			=   True
       lowercase							:  Union[str, Any] 			=   """panoptic""" in model_name
       if is_panoptic:
              lowercase							:  List[str] 			=   250
       else:
              lowercase							:  List[Any] 			=   91
              lowercase							:  List[str] 			=   """huggingface/label-files"""
              lowercase							:  Union[str, Any] 			=   """coco-detection-id2label.json"""
              lowercase							:  Optional[int] 			=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			repo_type="""dataset"""       )      ,			"""r"""       )       )
              lowercase							:  int 			=   {int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
              lowercase							:  List[Any] 			=   idalabel
              lowercase							:  List[Any] 			=   {v: k for k, v in idalabel.items()}

       # load image processor
       lowercase							:  Any 			=   """coco_panoptic""" if is_panoptic else """coco_detection"""
       lowercase							:  Dict 			=   DetrImageProcessor(format=SCREAMING_SNAKE_CASE__       )

       # prepare image
       lowercase							:  Optional[Any] 			=   prepare_img()
       lowercase							:  int 			=   image_processor(images=SCREAMING_SNAKE_CASE__      ,			return_tensors="""pt"""       )
       lowercase							:  int 			=   encoding["""pixel_values"""]

       logger.info(f"Converting model {model_name}..."       )

       # load original model from torch hub
       lowercase							:  Optional[int] 			=   torch.hub.load("""facebookresearch/detr"""      ,			SCREAMING_SNAKE_CASE__      ,			pretrained=SCREAMING_SNAKE_CASE__       ).eval()
       lowercase							:  Optional[int] 			=   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase							:  Optional[Any] 			=   """detr.""" + src
              rename_key(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   rename_backbone_keys(SCREAMING_SNAKE_CASE__       )
       # query, key and value matrices need special treatment
       read_in_q_k_v(SCREAMING_SNAKE_CASE__      ,			is_panoptic=SCREAMING_SNAKE_CASE__       )
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowercase							:  List[Any] 			=   """detr.model.""" if is_panoptic else """model."""
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("""detr"""       )
                         and not key.startswith("""class_labels_classifier"""       )
                         and not key.startswith("""bbox_predictor"""       )
                     ):
                            lowercase							:  Optional[Any] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Optional[Any] 			=   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase							:  Tuple 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  str 			=   val
                     elif key.startswith("""bbox_attention"""       ) or key.startswith("""mask_head"""       ):
                            continue
                     else:
                            lowercase							:  Any 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Dict 			=   val
              else:
                     if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
                            lowercase							:  List[str] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Union[str, Any] 			=   val
    # finally, create HuggingFace model and load state dict
       lowercase							:  int 			=   DetrForSegmentation(SCREAMING_SNAKE_CASE__       ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__       )
       model.load_state_dict(SCREAMING_SNAKE_CASE__       )
       model.eval()
       # verify our conversion
       lowercase							:  Union[str, Any] 			=   detr(SCREAMING_SNAKE_CASE__       )
       lowercase							:  int 			=   model(SCREAMING_SNAKE_CASE__       )
       assert torch.allclose(outputs.logits      ,			original_outputs["""pred_logits"""]      ,			atol=1e-4       )
       assert torch.allclose(outputs.pred_boxes      ,			original_outputs["""pred_boxes"""]      ,			atol=1e-4       )
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks      ,			original_outputs["""pred_masks"""]      ,			atol=1e-4       )

       # Save model and image processor
       logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."       )
       Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
       model.save_pretrained(SCREAMING_SNAKE_CASE__       )
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
  lowercase						:      Any							=							argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR model you'd like to convert."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  lowercase						:      List[Any]							=							parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowercase						:      Tuple							=							logging.get_logger(__name__)

lowercase						:      Optional[int]							=							OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

lowercase						:      str							=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                     lowercase							:  Optional[int] 			=   model_type_to_module_name(SCREAMING_SNAKE_CASE__       )

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

       for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(SCREAMING_SNAKE_CASE__      ,			"""__name__"""      ,			SCREAMING_SNAKE_CASE__       ) == class_name:
                     return processor

    # 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							:  Dict 			=   importlib.import_module("""transformers"""       )
       if hasattr(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       ):
              return getattr(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )

       return None




class __snake_case	:
    def __init__(						self   ):



           '''simple docstring'''







           raise EnvironmentError(
               """AutoProcessor is designed to be instantiated """
               """using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."""   )
    @classmethod
    @replace_list_option_in_docstrings(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						cls	,snake_case	,**snake_case   ):



           '''simple docstring'''







           lowercase							:  Any 			=   kwargs.pop("""config"""	,snake_case   )
           lowercase							:  Optional[int] 			=   kwargs.pop("""trust_remote_code"""	,snake_case   )
           lowercase							:  Union[str, Any] 			=   True

           lowercase							:  Optional[Any] 			=   None
           lowercase							:  Optional[Any] 			=   None

           # First, let's see if we have a preprocessor config.
           # Filter the kwargs for `get_file_from_repo`.
           lowercase							:  str 			=   {
               key: kwargs[key] for key in inspect.signature(snake_case   ).parameters.keys() if key in kwargs
           }
           # Let's start by checking whether the processor class is saved in an image processor
           lowercase							:  Dict 			=   get_file_from_repo(
               snake_case	,snake_case	,**snake_case   )
           if preprocessor_config_file is not None:
                  lowercase			,    lowercase							:  Dict 			=   ImageProcessingMixin.get_image_processor_dict(snake_case	,**snake_case   )
                  lowercase							:  Dict 			=   config_dict.get("""processor_class"""	,snake_case   )
                  if "AutoProcessor" in config_dict.get("""auto_map"""	,{}   ):
                         lowercase							:  List[str] 			=   config_dict["""auto_map"""]["""AutoProcessor"""]

        # If not found, let's check whether the processor class is saved in a feature extractor config
           if preprocessor_config_file is not None and processor_class is None:
                  lowercase			,    lowercase							:  Dict 			=   FeatureExtractionMixin.get_feature_extractor_dict(snake_case	,**snake_case   )
                  lowercase							:  Dict 			=   config_dict.get("""processor_class"""	,snake_case   )
                  if "AutoProcessor" in config_dict.get("""auto_map"""	,{}   ):
                         lowercase							:  Any 			=   config_dict["""auto_map"""]["""AutoProcessor"""]

           if processor_class is None:
                  # Next, let's check whether the processor class is saved in a tokenizer
                  lowercase							:  Any 			=   get_file_from_repo(
                      snake_case	,snake_case	,**snake_case   )
                  if tokenizer_config_file is not None:
                         with open(snake_case	,encoding="""utf-8"""   ) as reader:
                                lowercase							:  Optional[int] 			=   json.load(snake_case   )

                         lowercase							:  Union[str, Any] 			=   config_dict.get("""processor_class"""	,snake_case   )
                         if "AutoProcessor" in config_dict.get("""auto_map"""	,{}   ):
                                lowercase							:  Optional[Any] 			=   config_dict["""auto_map"""]["""AutoProcessor"""]

           if processor_class is None:
                  # Otherwise, load config, if it can be loaded.
                  if not isinstance(snake_case	,snake_case   ):
                         lowercase							:  Union[str, Any] 			=   AutoConfig.from_pretrained(
                             snake_case	,trust_remote_code=snake_case	,**snake_case   )

                  # And check if the config contains the processor class.
                  lowercase							:  str 			=   getattr(snake_case	,"""processor_class"""	,snake_case   )
                  if hasattr(snake_case	,"""auto_map"""   ) and "AutoProcessor" in config.auto_map:
                         lowercase							:  Tuple 			=   config.auto_map["""AutoProcessor"""]

           if processor_class is not None:
                  lowercase							:  List[str] 			=   processor_class_from_name(snake_case   )

           lowercase							:  str 			=   processor_auto_map is not None
           lowercase							:  str 			=   processor_class is not None or type(snake_case   ) in PROCESSOR_MAPPING
           lowercase							:  List[Any] 			=   resolve_trust_remote_code(
               snake_case	,snake_case	,snake_case	,snake_case   )

           if has_remote_code and trust_remote_code:
                  lowercase							:  Dict 			=   get_class_from_dynamic_module(
                      snake_case	,snake_case	,**snake_case   )
                  lowercase							:  str 			=   kwargs.pop("""code_revision"""	,snake_case   )
                  if os.path.isdir(snake_case   ):
                         processor_class.register_for_auto_class()
                  return processor_class.from_pretrained(
                      snake_case	,trust_remote_code=snake_case	,**snake_case   )
           elif processor_class is not None:
                  return processor_class.from_pretrained(
                      snake_case	,trust_remote_code=snake_case	,**snake_case   )
           # Last try: we use the PROCESSOR_MAPPING.
           elif type(snake_case   ) in PROCESSOR_MAPPING:
                  return PROCESSOR_MAPPING[type(snake_case   )].from_pretrained(snake_case	,**snake_case   )

           # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
           # tokenizer.
           try:
                  return AutoTokenizer.from_pretrained(
                      snake_case	,trust_remote_code=snake_case	,**snake_case   )
           except Exception:
                  try:
                         return AutoImageProcessor.from_pretrained(
                             snake_case	,trust_remote_code=snake_case	,**snake_case   )
                  except Exception:
                         pass

                  try:
                         return AutoFeatureExtractor.from_pretrained(
                             snake_case	,trust_remote_code=snake_case	,**snake_case   )
                  except Exception:
                         pass

           raise ValueError(
               f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
               """tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"""
               """the files of at least one of those processing classes."""   )
    @staticmethod
    def    _SCREAMING_SNAKE_CASE (						snake_case	,snake_case   ):



           '''simple docstring'''







           PROCESSOR_MAPPING.register(snake_case	,snake_case   )
