





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      A       (	__lowercase						):
      _snake_case							     =['''image_processor''', '''tokenizer''']
      _snake_case							     ='''LayoutLMv3ImageProcessor'''
      _snake_case							     =('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')



      def __init__(	self:						Any		,  _lowerCAmelCase:						List[str]=None		,  _lowerCAmelCase:						List[Any]=None		,  **_lowerCAmelCase:						Optional[Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=None
         if "feature_extractor" in kwargs:
            warnings.warn(
                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                " instead."		,  _lowerCAmelCase		,  )
            UpperCAmelCase_											=kwargs.pop("feature_extractor"       )

         UpperCAmelCase_											=image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError("You need to specify an `image_processor`."       )
         if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`."       )

         super().__init__(_lowerCAmelCase		,  _lowerCAmelCase       )



      def __call__(	self:						Optional[int]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,  _lowerCAmelCase:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,  _lowerCAmelCase:						Union[List[List[int]], List[List[List[int]]]] = None		,  _lowerCAmelCase:						Optional[Union[List[int], List[List[int]]]] = None		,  _lowerCAmelCase:						bool = True		,  _lowerCAmelCase:						Union[bool, str, PaddingStrategy] = False		,  _lowerCAmelCase:						Union[bool, str, TruncationStrategy] = None		,  _lowerCAmelCase:						Optional[int] = None		,  _lowerCAmelCase:						int = 0		,  _lowerCAmelCase:						Optional[int] = None		,  _lowerCAmelCase:						Optional[bool] = None		,  _lowerCAmelCase:						Optional[bool] = None		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						bool = True		,  _lowerCAmelCase:						Optional[Union[str, TensorType]] = None		,  **_lowerCAmelCase:						List[Any]		,  )						->    BatchEncoding:
         '''simple docstring'''

         if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."       )

         if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."       )

         # first, apply the image processor
         UpperCAmelCase_											=self.image_processor(images=_lowerCAmelCase		,  return_tensors=_lowerCAmelCase       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
               UpperCAmelCase_											=[text]  # add batch dimension (as the image processor always adds a batch dimension)
            UpperCAmelCase_											=features["words"]

         UpperCAmelCase_											=self.tokenizer(
             text=text if text is not None else features["words"]		,  text_pair=text_pair if text_pair is not None else None		,  boxes=boxes if boxes is not None else features["boxes"]		,  word_labels=_lowerCAmelCase		,  add_special_tokens=_lowerCAmelCase		,  padding=_lowerCAmelCase		,  truncation=_lowerCAmelCase		,  max_length=_lowerCAmelCase		,  stride=_lowerCAmelCase		,  pad_to_multiple_of=_lowerCAmelCase		,  return_token_type_ids=_lowerCAmelCase		,  return_attention_mask=_lowerCAmelCase		,  return_overflowing_tokens=_lowerCAmelCase		,  return_special_tokens_mask=_lowerCAmelCase		,  return_offsets_mapping=_lowerCAmelCase		,  return_length=_lowerCAmelCase		,  verbose=_lowerCAmelCase		,  return_tensors=_lowerCAmelCase		,  **_lowerCAmelCase		,  )

         # add pixel values
         UpperCAmelCase_											=features.pop("pixel_values"       )
         if return_overflowing_tokens is True:
            UpperCAmelCase_											=self.get_overflowing_images(_lowerCAmelCase		,  encoded_inputs["overflow_to_sample_mapping"]       )
         UpperCAmelCase_											=images

         return encoded_inputs



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Any       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

         if len(_lowerCAmelCase       ) != len(_lowerCAmelCase       ):
            raise ValueError(
                "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                F' {len(_lowerCAmelCase       )} and {len(_lowerCAmelCase       )}'       )

         return images_with_overflow



      def 	lowerCAmelCase__    (	self:						int		,  *_lowerCAmelCase:						Any		,  **_lowerCAmelCase:						Dict       )						->    Optional[int]:
         '''simple docstring'''

         return self.tokenizer.batch_decode(*_lowerCAmelCase		,  **_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  *_lowerCAmelCase:						Union[str, Any]		,  **_lowerCAmelCase:						Optional[Any]       )						->    Any:
         '''simple docstring'''

         return self.tokenizer.decode(*_lowerCAmelCase		,  **_lowerCAmelCase       )



      @property
      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

         return ["input_ids", "bbox", "attention_mask", "pixel_values"]



      @property
      def 	lowerCAmelCase__    (	self:						Tuple       )						->    int:
         '''simple docstring'''

         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,  _lowerCAmelCase		,  )
         return self.image_processor_class






      @property
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Dict:
         '''simple docstring'''

         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,  _lowerCAmelCase		,  )
         return self.image_processor






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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase  :   Optional[Any]						      =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowercase  :   str						      =[]
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}.cross_attn.out_proj.weight""",
             f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
         )
     )
     rename_keys.append(
         (
             f"""transformer.decoder.layers.{i}.cross_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"""))

     # q, k, v projections in self/cross-attention in decoder for conditional DETR
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", f"""decoder.layers.{i}.sa_qcontent_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", f"""decoder.layers.{i}.sa_kcontent_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", f"""decoder.layers.{i}.sa_qpos_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", f"""decoder.layers.{i}.sa_kpos_proj.weight""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.weight""", f"""decoder.layers.{i}.sa_v_proj.weight"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", f"""decoder.layers.{i}.ca_qcontent_proj.weight""")
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", f"""decoder.layers.{i}.ca_kcontent_proj.weight""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", f"""decoder.layers.{i}.ca_kpos_proj.weight""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.weight""", f"""decoder.layers.{i}.ca_v_proj.weight"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", f"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
     )

     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", f"""decoder.layers.{i}.sa_qcontent_proj.bias""")
     )
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", f"""decoder.layers.{i}.sa_kcontent_proj.bias""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", f"""decoder.layers.{i}.sa_qpos_proj.bias"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", f"""decoder.layers.{i}.sa_kpos_proj.bias"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.sa_v_proj.bias""", f"""decoder.layers.{i}.sa_v_proj.bias"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", f"""decoder.layers.{i}.ca_qcontent_proj.bias""")
     )
     # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", f"""decoder.layers.{i}.ca_kcontent_proj.bias""")
     )
     rename_keys.append((f"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", f"""decoder.layers.{i}.ca_kpos_proj.bias"""))
     rename_keys.append((f"""transformer.decoder.layers.{i}.ca_v_proj.bias""", f"""decoder.layers.{i}.ca_v_proj.bias"""))
     rename_keys.append(
         (f"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", f"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
     )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=state_dict.pop(lowercase__  )
   UpperCAmelCase_											=val




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         UpperCAmelCase_											=key.replace("backbone.0.body"  ,    "backbone.conv_encoder.model"  )
         UpperCAmelCase_											=value
      else:
         UpperCAmelCase_											=value

   return new_state_dict




def     a__ (					lowercase__  ,    lowercase__=False  ):






   '''simple docstring'''






   UpperCAmelCase_											=""
   if is_panoptic:
      UpperCAmelCase_											="conditional_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)
      UpperCAmelCase_											=state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
      UpperCAmelCase_											=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
      UpperCAmelCase_											=in_proj_weight[:2_5_6, :]
      UpperCAmelCase_											=in_proj_bias[:2_5_6]
      UpperCAmelCase_											=in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase_											=in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase_											=in_proj_weight[-2_5_6:, :]
      UpperCAmelCase_											=in_proj_bias[-2_5_6:]




def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_											=Image.open(requests.get(lowercase__  ,    stream=lowercase__  ).raw  )

   return im




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      UpperCAmelCase_											="resnet101"
   if "dc5" in model_name:
      UpperCAmelCase_											=True
   UpperCAmelCase_											="panoptic" in model_name
   if is_panoptic:
      UpperCAmelCase_											=2_5_0
   else:
      UpperCAmelCase_											=9_1
      UpperCAmelCase_											="huggingface/label-files"
      UpperCAmelCase_											="coco-detection-id2label.json"
      UpperCAmelCase_											=json.load(open(hf_hub_download(lowercase__  ,    lowercase__  ,    repo_type="dataset"  )  ,    "r"  )  )
      UpperCAmelCase_											={int(lowercase__  ): v for k, v in idalabel.items()}
      UpperCAmelCase_											=idalabel
      UpperCAmelCase_											={v: k for k, v in idalabel.items()}

   # load image processor
   UpperCAmelCase_											="coco_panoptic" if is_panoptic else "coco_detection"
   UpperCAmelCase_											=ConditionalDetrImageProcessor(format=lowercase__  )

   # prepare image
   UpperCAmelCase_											=prepare_img()
   UpperCAmelCase_											=image_processor(images=lowercase__  ,    return_tensors="pt"  )
   UpperCAmelCase_											=encoding["pixel_values"]

   logger.info(F'Converting model {model_name}...'  )

   # load original model from torch hub
   UpperCAmelCase_											=torch.hub.load("DeppMeng/ConditionalDETR"  ,    lowercase__  ,    pretrained=lowercase__  ).eval()
   UpperCAmelCase_											=conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         UpperCAmelCase_											="conditional_detr." + src
      rename_key(lowercase__  ,    lowercase__  ,    lowercase__  )
   UpperCAmelCase_											=rename_backbone_keys(lowercase__  )
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowercase__  ,    is_panoptic=lowercase__  )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   UpperCAmelCase_											="conditional_detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("conditional_detr"  )
             and not key.startswith("class_labels_classifier"  )
             and not key.startswith("bbox_predictor"  )
         ):
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
         elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
            continue
         else:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
      else:
         if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
    # finally, create HuggingFace model and load state dict
   UpperCAmelCase_											=ConditionalDetrForSegmentation(lowercase__  ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__  )
   model.load_state_dict(lowercase__  )
   model.eval()
   model.push_to_hub(repo_id=lowercase__  ,    organization="DepuMeng"  ,    commit_message="Add model"  )
   # verify our conversion
   UpperCAmelCase_											=conditional_detr(lowercase__  )
   UpperCAmelCase_											=model(lowercase__  )
   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(lowercase__  ).mkdir(exist_ok=lowercase__  )
   model.save_pretrained(lowercase__  )
   image_processor.save_pretrained(lowercase__  )


if __name__ == "__main__":
     __lowercase  :   Dict						      =argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""",
         default="""conditional_detr_resnet50""",
         type=str,
         help="""Name of the CONDITIONAL_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  :   Any						      =parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




