





"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)





def 							a__	(     _UpperCAmelCase		)			->     Tuple:

 '''simple docstring'''






 snake_case__										= CLIPSegTextConfig()
 snake_case__										= CLIPSegVisionConfig(patch_size=1_6		)

 snake_case__										= True if "refined" in model_name else False
 snake_case__										= 1_6 if "rd16" in model_name else 6_4

 snake_case__										= CLIPSegConfig.from_text_vision_configs(
     _UpperCAmelCase		,   _UpperCAmelCase		,   use_complex_transposed_convolution=_UpperCAmelCase		,   reduce_dim=_UpperCAmelCase		,   )
 return config





def 							a__	(     _UpperCAmelCase		)			->     int:

 '''simple docstring'''






 if "clip_model" in name:
  snake_case__										= name.replace("clip_model"		,   "clip"		)
 if "transformer" in name:
  if "visual" in name:
   snake_case__										= name.replace("visual.transformer"		,   "vision_model"		)
  else:
   snake_case__										= name.replace("transformer"		,   "text_model"		)
 if "resblocks" in name:
  snake_case__										= name.replace("resblocks"		,   "encoder.layers"		)
 if "ln_1" in name:
  snake_case__										= name.replace("ln_1"		,   "layer_norm1"		)
 if "ln_2" in name:
  snake_case__										= name.replace("ln_2"		,   "layer_norm2"		)
 if "c_fc" in name:
  snake_case__										= name.replace("c_fc"		,   "fc1"		)
 if "c_proj" in name:
  snake_case__										= name.replace("c_proj"		,   "fc2"		)
 if "attn" in name and "self" not in name:
  snake_case__										= name.replace("attn"		,   "self_attn"		)
 # text encoder
 if "token_embedding" in name:
  snake_case__										= name.replace("token_embedding"		,   "text_model.embeddings.token_embedding"		)
 if "positional_embedding" in name and "visual" not in name:
  snake_case__										= name.replace("positional_embedding"		,   "text_model.embeddings.position_embedding.weight"		)
 if "ln_final" in name:
  snake_case__										= name.replace("ln_final"		,   "text_model.final_layer_norm"		)
 # vision encoder
 if "visual.class_embedding" in name:
  snake_case__										= name.replace("visual.class_embedding"		,   "vision_model.embeddings.class_embedding"		)
 if "visual.conv1" in name:
  snake_case__										= name.replace("visual.conv1"		,   "vision_model.embeddings.patch_embedding"		)
 if "visual.positional_embedding" in name:
  snake_case__										= name.replace("visual.positional_embedding"		,   "vision_model.embeddings.position_embedding.weight"		)
 if "visual.ln_pre" in name:
  snake_case__										= name.replace("visual.ln_pre"		,   "vision_model.pre_layrnorm"		)
 if "visual.ln_post" in name:
  snake_case__										= name.replace("visual.ln_post"		,   "vision_model.post_layernorm"		)
 # projection layers
 if "visual.proj" in name:
  snake_case__										= name.replace("visual.proj"		,   "visual_projection.weight"		)
 if "text_projection" in name:
  snake_case__										= name.replace("text_projection"		,   "text_projection.weight"		)
 # decoder
 if "trans_conv" in name:
  snake_case__										= name.replace("trans_conv"		,   "transposed_convolution"		)
 if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
  snake_case__										= "decoder." + name
 if "blocks" in name:
  snake_case__										= name.replace("blocks"		,   "decoder.layers"		)
 if "linear1" in name:
  snake_case__										= name.replace("linear1"		,   "mlp.fc1"		)
 if "linear2" in name:
  snake_case__										= name.replace("linear2"		,   "mlp.fc2"		)
 if "norm1" in name and "layer_" not in name:
  snake_case__										= name.replace("norm1"		,   "layer_norm1"		)
 if "norm2" in name and "layer_" not in name:
  snake_case__										= name.replace("norm2"		,   "layer_norm2"		)

 return name





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Optional[Any]:

 '''simple docstring'''






 for key in orig_state_dict.copy().keys():
  snake_case__										= orig_state_dict.pop(_UpperCAmelCase		)

  if key.startswith("clip_model"		) and "attn.in_proj" in key:
   snake_case__										= key.split("."		)
   if "visual" in key:
    snake_case__										= int(key_split[4]		)
    snake_case__										= config.vision_config.hidden_size
    snake_case__										= "vision_model"
   else:
    snake_case__										= int(key_split[3]		)
    snake_case__										= config.text_config.hidden_size
    snake_case__										= "text_model"

   if "weight" in key:
    snake_case__										= val[:dim, :]
    snake_case__										= val[
        dim : dim * 2, :
    ]
    snake_case__										= val[-dim:, :]
   else:
    snake_case__										= val[:dim]
    snake_case__										= val[dim : dim * 2]
    snake_case__										= val[-dim:]
  elif "self_attn" in key and "out_proj" not in key:
   snake_case__										= key.split("."		)
   snake_case__										= int(key_split[1]		)
   snake_case__										= config.reduce_dim
   if "weight" in key:
    snake_case__										= val[:dim, :]
    snake_case__										= val[dim : dim * 2, :]
    snake_case__										= val[-dim:, :]
   else:
    snake_case__										= val[:dim]
    snake_case__										= val[dim : dim * 2]
    snake_case__										= val[-dim:]
  else:
   snake_case__										= rename_key(_UpperCAmelCase		)
   if "visual_projection" in new_name or "text_projection" in new_name:
    snake_case__										= val.T
   snake_case__										= val

 return orig_state_dict





def 							a__	(     )			->     Union[str, Any]:

 '''simple docstring'''






 snake_case__										= "http://images.cocodataset.org/val2017/000000039769.jpg"
 snake_case__										= Image.open(requests.get(_UpperCAmelCase		,   stream=_UpperCAmelCase		).raw		)
 return image





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= get_clipseg_config(_UpperCAmelCase		)
 snake_case__										= CLIPSegForImageSegmentation(_UpperCAmelCase		)
 model.eval()

 snake_case__										= torch.load(_UpperCAmelCase		,   map_location="cpu"		)

 # remove some keys
 for key in state_dict.copy().keys():
  if key.startswith("model"		):
   state_dict.pop(_UpperCAmelCase		,   _UpperCAmelCase		)

    # rename some keys
 snake_case__										= convert_state_dict(_UpperCAmelCase		,   _UpperCAmelCase		)
 snake_case__							,      snake_case__										= model.load_state_dict(_UpperCAmelCase		,   strict=_UpperCAmelCase		)

 if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
  raise ValueError("Missing keys that are not expected: {}".format(_UpperCAmelCase		)		)
 if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  raise ValueError(f"Unexpected keys: {unexpected_keys}"		)

 snake_case__										= ViTImageProcessor(size=3_5_2		)
 snake_case__										= CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"		)
 snake_case__										= CLIPSegProcessor(image_processor=_UpperCAmelCase		,   tokenizer=_UpperCAmelCase		)

 snake_case__										= prepare_img()
 snake_case__										= ["a glass", "something to fill", "wood", "a jar"]

 snake_case__										= processor(text=_UpperCAmelCase		,   images=[image] * len(_UpperCAmelCase		)		,   padding="max_length"		,   return_tensors="pt"		)

 with torch.no_grad():
  snake_case__										= model(**_UpperCAmelCase		)

 # verify values
 snake_case__										= torch.tensor([0.1110, -0.1882, 0.1645]		)
 snake_case__										= torch.tensor([0.2692, -0.7197, -0.1328]		)
 if model_name == "clipseg-rd64-refined":
  snake_case__										= torch.tensor(
      [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]		)
 elif model_name == "clipseg-rd64":
  snake_case__										= torch.tensor(
      [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]		)
 elif model_name == "clipseg-rd16":
  snake_case__										= torch.tensor(
      [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]		)
 else:
  raise ValueError(f"Model name {model_name} not supported."		)

 assert torch.allclose(outputs.logits[0, :3, :3]		,   _UpperCAmelCase		,   atol=1e-3		)
 assert torch.allclose(outputs.conditional_embeddings[0, :3]		,   _UpperCAmelCase		,   atol=1e-3		)
 assert torch.allclose(outputs.pooled_output[0, :3]		,   _UpperCAmelCase		,   atol=1e-3		)
 print("Looks ok!"		)

 if pytorch_dump_folder_path is not None:
  print(f"Saving model and processor to {pytorch_dump_folder_path}"		)
  model.save_pretrained(_UpperCAmelCase		)
  processor.save_pretrained(_UpperCAmelCase		)

 if push_to_hub:
  print(f"Pushing model and processor for {model_name} to the hub"		)
  model.push_to_hub(f"CIDAS/{model_name}"		)
  processor.push_to_hub(f"CIDAS/{model_name}"		)


if __name__ == "__main__":
   a__    :   List[Any]    						= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""clipseg-rd64""",
       type=str,
       choices=["""clipseg-rd16""", """clipseg-rd64""", """clipseg-rd64-refined"""],
       help=(
           """Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"""
           """ reduce dimension)"""
       ),
   )
   parser.add_argument(
       """--checkpoint_path""",
       default="""/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth""",
       type=str,
       help=(
           """Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"""
           """ the decoder weights."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   a__    :   Tuple    						= parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""


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()
a__    :   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)
a__    :   List[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}.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 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= state_dict.pop(_UpperCAmelCase		)
 snake_case__										= val





def 							a__	(     _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






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

 return new_state_dict





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=False		)			->     List[Any]:

 '''simple docstring'''






 snake_case__										= ""
 if is_panoptic:
  snake_case__										= "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)
  snake_case__										= state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight[:2_5_6, :]
  snake_case__										= in_proj_bias[:2_5_6]
  snake_case__										= in_proj_weight[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias[2_5_6:5_1_2]
  snake_case__										= in_proj_weight[-2_5_6:, :]
  snake_case__										= in_proj_bias[-2_5_6:]
 # 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
  snake_case__										= state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight[:2_5_6, :]
  snake_case__										= in_proj_bias[:2_5_6]
  snake_case__										= in_proj_weight[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias[2_5_6:5_1_2]
  snake_case__										= in_proj_weight[-2_5_6:, :]
  snake_case__										= in_proj_bias[-2_5_6:]
  # read in weights + bias of input projection layer of cross-attention
  snake_case__										= state_dict.pop(
      f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight_cross_attn[:2_5_6, :]
  snake_case__										= in_proj_bias_cross_attn[:2_5_6]
  snake_case__										= in_proj_weight_cross_attn[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias_cross_attn[2_5_6:5_1_2]
  snake_case__										= in_proj_weight_cross_attn[-2_5_6:, :]
  snake_case__										= in_proj_bias_cross_attn[-2_5_6:]





def 							a__	(     )			->     Optional[int]:

 '''simple docstring'''






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

 return im





@torch.no_grad()
def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






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

 # load image processor
 snake_case__										= "coco_panoptic" if is_panoptic else "coco_detection"
 snake_case__										= DetrImageProcessor(format=_UpperCAmelCase		)

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

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

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


if __name__ == "__main__":
   a__    :   List[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."""
   )
   a__    :   Optional[int]    						= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

