"""simple docstring"""


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__UpperCamelCase       :    List[str]         =		logging.get_logger(__name__)
@dataclass
class   UpperCAmelCase_	:
    snake_case_											=      42
    snake_case_											=      42
    snake_case_											=      None
    snake_case_											=      None
    def 		_lowerCamelCase      (	self			:       str		)						->    Optional[Any]:
       return json.dumps(dataclasses.asdict(self		)   ,						indent=2		) + "\n"
@dataclass(frozen=lowercase__   )
class   UpperCAmelCase_	:
    snake_case_											=      42
    snake_case_											=      None
    snake_case_											=      None
    snake_case_											=      None
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Tuple:
       return json.dumps(dataclasses.asdict(self		)		) + "\n"
class   UpperCAmelCase_	:
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Any		)						->    Dict:
       raise NotImplementedError()
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Tuple		)						->    Any:
       raise NotImplementedError()
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]		)						->    Dict:
       raise NotImplementedError()
    def 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       Any		)						->    int:
       raise NotImplementedError()
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Optional[int]:
       raise NotImplementedError()
    def 		_lowerCamelCase      (	self			:       Optional[Any]   ,						_lowercase			:       Any		)						->    Union[str, Any]:
       if len(self.get_labels()		) > 1:
          _lowercase							     =       self.get_labels()[int(example.label		)]
       return example




    @classmethod
    def 		_lowerCamelCase      (	cls			:       List[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Optional[int]=None		)						->    Union[str, Any]:
       with open(_lowercase   ,						"r"   ,						encoding="utf-8-sig"		) as f:
          return list(csv.reader(_lowercase   ,						delimiter="\t"   ,						quotechar=_lowercase		)		)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       Tuple   ,						_lowercase			:       Dict=None   ,						_lowercase			:       List[Any]=None   ,						_lowercase			:       Tuple="classification"   ,						_lowercase			:       List[str]=False		)						->    int:
       _lowercase							     =       [] if labels is None else labels
       _lowercase							     =       [] if examples is None else examples
       _lowercase							     =       mode
       _lowercase							     =       verbose
    def __len__(	self			:       Any		)						->    int:
       return len(self.examples		)
    def __getitem__(	self			:       Union[str, Any]   ,						_lowercase			:       Optional[Any]		)						->    Tuple:
       if isinstance(_lowercase   ,						_lowercase		):
          return SingleSentenceClassificationProcessor(labels=self.labels   ,						examples=self.examples[idx]		)
       return self.examples[idx]
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Optional[int]   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       List[Any]=""   ,						_lowercase			:       Any=0   ,						_lowercase			:       Tuple=1   ,						_lowercase			:       int=None   ,						_lowercase			:       Optional[int]=False   ,						**_lowercase			:       Any		)						->    Union[str, Any]:
       _lowercase							     =       cls(**_lowercase		)
       processor.add_examples_from_csv(
           _lowercase   ,						split_name=_lowercase   ,						column_label=_lowercase   ,						column_text=_lowercase   ,						column_id=_lowercase   ,						skip_first_row=_lowercase   ,						overwrite_labels=_lowercase   ,						overwrite_examples=_lowercase   ,						)
       return processor
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Optional[Any]   ,						_lowercase			:       str   ,						_lowercase			:       Union[str, Any]=None   ,						**_lowercase			:       Optional[int]		)						->    List[Any]:
       _lowercase							     =       cls(**_lowercase		)
       processor.add_examples(_lowercase   ,						labels=_lowercase		)
       return processor
    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       List[Any]=""   ,						_lowercase			:       Tuple=0   ,						_lowercase			:       Any=1   ,						_lowercase			:       Optional[Any]=None   ,						_lowercase			:       List[Any]=False   ,						_lowercase			:       List[Any]=False   ,						_lowercase			:       Union[str, Any]=False   ,						)						->    Any:
       _lowercase							     =       self._read_tsv(_lowercase		)
       if skip_first_row:
          _lowercase							     =       lines[1:]
       _lowercase							     =       []
       _lowercase							     =       []
       _lowercase							     =       []
       for i, line in enumerate(_lowercase		):
          texts.append(line[column_text]		)
          labels.append(line[column_label]		)
          if column_id is not None:
             ids.append(line[column_id]		)
          else:
             _lowercase							     =       f"""{split_name}-{i}""" if split_name else str(_lowercase		)
             ids.append(_lowercase		)

       return self.add_examples(
           _lowercase   ,						_lowercase   ,						_lowercase   ,						overwrite_labels=_lowercase   ,						overwrite_examples=_lowercase		)
    def 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Dict=None   ,						_lowercase			:       List[str]=None   ,						_lowercase			:       Optional[Any]=False   ,						_lowercase			:       Dict=False		)						->    int:
       if labels is not None and len(_lowercase		) != len(_lowercase		):
          raise ValueError(
              f"""Text and labels have mismatched lengths {len(_lowercase		)} and {len(_lowercase		)}"""		)
       if ids is not None and len(_lowercase		) != len(_lowercase		):
          raise ValueError(f"""Text and ids have mismatched lengths {len(_lowercase		)} and {len(_lowercase		)}"""		)
       if ids is None:
          _lowercase							     =       [None] * len(_lowercase		)
       if labels is None:
          _lowercase							     =       [None] * len(_lowercase		)
       _lowercase							     =       []
       _lowercase							     =       set()
       for text_or_text_and_label, label, guid in zip(_lowercase   ,						_lowercase   ,						_lowercase		):
          if isinstance(_lowercase   ,						(tuple, list)		) and label is None:
             _lowercase     ,  _lowercase							     =       text_or_text_and_label
          else:
             _lowercase							     =       text_or_text_and_label
          added_labels.add(_lowercase		)
          examples.append(InputExample(guid=_lowercase   ,						text_a=_lowercase   ,						text_b=_lowercase   ,						label=_lowercase		)		)

       # Update examples
       if overwrite_examples:
          _lowercase							     =       examples
       else:
          self.examples.extend(_lowercase		)

       # Update labels
       if overwrite_labels:
          _lowercase							     =       list(_lowercase		)
       else:
          _lowercase							     =       list(set(self.labels		).union(_lowercase		)		)

       return self.examples




    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]   ,						_lowercase			:       Optional[Any]=None   ,						_lowercase			:       List[str]=False   ,						_lowercase			:       int=0   ,						_lowercase			:       int=True   ,						_lowercase			:       List[str]=None   ,						)						->    Optional[Any]:
       if max_length is None:
          _lowercase							     =       tokenizer.max_len

       _lowercase							     =       {label: i for i, label in enumerate(self.labels		)}

       _lowercase							     =       []
       for ex_index, example in enumerate(self.examples		):
          if ex_index % 1_0_0_0_0 == 0:
             logger.info(f"""Tokenizing example {ex_index}"""		)

          _lowercase							     =       tokenizer.encode(
              example.text_a   ,						add_special_tokens=_lowercase   ,						max_length=min(_lowercase   ,						tokenizer.max_len		)   ,						)
          all_input_ids.append(_lowercase		)

       _lowercase							     =       max(len(_lowercase		) for input_ids in all_input_ids		)

       _lowercase							     =       []
       for ex_index, (input_ids, example) in enumerate(zip(_lowercase   ,						self.examples		)		):
          if ex_index % 1_0_0_0_0 == 0:
             logger.info(f"""Writing example {ex_index}/{len(self.examples		)}"""		)
          # The mask has 1 for real tokens and 0 for padding tokens. Only real
          # tokens are attended to.
          _lowercase							     =       [1 if mask_padding_with_zero else 0] * len(_lowercase		)

          # Zero-pad up to the sequence length.
          _lowercase							     =       batch_length - len(_lowercase		)
          if pad_on_left:
             _lowercase							     =       ([pad_token] * padding_length) + input_ids
             _lowercase							     =       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
          else:
             _lowercase							     =       input_ids + ([pad_token] * padding_length)
             _lowercase							     =       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

          if len(_lowercase		) != batch_length:
             raise ValueError(f"""Error with input length {len(_lowercase		)} vs {batch_length}"""		)
          if len(_lowercase		) != batch_length:
             raise ValueError(f"""Error with input length {len(_lowercase		)} vs {batch_length}"""		)

          if self.mode == "classification":
             _lowercase							     =       label_map[example.label]
          elif self.mode == "regression":
             _lowercase							     =       float(example.label		)
          else:
             raise ValueError(self.mode		)

          if ex_index < 5 and self.verbose:
             logger.info("*** Example ***"		)
             logger.info(f"""guid: {example.guid}"""		)
             logger.info(f"""input_ids: {" ".join([str(_lowercase		) for x in input_ids]		)}"""		)
             logger.info(f"""attention_mask: {" ".join([str(_lowercase		) for x in attention_mask]		)}"""		)
             logger.info(f"""label: {example.label} (id = {label})"""		)

          features.append(InputFeatures(input_ids=_lowercase   ,						attention_mask=_lowercase   ,						label=_lowercase		)		)

       if return_tensors is None:
          return features
       elif return_tensors == "tf":
          if not is_tf_available():
             raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"		)
          import tensorflow as tf

          def gen():
             for ex in features:
                yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

          _lowercase							     =       tf.data.Dataset.from_generator(
              _lowercase   ,						({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)   ,						({"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)}, tf.TensorShape([]		))   ,						)
          return dataset
       elif return_tensors == "pt":
          if not is_torch_available():
             raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"		)
          import torch
          from torch.utils.data import TensorDataset

          _lowercase							     =       torch.tensor([f.input_ids for f in features]   ,						dtype=torch.long		)
          _lowercase							     =       torch.tensor([f.attention_mask for f in features]   ,						dtype=torch.long		)
          if self.mode == "classification":
             _lowercase							     =       torch.tensor([f.label for f in features]   ,						dtype=torch.long		)
          elif self.mode == "regression":
             _lowercase							     =       torch.tensor([f.label for f in features]   ,						dtype=torch.float		)

          _lowercase							     =       TensorDataset(_lowercase   ,						_lowercase   ,						_lowercase		)
          return dataset
       else:
          raise ValueError("return_tensors should be one of 'tf' or 'pt'"		)
"""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()
__UpperCamelCase       :    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)
__UpperCamelCase       :    Optional[Any]         =		[]
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   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _lowercase							     =       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase							     =       key.replace("backbone.0.body",				"backbone.conv_encoder.model"       )
         _lowercase							     =       value
      else:
         _lowercase							     =       value

   return new_state_dict


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


def   __UpperCAmelCase     (	):
   _lowercase							     =       "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowercase							     =       Image.open(requests.get(_snake_case,				stream=_snake_case       ).raw       )

   return im


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

   # load image processor
   _lowercase							     =       "coco_panoptic" if is_panoptic else "coco_detection"
   _lowercase							     =       DetrImageProcessor(format=_snake_case       )

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

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

   # load original model from torch hub
   _lowercase							     =       torch.hub.load("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "detr." + src
      rename_key(_snake_case,				_snake_case,				_snake_case       )
   _lowercase							     =       rename_backbone_keys(_snake_case       )
   # query, key and value matrices need special treatment
   read_in_q_k_v(_snake_case,				is_panoptic=_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							     =       "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							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
    # finally, create HuggingFace model and load state dict
   _lowercase							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       detr(_snake_case       )
   _lowercase							     =       model(_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(_snake_case       ).mkdir(exist_ok=_snake_case       )
   model.save_pretrained(_snake_case       )
   image_processor.save_pretrained(_snake_case       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		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."
  )
  __UpperCamelCase       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)