






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       def __init__(				self	:int,      snake_case	:List[Any]):



          """simple docstring"""

          _lowercase              =data





       def __iter__(				self	:Any):



          """simple docstring"""

          for element in self.data:
             yield element





def        _snake_case   (_snake_case				:       int=True)					->	Tuple:
   _lowercase              =Accelerator(even_batches=_snake_case)
   assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
   return accelerator





def        _snake_case   (_snake_case				:       Accelerator      ,    _snake_case				:       int      ,    _snake_case				:       int      ,    _snake_case				:       bool = False)					->	List[str]:
   if iterable:
      _lowercase              =DummyIterableDataset(torch.as_tensor(range(_snake_case)))
   else:
      _lowercase              =TensorDataset(torch.as_tensor(range(_snake_case)))

   _lowercase              =DataLoader(_snake_case      ,    batch_size=_snake_case)
   _lowercase              =accelerator.prepare(_snake_case)

   return dl





def        _snake_case   (_snake_case				:       Accelerator      ,    _snake_case				:       int      ,    _snake_case				:       int      ,    _snake_case				:       List[int]      ,    _snake_case				:       List[int]      ,    )					->	Optional[Any]:
   _lowercase              =create_dataloader(accelerator=_snake_case      ,    dataset_size=_snake_case      ,    batch_size=_snake_case)

   _lowercase              =[len(batch[0]) for batch in dl]

   if accelerator.process_index == 0:
      assert batch_sizes == process_0_expected_batch_sizes
   elif accelerator.process_index == 1:
      assert batch_sizes == process_1_expected_batch_sizes





def        _snake_case   ()					->	Dict:
   _lowercase              =create_accelerator()

   # without padding, we would expect a different number of batches
   verify_dataloader_batch_sizes(
       _snake_case      ,    dataset_size=3      ,    batch_size=1      ,    process_0_expected_batch_sizes=[1, 1]      ,    process_1_expected_batch_sizes=[1, 1]      ,    )

   # without padding, we would expect the same number of batches, but different sizes
   verify_dataloader_batch_sizes(
       _snake_case      ,    dataset_size=7      ,    batch_size=2      ,    process_0_expected_batch_sizes=[2, 2]      ,    process_1_expected_batch_sizes=[2, 2]      ,    )





def        _snake_case   ()					->	Optional[Any]:
   _lowercase              =create_accelerator(even_batches=_snake_case)

   verify_dataloader_batch_sizes(
       _snake_case      ,    dataset_size=3      ,    batch_size=1      ,    process_0_expected_batch_sizes=[1, 1]      ,    process_1_expected_batch_sizes=[1]      ,    )

   verify_dataloader_batch_sizes(
       _snake_case      ,    dataset_size=7      ,    batch_size=2      ,    process_0_expected_batch_sizes=[2, 2]      ,    process_1_expected_batch_sizes=[2, 1]      ,    )





def        _snake_case   ()					->	str:
   _lowercase              =create_accelerator(even_batches=_snake_case)

   _lowercase              =torch.nn.Linear(1      ,    1)
   _lowercase              =accelerator.prepare(_snake_case)

   _lowercase              =create_dataloader(_snake_case      ,    dataset_size=3      ,    batch_size=1)

   _lowercase              =[]
   with accelerator.join_uneven_inputs([ddp_model]):
      for batch_idx, batch in enumerate(_snake_case):
         _lowercase              =ddp_model(batch[0].float())
         _lowercase              =output.sum()
         loss.backward()
         batch_idxs.append(_snake_case)

   accelerator.wait_for_everyone()

   if accelerator.process_index == 0:
      assert batch_idxs == [0, 1]
   elif accelerator.process_index == 1:
      assert batch_idxs == [0]





def        _snake_case   (_snake_case				:       List[Any])					->	str:
   with warnings.catch_warnings(record=_snake_case) as w:
      with accelerator.join_uneven_inputs([Mock()]):
         pass

      assert issubclass(w[-1].category      ,    _snake_case)
      assert "only supported for multi-GPU" in str(w[-1].message)





def        _snake_case   ()					->	str:
   _lowercase              =True
   _lowercase              =False
   _lowercase              =create_accelerator(even_batches=_snake_case)
   _lowercase              =torch.nn.Linear(1      ,    1)
   _lowercase              =accelerator.prepare(_snake_case)
   _lowercase              =create_dataloader(_snake_case      ,    dataset_size=3      ,    batch_size=1)
   _lowercase              =create_dataloader(_snake_case      ,    dataset_size=3      ,    batch_size=1)

   with accelerator.join_uneven_inputs([ddp_model]      ,    even_batches=_snake_case):
      _lowercase              =train_dl.batch_sampler.even_batches
      _lowercase              =valid_dl.batch_sampler.even_batches

   assert train_dl_overridden_value == overridden_even_batches
   assert valid_dl_overridden_value == overridden_even_batches
   assert train_dl.batch_sampler.even_batches == default_even_batches
   assert valid_dl.batch_sampler.even_batches == default_even_batches





def        _snake_case   ()					->	Tuple:
   _lowercase              =True
   _lowercase              =False
   _lowercase              =create_accelerator(even_batches=_snake_case)
   _lowercase              =torch.nn.Linear(1      ,    1)
   _lowercase              =accelerator.prepare(_snake_case)
   create_dataloader(_snake_case      ,    dataset_size=3      ,    batch_size=1      ,    iterable=_snake_case)
   _lowercase              =create_dataloader(_snake_case      ,    dataset_size=3      ,    batch_size=1)

   with warnings.catch_warnings():
      warnings.filterwarnings('ignore')
      try:
         with accelerator.join_uneven_inputs([ddp_model]      ,    even_batches=_snake_case):
            _lowercase              =batch_dl.batch_sampler.even_batches
      except AttributeError:
         # ensure attribute error is not raised when processing iterable dl
         raise AssertionError

   assert batch_dl_overridden_value == overridden_even_batches
   assert batch_dl.batch_sampler.even_batches == default_even_batches





def        _snake_case   ()					->	List[str]:
   _lowercase              =create_accelerator()
   _lowercase              =torch.nn.Linear(1      ,    1)
   _lowercase              =accelerator.prepare(_snake_case)
   create_dataloader(_snake_case      ,    dataset_size=3      ,    batch_size=1      ,    iterable=_snake_case)

   with warnings.catch_warnings(record=_snake_case) as w:
      with accelerator.join_uneven_inputs([ddp_model]      ,    even_batches=_snake_case):
         pass

      assert issubclass(w[-1].category      ,    _snake_case)
      assert "only supported for map-style datasets" in str(w[-1].message)





def        _snake_case   ()					->	Optional[Any]:
   _lowercase              =create_accelerator()

   accelerator.print('Test that even_batches variable ensures uniform batches across processes')
   test_default_ensures_even_batch_sizes()

   accelerator.print('Run tests with even_batches disabled')
   test_can_disable_even_batches()

   accelerator.print('Test joining uneven inputs')
   test_can_join_uneven_inputs()

   accelerator.print('Test overriding even_batches when joining uneven inputs')
   test_join_can_override_even_batches()

   accelerator.print('Test overriding even_batches for mixed dataloader types')
   test_join_can_override_for_mixed_type_dataloaders()

   accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders')
   test_join_raises_warning_for_iterable_when_overriding_even_batches()

   accelerator.print('Test join with non DDP distributed raises warning')
   _lowercase              =accelerator.state.distributed_type
   _lowercase              =DistributedType.FSDP
   test_join_raises_warning_for_non_ddp_distributed(_snake_case)
   _lowercase              =original_state


if __name__ == "__main__":
    main()










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()
_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE   =  []
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        _snake_case   (_snake_case				:       Optional[int]      ,    _snake_case				:       Dict      ,    _snake_case				:       Dict)					->	str:
   _lowercase              =state_dict.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       Any)					->	Union[str, Any]:
   _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        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Tuple=False)					->	str:
   _lowercase              =''
   if is_panoptic:
      _lowercase              ='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)
      _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[:256, :]
      _lowercase              =in_proj_bias[:256]
      _lowercase              =in_proj_weight[256:512, :]
      _lowercase              =in_proj_bias[256:512]
      _lowercase              =in_proj_weight[-256:, :]
      _lowercase              =in_proj_bias[-256:]





def        _snake_case   ()					->	List[Any]:
   _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        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       int)					->	Union[str, Any]:
   _lowercase              =ConditionalDetrConfig()
   # 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              =250
   else:
      _lowercase              =91
      _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              =ConditionalDetrImageProcessor(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('DeppMeng/ConditionalDETR'      ,    _snake_case      ,    pretrained=_snake_case).eval()
   _lowercase              =conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase              ='conditional_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              ='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')
         ):
            _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              =ConditionalDetrForSegmentation(_snake_case) if is_panoptic else ConditionalDetrForObjectDetection(_snake_case)
   model.load_state_dict(_snake_case)
   model.eval()
   model.push_to_hub(repo_id=_snake_case      ,    organization='DepuMeng'      ,    commit_message='Add model')
   # verify our conversion
   _lowercase              =conditional_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__":
    _SCREAMING_SNAKE_CASE   =  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."
    )
    _SCREAMING_SNAKE_CASE   =  parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


