






import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase			:					int         =	logging.getLogger(__name__)



@dataclass(frozen=a_			)
class 							_UpperCamelCase			:
      snake_case_  =							42
      snake_case_  =							42
      snake_case_  =							None
      snake_case_  =							None
      snake_case_  =							None



@dataclass(frozen=a_			)
class 							_UpperCamelCase			:
      snake_case_  =							42
      snake_case_  =							None
      snake_case_  =							None
      snake_case_  =							None
      snake_case_  =							None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset



    class 							_UpperCamelCase			(a_			):
          snake_case_  =							42


          def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase=False					,						__UpperCamelCase = False					,						)->	Any:
               __lowerCAmelCase								=					hans_processors[task]()

               __lowerCAmelCase								=					os.path.join(
                   __UpperCamelCase					,						"cached_{}_{}_{}_{}".format(
                       "dev" if evaluate else "train"					,						tokenizer.__class__.__name__					,						str(__UpperCamelCase	)					,						__UpperCamelCase					,						)					,						)
               __lowerCAmelCase								=					processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                    # HACK(label indices are swapped in RoBERTa pretrained model)
                    __lowerCAmelCase			, __lowerCAmelCase								=					label_list[2], label_list[1]
               __lowerCAmelCase								=					label_list

               # Make sure only the first process in distributed training processes the dataset,
               # and the others will use the cache.
               __lowerCAmelCase								=					cached_features_file + ".lock"
               with FileLock(__UpperCamelCase	):
                    if os.path.exists(__UpperCamelCase	) and not overwrite_cache:
                         logger.info(F"""Loading features from cached file {cached_features_file}"""	)
                         __lowerCAmelCase								=					torch.load(__UpperCamelCase	)
                    else:
                         logger.info(F"""Creating features from dataset file at {data_dir}"""	)

                         __lowerCAmelCase								=					(
                             processor.get_dev_examples(__UpperCamelCase	) if evaluate else processor.get_train_examples(__UpperCamelCase	)
                         )

                         logger.info("Training examples: %s"					,						len(__UpperCamelCase	)	)
                         __lowerCAmelCase								=					hans_convert_examples_to_features(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)
                         logger.info("Saving features into cached file %s"					,						__UpperCamelCase	)
                         torch.save(self.features					,						__UpperCamelCase	)


          def __len__(	self	)->	Dict:
               return len(self.features	)


          def __getitem__(	self					,						__UpperCamelCase	)->	InputFeatures:
               return self.features[i]


          def  __UpperCAmelCase		(	self	)->	Optional[int]:
               return self.label_list


if is_tf_available():
    import tensorflow as tf



    class 							_UpperCamelCase			:
          snake_case_  =							42


          def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = 1_2_8					,						__UpperCamelCase=False					,						__UpperCamelCase = False					,						)->	Tuple:
               __lowerCAmelCase								=					hans_processors[task]()
               __lowerCAmelCase								=					processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                    # HACK(label indices are swapped in RoBERTa pretrained model)
                    __lowerCAmelCase			, __lowerCAmelCase								=					label_list[2], label_list[1]
               __lowerCAmelCase								=					label_list

               __lowerCAmelCase								=					processor.get_dev_examples(__UpperCamelCase	) if evaluate else processor.get_train_examples(__UpperCamelCase	)
               __lowerCAmelCase								=					hans_convert_examples_to_features(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

               def gen():
                    for ex_index, ex in tqdm.tqdm(enumerate(self.features	)					,						desc="convert examples to features"	):
                         if ex_index % 1_0_0_0_0 == 0:
                              logger.info("Writing example %d of %d" % (ex_index, len(__UpperCamelCase	))	)

                         yield (
                             {
                                 "example_id": 0,
                                 "input_ids": ex.input_ids,
                                 "attention_mask": ex.attention_mask,
                                 "token_type_ids": ex.token_type_ids,
                             },
                             ex.label,
                         )

               __lowerCAmelCase								=					tf.data.Dataset.from_generator(
                   __UpperCamelCase					,						(
                       {
                           "example_id": tf.intaa,
                           "input_ids": tf.intaa,
                           "attention_mask": tf.intaa,
                           "token_type_ids": tf.intaa,
                       },
                       tf.intaa,
                   )					,						(
                       {
                           "example_id": tf.TensorShape([]	),
                           "input_ids": tf.TensorShape([None, None]	),
                           "attention_mask": tf.TensorShape([None, None]	),
                           "token_type_ids": tf.TensorShape([None, None]	),
                       },
                       tf.TensorShape([]	),
                   )					,						)


          def  __UpperCAmelCase		(	self	)->	Optional[int]:
               return self.dataset


          def __len__(	self	)->	List[Any]:
               return len(self.features	)


          def __getitem__(	self					,						__UpperCamelCase	)->	InputFeatures:
               return self.features[i]


          def  __UpperCAmelCase		(	self	)->	int:
               return self.label_list



class 							_UpperCamelCase			(a_			):


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	List[str]:
           return self._create_examples(self._read_tsv(os.path.join(__UpperCamelCase					,						"heuristics_train_set.txt"	)	)					,						"train"	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Dict:
           return self._create_examples(self._read_tsv(os.path.join(__UpperCamelCase					,						"heuristics_evaluation_set.txt"	)	)					,						"dev"	)


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           return ["contradiction", "entailment", "neutral"]


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Optional[Any]:
           __lowerCAmelCase								=					[]
           for i, line in enumerate(__UpperCamelCase	):
                if i == 0:
                     continue
                __lowerCAmelCase								=					"%s-%s" % (set_type, line[0])
                __lowerCAmelCase								=					line[5]
                __lowerCAmelCase								=					line[6]
                __lowerCAmelCase								=					line[7][2:] if line[7].startswith("ex"	) else line[7]
                __lowerCAmelCase								=					line[0]
                examples.append(InputExample(guid=__UpperCamelCase					,						text_a=__UpperCamelCase					,						text_b=__UpperCamelCase					,						label=__UpperCamelCase					,						pairID=__UpperCamelCase	)	)
           return examples





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, ):
     __lowerCAmelCase								=					{label: i for i, label in enumerate(__snake_case       )}

     __lowerCAmelCase								=					[]
     for ex_index, example in tqdm.tqdm(enumerate(__snake_case       )					, desc="convert examples to features"       ):
          if ex_index % 1_0000 == 0:
               logger.info("Writing example %d" % (ex_index)       )

          __lowerCAmelCase								=					tokenizer(
              example.text_a					, example.text_b					, add_special_tokens=__snake_case					, max_length=__snake_case					, padding="max_length"					, truncation=__snake_case					, return_overflowing_tokens=__snake_case					, )

          __lowerCAmelCase								=					label_map[example.label] if example.label in label_map else 0

          __lowerCAmelCase								=					int(example.pairID       )

          features.append(InputFeatures(**__snake_case					, label=__snake_case					, pairID=__snake_case       )       )

     for i, example in enumerate(examples[:5]       ):
          logger.info("*** Example ***"       )
          logger.info(F"""guid: {example}"""       )
          logger.info(F"""features: {features[i]}"""       )

     return features


lowerCamelCase			:					Union[str, Any]         =	{
    '''hans''': 3,
}



lowerCamelCase			:					Optional[int]         =	{
    '''hans''': HansProcessor,
}














import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase			:					str         =	logging.get_logger(__name__)





def       __lowerCAmelCase      (						__snake_case       ):
     # initialize config
     if "resnet-50" in model_name:
          __lowerCAmelCase								=					ResNetConfig.from_pretrained("microsoft/resnet-50"       )
     elif "resnet-101" in model_name:
          __lowerCAmelCase								=					ResNetConfig.from_pretrained("microsoft/resnet-101"       )
     else:
          raise ValueError("Model name should include either resnet50 or resnet101"       )

     __lowerCAmelCase								=					DetrConfig(use_timm_backbone=__snake_case					, backbone_config=__snake_case       )

     # set label attributes
     __lowerCAmelCase								=					"panoptic" in model_name
     if is_panoptic:
          __lowerCAmelCase								=					250
     else:
          __lowerCAmelCase								=					91
          __lowerCAmelCase								=					"huggingface/label-files"
          __lowerCAmelCase								=					"coco-detection-id2label.json"
          __lowerCAmelCase								=					json.load(open(hf_hub_download(__snake_case					, __snake_case					, repo_type="dataset"       )					, "r"       )       )
          __lowerCAmelCase								=					{int(__snake_case       ): v for k, v in idalabel.items()}
          __lowerCAmelCase								=					idalabel
          __lowerCAmelCase								=					{v: k for k, v in idalabel.items()}

     return config, is_panoptic





def       __lowerCAmelCase      (						__snake_case       ):
     # here we list all keys to be renamed (original name on the left, our name on the right)
     __lowerCAmelCase								=					[]

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")       )
     rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")       )
     rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")       )
     rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")       )
     rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")       )
     # stages
     for stage_idx in range(len(config.backbone_config.depths       )       ):
          for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                        )       )
               # 3 convs
               for i in range(3       ):
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                        )       )
                    rename_keys.append(
                        (
                            F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                            F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                        )       )
    # fmt: on

     for i in range(config.encoder_layers       ):
          # 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"),
         ]       )

     return rename_keys





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					state_dict.pop(__snake_case       )
     __lowerCAmelCase								=					val





def       __lowerCAmelCase      (						__snake_case					, __snake_case=False       ):
     __lowerCAmelCase								=					""
     if is_panoptic:
          __lowerCAmelCase								=					"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)
          __lowerCAmelCase								=					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
          __lowerCAmelCase								=					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
          __lowerCAmelCase								=					in_proj_weight[:256, :]
          __lowerCAmelCase								=					in_proj_bias[:256]
          __lowerCAmelCase								=					in_proj_weight[256:512, :]
          __lowerCAmelCase								=					in_proj_bias[256:512]
          __lowerCAmelCase								=					in_proj_weight[-256:, :]
          __lowerCAmelCase								=					in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6       ):
          # read in weights + bias of input projection layer of self-attention
          __lowerCAmelCase								=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
          __lowerCAmelCase								=					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
          __lowerCAmelCase								=					in_proj_weight[:256, :]
          __lowerCAmelCase								=					in_proj_bias[:256]
          __lowerCAmelCase								=					in_proj_weight[256:512, :]
          __lowerCAmelCase								=					in_proj_bias[256:512]
          __lowerCAmelCase								=					in_proj_weight[-256:, :]
          __lowerCAmelCase								=					in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          __lowerCAmelCase								=					state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
          __lowerCAmelCase								=					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
          __lowerCAmelCase								=					in_proj_weight_cross_attn[:256, :]
          __lowerCAmelCase								=					in_proj_bias_cross_attn[:256]
          __lowerCAmelCase								=					in_proj_weight_cross_attn[256:512, :]
          __lowerCAmelCase								=					in_proj_bias_cross_attn[256:512]
          __lowerCAmelCase								=					in_proj_weight_cross_attn[-256:, :]
          __lowerCAmelCase								=					in_proj_bias_cross_attn[-256:]





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

     return im





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case=None					, __snake_case=False       ):
     __lowerCAmelCase			, __lowerCAmelCase								=					get_detr_config(__snake_case       )

     # load original model from torch hub
     __lowerCAmelCase								=					{
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(F"""Converting model {model_name}..."""       )
     __lowerCAmelCase								=					torch.hub.load("facebookresearch/detr"					, model_name_to_original_name[model_name]					, pretrained=__snake_case       ).eval()
     __lowerCAmelCase								=					detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(__snake_case       ):
          if is_panoptic:
               __lowerCAmelCase								=					"detr." + src
          rename_key(__snake_case					, __snake_case					, __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
     __lowerCAmelCase								=					"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"       )
               ):
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
               elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
                    continue
               else:
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val
          else:
               if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
                    __lowerCAmelCase								=					state_dict.pop(__snake_case       )
                    __lowerCAmelCase								=					val

    # finally, create HuggingFace model and load state dict
     __lowerCAmelCase								=					DetrForSegmentation(__snake_case       ) if is_panoptic else DetrForObjectDetection(__snake_case       )
     model.load_state_dict(__snake_case       )
     model.eval()

     # verify our conversion on an image
     __lowerCAmelCase								=					"coco_panoptic" if is_panoptic else "coco_detection"
     __lowerCAmelCase								=					DetrImageProcessor(format=__snake_case       )

     __lowerCAmelCase								=					processor(images=prepare_img()					, return_tensors="pt"       )
     __lowerCAmelCase								=					encoding["pixel_values"]

     __lowerCAmelCase								=					detr(__snake_case       )
     __lowerCAmelCase								=					model(__snake_case       )

     assert torch.allclose(outputs.logits					, original_outputs["pred_logits"]					, atol=1E-3       )
     assert torch.allclose(outputs.pred_boxes					, original_outputs["pred_boxes"]					, atol=1E-3       )
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks					, original_outputs["pred_masks"]					, atol=1E-4       )
     print("Looks ok!"       )

     if pytorch_dump_folder_path is not None:
          # 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       )
          processor.save_pretrained(__snake_case       )

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info("Uploading PyTorch model and image processor to the hub..."       )
          model.push_to_hub(F"""nielsr/{model_name}"""       )
          processor.push_to_hub(F"""nielsr/{model_name}"""       )


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

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     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.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
 lowerCamelCase			:					int         =	parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






