code: 
  
  
  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_														=      logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  A_														=      []
  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				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      ):
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       state_dict.pop(snake_case_      )
  					SCREAMING_SNAKE_CASE_	      =       val
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      ):
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       OrderedDict()
  					for key, value in state_dict.items():
  										if "backbone.0.body" in key:
  															SCREAMING_SNAKE_CASE_	      =       key.replace('''backbone.0.body'''				,'''backbone.conv_encoder.model'''      )
  															SCREAMING_SNAKE_CASE_	      =       value
  										else:
  															SCREAMING_SNAKE_CASE_	      =       value
  
  					return new_state_dict
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase=False      ):
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       ''''''
  					if is_panoptic:
  										SCREAMING_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)
  										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
  										SCREAMING_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
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[:256, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[:256]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[256:512, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[256:512]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[-256:, :]
  										SCREAMING_SNAKE_CASE_	      =       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
  										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
  										SCREAMING_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
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[:256, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[:256]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[256:512, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[256:512]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight[-256:, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias[-256:]
  										# read in weights + bias of input projection layer of cross-attention
  										SCREAMING_SNAKE_CASE_	      =       state_dict.pop(
  										    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''      )
  										SCREAMING_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
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight_cross_attn[:256, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias_cross_attn[:256]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight_cross_attn[256:512, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias_cross_attn[256:512]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_weight_cross_attn[-256:, :]
  										SCREAMING_SNAKE_CASE_	      =       in_proj_bias_cross_attn[-256:]
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     ):
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  					SCREAMING_SNAKE_CASE_	      =       Image.open(requests.get(snake_case_				,stream=snake_case_      ).raw      )
  
  					return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      ):
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       DetrConfig()
  					# set backbone and dilation attributes
  					if "resnet101" in model_name:
  										SCREAMING_SNAKE_CASE_	      =       '''resnet101'''
  					if "dc5" in model_name:
  										SCREAMING_SNAKE_CASE_	      =       True
  					SCREAMING_SNAKE_CASE_	      =       '''panoptic''' in model_name
  					if is_panoptic:
  										SCREAMING_SNAKE_CASE_	      =       250
  					else:
  										SCREAMING_SNAKE_CASE_	      =       91
  										SCREAMING_SNAKE_CASE_	      =       '''huggingface/label-files'''
  										SCREAMING_SNAKE_CASE_	      =       '''coco-detection-id2label.json'''
  										SCREAMING_SNAKE_CASE_	      =       json.load(open(hf_hub_download(snake_case_				,snake_case_				,repo_type='''dataset'''      )				,'''r'''      )      )
  										SCREAMING_SNAKE_CASE_	      =       {int(snake_case_      ): v for k, v in idalabel.items()}
  										SCREAMING_SNAKE_CASE_	      =       idalabel
  										SCREAMING_SNAKE_CASE_	      =       {v: k for k, v in idalabel.items()}
  
  					# load image processor
  					SCREAMING_SNAKE_CASE_	      =       '''coco_panoptic''' if is_panoptic else '''coco_detection'''
  					SCREAMING_SNAKE_CASE_	      =       DetrImageProcessor(format=snake_case_      )
  
  					# prepare image
  					SCREAMING_SNAKE_CASE_	      =       prepare_img()
  					SCREAMING_SNAKE_CASE_	      =       image_processor(images=snake_case_				,return_tensors='''pt'''      )
  					SCREAMING_SNAKE_CASE_	      =       encoding['''pixel_values''']
  
  					logger.info(f'''Converting model {model_name}...'''      )
  
  					# load original model from torch hub
  					SCREAMING_SNAKE_CASE_	      =       torch.hub.load('''facebookresearch/detr'''				,snake_case_				,pretrained=snake_case_      ).eval()
  					SCREAMING_SNAKE_CASE_	      =       detr.state_dict()
  					# rename keys
  					for src, dest in rename_keys:
  										if is_panoptic:
  															SCREAMING_SNAKE_CASE_	      =       '''detr.''' + src
  										rename_key(snake_case_				,snake_case_				,snake_case_      )
  					SCREAMING_SNAKE_CASE_	      =       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
  					SCREAMING_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'''      )
  															):
  																				SCREAMING_SNAKE_CASE_	      =       state_dict.pop(snake_case_      )
  																				SCREAMING_SNAKE_CASE_	      =       val
  															elif "class_labels_classifier" in key or "bbox_predictor" in key:
  																				SCREAMING_SNAKE_CASE_	      =       state_dict.pop(snake_case_      )
  																				SCREAMING_SNAKE_CASE_	      =       val
  															elif key.startswith('''bbox_attention'''      ) or key.startswith('''mask_head'''      ):
  																				continue
  															else:
  																				SCREAMING_SNAKE_CASE_	      =       state_dict.pop(snake_case_      )
  																				SCREAMING_SNAKE_CASE_	      =       val
  										else:
  															if not key.startswith('''class_labels_classifier'''      ) and not key.startswith('''bbox_predictor'''      ):
  																				SCREAMING_SNAKE_CASE_	      =       state_dict.pop(snake_case_      )
  																				SCREAMING_SNAKE_CASE_	      =       val
      # finally, create HuggingFace model and load state dict
  					SCREAMING_SNAKE_CASE_	      =       DetrForSegmentation(snake_case_      ) if is_panoptic else DetrForObjectDetection(snake_case_      )
  					model.load_state_dict(snake_case_      )
  					model.eval()
  					# verify our conversion
  					SCREAMING_SNAKE_CASE_	      =       detr(snake_case_      )
  					SCREAMING_SNAKE_CASE_	      =       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__":
  		A_														=      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_														=      parser.parse_args()
  		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["MarianTokenizer"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "MarianForCausalLM",
  		    "MarianModel",
  		    "MarianMTModel",
  		    "MarianPreTrainedModel",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  