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()
  _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}.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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->int:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     state_dict.pop(lowerCamelCase__					)
    a__     		=     val
  def 				__lowercase	(						lowerCamelCase__					)				->Dict:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     OrderedDict()
    for key, value in state_dict.items():
      if "backbone.0.body" in key:
        a__     		=     key.replace("backbone.0.body" ,				"backbone.conv_encoder.model"					)
        a__     		=     value
      else:
        a__     		=     value
  
    return new_state_dict
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__=False					)				->Any:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     ""
    if is_panoptic:
      a__     		=     "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)
      a__     		=     state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"					)
      a__     		=     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
      a__     		=     in_proj_weight[:2_56, :]
      a__     		=     in_proj_bias[:2_56]
      a__     		=     in_proj_weight[2_56:5_12, :]
      a__     		=     in_proj_bias[2_56:5_12]
      a__     		=     in_proj_weight[-2_56:, :]
      a__     		=     in_proj_bias[-2_56:]
    # 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
      a__     		=     state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
      a__     		=     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
      a__     		=     in_proj_weight[:2_56, :]
      a__     		=     in_proj_bias[:2_56]
      a__     		=     in_proj_weight[2_56:5_12, :]
      a__     		=     in_proj_bias[2_56:5_12]
      a__     		=     in_proj_weight[-2_56:, :]
      a__     		=     in_proj_bias[-2_56:]
      # read in weights + bias of input projection layer of cross-attention
      a__     		=     state_dict.pop(
          f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"					)
      a__     		=     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
      a__     		=     in_proj_weight_cross_attn[:2_56, :]
      a__     		=     in_proj_bias_cross_attn[:2_56]
      a__     		=     in_proj_weight_cross_attn[2_56:5_12, :]
      a__     		=     in_proj_bias_cross_attn[2_56:5_12]
      a__     		=     in_proj_weight_cross_attn[-2_56:, :]
      a__     		=     in_proj_bias_cross_attn[-2_56:]
  def 				__lowercase	(						)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     "http://images.cocodataset.org/val2017/000000039769.jpg"
    a__     		=     Image.open(requests.get(lowerCamelCase__ ,				stream=lowerCamelCase__					).raw					)
  
    return im
  @torch.no_grad()
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
      a__     		=     "resnet101"
    if "dc5" in model_name:
      a__     		=     True
    a__     		=     "panoptic" in model_name
    if is_panoptic:
      a__     		=     2_50
    else:
      a__     		=     91
      a__     		=     "huggingface/label-files"
      a__     		=     "coco-detection-id2label.json"
      a__     		=     json.load(open(hf_hub_download(lowerCamelCase__ ,				lowerCamelCase__ ,				repo_type="dataset"					) ,				"r"					)					)
      a__     		=     {int(lowerCamelCase__					): v for k, v in idalabel.items()}
      a__     		=     idalabel
      a__     		=     {v: k for k, v in idalabel.items()}
  
    # load image processor
    a__     		=     "coco_panoptic" if is_panoptic else "coco_detection"
    a__     		=     DetrImageProcessor(format=lowerCamelCase__					)
  
    # prepare image
    a__     		=     prepare_img()
    a__     		=     image_processor(images=lowerCamelCase__ ,				return_tensors="pt"					)
    a__     		=     encoding["pixel_values"]
  
    logger.info(f"Converting model {model_name}..."					)
  
    # load original model from torch hub
    a__     		=     torch.hub.load("facebookresearch/detr" ,				lowerCamelCase__ ,				pretrained=lowerCamelCase__					).eval()
    a__     		=     detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
      if is_panoptic:
        a__     		=     "detr." + src
      rename_key(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)
    a__     		=     rename_backbone_keys(lowerCamelCase__					)
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowerCamelCase__ ,				is_panoptic=lowerCamelCase__					)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    a__     		=     "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"					)
        ):
          a__     		=     state_dict.pop(lowerCamelCase__					)
          a__     		=     val
        elif "class_labels_classifier" in key or "bbox_predictor" in key:
          a__     		=     state_dict.pop(lowerCamelCase__					)
          a__     		=     val
        elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
          continue
        else:
          a__     		=     state_dict.pop(lowerCamelCase__					)
          a__     		=     val
      else:
        if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
          a__     		=     state_dict.pop(lowerCamelCase__					)
          a__     		=     val
      # finally, create HuggingFace model and load state dict
    a__     		=     DetrForSegmentation(lowerCamelCase__					) if is_panoptic else DetrForObjectDetection(lowerCamelCase__					)
    model.load_state_dict(lowerCamelCase__					)
    model.eval()
    # verify our conversion
    a__     		=     detr(lowerCamelCase__					)
    a__     		=     model(lowerCamelCase__					)
    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(lowerCamelCase__					).mkdir(exist_ok=lowerCamelCase__					)
    model.save_pretrained(lowerCamelCase__					)
    image_processor.save_pretrained(lowerCamelCase__					)
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    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."""
       )
       _SCREAMING_SNAKE_CASE   						=    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_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  