code: 
  
  
  
  """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()
  _lowerCAmelCase					:Any  =      logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  _lowerCAmelCase					:Union[str, 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 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	int  , UpperCamelCase__	:	List[Any]							):
      _UpperCAmelCase							:						str       							=					state_dict.pop(UpperCamelCase__							)
      _UpperCAmelCase							:						List[Any]       							=					val
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Dict							):
      _UpperCAmelCase							:						Dict       							=					OrderedDict()
      for key, value in state_dict.items():
          if "backbone.0.body" in key:
              _UpperCAmelCase							:						List[str]       							=					key.replace('''backbone.0.body'''  , '''backbone.conv_encoder.model'''							)
              _UpperCAmelCase							:						List[Any]       							=					value
          else:
              _UpperCAmelCase							:						Any       							=					value
  
      return new_state_dict
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Optional[Any]=False							):
      _UpperCAmelCase							:						Tuple       							=					''''''
      if is_panoptic:
          _UpperCAmelCase							:						Optional[int]       							=					'''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)
          _UpperCAmelCase							:						List[Any]       							=					state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'							)
          _UpperCAmelCase							:						int       							=					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
          _UpperCAmelCase							:						str       							=					in_proj_weight[:256, :]
          _UpperCAmelCase							:						Dict       							=					in_proj_bias[:256]
          _UpperCAmelCase							:						str       							=					in_proj_weight[256:512, :]
          _UpperCAmelCase							:						Any       							=					in_proj_bias[256:512]
          _UpperCAmelCase							:						int       							=					in_proj_weight[-256:, :]
          _UpperCAmelCase							:						Tuple       							=					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
          _UpperCAmelCase							:						str       							=					state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'							)
          _UpperCAmelCase							:						str       							=					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
          _UpperCAmelCase							:						Dict       							=					in_proj_weight[:256, :]
          _UpperCAmelCase							:						List[Any]       							=					in_proj_bias[:256]
          _UpperCAmelCase							:						Any       							=					in_proj_weight[256:512, :]
          _UpperCAmelCase							:						str       							=					in_proj_bias[256:512]
          _UpperCAmelCase							:						Union[str, Any]       							=					in_proj_weight[-256:, :]
          _UpperCAmelCase							:						Dict       							=					in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          _UpperCAmelCase							:						Optional[int]       							=					state_dict.pop(
              F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'							)
          _UpperCAmelCase							:						int       							=					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
          _UpperCAmelCase							:						List[str]       							=					in_proj_weight_cross_attn[:256, :]
          _UpperCAmelCase							:						Optional[Any]       							=					in_proj_bias_cross_attn[:256]
          _UpperCAmelCase							:						List[str]       							=					in_proj_weight_cross_attn[256:512, :]
          _UpperCAmelCase							:						Union[str, Any]       							=					in_proj_bias_cross_attn[256:512]
          _UpperCAmelCase							:						Optional[Any]       							=					in_proj_weight_cross_attn[-256:, :]
          _UpperCAmelCase							:						Optional[int]       							=					in_proj_bias_cross_attn[-256:]
  
  
  
  
  
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						Tuple       							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _UpperCAmelCase							:						List[str]       							=					Image.open(requests.get(UpperCamelCase__  , stream=UpperCamelCase__							).raw							)
  
      return im
  
  
  
  
  
  @torch.no_grad()
  def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	Optional[Any]							):
      _UpperCAmelCase							:						List[str]       							=					DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
          _UpperCAmelCase							:						Union[str, Any]       							=					'''resnet101'''
      if "dc5" in model_name:
          _UpperCAmelCase							:						str       							=					True
      _UpperCAmelCase							:						Dict       							=					'''panoptic''' in model_name
      if is_panoptic:
          _UpperCAmelCase							:						Any       							=					250
      else:
          _UpperCAmelCase							:						Optional[int]       							=					91
          _UpperCAmelCase							:						str       							=					'''huggingface/label-files'''
          _UpperCAmelCase							:						List[str]       							=					'''coco-detection-id2label.json'''
          _UpperCAmelCase							:						Optional[Any]       							=					json.load(open(hf_hub_download(UpperCamelCase__  , UpperCamelCase__  , repo_type='''dataset'''							)  , '''r'''							)							)
          _UpperCAmelCase							:						Optional[int]       							=					{int(UpperCamelCase__							): v for k, v in idalabel.items()}
          _UpperCAmelCase							:						Optional[Any]       							=					idalabel
          _UpperCAmelCase							:						int       							=					{v: k for k, v in idalabel.items()}
  
      # load image processor
      _UpperCAmelCase							:						str       							=					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
      _UpperCAmelCase							:						Any       							=					DetrImageProcessor(format=UpperCamelCase__							)
  
      # prepare image
      _UpperCAmelCase							:						List[Any]       							=					prepare_img()
      _UpperCAmelCase							:						Any       							=					image_processor(images=UpperCamelCase__  , return_tensors='''pt'''							)
      _UpperCAmelCase							:						List[Any]       							=					encoding['''pixel_values''']
  
      logger.info(F'Converting model {model_name}...'							)
  
      # load original model from torch hub
      _UpperCAmelCase							:						int       							=					torch.hub.load('''facebookresearch/detr'''  , UpperCamelCase__  , pretrained=UpperCamelCase__							).eval()
      _UpperCAmelCase							:						Optional[Any]       							=					detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
          if is_panoptic:
              _UpperCAmelCase							:						Dict       							=					'''detr.''' + src
          rename_key(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
      _UpperCAmelCase							:						str       							=					rename_backbone_keys(UpperCamelCase__							)
      # query, key and value matrices need special treatment
      read_in_q_k_v(UpperCamelCase__  , is_panoptic=UpperCamelCase__							)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      _UpperCAmelCase							:						Tuple       							=					'''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'''							)
              ):
                  _UpperCAmelCase							:						str       							=					state_dict.pop(UpperCamelCase__							)
                  _UpperCAmelCase							:						Union[str, Any]       							=					val
              elif "class_labels_classifier" in key or "bbox_predictor" in key:
                  _UpperCAmelCase							:						int       							=					state_dict.pop(UpperCamelCase__							)
                  _UpperCAmelCase							:						Optional[int]       							=					val
              elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
                  continue
              else:
                  _UpperCAmelCase							:						Any       							=					state_dict.pop(UpperCamelCase__							)
                  _UpperCAmelCase							:						Dict       							=					val
          else:
              if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
                  _UpperCAmelCase							:						int       							=					state_dict.pop(UpperCamelCase__							)
                  _UpperCAmelCase							:						Union[str, Any]       							=					val
      # finally, create HuggingFace model and load state dict
      _UpperCAmelCase							:						Optional[int]       							=					DetrForSegmentation(UpperCamelCase__							) if is_panoptic else DetrForObjectDetection(UpperCamelCase__							)
      model.load_state_dict(UpperCamelCase__							)
      model.eval()
      # verify our conversion
      _UpperCAmelCase							:						Optional[int]       							=					detr(UpperCamelCase__							)
      _UpperCAmelCase							:						Any       							=					model(UpperCamelCase__							)
      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(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
      model.save_pretrained(UpperCamelCase__							)
      image_processor.save_pretrained(UpperCamelCase__							)
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:str  =      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.'
    )
    _lowerCAmelCase					:int  =      parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  # Lint as: python3
  import sys
  from collections.abc import Mapping
  from typing import TYPE_CHECKING
  
  import numpy as np
  import pyarrow as pa
  
  from .. import config
  from ..utils.py_utils import map_nested
  from .formatting import TensorFormatter
  
  
  if TYPE_CHECKING:
    import tensorflow as tf
  class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A=None ,			**A	)							->      List[str]:
           super().__init__(features=A	)
           _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           import tensorflow as tf
  
           if isinstance(A ,			A	) and column:
               if all(
                   isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                   return tf.stack(A	)
               elif all(
                   isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                   for x in column	):
                   # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                   return tf.ragged.stack(A	)
  
           return column
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           import tensorflow as tf
  
           if value is None:
               return value
  
           _UpperCAmelCase							:						Any       							=					{}
  
           if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
               _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
           elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
               _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image
  
               if isinstance(A ,			PIL.Image.Image	):
                   _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)
  
           return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
           import tensorflow as tf
  
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch
  
               if isinstance(A ,			torch.Tensor	):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
           if hasattr(A ,			'''__array__'''	) and not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A ,			np.ndarray	):
               if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
           elif isinstance(A ,			(list, tuple)	):
               return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
           return self._tensorize(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
           _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
           _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
           return self.recursive_tensorize(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
           _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
           _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
           _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
           _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
           return column
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
           _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
           _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
           for column_name in batch:
               _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
           return batch
  
  
  