
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__	:  Dict      =   logging.get_logger(__name__)






def 						lowerCamelCase__							(      )   ->     Union[str, Any]:
   _A:						Optional[Any]	    = DetaConfig(
       num_queries=9_00	,	encoder_ffn_dim=20_48	,	decoder_ffn_dim=20_48	,	num_feature_levels=5	,	assign_first_stage=a	,	with_box_refine=a	,	two_stage=a	,	)

   # set labels
   _A:						List[str]	    = 91
   _A:						Dict	    = '''huggingface/label-files'''
   _A:						str	    = '''coco-detection-id2label.json'''
   _A:						int	    = json.load(open(cached_download(hf_hub_url(a	,	a	,	repo_type='''dataset'''   )   )	,	'''r'''   )   )
   _A:						Optional[int]	    = {int(a   ): v for k, v in idalabel.items()}
   _A:						List[Any]	    = idalabel
   _A:						Tuple	    = {v: k for k, v in idalabel.items()}

   return config






def 						lowerCamelCase__							(      a   )   ->     Union[str, Any]:
   _A:						Optional[int]	    = []

   # stem
   # fmt: off
   rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')   )
   rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')   )
   rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')   )
   rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')   )
   rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.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"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                )   )
    # transformer encoder
   for i in range(config.encoder_layers   ):
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")   )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")   )

   # transformer decoder
   for i in range(config.decoder_layers   ):
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")   )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")   )

   # fmt: on

   return rename_keys






def 						lowerCamelCase__							(      a	,	a	,	a   )   ->     Dict:
   _A:						Any	    = dct.pop(a   )
   _A:						Optional[Any]	    = val






def 						lowerCamelCase__							(      a	,	a   )   ->     Optional[Any]:
   # transformer decoder self-attention layers
   _A:						int	    = config.d_model
   for i in range(config.decoder_layers   ):
      # read in weights + bias of input projection layer of self-attention
      _A:						Optional[int]	    = state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
      _A:						Optional[Any]	    = state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
      # next, add query, keys and values (in that order) to the state dict
      _A:						Optional[int]	    = in_proj_weight[:hidden_size, :]
      _A:						List[Any]	    = in_proj_bias[:hidden_size]
      _A:						int	    = in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      _A:						Any	    = in_proj_bias[hidden_size : hidden_size * 2]
      _A:						List[str]	    = in_proj_weight[-hidden_size:, :]
      _A:						List[str]	    = in_proj_bias[-hidden_size:]






def 						lowerCamelCase__							(      )   ->     str:
   _A:						List[Any]	    = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A:						Optional[Any]	    = Image.open(requests.get(a	,	stream=a   ).raw   )

   return im






@torch.no_grad()
def 						lowerCamelCase__							(      a	,	a	,	a   )   ->     int:
   _A:						Union[str, Any]	    = get_deta_config()

   # load original state dict
   if model_name == "deta-resnet-50":
      _A:						Dict	    = '''adet_checkpoint0011.pth'''
   elif model_name == "deta-resnet-50-24-epochs":
      _A:						str	    = '''adet_2x_checkpoint0023.pth'''
   else:
      raise ValueError(f"""Model name {model_name} not supported"""   )
   _A:						Union[str, Any]	    = hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,	filename=a   )
   _A:						Any	    = torch.load(a	,	map_location='''cpu'''   )['''model''']

   # rename keys
   _A:						Dict	    = create_rename_keys(a   )
   for src, dest in rename_keys:
      rename_key(a	,	a	,	a   )
   read_in_decoder_q_k_v(a	,	a   )

   # fix some prefixes
   for key in state_dict.copy().keys():
      if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
         _A:						str	    = state_dict.pop(a   )
         _A:						Union[str, Any]	    = val
      if "input_proj" in key:
         _A:						Dict	    = state_dict.pop(a   )
         _A:						Dict	    = val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         _A:						Dict	    = state_dict.pop(a   )
         _A:						Optional[Any]	    = val

    # finally, create HuggingFace model and load state dict
   _A:						Any	    = DetaForObjectDetection(a   )
   model.load_state_dict(a   )
   model.eval()

   _A:						Tuple	    = '''cuda''' if torch.cuda.is_available() else '''cpu'''
   model.to(a   )

   # load image processor
   _A:						Any	    = DetaImageProcessor(format='''coco_detection'''   )

   # verify our conversion on image
   _A:						Dict	    = prepare_img()
   _A:						Tuple	    = processor(images=a	,	return_tensors='''pt'''   )
   _A:						Dict	    = encoding['''pixel_values''']
   _A:						Union[str, Any]	    = model(pixel_values.to(a   )   )

   # verify logits
   if model_name == "deta-resnet-50":
      _A:						Optional[int]	    = torch.tensor(
          [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
      _A:						List[str]	    = torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]   )
   elif model_name == "deta-resnet-50-24-epochs":
      _A:						int	    = torch.tensor(
          [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
      _A:						Optional[Any]	    = torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]   )

   assert torch.allclose(outputs.logits[0, :3, :3]	,	expected_logits.to(a   )	,	atol=1E-4   )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,	expected_boxes.to(a   )	,	atol=1E-4   )
   print('''Everything ok!'''   )

   if pytorch_dump_folder_path:
      # Save model and processor
      logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""   )
      Path(a   ).mkdir(exist_ok=a   )
      model.save_pretrained(a   )
      processor.save_pretrained(a   )

   # Push to hub
   if push_to_hub:
      print('''Pushing model and processor to hub...'''   )
      model.push_to_hub(f"""jozhang97/{model_name}"""   )
      processor.push_to_hub(f"""jozhang97/{model_name}"""   )


if __name__ == "__main__":
   UpperCAmelCase__	:  List[Any]      =   argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       type=str,
       default='deta-resnet-50',
       choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
       help='Name of the 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 or not to push the converted model to the 🤗 hub.'
   )
   UpperCAmelCase__	:  Optional[Any]      =   parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





UpperCAmelCase__	:  str      =   1000






def 						lowerCamelCase__							(      a   )   ->     float:
   return x * x






def 						lowerCamelCase__							(      a	,	a	,	a	,	a = 4   )   ->     float:
   assert callable(
       a   ), f"""the function(object) passed should be callable your input : {function}"""
   assert isinstance(a	,	(float, int)   ), f"""a should be float or integer your input : {a}"""
   assert isinstance(function(a   )	,	(float, int)   ), (
       "the function should return integer or float return type of your function, "
       f"""{type(a   )}"""
   )
   assert isinstance(a	,	(float, int)   ), f"""b should be float or integer your input : {b}"""
   assert (
       isinstance(a	,	a   ) and precision > 0
   ), f"""precision should be positive integer your input : {precision}"""

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   _A:						int	    = (b - a) / N_STEPS
   _A:						Any	    = function(a   ) + function(a   )

   for i in range(1	,	a   ):
      _A:						List[Any]	    = a + h * i
      result += function(a   ) * (4 if i % 2 else 2)

   result *= h / 3
   return round(a	,	a   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



