



"""simple docstring"""







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()
lowercase__						:							Tuple        =      logging.get_logger(__name__)

def 							UpperCamelCase_       (       )		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[Any]								=					DetaConfig(
      num_queries=900       ,  encoder_ffn_dim=2048       ,  decoder_ffn_dim=2048       ,  num_feature_levels=5       ,  assign_first_stage=lowerCAmelCase__       ,  with_box_refine=lowerCAmelCase__       ,  two_stage=lowerCAmelCase__       ,  )

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

  return config

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any		)		->  int:

  """simple docstring"""




  lowerCAmelCase_  :							Any								=					[]

  # 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 							UpperCamelCase_       (       lowerCAmelCase__ :							Union[str, Any]       ,  lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							Dict		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Any								=					dct.pop(lowerCAmelCase__		)
  lowerCAmelCase_  :							List[str]								=					val

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Tuple       ,  lowerCAmelCase__ :							Optional[int]		)		->  Optional[int]:

  """simple docstring"""




  lowerCAmelCase_  :							str								=					config.d_model
  for i in range(config.decoder_layers		):
    # read in weights + bias of input projection layer of self-attention
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Tuple								=					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
    lowerCAmelCase_  :							Union[str, Any]								=					in_proj_weight[:hidden_size, :]
    lowerCAmelCase_  :							List[str]								=					in_proj_bias[:hidden_size]
    lowerCAmelCase_  :							Tuple								=					in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    lowerCAmelCase_  :							List[Any]								=					in_proj_bias[hidden_size : hidden_size * 2]
    lowerCAmelCase_  :							Union[str, Any]								=					in_proj_weight[-hidden_size:, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[-hidden_size:]

def 							UpperCamelCase_       (       )		->  Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Dict								=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_  :							Any								=					Image.open(requests.get(lowerCAmelCase__       ,  stream=lowerCAmelCase__		).raw		)

  return im

@torch.no_grad()
def 							UpperCamelCase_       (       lowerCAmelCase__ :							Optional[Any]       ,  lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Tuple		)		->  str:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[int]								=					get_deta_config()

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

  # rename keys
  lowerCAmelCase_  :							Union[str, Any]								=					create_rename_keys(lowerCAmelCase__		)
  for src, dest in rename_keys:
    rename_key(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__		)
  read_in_decoder_q_k_v(lowerCAmelCase__       ,  lowerCAmelCase__		)

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      lowerCAmelCase_  :							Any								=					state_dict.pop(lowerCAmelCase__		)
      lowerCAmelCase_  :							Any								=					val
    if "input_proj" in key:
      lowerCAmelCase_  :							Dict								=					state_dict.pop(lowerCAmelCase__		)
      lowerCAmelCase_  :							int								=					val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      lowerCAmelCase_  :							List[str]								=					state_dict.pop(lowerCAmelCase__		)
      lowerCAmelCase_  :							List[Any]								=					val

    # finally, create HuggingFace model and load state dict
  lowerCAmelCase_  :							str								=					DetaForObjectDetection(lowerCAmelCase__		)
  model.load_state_dict(lowerCAmelCase__		)
  model.eval()

  lowerCAmelCase_  :							int								=					'cuda' if torch.cuda.is_available() else 'cpu'
  model.to(lowerCAmelCase__		)

  # load image processor
  lowerCAmelCase_  :							int								=					DetaImageProcessor(format='coco_detection'		)

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

  # verify logits
  if model_name == "deta-resnet-50":
    lowerCAmelCase_  :							List[str]								=					torch.tensor(
        [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]		)
    lowerCAmelCase_  :							List[Any]								=					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":
    lowerCAmelCase_  :							Optional[Any]								=					torch.tensor(
        [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]		)
    lowerCAmelCase_  :							Tuple								=					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(lowerCAmelCase__		)       ,  atol=1e-4		)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,  expected_boxes.to(lowerCAmelCase__		)       ,  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(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
    model.save_pretrained(lowerCAmelCase__		)
    processor.save_pretrained(lowerCAmelCase__		)

  # 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__":
     lowercase__						:							Optional[int]        =      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."""
     )
     lowercase__						:							List[Any]        =      parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''

import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__         =     [(1,)]




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			snake_case__          =						bool(os.getenv("""RUN_SLOW"""       ,      0	)	)
			if run_slow:
						assert True
			else:
						assert False, "should have been skipped"




def    snake_case__     (  )			->       Dict:





			'''simple docstring'''






			snake_case__          =						bool(os.getenv("""RUN_SLOW"""       ,      0	)	)
			if run_slow and torch_device == "cuda":
						assert True
			else:
						assert False, "should have been skipped"





@require_torch
class __magic_name__(						unittest.TestCase      ):






						@slow
						@require_torch_gpu
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									check_slow_torch_cuda()






						@require_torch_gpu
						@slow
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									check_slow_torch_cuda()







						@parameterized.expand(__UpperCamelCase       )
						@slow
						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int=None       ):



									'''simple docstring'''






									check_slow()







@slow
@require_torch_gpu
def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			check_slow_torch_cuda()




@require_torch_gpu
@slow
def    snake_case__     (  )			->       List[Any]:





			'''simple docstring'''






			check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param"""       ,      [1]	)
def    snake_case__     (  a	)			->       Union[str, Any]:





			'''simple docstring'''






			check_slow()




@pytest.mark.parametrize("""param"""       ,      [1]	)
@slow
def    snake_case__     (  a	)			->       Dict:





			'''simple docstring'''






			check_slow()