





"""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()
__UpperCamelCase			:		Any		    =   logging.get_logger(__name__)

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      DetaConfig(
     num_queries=900   ,			encoder_ffn_dim=2048   ,			decoder_ffn_dim=2048   ,			num_feature_levels=5   ,			assign_first_stage=_UpperCAmelCase   ,			with_box_refine=_UpperCAmelCase   ,			two_stage=_UpperCAmelCase   ,			)

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

 return config

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Tuple       ):
 lowerCAmelCase  							=      []

 # 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 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[str]   ,			_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  List[str]       ):
 lowerCAmelCase  							=      dct.pop(_UpperCAmelCase       )
 lowerCAmelCase  							=      val

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  int   ,			_UpperCAmelCase :  Union[str, Any]       ):
 # transformer decoder self-attention layers
 lowerCAmelCase  							=      config.d_model
 for i in range(config.decoder_layers       ):
  # read in weights + bias of input projection layer of self-attention
  lowerCAmelCase  							=      state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
  lowerCAmelCase  							=      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  							=      in_proj_weight[:hidden_size, :]
  lowerCAmelCase  							=      in_proj_bias[:hidden_size]
  lowerCAmelCase  							=      in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  lowerCAmelCase  							=      in_proj_bias[hidden_size : hidden_size * 2]
  lowerCAmelCase  							=      in_proj_weight[-hidden_size:, :]
  lowerCAmelCase  							=      in_proj_bias[-hidden_size:]

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'http://images.cocodataset.org/val2017/000000039769.jpg'
 lowerCAmelCase  							=      Image.open(requests.get(_UpperCAmelCase   ,			stream=_UpperCAmelCase       ).raw       )

 return im

@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Optional[Any]   ,			_UpperCAmelCase :  Tuple   ,			_UpperCAmelCase :  Optional[Any]       ):
 lowerCAmelCase  							=      get_deta_config()

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

 # rename keys
 lowerCAmelCase  							=      create_rename_keys(_UpperCAmelCase       )
 for src, dest in rename_keys:
  rename_key(_UpperCAmelCase   ,			_UpperCAmelCase   ,			_UpperCAmelCase       )
 read_in_decoder_q_k_v(_UpperCAmelCase   ,			_UpperCAmelCase       )

 # 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  							=      state_dict.pop(_UpperCAmelCase       )
   lowerCAmelCase  							=      val
  if "input_proj" in key:
   lowerCAmelCase  							=      state_dict.pop(_UpperCAmelCase       )
   lowerCAmelCase  							=      val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   lowerCAmelCase  							=      state_dict.pop(_UpperCAmelCase       )
   lowerCAmelCase  							=      val

    # finally, create HuggingFace model and load state dict
 lowerCAmelCase  							=      DetaForObjectDetection(_UpperCAmelCase       )
 model.load_state_dict(_UpperCAmelCase       )
 model.eval()

 lowerCAmelCase  							=      'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(_UpperCAmelCase       )

 # load image processor
 lowerCAmelCase  							=      DetaImageProcessor(format='coco_detection'       )

 # verify our conversion on image
 lowerCAmelCase  							=      prepare_img()
 lowerCAmelCase  							=      processor(images=_UpperCAmelCase   ,			return_tensors='pt'       )
 lowerCAmelCase  							=      encoding['pixel_values']
 lowerCAmelCase  							=      model(pixel_values.to(_UpperCAmelCase       )       )

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

 # 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			:		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.'''
       )
       __UpperCamelCase			:		Optional[Any]		    =   parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCamelCase			:		List[Any]		    =   logging.get_logger(__name__)

__UpperCamelCase			:		Union[str, Any]		    =   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
       import detectrona







class 	a				(					a__   ):
 snake_case__    	=    '''layoutlmv2'''



 def __init__(			self							,    _snake_case=3_05_22							,    _snake_case=7_68							,    _snake_case=12							,    _snake_case=12							,    _snake_case=30_72							,    _snake_case="gelu"							,    _snake_case=0.1							,    _snake_case=0.1							,    _snake_case=5_12							,    _snake_case=2							,    _snake_case=0.02							,    _snake_case=1E-12							,    _snake_case=0							,    _snake_case=10_24							,    _snake_case=1_28							,    _snake_case=32							,    _snake_case=True							,    _snake_case=2_56							,    _snake_case=64							,    _snake_case=True							,    _snake_case=[7, 7, 2_56]							,    _snake_case=1_28							,    _snake_case=1_28							,    _snake_case=True							,    _snake_case=True							,    _snake_case=False							,    _snake_case=None							,    **_snake_case							,    ):







  """simple docstring"""






  super().__init__(
      vocab_size=_snake_case							,    hidden_size=_snake_case							,    num_hidden_layers=_snake_case							,    num_attention_heads=_snake_case							,    intermediate_size=_snake_case							,    hidden_act=_snake_case							,    hidden_dropout_prob=_snake_case							,    attention_probs_dropout_prob=_snake_case							,    max_position_embeddings=_snake_case							,    type_vocab_size=_snake_case							,    initializer_range=_snake_case							,    layer_norm_eps=_snake_case							,    pad_token_id=_snake_case							,    **_snake_case							,    )
  lowerCAmelCase  							=      max_ad_position_embeddings
  lowerCAmelCase  							=      max_rel_pos
  lowerCAmelCase  							=      rel_pos_bins
  lowerCAmelCase  							=      fast_qkv
  lowerCAmelCase  							=      max_rel_ad_pos
  lowerCAmelCase  							=      rel_ad_pos_bins
  lowerCAmelCase  							=      convert_sync_batchnorm
  lowerCAmelCase  							=      image_feature_pool_shape
  lowerCAmelCase  							=      coordinate_size
  lowerCAmelCase  							=      shape_size
  lowerCAmelCase  							=      has_relative_attention_bias
  lowerCAmelCase  							=      has_spatial_attention_bias
  lowerCAmelCase  							=      has_visual_segment_embedding
  lowerCAmelCase  							=      (
      detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  )



 @classmethod
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return {
      "MODEL.MASK_ON": True,
      "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
      "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
      "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
      "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
      "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
      "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
      "MODEL.POST_NMS_TOPK_TEST": 10_00,
      "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
      "MODEL.ROI_HEADS.NUM_CLASSES": 5,
      "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
      "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
      "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
      "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
      "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
      "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
      "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
      "MODEL.RESNETS.DEPTH": 1_01,
      "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
      "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.RESNETS.NUM_GROUPS": 32,
      "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
      "MODEL.RESNETS.STRIDE_IN_1X1": False,
  }



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      detectrona.config.get_cfg()
  for k, v in self.detectrona_config_args.items():
   lowerCAmelCase  							=      k.split('.'			)
   lowerCAmelCase  							=      detectrona_config
   for attribute in attributes[:-1]:
    lowerCAmelCase  							=      getattr(_snake_case							,    _snake_case			)
   setattr(_snake_case							,    attributes[-1]							,    _snake_case			)

  return detectrona_config





