





"""simple docstring"""




import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_     :						Optional[int]						=					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 encoder + 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.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  state_dict.pop(_UpperCAmelCase					)
  A_  :       str										=  val





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       str										=  OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      A_  :       List[str]										=  key.replace('backbone.0.body' ,					'backbone.conv_encoder.model'					)
      A_  :       Tuple										=  value
    else:
      A_  :       Union[str, Any]										=  value

  return new_state_dict





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  ''

  # 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_  :       str										=  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
    A_  :       Tuple										=  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_  :       int										=  in_proj_weight[:256, :]
    A_  :       Dict										=  in_proj_bias[:256]
    A_  :       Optional[int]										=  in_proj_weight[256:512, :]
    A_  :       Any										=  in_proj_bias[256:512]
    A_  :       Optional[Any]										=  in_proj_weight[-256:, :]
    A_  :       List[Any]										=  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
    A_  :       Dict										=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
    A_  :       int										=  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_  :       Union[str, Any]										=  in_proj_weight[:256, :]
    A_  :       Any										=  in_proj_bias[:256]
    A_  :       Dict										=  in_proj_weight[256:512, :]
    A_  :       Union[str, Any]										=  in_proj_bias[256:512]
    A_  :       Any										=  in_proj_weight[-256:, :]
    A_  :       Tuple										=  in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    A_  :       Union[str, Any]										=  state_dict.pop(
        f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
    A_  :       List[str]										=  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_  :       str										=  in_proj_weight_cross_attn[:256, :]
    A_  :       int										=  in_proj_bias_cross_attn[:256]
    A_  :       List[Any]										=  in_proj_weight_cross_attn[256:512, :]
    A_  :       Any										=  in_proj_bias_cross_attn[256:512]
    A_  :       str										=  in_proj_weight_cross_attn[-256:, :]
    A_  :       int										=  in_proj_bias_cross_attn[-256:]





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_						,       A_  :       Tuple										=  image.size
  A_  :       Tuple										=  max(_UpperCAmelCase ,					_UpperCAmelCase					)
  A_  :       str										=  800 if 'detection' in checkpoint_url else 1000
  A_  :       Optional[int]										=  target_max_size / current_max_size
  A_  :       Union[str, Any]										=  image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

  return resized_image





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  F.to_tensor(_UpperCAmelCase					)
  A_  :       List[str]										=  F.normalize(_UpperCAmelCase ,					mean=[0.485, 0.456, 0.406] ,					std=[0.229, 0.224, 0.225]					)
  return image





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  logger.info('Converting model...'					)

  # load original state dict
  A_  :       str										=  torch.hub.load_state_dict_from_url(_UpperCAmelCase ,					map_location='cpu'					)
  # rename keys
  for src, dest in rename_keys:
    rename_key(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  A_  :       Optional[int]										=  rename_backbone_keys(_UpperCAmelCase					)
  # query, key and value matrices need special treatment
  read_in_q_k_v(_UpperCAmelCase					)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  A_  :       Optional[int]										=  'model.'
  for key in state_dict.copy().keys():
    if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
      A_  :       Tuple										=  state_dict.pop(_UpperCAmelCase					)
      A_  :       str										=  val
    # create HuggingFace model and load state dict
  A_  :       List[str]										=  TableTransformerConfig(
      backbone='resnet18' ,					mask_loss_coefficient=1 ,					dice_loss_coefficient=1 ,					ce_loss_coefficient=1 ,					bbox_loss_coefficient=5 ,					giou_loss_coefficient=2 ,					eos_coefficient=0.4 ,					class_cost=1 ,					bbox_cost=5 ,					giou_cost=2 ,					)

  if "detection" in checkpoint_url:
    A_  :       Union[str, Any]										=  15
    A_  :       Union[str, Any]										=  2
    A_  :       int										=  {0: 'table', 1: 'table rotated'}
    A_  :       str										=  idalabel
    A_  :       str										=  {v: k for k, v in idalabel.items()}
  else:
    A_  :       Dict										=  125
    A_  :       str										=  6
    A_  :       Optional[int]										=  {
        0: 'table',
        1: 'table column',
        2: 'table row',
        3: 'table column header',
        4: 'table projected row header',
        5: 'table spanning cell',
    }
    A_  :       int										=  idalabel
    A_  :       List[str]										=  {v: k for k, v in idalabel.items()}

  A_  :       Any										=  DetrImageProcessor(
      format='coco_detection' ,					max_size=800 if 'detection' in checkpoint_url else 1000					)
  A_  :       Tuple										=  TableTransformerForObjectDetection(_UpperCAmelCase					)
  model.load_state_dict(_UpperCAmelCase					)
  model.eval()

  # verify our conversion
  A_  :       Union[str, Any]										=  'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
  A_  :       Any										=  hf_hub_download(repo_id='nielsr/example-pdf' ,					repo_type='dataset' ,					filename=_UpperCAmelCase					)
  A_  :       Optional[Any]										=  Image.open(_UpperCAmelCase					).convert('RGB'					)
  A_  :       Dict										=  normalize(resize(_UpperCAmelCase ,					_UpperCAmelCase					)					).unsqueeze(0					)

  A_  :       str										=  model(_UpperCAmelCase					)

  if "detection" in checkpoint_url:
    A_  :       Any										=  (1, 15, 3)
    A_  :       List[Any]										=  torch.tensor(
        [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]					)
    A_  :       Dict										=  torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]					)

  else:
    A_  :       Tuple										=  (1, 125, 7)
    A_  :       int										=  torch.tensor(
        [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]					)
    A_  :       Dict										=  torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]					)

  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, :3, :3] ,					_UpperCAmelCase ,					atol=1E-4					)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,					_UpperCAmelCase ,					atol=1E-4					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    # 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 push_to_hub:
    # Push model to HF hub
    logger.info('Pushing model to the hub...'					)
    A_  :       Any										=  (
        'microsoft/table-transformer-detection'
        if 'detection' in checkpoint_url
        else 'microsoft/table-transformer-structure-recognition'
    )
    model.push_to_hub(_UpperCAmelCase					)
    image_processor.push_to_hub(_UpperCAmelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						List[str]						=					argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_url',
       default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
       type=str,
       choices=[
           'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
           'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
       ],
       help='URL of the Table Transformer checkpoint 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.'
   )
   lowerCamelCase_     :						Any						=					parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""




import os





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Any										=  os.path.join(os.path.dirname(_UpperCAmelCase					) ,					'num.txt'					)
  with open(_UpperCAmelCase					) as file_hand:
    return str(sum(int(_UpperCAmelCase					) for line in file_hand					)					)[:10]


if __name__ == "__main__":
   print(solution())