





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()
A             =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A             =							[]
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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Union[str, Any]   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   ,   SCREAMING_SNAKE_CASE		:str   )      ->   Union[str, Any]:
 __lowerCAmelCase					:	Union[str, Any]      =       state_dict.pop(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Union[str, Any]      =       val






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   )      ->   Optional[Any]:
 __lowerCAmelCase					:	Dict      =       OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __lowerCAmelCase					:	List[Any]      =       key.replace("""backbone.0.body"""   ,   """backbone.conv_encoder.model"""   )
   __lowerCAmelCase					:	int      =       value
  else:
   __lowerCAmelCase					:	Dict      =       value

 return new_state_dict






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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Union[str, Any]   ,   SCREAMING_SNAKE_CASE		:Tuple   )      ->   Optional[Any]:
 __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       image.size
 __lowerCAmelCase					:	List[str]      =       max(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Union[str, Any]      =       800 if """detection""" in checkpoint_url else 1_000
 __lowerCAmelCase					:	Any      =       target_max_size / current_max_size
 __lowerCAmelCase					:	Union[str, Any]      =       image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

 return resized_image






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   List[str]:
 __lowerCAmelCase					:	Optional[int]      =       F.to_tensor(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[Any]      =       F.normalize(SCREAMING_SNAKE_CASE   ,   mean=[0.4_85, 0.4_56, 0.4_06]   ,   std=[0.2_29, 0.2_24, 0.2_25]   )
 return image






@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   List[str]:
 logger.info("""Converting model..."""   )

 # load original state dict
 __lowerCAmelCase					:	List[str]      =       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE   ,   map_location="""cpu"""   )
 # rename keys
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Union[str, Any]      =       rename_backbone_keys(SCREAMING_SNAKE_CASE   )
 # query, key and value matrices need special treatment
 read_in_q_k_v(SCREAMING_SNAKE_CASE   )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 __lowerCAmelCase					:	int      =       """model."""
 for key in state_dict.copy().keys():
  if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
   __lowerCAmelCase					:	List[str]      =       state_dict.pop(SCREAMING_SNAKE_CASE   )
   __lowerCAmelCase					:	Tuple      =       val
    # create HuggingFace model and load state dict
 __lowerCAmelCase					:	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:
  __lowerCAmelCase					:	Any      =       15
  __lowerCAmelCase					:	Dict      =       2
  __lowerCAmelCase					:	int      =       {0: """table""", 1: """table rotated"""}
  __lowerCAmelCase					:	Optional[int]      =       idalabel
  __lowerCAmelCase					:	Optional[int]      =       {v: k for k, v in idalabel.items()}
 else:
  __lowerCAmelCase					:	Optional[Any]      =       125
  __lowerCAmelCase					:	int      =       6
  __lowerCAmelCase					:	Tuple      =       {
      0: """table""",
      1: """table column""",
      2: """table row""",
      3: """table column header""",
      4: """table projected row header""",
      5: """table spanning cell""",
  }
  __lowerCAmelCase					:	int      =       idalabel
  __lowerCAmelCase					:	List[str]      =       {v: k for k, v in idalabel.items()}

 __lowerCAmelCase					:	Union[str, Any]      =       DetrImageProcessor(
     format="""coco_detection"""   ,   max_size=800 if """detection""" in checkpoint_url else 1_000   )
 __lowerCAmelCase					:	Optional[Any]      =       TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE   )
 model.load_state_dict(SCREAMING_SNAKE_CASE   )
 model.eval()

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

 __lowerCAmelCase					:	Optional[int]      =       model(SCREAMING_SNAKE_CASE   )

 if "detection" in checkpoint_url:
  __lowerCAmelCase					:	Dict      =       (1, 15, 3)
  __lowerCAmelCase					:	List[Any]      =       torch.tensor(
      [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]   )
  __lowerCAmelCase					:	str      =       torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]   )

 else:
  __lowerCAmelCase					:	List[str]      =       (1, 125, 7)
  __lowerCAmelCase					:	Optional[Any]      =       torch.tensor(
      [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]   )
  __lowerCAmelCase					:	Optional[Any]      =       torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]   )

 assert outputs.logits.shape == expected_shape
 assert torch.allclose(outputs.logits[0, :3, :3]   ,   SCREAMING_SNAKE_CASE   ,   atol=1e-4   )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,   SCREAMING_SNAKE_CASE   ,   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(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
  model.save_pretrained(SCREAMING_SNAKE_CASE   )
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE   )

 if push_to_hub:
  # Push model to HF hub
  logger.info("""Pushing model to the hub..."""   )
  __lowerCAmelCase					:	Optional[int]      =       (
      """microsoft/table-transformer-detection"""
      if """detection""" in checkpoint_url
      else """microsoft/table-transformer-structure-recognition"""
  )
  model.push_to_hub(SCREAMING_SNAKE_CASE   )
  image_processor.push_to_hub(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      A             =							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.'
      )
      A             =							parser.parse_args()
      convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   Dict:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 if n == 0:
  return 0
 __lowerCAmelCase					:	Union[str, Any]      =       float("""-inf"""   )
 for i in range(1   ,   n + 1   ):
  __lowerCAmelCase					:	Union[str, Any]      =       max(
      SCREAMING_SNAKE_CASE   ,   prices[i - 1] + naive_cut_rod_recursive(n - i   ,   SCREAMING_SNAKE_CASE   )   )

 return max_revue






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[str]:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       [float("""-inf"""   ) for _ in range(n + 1   )]
 return _top_down_cut_rod_recursive(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[str]:
 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowerCAmelCase					:	str      =       float("""-inf"""   )
  for i in range(1   ,   n + 1   ):
   __lowerCAmelCase					:	List[Any]      =       max(
       SCREAMING_SNAKE_CASE   ,   prices[i - 1] + _top_down_cut_rod_recursive(n - i   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   ,   )

  __lowerCAmelCase					:	List[str]      =       max_revenue

 return max_rev[n]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   Union[str, Any]:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowerCAmelCase					:	Optional[int]      =       [float("""-inf"""   ) for _ in range(n + 1   )]
 __lowerCAmelCase					:	List[str]      =       0

 for i in range(1   ,   n + 1   ):
  __lowerCAmelCase					:	Optional[Any]      =       max_rev[i]
  for j in range(1   ,   i + 1   ):
   __lowerCAmelCase					:	Optional[int]      =       max(SCREAMING_SNAKE_CASE   ,   prices[j - 1] + max_rev[i - j]   )

  __lowerCAmelCase					:	Optional[int]      =       max_revenue_i

 return max_rev[n]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[Any]:
 if n < 0:
  __lowerCAmelCase					:	Any      =       F'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(SCREAMING_SNAKE_CASE   )

 if n > len(SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase					:	Any      =       (
      """Each integral piece of rod must have a corresponding price. """
      F'''Got n = {n} but length of prices = {len(SCREAMING_SNAKE_CASE   )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[int]:
 __lowerCAmelCase					:	Tuple      =       [6, 10, 12, 15, 20, 23]
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowerCAmelCase					:	Union[str, Any]      =       36

 __lowerCAmelCase					:	Optional[int]      =       top_down_cut_rod(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       bottom_up_cut_rod(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       naive_cut_rod_recursive(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
      main()