




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_					    = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_					    = []
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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	str:
  lowerCAmelCase__				:  Tuple		       =     state_dict.pop(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[int]		       =     val



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[str]:
  lowerCAmelCase__				:  Optional[int]		       =     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__				:  Union[str, Any]		       =     value
    else:
      lowerCAmelCase__				:  Dict		       =     value

  return new_state_dict



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



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Any:
  lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     image.size
  lowerCAmelCase__				:  int		       =     max(UpperCamelCase	,      UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     800 if '''detection''' in checkpoint_url else 1000
  lowerCAmelCase__				:  Optional[int]		       =     target_max_size / current_max_size
  lowerCAmelCase__				:  Optional[Any]		       =     image.resize((int(round(scale * width      )      ), int(round(scale * height      )      ))      )

  return resized_image



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  Optional[Any]		       =     F.to_tensor(UpperCamelCase      )
  lowerCAmelCase__				:  Any		       =     F.normalize(UpperCamelCase	,      mean=[0.485, 0.456, 0.406]	,      std=[0.229, 0.224, 0.225]      )
  return image



@torch.no_grad()
def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[int]:
  logger.info('''Converting model...'''      )

  # load original state dict
  lowerCAmelCase__				:  Union[str, Any]		       =     torch.hub.load_state_dict_from_url(UpperCamelCase	,      map_location='''cpu'''      )
  # rename keys
  for src, dest in rename_keys:
    rename_key(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )
  lowerCAmelCase__				:  Union[str, Any]		       =     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
  lowerCAmelCase__				:  Optional[int]		       =     '''model.'''
  for key in state_dict.copy().keys():
    if not key.startswith('''class_labels_classifier'''      ) and not key.startswith('''bbox_predictor'''      ):
      lowerCAmelCase__				:  int		       =     state_dict.pop(UpperCamelCase      )
      lowerCAmelCase__				:  List[Any]		       =     val
    # create HuggingFace model and load state dict
  lowerCAmelCase__				:  Dict		       =     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__				:  List[str]		       =     15
    lowerCAmelCase__				:  str		       =     2
    lowerCAmelCase__				:  Optional[int]		       =     {0: '''table''', 1: '''table rotated'''}
    lowerCAmelCase__				:  str		       =     idalabel
    lowerCAmelCase__				:  Any		       =     {v: k for k, v in idalabel.items()}
  else:
    lowerCAmelCase__				:  Optional[Any]		       =     125
    lowerCAmelCase__				:  int		       =     6
    lowerCAmelCase__				:  str		       =     {
        0: '''table''',
        1: '''table column''',
        2: '''table row''',
        3: '''table column header''',
        4: '''table projected row header''',
        5: '''table spanning cell''',
    }
    lowerCAmelCase__				:  Dict		       =     idalabel
    lowerCAmelCase__				:  Union[str, Any]		       =     {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 1000      )
  lowerCAmelCase__				:  Dict		       =     TableTransformerForObjectDetection(UpperCamelCase      )
  model.load_state_dict(UpperCamelCase      )
  model.eval()

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

  lowerCAmelCase__				:  Dict		       =     model(UpperCamelCase      )

  if "detection" in checkpoint_url:
    lowerCAmelCase__				:  int		       =     (1, 15, 3)
    lowerCAmelCase__				:  Dict		       =     torch.tensor(
        [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]      )
    lowerCAmelCase__				:  Dict		       =     torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]      )

  else:
    lowerCAmelCase__				:  Optional[Any]		       =     (1, 125, 7)
    lowerCAmelCase__				:  Tuple		       =     torch.tensor(
        [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]      )
    lowerCAmelCase__				:  Union[str, Any]		       =     torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]      )

  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...'''      )
    lowerCAmelCase__				:  List[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_					    = 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_					    = parser.parse_args()
     convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from abc import ABC, abstractmethod
from argparse import ArgumentParser






class  _lowerCAmelCase     (  _lowercase	):

      @staticmethod
      @abstractmethod
      def 	__magic_name__(   __UpperCAmelCase		):
        raise NotImplementedError()







      @abstractmethod
      def 	__magic_name__(   self		):
        raise NotImplementedError()

