

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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[Any]:
       A_		   = state_dict.pop(lowerCamelCase__			)
       A_		   = val
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       A_		   = OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     A_		   = key.replace("""backbone.0.body""",    """backbone.conv_encoder.model"""			)
                     A_		   = value
              else:
                     A_		   = value

       return new_state_dict
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       A_		   = ''''''

       # 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_		   = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''			)
              A_		   = 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_		   = in_proj_weight[:2_56, :]
              A_		   = in_proj_bias[:2_56]
              A_		   = in_proj_weight[2_56:5_12, :]
              A_		   = in_proj_bias[2_56:5_12]
              A_		   = in_proj_weight[-2_56:, :]
              A_		   = in_proj_bias[-2_56:]
       # 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_		   = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
              A_		   = 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_		   = in_proj_weight[:2_56, :]
              A_		   = in_proj_bias[:2_56]
              A_		   = in_proj_weight[2_56:5_12, :]
              A_		   = in_proj_bias[2_56:5_12]
              A_		   = in_proj_weight[-2_56:, :]
              A_		   = in_proj_bias[-2_56:]
              # read in weights + bias of input projection layer of cross-attention
              A_		   = state_dict.pop(
                  F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''			)
              A_		   = 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_		   = in_proj_weight_cross_attn[:2_56, :]
              A_		   = in_proj_bias_cross_attn[:2_56]
              A_		   = in_proj_weight_cross_attn[2_56:5_12, :]
              A_		   = in_proj_bias_cross_attn[2_56:5_12]
              A_		   = in_proj_weight_cross_attn[-2_56:, :]
              A_		   = in_proj_bias_cross_attn[-2_56:]
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       A_		   = image.size
       A_		   = max(lowerCamelCase__,    lowerCamelCase__			)
       A_		   = 8_00 if '''detection''' in checkpoint_url else 10_00
       A_		   = target_max_size / current_max_size
       A_		   = image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

       return resized_image
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     str:
       A_		   = F.to_tensor(lowerCamelCase__			)
       A_		   = F.normalize(lowerCamelCase__,    mean=[0.485, 0.456, 0.406],    std=[0.229, 0.224, 0.225]			)
       return image
@torch.no_grad()
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     str:
       logger.info("""Converting model..."""			)

       # load original state dict
       A_		   = torch.hub.load_state_dict_from_url(lowerCamelCase__,    map_location="""cpu"""			)
       # rename keys
       for src, dest in rename_keys:
              rename_key(lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__			)
       A_		   = rename_backbone_keys(lowerCamelCase__			)
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowerCamelCase__			)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       A_		   = '''model.'''
       for key in state_dict.copy().keys():
              if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
                     A_		   = state_dict.pop(lowerCamelCase__			)
                     A_		   = val
    # create HuggingFace model and load state dict
       A_		   = 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_		   = 15
              A_		   = 2
              A_		   = {0: '''table''', 1: '''table rotated'''}
              A_		   = idalabel
              A_		   = {v: k for k, v in idalabel.items()}
       else:
              A_		   = 1_25
              A_		   = 6
              A_		   = {
                  0: '''table''',
                  1: '''table column''',
                  2: '''table row''',
                  3: '''table column header''',
                  4: '''table projected row header''',
                  5: '''table spanning cell''',
              }
              A_		   = idalabel
              A_		   = {v: k for k, v in idalabel.items()}

       A_		   = DetrImageProcessor(
           format="""coco_detection""",    max_size=8_00 if """detection""" in checkpoint_url else 10_00			)
       A_		   = TableTransformerForObjectDetection(lowerCamelCase__			)
       model.load_state_dict(lowerCamelCase__			)
       model.eval()

       # verify our conversion
       A_		   = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
       A_		   = hf_hub_download(repo_id="""nielsr/example-pdf""",    repo_type="""dataset""",    filename=lowerCamelCase__			)
       A_		   = Image.open(lowerCamelCase__			).convert("""RGB"""			)
       A_		   = normalize(resize(lowerCamelCase__,    lowerCamelCase__			)			).unsqueeze(0			)

       A_		   = model(lowerCamelCase__			)

       if "detection" in checkpoint_url:
              A_		   = (1, 15, 3)
              A_		   = 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_		   = 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_		   = (1, 1_25, 7)
              A_		   = 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_		   = 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],    lowerCamelCase__,    atol=1e-4			)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3],    lowerCamelCase__,    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(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
              model.save_pretrained(lowerCamelCase__			)
              image_processor.save_pretrained(lowerCamelCase__			)

       if push_to_hub:
              # Push model to HF hub
              logger.info("""Pushing model to the hub..."""			)
              A_		   = (
                  '''microsoft/table-transformer-detection'''
                  if '''detection''' in checkpoint_url
                  else '''microsoft/table-transformer-structure-recognition'''
              )
              model.push_to_hub(lowerCamelCase__			)
              image_processor.push_to_hub(lowerCamelCase__			)


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)



'''simple docstring'''







from functools import lru_cache
@lru_cache
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if num < 0:
              raise ValueError("""Number should not be negative."""			)

       return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
