





"""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_       =		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       (							a_					:		Tuple			,       a_					:		Tuple			,       a_					:		Optional[int]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :str		    =  state_dict.pop(a_							)
      __SCREAMING_SNAKE_CASE       :Any		    =  val



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

      return new_state_dict



def 					__lowerCamelCase       (							a_					:		Optional[int]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :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)
            __SCREAMING_SNAKE_CASE       :int		    =  state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
            __SCREAMING_SNAKE_CASE       :str		    =  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
            __SCREAMING_SNAKE_CASE       :Tuple		    =  in_proj_weight[:2_56, :]
            __SCREAMING_SNAKE_CASE       :Dict		    =  in_proj_bias[:2_56]
            __SCREAMING_SNAKE_CASE       :Dict		    =  in_proj_weight[2_56:5_12, :]
            __SCREAMING_SNAKE_CASE       :List[str]		    =  in_proj_bias[2_56:5_12]
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  in_proj_weight[-2_56:, :]
            __SCREAMING_SNAKE_CASE       :Dict		    =  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
            __SCREAMING_SNAKE_CASE       :str		    =  state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
            __SCREAMING_SNAKE_CASE       :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
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  in_proj_weight[:2_56, :]
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  in_proj_bias[:2_56]
            __SCREAMING_SNAKE_CASE       :int		    =  in_proj_weight[2_56:5_12, :]
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  in_proj_bias[2_56:5_12]
            __SCREAMING_SNAKE_CASE       :List[str]		    =  in_proj_weight[-2_56:, :]
            __SCREAMING_SNAKE_CASE       :Any		    =  in_proj_bias[-2_56:]
            # read in weights + bias of input projection layer of cross-attention
            __SCREAMING_SNAKE_CASE       :List[str]		    =  state_dict.pop(
                f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''							)
            __SCREAMING_SNAKE_CASE       :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
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  in_proj_weight_cross_attn[:2_56, :]
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  in_proj_bias_cross_attn[:2_56]
            __SCREAMING_SNAKE_CASE       :Any		    =  in_proj_weight_cross_attn[2_56:5_12, :]
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  in_proj_bias_cross_attn[2_56:5_12]
            __SCREAMING_SNAKE_CASE       :List[str]		    =  in_proj_weight_cross_attn[-2_56:, :]
            __SCREAMING_SNAKE_CASE       :str		    =  in_proj_bias_cross_attn[-2_56:]



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		List[Any]							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[int]		    =  image.size
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  max(a_			,       a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  8_00 if '''detection''' in checkpoint_url else 10_00
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  target_max_size / current_max_size
      __SCREAMING_SNAKE_CASE       :str		    =  image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

      return resized_image



def 					__lowerCamelCase       (							a_					:		int							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :str		    =  F.to_tensor(a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  F.normalize(a_			,       mean=[0.485, 0.456, 0.406]			,       std=[0.229, 0.224, 0.225]							)
      return image



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Tuple			,       a_					:		Dict							)      ->				Dict:
      logger.info('''Converting model...'''							)

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

      __SCREAMING_SNAKE_CASE       :str		    =  DetrImageProcessor(
          format='''coco_detection'''			,       max_size=8_00 if '''detection''' in checkpoint_url else 10_00							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  TableTransformerForObjectDetection(a_							)
      model.load_state_dict(a_							)
      model.eval()

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

      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model(a_							)

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

      else:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  (1, 1_25, 7)
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.tensor(
                [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]							)
            __SCREAMING_SNAKE_CASE       :Tuple		    =  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]			,       a_			,       atol=1e-4							)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,       a_			,       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(a_							).mkdir(exist_ok=a_							)
            model.save_pretrained(a_							)
            image_processor.save_pretrained(a_							)

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


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"""
def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int							)      ->				int:
      return int((input_a, input_a).count(0							) == 0							)



def 					__lowerCamelCase       (							)      ->				None:
      assert and_gate(0			,       0							) == 0
      assert and_gate(0			,       1							) == 0
      assert and_gate(1			,       0							) == 0
      assert and_gate(1			,       1							) == 1


if __name__ == "__main__":
  test_and_gate()
  print(and_gate(1, 0))
  print(and_gate(0, 0))
  print(and_gate(0, 1))
  print(and_gate(1, 1))