





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()
_snake_case  :    Any						    =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case  :    Dict						    =  []
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   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			List[str]	):
   __lowerCAmelCase								=	state_dict.pop(UpperCamelCase_	)
   __lowerCAmelCase								=	val

def   a_    ( lowerCAmelCase_      :			Optional[Any]	):
   __lowerCAmelCase								=	OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         __lowerCAmelCase								=	key.replace('backbone.0.body',	'backbone.conv_encoder.model'	)
         __lowerCAmelCase								=	value
      else:
         __lowerCAmelCase								=	value

   return new_state_dict

def   a_    ( lowerCAmelCase_      :			Any	):
   __lowerCAmelCase								=	''

   # 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								=	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
      __lowerCAmelCase								=	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								=	in_proj_weight[:256, :]
      __lowerCAmelCase								=	in_proj_bias[:256]
      __lowerCAmelCase								=	in_proj_weight[256:512, :]
      __lowerCAmelCase								=	in_proj_bias[256:512]
      __lowerCAmelCase								=	in_proj_weight[-256:, :]
      __lowerCAmelCase								=	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								=	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
      __lowerCAmelCase								=	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								=	in_proj_weight[:256, :]
      __lowerCAmelCase								=	in_proj_bias[:256]
      __lowerCAmelCase								=	in_proj_weight[256:512, :]
      __lowerCAmelCase								=	in_proj_bias[256:512]
      __lowerCAmelCase								=	in_proj_weight[-256:, :]
      __lowerCAmelCase								=	in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      __lowerCAmelCase								=	state_dict.pop(
          F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
      __lowerCAmelCase								=	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								=	in_proj_weight_cross_attn[:256, :]
      __lowerCAmelCase								=	in_proj_bias_cross_attn[:256]
      __lowerCAmelCase								=	in_proj_weight_cross_attn[256:512, :]
      __lowerCAmelCase								=	in_proj_bias_cross_attn[256:512]
      __lowerCAmelCase								=	in_proj_weight_cross_attn[-256:, :]
      __lowerCAmelCase								=	in_proj_bias_cross_attn[-256:]

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

   return resized_image

def   a_    ( lowerCAmelCase_      :			List[str]	):
   __lowerCAmelCase								=	F.to_tensor(UpperCamelCase_	)
   __lowerCAmelCase								=	F.normalize(UpperCamelCase_,	mean=[0.485, 0.456, 0.406],	std=[0.229, 0.224, 0.225]	)
   return image

@torch.no_grad()
def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Any	):
   logger.info('Converting model...'	)

   # load original state dict
   __lowerCAmelCase								=	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								=	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								=	'model.'
   for key in state_dict.copy().keys():
      if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
         __lowerCAmelCase								=	state_dict.pop(UpperCamelCase_	)
         __lowerCAmelCase								=	val
    # create HuggingFace model and load state dict
   __lowerCAmelCase								=	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								=	15
      __lowerCAmelCase								=	2
      __lowerCAmelCase								=	{0: 'table', 1: 'table rotated'}
      __lowerCAmelCase								=	idalabel
      __lowerCAmelCase								=	{v: k for k, v in idalabel.items()}
   else:
      __lowerCAmelCase								=	125
      __lowerCAmelCase								=	6
      __lowerCAmelCase								=	{
          0: 'table',
          1: 'table column',
          2: 'table row',
          3: 'table column header',
          4: 'table projected row header',
          5: 'table spanning cell',
      }
      __lowerCAmelCase								=	idalabel
      __lowerCAmelCase								=	{v: k for k, v in idalabel.items()}

   __lowerCAmelCase								=	DetrImageProcessor(
       format='coco_detection',	max_size=800 if 'detection' in checkpoint_url else 1000	)
   __lowerCAmelCase								=	TableTransformerForObjectDetection(UpperCamelCase_	)
   model.load_state_dict(UpperCamelCase_	)
   model.eval()

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

   __lowerCAmelCase								=	model(UpperCamelCase_	)

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

   else:
      __lowerCAmelCase								=	(1, 125, 7)
      __lowerCAmelCase								=	torch.tensor(
          [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]	)
      __lowerCAmelCase								=	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								=	(
          '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__":
     _snake_case  :    int						    =  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.'
     )
     _snake_case  :    List[Any]						    =  parser.parse_args()
     convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_				):

		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'float' object cannot be interpreted as an integer"""				)
		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'str' object cannot be interpreted as an integer"""				)

		if num == 0:
				return "0b0"

		__SCREAMING_SNAKE_CASE            = False

		if num < 0:
				__SCREAMING_SNAKE_CASE            = True
				__SCREAMING_SNAKE_CASE            = -num

		__SCREAMING_SNAKE_CASE            = []
		while num > 0:
				binary.insert(0  ,				num % 2				)
				num >>= 1

		if negative:
				return "-0b" + "".join(str(UpperCamelCase_				) for e in binary				)

		return "0b" + "".join(str(UpperCamelCase_				) for e in binary				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




