
'''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()
lowercase    :							List[str]      =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase    :							List[str]      =					[]
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__	(   __A			,				__A			,				__A	)       ->	str:
	_snake_case   						=		state_dict.pop(__A	)
	_snake_case   						=		val







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Any:
	_snake_case   						=		OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			_snake_case   						=		key.replace('backbone.0.body'			,				'backbone.conv_encoder.model'	)
			_snake_case   						=		value
		else:
			_snake_case   						=		value

	return new_state_dict







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	List[str]:
	_snake_case   						=		''

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Union[str, Any]:
	_snake_case						, _snake_case   						=		image.size
	_snake_case   						=		max(__A			,				__A	)
	_snake_case   						=		800 if 'detection' in checkpoint_url else 1_000
	_snake_case   						=		target_max_size / current_max_size
	_snake_case   						=		image.resize((int(round(scale * width	)	), int(round(scale * height	)	))	)

	return resized_image







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	_snake_case   						=		F.to_tensor(__A	)
	_snake_case   						=		F.normalize(__A			,				mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]			,				std=[0.2_2_9, 0.2_2_4, 0.2_2_5]	)
	return image







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Any:
	logger.info('Converting model...'	)

	# load original state dict
	_snake_case   						=		torch.hub.load_state_dict_from_url(__A			,				map_location='cpu'	)
	# rename keys
	for src, dest in rename_keys:
		rename_key(__A			,				__A			,				__A	)
	_snake_case   						=		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
	_snake_case   						=		'model.'
	for key in state_dict.copy().keys():
		if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
			_snake_case   						=		state_dict.pop(__A	)
			_snake_case   						=		val
    # create HuggingFace model and load state dict
	_snake_case   						=		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:
		_snake_case   						=		15
		_snake_case   						=		2
		_snake_case   						=		{0: 'table', 1: 'table rotated'}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}
	else:
		_snake_case   						=		125
		_snake_case   						=		6
		_snake_case   						=		{
		    0: 'table',
		    1: 'table column',
		    2: 'table row',
		    3: 'table column header',
		    4: 'table projected row header',
		    5: 'table spanning cell',
		}
		_snake_case   						=		idalabel
		_snake_case   						=		{v: k for k, v in idalabel.items()}

	_snake_case   						=		DetrImageProcessor(
	    format='coco_detection'			,				max_size=800 if 'detection' in checkpoint_url else 1_000	)
	_snake_case   						=		TableTransformerForObjectDetection(__A	)
	model.load_state_dict(__A	)
	model.eval()

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

	_snake_case   						=		model(__A	)

	if "detection" in checkpoint_url:
		_snake_case   						=		(1, 15, 3)
		_snake_case   						=		torch.tensor(
		    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]	)
		_snake_case   						=		torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]	)

	else:
		_snake_case   						=		(1, 125, 7)
		_snake_case   						=		torch.tensor(
		    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]	)
		_snake_case   						=		torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]	)

	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...'	)
		_snake_case   						=		(
		    '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__":
					lowercase    :							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."
					)
					lowercase    :							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   (							a_     :		int							)			->      bool:
      if not isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :int						=  f'''Input value of [number={number}] must be an integer'''
            raise TypeError(a_							)
      if number < 0:
            return False
      __SCREAMING_SNAKE_CASE   :Any						=  number * number
      while number > 0:
            if number % 10 != number_square % 10:
                  return False
            number //= 10
            number_square //= 10
      return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()