
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()
SCREAMING_SNAKE_CASE__							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE__							=				[]
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   (		SCREAMING_SNAKE_CASE       :      List[str]    ,							SCREAMING_SNAKE_CASE       :      List[Any]    ,							SCREAMING_SNAKE_CASE       :      Dict			)     ->						Optional[Any]:
				__lowercase								=					state_dict.pop(_UpperCAmelCase			)
				__lowercase								=					val




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

				return new_state_dict




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[Any]			)     ->						Any:
				__lowercase								=					''

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




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[Any]    ,							SCREAMING_SNAKE_CASE       :      Dict			)     ->						Optional[Any]:
				__lowercase								=					image.size
				__lowercase								=					max(_UpperCAmelCase    ,							_UpperCAmelCase			)
				__lowercase								=					800 if 'detection' in checkpoint_url else 1000
				__lowercase								=					target_max_size / current_max_size
				__lowercase								=					image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

				return resized_image




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Any			)     ->						Optional[Any]:
				__lowercase								=					F.to_tensor(_UpperCAmelCase			)
				__lowercase								=					F.normalize(_UpperCAmelCase    ,							mean=[0.485, 0.456, 0.406]    ,							std=[0.229, 0.224, 0.225]			)
				return image




@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[Any]    ,							SCREAMING_SNAKE_CASE       :      str    ,							SCREAMING_SNAKE_CASE       :      Union[str, Any]			)     ->						List[str]:
				logger.info('Converting model...'			)

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

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

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

				__lowercase								=					model(_UpperCAmelCase			)

				if "detection" in checkpoint_url:
								__lowercase								=					(1, 15, 3)
								__lowercase								=					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]]			)
								__lowercase								=					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:
								__lowercase								=					(1, 125, 7)
								__lowercase								=					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]]			)
								__lowercase								=					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]    ,							_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...'			)
								__lowercase								=					(
								    '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__":
							SCREAMING_SNAKE_CASE__							=				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."""
							)
							SCREAMING_SNAKE_CASE__							=				parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""




def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  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"

  A_  :       str										=  False

  if num < 0:
    A_  :       Dict										=  True
    A_  :       Union[str, Any]										=  -num

  A_  :       list[int]										=  []
  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()