


'''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()
snake_case_     :       Optional[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_     :       int						=  []
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_      (  SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			str   ) ->	Any:
						UpperCAmelCase_			:      List[str]     						=  state_dict.pop(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[Any]     						=  val





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	str:
						UpperCAmelCase_			:      int     						=  OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		UpperCAmelCase_			:      Dict     						=  key.replace('''backbone.0.body''',			'''backbone.conv_encoder.model'''   )
																		UpperCAmelCase_			:      Dict     						=  value
												else:
																		UpperCAmelCase_			:      Tuple     						=  value

						return new_state_dict





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any   ) ->	Dict:
						UpperCAmelCase_			:      int     						=  ''''''

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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Tuple   ) ->	Dict:
						UpperCAmelCase_			:      List[str]     						=  image.size
						UpperCAmelCase_			:      str     						=  max(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      str     						=  800 if '''detection''' in checkpoint_url else 1000
						UpperCAmelCase_			:      int     						=  target_max_size / current_max_size
						UpperCAmelCase_			:      Dict     						=  image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

						return resized_image





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any   ) ->	Optional[int]:
						UpperCAmelCase_			:      Optional[int]     						=  F.to_tensor(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Any     						=  F.normalize(SCREAMING_SNAKE_CASE__,			mean=[0.4_85, 0.4_56, 0.4_06],			std=[0.2_29, 0.2_24, 0.2_25]   )
						return image





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			int   ) ->	List[str]:
						logger.info('''Converting model...'''   )

						# load original state dict
						UpperCAmelCase_			:      Any     						=  torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__,			map_location='''cpu'''   )
						# rename keys
						for src, dest in rename_keys:
												rename_key(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Union[str, Any]     						=  rename_backbone_keys(SCREAMING_SNAKE_CASE__   )
						# query, key and value matrices need special treatment
						read_in_q_k_v(SCREAMING_SNAKE_CASE__   )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						UpperCAmelCase_			:      List[str]     						=  '''model.'''
						for key in state_dict.copy().keys():
												if not key.startswith('''class_labels_classifier'''   ) and not key.startswith('''bbox_predictor'''   ):
																		UpperCAmelCase_			:      Union[str, Any]     						=  state_dict.pop(SCREAMING_SNAKE_CASE__   )
																		UpperCAmelCase_			:      List[Any]     						=  val
    # create HuggingFace model and load state dict
						UpperCAmelCase_			:      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:
												UpperCAmelCase_			:      Optional[Any]     						=  15
												UpperCAmelCase_			:      str     						=  2
												UpperCAmelCase_			:      Dict     						=  {0: '''table''', 1: '''table rotated'''}
												UpperCAmelCase_			:      Dict     						=  idalabel
												UpperCAmelCase_			:      str     						=  {v: k for k, v in idalabel.items()}
						else:
												UpperCAmelCase_			:      Union[str, Any]     						=  125
												UpperCAmelCase_			:      Union[str, Any]     						=  6
												UpperCAmelCase_			:      Union[str, Any]     						=  {
												    0: '''table''',
												    1: '''table column''',
												    2: '''table row''',
												    3: '''table column header''',
												    4: '''table projected row header''',
												    5: '''table spanning cell''',
												}
												UpperCAmelCase_			:      Optional[Any]     						=  idalabel
												UpperCAmelCase_			:      List[str]     						=  {v: k for k, v in idalabel.items()}

						UpperCAmelCase_			:      Any     						=  DetrImageProcessor(
						    format='''coco_detection''',			max_size=800 if '''detection''' in checkpoint_url else 1000   )
						UpperCAmelCase_			:      Any     						=  TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE__   )
						model.load_state_dict(SCREAMING_SNAKE_CASE__   )
						model.eval()

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

						UpperCAmelCase_			:      Dict     						=  model(SCREAMING_SNAKE_CASE__   )

						if "detection" in checkpoint_url:
												UpperCAmelCase_			:      int     						=  (1, 15, 3)
												UpperCAmelCase_			:      Tuple     						=  torch.tensor(
												    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]   )
												UpperCAmelCase_			:      List[str]     						=  torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]   )

						else:
												UpperCAmelCase_			:      Dict     						=  (1, 125, 7)
												UpperCAmelCase_			:      Optional[int]     						=  torch.tensor(
												    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]   )
												UpperCAmelCase_			:      int     						=  torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]   )

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :3, :3],			SCREAMING_SNAKE_CASE__,			atol=1E-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3],			SCREAMING_SNAKE_CASE__,			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(SCREAMING_SNAKE_CASE__   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__   )
												model.save_pretrained(SCREAMING_SNAKE_CASE__   )
												image_processor.save_pretrained(SCREAMING_SNAKE_CASE__   )

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


if __name__ == "__main__":
					snake_case_     :       str						=  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_     :       Dict						=  parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			int   ) ->	int:
						return abs(SCREAMING_SNAKE_CASE__   ) if a == 0 else greatest_common_divisor(b % a,			SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			int   ) ->	int:
						while y:  # --> when y=0 then loop will terminate and return x as final GCD.
												UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  y, x % y
						return abs(SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  ) ->	Optional[int]:
						try:
												UpperCAmelCase_			:      Optional[Any]     						=  input('''Enter two integers separated by comma (,): '''   ).split(''','''   )
												UpperCAmelCase_			:      Optional[int]     						=  int(nums[0]   )
												UpperCAmelCase_			:      List[Any]     						=  int(nums[1]   )
												print(
												    F"""greatest_common_divisor({num_a}, {num_a}) = """
												    F"""{greatest_common_divisor(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )}"""   )
												print(F"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )}"""   )
						except (IndexError, UnboundLocalError, ValueError):
												print('''Wrong input'''   )


if __name__ == "__main__":
					main()





