


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()
_A		:				Optional[int]									=       logging.get_logger(__name__)

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



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

   return new_state_dict



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



def     __lowerCAmelCase						(							snake_case	:    Any						,      snake_case	:    int )       ->							Any:
   __lowerCamelCase:				List[str]     =	image.size
   __lowerCamelCase:				List[Any]     =	max(snake_case						,      snake_case )
   __lowerCamelCase:				List[str]     =	800 if """detection""" in checkpoint_url else 1000
   __lowerCamelCase:				List[Any]     =	target_max_size / current_max_size
   __lowerCamelCase:				List[Any]     =	image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )

   return resized_image



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



@torch.no_grad()
def     __lowerCAmelCase						(							snake_case	:    List[str]						,      snake_case	:    Tuple						,      snake_case	:    List[Any] )       ->							Tuple:
   logger.info("""Converting model...""" )

   # load original state dict
   __lowerCamelCase:				Any     =	torch.hub.load_state_dict_from_url(snake_case						,      map_location="""cpu""" )
   # rename keys
   for src, dest in rename_keys:
      rename_key(snake_case						,      snake_case						,      snake_case )
   __lowerCamelCase:				Union[str, Any]     =	rename_backbone_keys(snake_case )
   # query, key and value matrices need special treatment
   read_in_q_k_v(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
   __lowerCamelCase:				Optional[int]     =	"""model."""
   for key in state_dict.copy().keys():
      if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
         __lowerCamelCase:				Dict     =	state_dict.pop(snake_case )
         __lowerCamelCase:				Any     =	val
    # create HuggingFace model and load state dict
   __lowerCamelCase:				List[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:
      __lowerCamelCase:				Union[str, Any]     =	15
      __lowerCamelCase:				List[Any]     =	2
      __lowerCamelCase:				Optional[int]     =	{0: """table""", 1: """table rotated"""}
      __lowerCamelCase:				Optional[Any]     =	idalabel
      __lowerCamelCase:				Dict     =	{v: k for k, v in idalabel.items()}
   else:
      __lowerCamelCase:				int     =	125
      __lowerCamelCase:				Optional[Any]     =	6
      __lowerCamelCase:				List[Any]     =	{
          0: """table""",
          1: """table column""",
          2: """table row""",
          3: """table column header""",
          4: """table projected row header""",
          5: """table spanning cell""",
      }
      __lowerCamelCase:				int     =	idalabel
      __lowerCamelCase:				Dict     =	{v: k for k, v in idalabel.items()}

   __lowerCamelCase:				List[str]     =	DetrImageProcessor(
       format="""coco_detection"""						,      max_size=800 if """detection""" in checkpoint_url else 1000 )
   __lowerCamelCase:				List[str]     =	TableTransformerForObjectDetection(snake_case )
   model.load_state_dict(snake_case )
   model.eval()

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

   __lowerCamelCase:				Optional[int]     =	model(snake_case )

   if "detection" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	(1, 15, 3)
      __lowerCamelCase:				Union[str, Any]     =	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]] )
      __lowerCamelCase:				Dict     =	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:
      __lowerCamelCase:				int     =	(1, 125, 7)
      __lowerCamelCase:				List[Any]     =	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]] )
      __lowerCamelCase:				Dict     =	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]						,      snake_case						,      atol=1e-4 )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,      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(snake_case ).mkdir(exist_ok=snake_case )
      model.save_pretrained(snake_case )
      image_processor.save_pretrained(snake_case )

   if push_to_hub:
      # Push model to HF hub
      logger.info("""Pushing model to the hub...""" )
      __lowerCamelCase:				str     =	(
          """microsoft/table-transformer-detection"""
          if """detection""" in checkpoint_url
          else """microsoft/table-transformer-structure-recognition"""
      )
      model.push_to_hub(snake_case )
      image_processor.push_to_hub(snake_case )


if __name__ == "__main__":
   _A		:				Optional[Any]									=       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.'''
   )
   _A		:				Any									=       parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






def     __lowerCAmelCase						(							snake_case	:    int = 1000000 )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	set(range(3						,      snake_case						,      2 ) )
   primes.add(2 )
   for p in range(3						,      snake_case						,      2 ):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p						,      snake_case						,      snake_case ) ) )

   __lowerCamelCase:				Any     =	[float(snake_case ) for n in range(limit + 1 )]

   for p in primes:
      for n in range(snake_case						,      limit + 1						,      snake_case ):
         phi[n] *= 1 - 1 / p

   return int(sum(phi[2:] ) )


if __name__ == "__main__":
   print(F"""{solution() = }""")


