'''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()
a_						:       Tuple      			=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_						:       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 __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						state_dict.pop(UpperCAmelCase_	)
 lowerCamelCase_  				=						val





def __snake_case      (   UpperCAmelCase_		:		Tuple	):
 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 __snake_case      (   UpperCAmelCase_		:		int	):
 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 __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		int	):
 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 __snake_case      (   UpperCAmelCase_		:		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 __snake_case      (   UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Union[str, 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__":
     a_						:       Tuple      			=				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)







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

 def get_lens(UpperCAmelCase_		:		str	):
  lowerCamelCase_  				=						tqdm(
      DataLoader(UpperCAmelCase_      ,       batch_size=512      ,       num_workers=8      ,       shuffle=UpperCAmelCase_      ,       collate_fn=ds.collate_fn	)      ,       desc=str(ds.len_file	)      ,       )
  lowerCamelCase_  				=						[]
  for batch in dl:
   lowerCamelCase_  				=						batch["input_ids"].ne(UpperCAmelCase_	).sum(1	).tolist()
   lowerCamelCase_  				=						batch["labels"].ne(UpperCAmelCase_	).sum(1	).tolist()
   if consider_target:
    for src, tgt in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
     max_lens.append(max(UpperCAmelCase_      ,       UpperCAmelCase_	)	)
   else:
    max_lens.extend(UpperCAmelCase_	)
  return max_lens

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)



