


'''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     =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__a     =			[]
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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   UpperCAmelCase							=     state_dict.pop(lowerCAmelCase_   )
   UpperCAmelCase							=     val
def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[str]:
   UpperCAmelCase							=     OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         UpperCAmelCase							=     key.replace("""backbone.0.body"""  ,						"""backbone.conv_encoder.model"""   )
         UpperCAmelCase							=     value
      else:
         UpperCAmelCase							=     value

   return new_state_dict
def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[str]:
   UpperCAmelCase							=     """"""

   # 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							=     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
      UpperCAmelCase							=     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							=     in_proj_weight[:2_5_6, :]
      UpperCAmelCase							=     in_proj_bias[:2_5_6]
      UpperCAmelCase							=     in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase							=     in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase							=     in_proj_weight[-2_5_6:, :]
      UpperCAmelCase							=     in_proj_bias[-2_5_6:]
   # 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							=     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
      UpperCAmelCase							=     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							=     in_proj_weight[:2_5_6, :]
      UpperCAmelCase							=     in_proj_bias[:2_5_6]
      UpperCAmelCase							=     in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase							=     in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase							=     in_proj_weight[-2_5_6:, :]
      UpperCAmelCase							=     in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      UpperCAmelCase							=     state_dict.pop(
          F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
      UpperCAmelCase							=     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							=     in_proj_weight_cross_attn[:2_5_6, :]
      UpperCAmelCase							=     in_proj_bias_cross_attn[:2_5_6]
      UpperCAmelCase							=     in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      UpperCAmelCase							=     in_proj_bias_cross_attn[2_5_6:5_1_2]
      UpperCAmelCase							=     in_proj_weight_cross_attn[-2_5_6:, :]
      UpperCAmelCase							=     in_proj_bias_cross_attn[-2_5_6:]
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase				,  UpperCAmelCase							=     image.size
   UpperCAmelCase							=     max(lowerCAmelCase_  ,						lowerCAmelCase_   )
   UpperCAmelCase							=     8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
   UpperCAmelCase							=     target_max_size / current_max_size
   UpperCAmelCase							=     image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

   return resized_image
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   UpperCAmelCase							=     F.to_tensor(lowerCAmelCase_   )
   UpperCAmelCase							=     F.normalize(lowerCAmelCase_  ,						mean=[0.485, 0.456, 0.406]  ,						std=[0.229, 0.224, 0.225]   )
   return image
@torch.no_grad()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->int:
   logger.info("""Converting model..."""   )

   # load original state dict
   UpperCAmelCase							=     torch.hub.load_state_dict_from_url(lowerCAmelCase_  ,						map_location="""cpu"""   )
   # rename keys
   for src, dest in rename_keys:
      rename_key(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
   UpperCAmelCase							=     rename_backbone_keys(lowerCAmelCase_   )
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowerCAmelCase_   )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   UpperCAmelCase							=     """model."""
   for key in state_dict.copy().keys():
      if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
         UpperCAmelCase							=     state_dict.pop(lowerCAmelCase_   )
         UpperCAmelCase							=     val
    # create HuggingFace model and load state dict
   UpperCAmelCase							=     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							=     1_5
      UpperCAmelCase							=     2
      UpperCAmelCase							=     {0: """table""", 1: """table rotated"""}
      UpperCAmelCase							=     idalabel
      UpperCAmelCase							=     {v: k for k, v in idalabel.items()}
   else:
      UpperCAmelCase							=     1_2_5
      UpperCAmelCase							=     6
      UpperCAmelCase							=     {
          0: """table""",
          1: """table column""",
          2: """table row""",
          3: """table column header""",
          4: """table projected row header""",
          5: """table spanning cell""",
      }
      UpperCAmelCase							=     idalabel
      UpperCAmelCase							=     {v: k for k, v in idalabel.items()}

   UpperCAmelCase							=     DetrImageProcessor(
       format="""coco_detection"""  ,						max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0   )
   UpperCAmelCase							=     TableTransformerForObjectDetection(lowerCAmelCase_   )
   model.load_state_dict(lowerCAmelCase_   )
   model.eval()

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

   UpperCAmelCase							=     model(lowerCAmelCase_   )

   if "detection" in checkpoint_url:
      UpperCAmelCase							=     (1, 1_5, 3)
      UpperCAmelCase							=     torch.tensor(
          [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]   )
      UpperCAmelCase							=     torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]   )

   else:
      UpperCAmelCase							=     (1, 1_2_5, 7)
      UpperCAmelCase							=     torch.tensor(
          [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]   )
      UpperCAmelCase							=     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]  ,						lowerCAmelCase_  ,						atol=1e-4   )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,						lowerCAmelCase_  ,						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(lowerCAmelCase_   ).mkdir(exist_ok=lowerCAmelCase_   )
      model.save_pretrained(lowerCAmelCase_   )
      image_processor.save_pretrained(lowerCAmelCase_   )

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


if __name__ == "__main__":
 __a     =			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     =			parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     tokenizer(example["""content"""]  ,						truncation=lowerCAmelCase_   )["""input_ids"""]
   UpperCAmelCase							=     len(example["""content"""]   ) / len(output["""input_ids"""]   )
   return output


__a     =			HfArgumentParser(PretokenizationArguments)
__a     =			parser.parse_args()
if args.num_workers is None:
 __a     =			multiprocessing.cpu_count()
__a     =			AutoTokenizer.from_pretrained(args.tokenizer_dir)

__a     =			time.time()
__a     =			load_dataset(args.dataset_name, split="""train""")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

__a     =			time.time()
__a     =			ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

__a     =			time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")



