


'''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__  :					List[Any]        =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a__  :					List[str]        =       []
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       (     __A      ,			__A      ,			__A    )							->		Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__										=				state_dict.pop(__A    )
      UpperCamelCase__										=				val







def _UpperCamelCase       (     __A    )							->		Any:


      '''simple docstring'''



      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       (     __A    )							->		List[Any]:


      '''simple docstring'''



      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[:256, :]
            UpperCamelCase__										=				in_proj_bias[:256]
            UpperCamelCase__										=				in_proj_weight[256:512, :]
            UpperCamelCase__										=				in_proj_bias[256:512]
            UpperCamelCase__										=				in_proj_weight[-256:, :]
            UpperCamelCase__										=				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__										=				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[:256, :]
            UpperCamelCase__										=				in_proj_bias[:256]
            UpperCamelCase__										=				in_proj_weight[256:512, :]
            UpperCamelCase__										=				in_proj_bias[256:512]
            UpperCamelCase__										=				in_proj_weight[-256:, :]
            UpperCamelCase__										=				in_proj_bias[-256:]
            # 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[:256, :]
            UpperCamelCase__										=				in_proj_bias_cross_attn[:256]
            UpperCamelCase__										=				in_proj_weight_cross_attn[256:512, :]
            UpperCamelCase__										=				in_proj_bias_cross_attn[256:512]
            UpperCamelCase__										=				in_proj_weight_cross_attn[-256:, :]
            UpperCamelCase__										=				in_proj_bias_cross_attn[-256:]







def _UpperCamelCase       (     __A      ,			__A    )							->		Dict:


      '''simple docstring'''



      UpperCamelCase__					,	UpperCamelCase__										=				image.size
      UpperCamelCase__										=				max(__A      ,			__A    )
      UpperCamelCase__										=				800 if "detection" in checkpoint_url else 1000
      UpperCamelCase__										=				target_max_size / current_max_size
      UpperCamelCase__										=				image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

      return resized_image







def _UpperCamelCase       (     __A    )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__										=				F.to_tensor(__A    )
      UpperCamelCase__										=				F.normalize(__A      ,			mean=[0.485, 0.456, 0.406]      ,			std=[0.229, 0.224, 0.225]    )
      return image







@torch.no_grad()
def _UpperCamelCase       (     __A      ,			__A      ,			__A    )							->		List[Any]:


      '''simple docstring'''



      logger.info("Converting model..."    )

      # load original state dict
      UpperCamelCase__										=				torch.hub.load_state_dict_from_url(__A      ,			map_location="cpu"    )
      # rename keys
      for src, dest in rename_keys:
            rename_key(__A      ,			__A      ,			__A    )
      UpperCamelCase__										=				rename_backbone_keys(__A    )
      # query, key and value matrices need special treatment
      read_in_q_k_v(__A    )
      # 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(__A    )
                  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__										=				15
            UpperCamelCase__										=				2
            UpperCamelCase__										=				{0: "table", 1: "table rotated"}
            UpperCamelCase__										=				idalabel
            UpperCamelCase__										=				{v: k for k, v in idalabel.items()}
      else:
            UpperCamelCase__										=				125
            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=800 if "detection" in checkpoint_url else 1000    )
      UpperCamelCase__										=				TableTransformerForObjectDetection(__A    )
      model.load_state_dict(__A    )
      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=__A    )
      UpperCamelCase__										=				Image.open(__A    ).convert("RGB"    )
      UpperCamelCase__										=				normalize(resize(__A      ,			__A    )    ).unsqueeze(0    )

      UpperCamelCase__										=				model(__A    )

      if "detection" in checkpoint_url:
            UpperCamelCase__										=				(1, 15, 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, 125, 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]      ,			__A      ,			atol=1E-4    )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,			__A      ,			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(__A    ).mkdir(exist_ok=__A    )
            model.save_pretrained(__A    )
            image_processor.save_pretrained(__A    )

      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(__A    )
            image_processor.push_to_hub(__A    )


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












from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

