






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

# here we list all keys to be renamed (original name on the left, our name on the right)
__A							:							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_     (    lowercase__				,     lowercase__				,     lowercase__)     ->   List[str]:
 lowerCamelCase__  =     state_dict.pop(_lowercase)
 lowerCamelCase__  =     val






def    lowerCamelCase_     (    lowercase__)     ->   Optional[Any]:
 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    lowerCamelCase_     (    lowercase__)     ->   Optional[Any]:
 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    lowerCamelCase_     (    lowercase__				,     lowercase__)     ->   List[Any]:
 lowerCamelCase__  =     image.size
 lowerCamelCase__  =     max(_lowercase				,     _lowercase)
 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    lowerCamelCase_     (    lowercase__)     ->   Optional[Any]:
 lowerCamelCase__  =     F.to_tensor(_lowercase)
 lowerCamelCase__  =     F.normalize(_lowercase				,     mean=[0.485, 0.456, 0.406]				,     std=[0.229, 0.224, 0.225])
 return image






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

 # load original state dict
 lowerCamelCase__  =     torch.hub.load_state_dict_from_url(_lowercase				,     map_location="cpu")
 # rename keys
 for src, dest in rename_keys:
  rename_key(_lowercase				,     _lowercase				,     _lowercase)
 lowerCamelCase__  =     rename_backbone_keys(_lowercase)
 # query, key and value matrices need special treatment
 read_in_q_k_v(_lowercase)
 # 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(_lowercase)
   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(_lowercase)
 model.load_state_dict(_lowercase)
 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=_lowercase)
 lowerCamelCase__  =     Image.open(_lowercase).convert("RGB")
 lowerCamelCase__  =     normalize(resize(_lowercase				,     _lowercase)).unsqueeze(0)

 lowerCamelCase__  =     model(_lowercase)

 if "detection" in checkpoint_url:
  lowerCamelCase__  =     (1, 15, 3)
  lowerCamelCase__  =     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__  =     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__  =     (1, 125, 7)
  lowerCamelCase__  =     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__  =     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]				,     _lowercase				,     atol=1E-4)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,     _lowercase				,     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(_lowercase).mkdir(exist_ok=_lowercase)
  model.save_pretrained(_lowercase)
  image_processor.save_pretrained(_lowercase)

 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(_lowercase)
  image_processor.push_to_hub(_lowercase)


if __name__ == "__main__":
 __A							:							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[int]  							=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		cva.imread(
     str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
 )
 # turn image in gray scale value
 __A							:							int  							=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
 # get image shape
 __A							,			__A							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

 # add all rotated images in a list
 __A							:							List[Any]  							=		[
     gray_img,
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
 ]

 # plot different image rotations
 __A							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
 for i, image in enumerate(images):
  plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
  plt.title(titles[i])
  plt.axis("""off""")
  plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
 plt.show()

