



"""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()
lowerCAmelCase__      =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__      =    []
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_  (				A_ :		str,    A_ :		Dict,    A_ :		List[str]						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			state_dict.pop(A_						)
       _lowerCamelCase						:    Union[str, Any]         =			val






def 	snake_case_  (				A_ :		Tuple						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     _lowerCamelCase						:    Union[str, Any]         =			key.replace('''backbone.0.body''',    '''backbone.conv_encoder.model'''						)
                     _lowerCamelCase						:    Union[str, Any]         =			value
              else:
                     _lowerCamelCase						:    Tuple         =			value

       return new_state_dict






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, 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						:    int         =			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
              _lowerCamelCase						:    Union[str, Any]         =			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						:    Union[str, Any]         =			in_proj_weight[:2_56, :]
              _lowerCamelCase						:    int         =			in_proj_bias[:2_56]
              _lowerCamelCase						:    Any         =			in_proj_weight[2_56:5_12, :]
              _lowerCamelCase						:    Union[str, Any]         =			in_proj_bias[2_56:5_12]
              _lowerCamelCase						:    Optional[Any]         =			in_proj_weight[-2_56:, :]
              _lowerCamelCase						:    Optional[Any]         =			in_proj_bias[-2_56:]
       # 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						:    List[str]         =			state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''						)
              _lowerCamelCase						:    int         =			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						:    Optional[Any]         =			in_proj_weight[:2_56, :]
              _lowerCamelCase						:    Tuple         =			in_proj_bias[:2_56]
              _lowerCamelCase						:    List[Any]         =			in_proj_weight[2_56:5_12, :]
              _lowerCamelCase						:    Dict         =			in_proj_bias[2_56:5_12]
              _lowerCamelCase						:    Union[str, Any]         =			in_proj_weight[-2_56:, :]
              _lowerCamelCase						:    List[str]         =			in_proj_bias[-2_56:]
              # read in weights + bias of input projection layer of cross-attention
              _lowerCamelCase						:    Optional[Any]         =			state_dict.pop(
                  F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''						)
              _lowerCamelCase						:    List[str]         =			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						:    Tuple         =			in_proj_weight_cross_attn[:2_56, :]
              _lowerCamelCase						:    Optional[int]         =			in_proj_bias_cross_attn[:2_56]
              _lowerCamelCase						:    Union[str, Any]         =			in_proj_weight_cross_attn[2_56:5_12, :]
              _lowerCamelCase						:    Union[str, Any]         =			in_proj_bias_cross_attn[2_56:5_12]
              _lowerCamelCase						:    List[str]         =			in_proj_weight_cross_attn[-2_56:, :]
              _lowerCamelCase						:    Tuple         =			in_proj_bias_cross_attn[-2_56:]






def 	snake_case_  (				A_ :		Dict,    A_ :		List[str]						):


       '''simple docstring'''





       _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			image.size
       _lowerCamelCase						:    Any         =			max(A_,    A_						)
       _lowerCamelCase						:    str         =			8_00 if '''detection''' in checkpoint_url else 10_00
       _lowerCamelCase						:    str         =			target_max_size / current_max_size
       _lowerCamelCase						:    List[Any]         =			image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

       return resized_image






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			F.to_tensor(A_						)
       _lowerCamelCase						:    Optional[Any]         =			F.normalize(A_,    mean=[0.485, 0.456, 0.406],    std=[0.229, 0.224, 0.225]						)
       return image






@torch.no_grad()
def 	snake_case_  (				A_ :		List[str],    A_ :		int,    A_ :		str						):


       '''simple docstring'''





       logger.info('''Converting model...'''						)

       # load original state dict
       _lowerCamelCase						:    Optional[int]         =			torch.hub.load_state_dict_from_url(A_,    map_location='''cpu'''						)
       # rename keys
       for src, dest in rename_keys:
              rename_key(A_,    A_,    A_						)
       _lowerCamelCase						:    int         =			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
       _lowerCamelCase						:    Dict         =			'''model.'''
       for key in state_dict.copy().keys():
              if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
                     _lowerCamelCase						:    Any         =			state_dict.pop(A_						)
                     _lowerCamelCase						:    int         =			val
    # create HuggingFace model and load state dict
       _lowerCamelCase						:    Union[str, 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						:    str         =			15
              _lowerCamelCase						:    Dict         =			2
              _lowerCamelCase						:    List[Any]         =			{0: '''table''', 1: '''table rotated'''}
              _lowerCamelCase						:    Optional[Any]         =			idalabel
              _lowerCamelCase						:    Union[str, Any]         =			{v: k for k, v in idalabel.items()}
       else:
              _lowerCamelCase						:    Union[str, Any]         =			1_25
              _lowerCamelCase						:    Tuple         =			6
              _lowerCamelCase						:    str         =			{
                  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						:    Tuple         =			{v: k for k, v in idalabel.items()}

       _lowerCamelCase						:    str         =			DetrImageProcessor(
           format='''coco_detection''',    max_size=8_00 if '''detection''' in checkpoint_url else 10_00						)
       _lowerCamelCase						:    List[Any]         =			TableTransformerForObjectDetection(A_						)
       model.load_state_dict(A_						)
       model.eval()

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

       _lowerCamelCase						:    str         =			model(A_						)

       if "detection" in checkpoint_url:
              _lowerCamelCase						:    int         =			(1, 15, 3)
              _lowerCamelCase						:    str         =			torch.tensor(
                  [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]						)
              _lowerCamelCase						:    List[str]         =			torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]						)

       else:
              _lowerCamelCase						:    Dict         =			(1, 1_25, 7)
              _lowerCamelCase						:    str         =			torch.tensor(
                  [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]						)
              _lowerCamelCase						:    int         =			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...'''						)
              _lowerCamelCase						:    Dict         =			(
                  '''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__":
   lowerCAmelCase__      =    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.'''
   )
   lowerCAmelCase__      =    parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""






import argparse


lowerCAmelCase__      =    '''docs/source/_static/js/custom.js'''






def 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       with open(A_,    encoding='''utf-8''',    newline='''\n'''						) as f:
              _lowerCamelCase						:    int         =			f.readlines()
       _lowerCamelCase						:    List[str]         =			0

       # First let's put the right version
       while not lines[index].startswith('''const stableVersion ='''						):
              index += 1
       _lowerCamelCase						:    List[Any]         =			F'''const stableVersion = "v{version}"\n'''

       # Then update the dictionary
       while not lines[index].startswith('''const versionMapping = {'''						):
              index += 1

       # We go until the end
       while not lines[index].startswith('''}'''						):
              index += 1
       # We add the new version at the end
       lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

       with open(A_,    '''w''',    encoding='''utf-8''',    newline='''\n'''						) as f:
              f.writelines(A_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    argparse.ArgumentParser()
   parser.add_argument('''--version''', help='''Release version.''')
   lowerCAmelCase__      =    parser.parse_args()
   update_custom_js(args.version)


