





"""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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     state_dict.pop(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     val

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Optional[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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     image.size
       _UpperCAmelCase     =     max(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       _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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




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

@torch.no_grad()
def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




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

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

       _UpperCAmelCase     =     model(SCREAMING_SNAKE_CASE							)

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

       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(SCREAMING_SNAKE_CASE							)
              image_processor.push_to_hub(SCREAMING_SNAKE_CASE							)


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 sys

lowerCAmelCase_			=							(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     1
       for digit in s:
              product *= int(SCREAMING_SNAKE_CASE							)
       return product

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE = N							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     -sys.maxsize - 1
       _UpperCAmelCase     =     n[:13]
       _UpperCAmelCase     =     13
       while cur_index < len(SCREAMING_SNAKE_CASE							) - 13:
              if int(n[cur_index]							) >= int(substr[0]							):
                     _UpperCAmelCase     =     substr[1:] + n[cur_index]
                     cur_index += 1
              else:
                     _UpperCAmelCase     =     max(SCREAMING_SNAKE_CASE,str_eval(SCREAMING_SNAKE_CASE							)							)
                     _UpperCAmelCase     =     n[cur_index : cur_index + 13]
                     cur_index += 13
       return largest_product


if __name__ == "__main__":
    print(F'''{solution() = }''')






