




'''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()
UpperCamelCase_						:      str       =						logging.get_logger(__name__)

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

     """simple docstring"""




     _snake_case							=				state_dict.pop(_UpperCamelCase   )
     _snake_case							=				val





def      __a     (       _UpperCamelCase:   Optional[Any]   )						->			Union[str, Any]:

     """simple docstring"""




     _snake_case							=				OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               _snake_case							=				key.replace("backbone.0.body" ,						"backbone.conv_encoder.model"   )
               _snake_case							=				value
          else:
               _snake_case							=				value

     return new_state_dict





def      __a     (       _UpperCamelCase:   int   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				""

     # 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)
          _snake_case							=				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
          _snake_case							=				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
          _snake_case							=				in_proj_weight[:256, :]
          _snake_case							=				in_proj_bias[:256]
          _snake_case							=				in_proj_weight[256:512, :]
          _snake_case							=				in_proj_bias[256:512]
          _snake_case							=				in_proj_weight[-256:, :]
          _snake_case							=				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
          _snake_case							=				state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
          _snake_case							=				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
          _snake_case							=				in_proj_weight[:256, :]
          _snake_case							=				in_proj_bias[:256]
          _snake_case							=				in_proj_weight[256:512, :]
          _snake_case							=				in_proj_bias[256:512]
          _snake_case							=				in_proj_weight[-256:, :]
          _snake_case							=				in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          _snake_case							=				state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
          _snake_case							=				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
          _snake_case							=				in_proj_weight_cross_attn[:256, :]
          _snake_case							=				in_proj_bias_cross_attn[:256]
          _snake_case							=				in_proj_weight_cross_attn[256:512, :]
          _snake_case							=				in_proj_bias_cross_attn[256:512]
          _snake_case							=				in_proj_weight_cross_attn[-256:, :]
          _snake_case							=				in_proj_bias_cross_attn[-256:]





def      __a     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   Any   )						->			List[str]:

     """simple docstring"""




     _snake_case							, _snake_case							=				image.size
     _snake_case							=				max(_UpperCamelCase ,						_UpperCamelCase   )
     _snake_case							=				800 if "detection" in checkpoint_url else 1_000
     _snake_case							=				target_max_size / current_max_size
     _snake_case							=				image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

     return resized_image





def      __a     (       _UpperCamelCase:   Tuple   )						->			Optional[Any]:

     """simple docstring"""




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





@torch.no_grad()
def      __a     (       _UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   str ,						_UpperCamelCase:   Optional[int]   )						->			List[Any]:

     """simple docstring"""




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

     # load original state dict
     _snake_case							=				torch.hub.load_state_dict_from_url(_UpperCamelCase ,						map_location="cpu"   )
     # rename keys
     for src, dest in rename_keys:
          rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
     _snake_case							=				rename_backbone_keys(_UpperCamelCase   )
     # query, key and value matrices need special treatment
     read_in_q_k_v(_UpperCamelCase   )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _snake_case							=				"model."
     for key in state_dict.copy().keys():
          if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
               _snake_case							=				state_dict.pop(_UpperCamelCase   )
               _snake_case							=				val
    # create HuggingFace model and load state dict
     _snake_case							=				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:
          _snake_case							=				15
          _snake_case							=				2
          _snake_case							=				{0: "table", 1: "table rotated"}
          _snake_case							=				idalabel
          _snake_case							=				{v: k for k, v in idalabel.items()}
     else:
          _snake_case							=				125
          _snake_case							=				6
          _snake_case							=				{
              0: "table",
              1: "table column",
              2: "table row",
              3: "table column header",
              4: "table projected row header",
              5: "table spanning cell",
          }
          _snake_case							=				idalabel
          _snake_case							=				{v: k for k, v in idalabel.items()}

     _snake_case							=				DetrImageProcessor(
         format="coco_detection" ,						max_size=800 if "detection" in checkpoint_url else 1_000   )
     _snake_case							=				TableTransformerForObjectDetection(_UpperCamelCase   )
     model.load_state_dict(_UpperCamelCase   )
     model.eval()

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

     _snake_case							=				model(_UpperCamelCase   )

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

     else:
          _snake_case							=				(1, 125, 7)
          _snake_case							=				torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]   )
          _snake_case							=				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] ,						_UpperCamelCase ,						atol=1E-4   )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,						_UpperCamelCase ,						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(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
          model.save_pretrained(_UpperCamelCase   )
          image_processor.save_pretrained(_UpperCamelCase   )

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


if __name__ == "__main__":
   UpperCamelCase_						:      Dict       =						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.'''
   )
   UpperCamelCase_						:      str       =						parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





from __future__ import annotations

from typing import Any





def      __a     (       _UpperCamelCase:   list[Any]   )						->			None:

     """simple docstring"""




     create_state_space_tree(_UpperCamelCase ,						[] ,						0   )





def      __a     (       _UpperCamelCase:   list[Any] ,						_UpperCamelCase:   list[Any] ,						_UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     if index == len(_UpperCamelCase   ):
          print(_UpperCamelCase   )
          return

     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.append(sequence[index]   )
     create_state_space_tree(_UpperCamelCase ,						_UpperCamelCase ,						index + 1   )
     current_subsequence.pop()


if __name__ == "__main__":
   UpperCamelCase_						:      list[Any]       =						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['''A''', '''B''', '''C'''])
   generate_all_subsequences(seq)


