





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()
lowercase :			Any     		=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase :			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_      (						A__						, A__						, A__							)    ->						List[Any]:
       a__		:      Optional[Any]					=							state_dict.pop(_lowerCamelCase							)
       a__		:      Optional[int]					=							val



def A_      (						A__							)    ->						Tuple:
       a__		:      Tuple					=							OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     a__		:      Optional[Any]					=							key.replace('backbone.0.body'						, 'backbone.conv_encoder.model'							)
                     a__		:      Dict					=							value
              else:
                     a__		:      Union[str, Any]					=							value

       return new_state_dict



def A_      (						A__							)    ->						Tuple:
       a__		:      Optional[int]					=							''

       # 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)
              a__		:      Optional[Any]					=							state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'							)
              a__		:      Optional[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
              a__		:      Dict					=							in_proj_weight[:256, :]
              a__		:      Dict					=							in_proj_bias[:256]
              a__		:      List[str]					=							in_proj_weight[256:512, :]
              a__		:      Optional[Any]					=							in_proj_bias[256:512]
              a__		:      Optional[int]					=							in_proj_weight[-256:, :]
              a__		:      Dict					=							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
              a__		:      Dict					=							state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'							)
              a__		:      Optional[Any]					=							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
              a__		:      int					=							in_proj_weight[:256, :]
              a__		:      List[Any]					=							in_proj_bias[:256]
              a__		:      Tuple					=							in_proj_weight[256:512, :]
              a__		:      Optional[int]					=							in_proj_bias[256:512]
              a__		:      Any					=							in_proj_weight[-256:, :]
              a__		:      int					=							in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              a__		:      Tuple					=							state_dict.pop(
                  F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'							)
              a__		:      int					=							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
              a__		:      Any					=							in_proj_weight_cross_attn[:256, :]
              a__		:      Optional[int]					=							in_proj_bias_cross_attn[:256]
              a__		:      List[Any]					=							in_proj_weight_cross_attn[256:512, :]
              a__		:      Optional[int]					=							in_proj_bias_cross_attn[256:512]
              a__		:      Optional[int]					=							in_proj_weight_cross_attn[-256:, :]
              a__		:      Union[str, Any]					=							in_proj_bias_cross_attn[-256:]



def A_      (						A__						, A__							)    ->						List[str]:
       a__					,				a__		:      List[str]					=							image.size
       a__		:      Any					=							max(_lowerCamelCase						, _lowerCamelCase							)
       a__		:      int					=							800 if 'detection' in checkpoint_url else 1000
       a__		:      int					=							target_max_size / current_max_size
       a__		:      Dict					=							image.resize((int(round(scale * width							)							), int(round(scale * height							)							))							)

       return resized_image



def A_      (						A__							)    ->						Tuple:
       a__		:      Union[str, Any]					=							F.to_tensor(_lowerCamelCase							)
       a__		:      int					=							F.normalize(_lowerCamelCase						, mean=[0.4_85, 0.4_56, 0.4_06]						, std=[0.2_29, 0.2_24, 0.2_25]							)
       return image



@torch.no_grad()
def A_      (						A__						, A__						, A__							)    ->						Optional[Any]:
       logger.info('Converting model...'							)

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

       a__		:      int					=							DetrImageProcessor(
           format='coco_detection'						, max_size=800 if 'detection' in checkpoint_url else 1000							)
       a__		:      Tuple					=							TableTransformerForObjectDetection(_lowerCamelCase							)
       model.load_state_dict(_lowerCamelCase							)
       model.eval()

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

       a__		:      int					=							model(_lowerCamelCase							)

       if "detection" in checkpoint_url:
              a__		:      List[Any]					=							(1, 15, 3)
              a__		:      List[str]					=							torch.tensor(
                  [[-6.78_97, -16.9985, 6.79_37], [-8.01_86, -22.2192, 6.96_77], [-7.31_17, -21.0708, 7.40_55]]							)
              a__		:      str					=							torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]							)

       else:
              a__		:      Any					=							(1, 125, 7)
              a__		:      str					=							torch.tensor(
                  [[-18.1430, -8.32_14, 4.82_74], [-18.4685, -7.13_61, -4.26_67], [-26.3693, -9.34_29, -4.99_62]]							)
              a__		:      Any					=							torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]							)

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :3, :3]						, _lowerCamelCase						, atol=1E-4							)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]						, _lowerCamelCase						, 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(_lowerCamelCase							).mkdir(exist_ok=_lowerCamelCase							)
              model.save_pretrained(_lowerCamelCase							)
              image_processor.save_pretrained(_lowerCamelCase							)

       if push_to_hub:
              # Push model to HF hub
              logger.info('Pushing model to the hub...'							)
              a__		:      List[str]					=							(
                  'microsoft/table-transformer-detection'
                  if 'detection' in checkpoint_url
                  else 'microsoft/table-transformer-structure-recognition'
              )
              model.push_to_hub(_lowerCamelCase							)
              image_processor.push_to_hub(_lowerCamelCase							)


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









'''simple docstring'''

from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :List[str]		,			lowercase_ :int = 7_68		,			)->				str:
     super().__init__()

     A__							=     nn.Parameter(torch.zeros(1		,			lowercase_				)				)
     A__							=     nn.Parameter(torch.ones(1		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Optional[Union[str, torch.device]] = None		,			lowercase_ :Optional[torch.dtype] = None		,			)->				Tuple:
     A__							=     nn.Parameter(self.mean.to(lowercase_				).to(lowercase_				)				)
     A__							=     nn.Parameter(self.std.to(lowercase_				).to(lowercase_				)				)
     return self
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :str				)->				Union[str, Any]:
     A__							=     (embeds - self.mean) * 1.0 / self.std
     return embeds



 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :List[Any]				)->				Tuple:
     A__							=     (embeds * self.std) + self.mean
     return embeds



