


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__SCREAMING_SNAKE_CASE      		=    []
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       ):





       """simple docstring"""







       _a							 =					state_dict.pop(a       )
       _a							 =					val



def     __a   (	a       ):





       """simple docstring"""







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

       return new_state_dict



def     __a   (	a       ):





       """simple docstring"""







       _a							 =					""

       # 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							 =					state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'       )
              _a							 =					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							 =					in_proj_weight[:2_5_6, :]
              _a							 =					in_proj_bias[:2_5_6]
              _a							 =					in_proj_weight[2_5_6:5_1_2, :]
              _a							 =					in_proj_bias[2_5_6:5_1_2]
              _a							 =					in_proj_weight[-2_5_6:, :]
              _a							 =					in_proj_bias[-2_5_6:]
       # 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							 =					state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
              _a							 =					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							 =					in_proj_weight[:2_5_6, :]
              _a							 =					in_proj_bias[:2_5_6]
              _a							 =					in_proj_weight[2_5_6:5_1_2, :]
              _a							 =					in_proj_bias[2_5_6:5_1_2]
              _a							 =					in_proj_weight[-2_5_6:, :]
              _a							 =					in_proj_bias[-2_5_6:]
              # read in weights + bias of input projection layer of cross-attention
              _a							 =					state_dict.pop(
                  F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'       )
              _a							 =					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							 =					in_proj_weight_cross_attn[:2_5_6, :]
              _a							 =					in_proj_bias_cross_attn[:2_5_6]
              _a							 =					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
              _a							 =					in_proj_bias_cross_attn[2_5_6:5_1_2]
              _a							 =					in_proj_weight_cross_attn[-2_5_6:, :]
              _a							 =					in_proj_bias_cross_attn[-2_5_6:]



def     __a   (	a,		a       ):





       """simple docstring"""







       _a							 =					image.size
       _a							 =					max(a,		a       )
       _a							 =					8_0_0 if "detection" in checkpoint_url else 1_0_0_0
       _a							 =					target_max_size / current_max_size
       _a							 =					image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

       return resized_image



def     __a   (	a       ):





       """simple docstring"""







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



@torch.no_grad()
def     __a   (	a,		a,		a       ):





       """simple docstring"""







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

       # load original state dict
       _a							 =					torch.hub.load_state_dict_from_url(a,		map_location="cpu"       )
       # rename keys
       for src, dest in rename_keys:
              rename_key(a,		a,		a       )
       _a							 =					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
       _a							 =					"model."
       for key in state_dict.copy().keys():
              if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
                     _a							 =					state_dict.pop(a       )
                     _a							 =					val
    # create HuggingFace model and load state dict
       _a							 =					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							 =					1_5
              _a							 =					2
              _a							 =					{0: "table", 1: "table rotated"}
              _a							 =					idalabel
              _a							 =					{v: k for k, v in idalabel.items()}
       else:
              _a							 =					1_2_5
              _a							 =					6
              _a							 =					{
                  0: "table",
                  1: "table column",
                  2: "table row",
                  3: "table column header",
                  4: "table projected row header",
                  5: "table spanning cell",
              }
              _a							 =					idalabel
              _a							 =					{v: k for k, v in idalabel.items()}

       _a							 =					DetrImageProcessor(
           format="coco_detection",		max_size=8_0_0 if "detection" in checkpoint_url else 1_0_0_0       )
       _a							 =					TableTransformerForObjectDetection(a       )
       model.load_state_dict(a       )
       model.eval()

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

       _a							 =					model(a       )

       if "detection" in checkpoint_url:
              _a							 =					(1, 1_5, 3)
              _a							 =					torch.tensor(
                  [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]       )
              _a							 =					torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]       )

       else:
              _a							 =					(1, 1_2_5, 7)
              _a							 =					torch.tensor(
                  [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]       )
              _a							 =					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..."       )
              _a							 =					(
                  "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__":
   __SCREAMING_SNAKE_CASE      		=    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."""
   )
   __SCREAMING_SNAKE_CASE      		=    parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



_lowerCAmelCase  :      int			 ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowerCAmelCase  :      List[str]			 =[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowerCAmelCase  :      int			 ={
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}