

"""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		     =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCamelCase		     =    []
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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Dict:
 lowerCAmelCase__   :				List[str] 			=				state_dict.pop(lowercase__   )
 lowerCAmelCase__   :				int 			=				val



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				str 			=				OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   lowerCAmelCase__   :				List[Any] 			=				key.replace("backbone.0.body"					,					"backbone.conv_encoder.model"   )
   lowerCAmelCase__   :				List[Any] 			=				value
  else:
   lowerCAmelCase__   :				Optional[int] 			=				value

 return new_state_dict



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				str 			=				""

 # 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__   :				Optional[int] 			=				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
  lowerCAmelCase__   :				Tuple 			=				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__   :				Tuple 			=				in_proj_weight[:2_5_6, :]
  lowerCAmelCase__   :				Optional[int] 			=				in_proj_bias[:2_5_6]
  lowerCAmelCase__   :				Dict 			=				in_proj_weight[2_5_6:5_1_2, :]
  lowerCAmelCase__   :				Dict 			=				in_proj_bias[2_5_6:5_1_2]
  lowerCAmelCase__   :				Tuple 			=				in_proj_weight[-2_5_6:, :]
  lowerCAmelCase__   :				Any 			=				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
  lowerCAmelCase__   :				Optional[Any] 			=				state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
  lowerCAmelCase__   :				List[str] 			=				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__   :				Dict 			=				in_proj_weight[:2_5_6, :]
  lowerCAmelCase__   :				Any 			=				in_proj_bias[:2_5_6]
  lowerCAmelCase__   :				int 			=				in_proj_weight[2_5_6:5_1_2, :]
  lowerCAmelCase__   :				Any 			=				in_proj_bias[2_5_6:5_1_2]
  lowerCAmelCase__   :				Tuple 			=				in_proj_weight[-2_5_6:, :]
  lowerCAmelCase__   :				List[Any] 			=				in_proj_bias[-2_5_6:]
  # 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[Any] 			=				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__   :				Union[str, Any] 			=				in_proj_weight_cross_attn[:2_5_6, :]
  lowerCAmelCase__   :				Optional[Any] 			=				in_proj_bias_cross_attn[:2_5_6]
  lowerCAmelCase__   :				Dict 			=				in_proj_weight_cross_attn[2_5_6:5_1_2, :]
  lowerCAmelCase__   :				Optional[Any] 			=				in_proj_bias_cross_attn[2_5_6:5_1_2]
  lowerCAmelCase__   :				List[Any] 			=				in_proj_weight_cross_attn[-2_5_6:, :]
  lowerCAmelCase__   :				Tuple 			=				in_proj_bias_cross_attn[-2_5_6:]



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				image.size
 lowerCAmelCase__   :				Optional[Any] 			=				max(lowercase__					,					lowercase__   )
 lowerCAmelCase__   :				List[str] 			=				8_0_0 if "detection" in checkpoint_url else 1_0_0_0
 lowerCAmelCase__   :				List[str] 			=				target_max_size / current_max_size
 lowerCAmelCase__   :				str 			=				image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

 return resized_image



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 lowerCAmelCase__   :				Union[str, Any] 			=				F.to_tensor(lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				F.normalize(lowercase__					,					mean=[0.485, 0.456, 0.406]					,					std=[0.229, 0.224, 0.225]   )
 return image



@torch.no_grad()
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Union[str, Any]:
 logger.info("Converting model..."   )

 # load original state dict
 lowerCAmelCase__   :				Optional[Any] 			=				torch.hub.load_state_dict_from_url(lowercase__					,					map_location="cpu"   )
 # rename keys
 for src, dest in rename_keys:
  rename_key(lowercase__					,					lowercase__					,					lowercase__   )
 lowerCAmelCase__   :				Dict 			=				rename_backbone_keys(lowercase__   )
 # query, key and value matrices need special treatment
 read_in_q_k_v(lowercase__   )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 lowerCAmelCase__   :				Optional[int] 			=				"model."
 for key in state_dict.copy().keys():
  if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
   lowerCAmelCase__   :				Dict 			=				state_dict.pop(lowercase__   )
   lowerCAmelCase__   :				Union[str, Any] 			=				val
    # create HuggingFace model and load state dict
 lowerCAmelCase__   :				str 			=				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__   :				Any 			=				1_5
  lowerCAmelCase__   :				Optional[int] 			=				2
  lowerCAmelCase__   :				Union[str, Any] 			=				{0: "table", 1: "table rotated"}
  lowerCAmelCase__   :				Any 			=				idalabel
  lowerCAmelCase__   :				Tuple 			=				{v: k for k, v in idalabel.items()}
 else:
  lowerCAmelCase__   :				str 			=				1_2_5
  lowerCAmelCase__   :				Any 			=				6
  lowerCAmelCase__   :				Optional[int] 			=				{
      0: "table",
      1: "table column",
      2: "table row",
      3: "table column header",
      4: "table projected row header",
      5: "table spanning cell",
  }
  lowerCAmelCase__   :				str 			=				idalabel
  lowerCAmelCase__   :				Union[str, Any] 			=				{v: k for k, v in idalabel.items()}

 lowerCAmelCase__   :				Optional[int] 			=				DetrImageProcessor(
     format="coco_detection"					,					max_size=8_0_0 if "detection" in checkpoint_url else 1_0_0_0   )
 lowerCAmelCase__   :				Optional[int] 			=				TableTransformerForObjectDetection(lowercase__   )
 model.load_state_dict(lowercase__   )
 model.eval()

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

 lowerCAmelCase__   :				Any 			=				model(lowercase__   )

 if "detection" in checkpoint_url:
  lowerCAmelCase__   :				Union[str, Any] 			=				(1, 1_5, 3)
  lowerCAmelCase__   :				Optional[int] 			=				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]]   )
  lowerCAmelCase__   :				Optional[int] 			=				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:
  lowerCAmelCase__   :				int 			=				(1, 1_2_5, 7)
  lowerCAmelCase__   :				List[str] 			=				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]]   )
  lowerCAmelCase__   :				str 			=				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]					,					lowercase__					,					atol=1E-4   )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,					lowercase__					,					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(lowercase__   ).mkdir(exist_ok=lowercase__   )
  model.save_pretrained(lowercase__   )
  image_processor.save_pretrained(lowercase__   )

 if push_to_hub:
  # Push model to HF hub
  logger.info("Pushing model to the hub..."   )
  lowerCAmelCase__   :				Optional[Any] 			=				(
      "microsoft/table-transformer-detection"
      if "detection" in checkpoint_url
      else "microsoft/table-transformer-structure-recognition"
  )
  model.push_to_hub(lowercase__   )
  image_processor.push_to_hub(lowercase__   )


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









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						list:
 lowerCAmelCase__   :				List[str] 			=				len(lowercase__   )
 lowerCAmelCase__   :				Dict 			=				[]
 for i in range(len(lowercase__   ) - pat_len + 1   ):
  lowerCAmelCase__   :				Union[str, Any] 			=				True
  for j in range(lowercase__   ):
   if s[i + j] != pattern[j]:
    lowerCAmelCase__   :				int 			=				False
    break
  if match_found:
   position.append(lowercase__   )
 return position


if __name__ == "__main__":
    assert naive_pattern_search("""ABCDEFG""", """DE""") == [3]
    print(naive_pattern_search("""ABAAABCDBBABCDDEBCABC""", """ABC"""))






