


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       :		int         =				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       :		Tuple         =				[]
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     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       state_dict.pop(_a		)
     lowercase_       :List[Any]     =       val




def     UpperCamelCase    (      _a		)					->							Tuple:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               lowercase_       :Any     =       key.replace('''backbone.0.body'''       ,		'''backbone.conv_encoder.model'''		)
               lowercase_       :Dict     =       value
          else:
               lowercase_       :int     =       value

     return new_state_dict




def     UpperCamelCase    (      _a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Tuple     =       ''''''

     # 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)
          lowercase_       :Dict     =       state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
          lowercase_       :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
          lowercase_       :Dict     =       in_proj_weight[:2_5_6, :]
          lowercase_       :List[Any]     =       in_proj_bias[:2_5_6]
          lowercase_       :List[str]     =       in_proj_weight[2_5_6:5_1_2, :]
          lowercase_       :Optional[Any]     =       in_proj_bias[2_5_6:5_1_2]
          lowercase_       :Optional[int]     =       in_proj_weight[-2_5_6:, :]
          lowercase_       :Optional[int]     =       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
          lowercase_       :Optional[int]     =       state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
          lowercase_       :Dict     =       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
          lowercase_       :List[Any]     =       in_proj_weight[:2_5_6, :]
          lowercase_       :Tuple     =       in_proj_bias[:2_5_6]
          lowercase_       :Tuple     =       in_proj_weight[2_5_6:5_1_2, :]
          lowercase_       :Optional[int]     =       in_proj_bias[2_5_6:5_1_2]
          lowercase_       :Dict     =       in_proj_weight[-2_5_6:, :]
          lowercase_       :Optional[int]     =       in_proj_bias[-2_5_6:]
          # read in weights + bias of input projection layer of cross-attention
          lowercase_       :str     =       state_dict.pop(
              f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
          lowercase_       :Union[str, 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
          lowercase_       :List[Any]     =       in_proj_weight_cross_attn[:2_5_6, :]
          lowercase_       :Any     =       in_proj_bias_cross_attn[:2_5_6]
          lowercase_       :int     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
          lowercase_       :Tuple     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
          lowercase_       :List[Any]     =       in_proj_weight_cross_attn[-2_5_6:, :]
          lowercase_       :List[Any]     =       in_proj_bias_cross_attn[-2_5_6:]




def     UpperCamelCase    (      _a       ,		_a		)					->							str:
     '''simple docstring'''





     lowercase_    ,      lowercase_       :Tuple     =       image.size
     lowercase_       :int     =       max(_a       ,		_a		)
     lowercase_       :Optional[Any]     =       8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0
     lowercase_       :List[str]     =       target_max_size / current_max_size
     lowercase_       :int     =       image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

     return resized_image




def     UpperCamelCase    (      _a		)					->							Dict:
     '''simple docstring'''





     lowercase_       :List[str]     =       F.to_tensor(_a		)
     lowercase_       :Any     =       F.normalize(_a       ,		mean=[0.485, 0.456, 0.406]       ,		std=[0.229, 0.224, 0.225]		)
     return image




@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





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

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

     lowercase_       :List[str]     =       DetrImageProcessor(
         format='''coco_detection'''       ,		max_size=8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0		)
     lowercase_       :int     =       TableTransformerForObjectDetection(_a		)
     model.load_state_dict(_a		)
     model.eval()

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

     lowercase_       :Union[str, Any]     =       model(_a		)

     if "detection" in checkpoint_url:
          lowercase_       :Union[str, Any]     =       (1, 1_5, 3)
          lowercase_       :str     =       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]]		)
          lowercase_       :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:
          lowercase_       :Optional[int]     =       (1, 1_2_5, 7)
          lowercase_       :Tuple     =       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]]		)
          lowercase_       :Tuple     =       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]       ,		_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...'''		)
          lowercase_       :Any     =       (
              '''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       :		Any         =				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       :		Optional[Any]         =				parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     def count_of_possible_combinations(_a		) -> int:
          if target < 0:
               return 0
          if target == 0:
               return 1
          return sum(count_of_possible_combinations(target - item		) for item in array		)

     return count_of_possible_combinations(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     def count_of_possible_combinations_with_dp_array(
         _a       ,		_a		) -> int:
          if target < 0:
               return 0
          if target == 0:
               return 1
          if dp_array[target] != -1:
               return dp_array[target]
          lowercase_       :Optional[int]     =       sum(
              count_of_possible_combinations_with_dp_array(target - item       ,		_a		)
              for item in array		)
          lowercase_       :List[Any]     =       answer
          return answer

     lowercase_       :Dict     =       [-1] * (target + 1)
     return count_of_possible_combinations_with_dp_array(_a       ,		_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       [0] * (target + 1)
     lowercase_       :int     =       1

     for i in range(1       ,		target + 1		):
          for j in range(_a		):
               if i - array[j] >= 0:
                    dp_array[i] += dp_array[i - array[j]]

     return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   SCREAMING_SNAKE_CASE       :		Optional[Any]         =				3
   SCREAMING_SNAKE_CASE       :		Tuple         =				5
   SCREAMING_SNAKE_CASE       :		List[Any]         =				[1, 2, 5]
   print(combination_sum_iv(n, array, target))

