




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

# here we list all keys to be renamed (original name on the left, our name on the right)
A_						=				[]
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__       (snake_case__							:					int		,   snake_case__							:					Tuple		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  state_dict.pop(snake_case__    )
  _snake_case							:		Optional[Any]								=  val


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      _snake_case							:		Dict								=  key.replace("""backbone.0.body"""		,   """backbone.conv_encoder.model"""    )
      _snake_case							:		List[Any]								=  value
    else:
      _snake_case							:		Optional[Any]								=  value

  return new_state_dict


def 					UpperCAmelCase__       (snake_case__							:					Tuple    ):
  """simple docstring"""

  _snake_case							:		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)
    _snake_case							:		Dict								=  state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"    )
    _snake_case							:		Optional[int]								=  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							:		int								=  in_proj_weight[:2_56, :]
    _snake_case							:		Union[str, Any]								=  in_proj_bias[:2_56]
    _snake_case							:		List[str]								=  in_proj_weight[2_56:5_12, :]
    _snake_case							:		Optional[Any]								=  in_proj_bias[2_56:5_12]
    _snake_case							:		int								=  in_proj_weight[-2_56:, :]
    _snake_case							:		Any								=  in_proj_bias[-2_56:]
  # 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							:		int								=  state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"    )
    _snake_case							:		Optional[int]								=  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							:		str								=  in_proj_weight[:2_56, :]
    _snake_case							:		Optional[int]								=  in_proj_bias[:2_56]
    _snake_case							:		Optional[Any]								=  in_proj_weight[2_56:5_12, :]
    _snake_case							:		Optional[int]								=  in_proj_bias[2_56:5_12]
    _snake_case							:		Dict								=  in_proj_weight[-2_56:, :]
    _snake_case							:		List[str]								=  in_proj_bias[-2_56:]
    # read in weights + bias of input projection layer of cross-attention
    _snake_case							:		List[Any]								=  state_dict.pop(
        F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"    )
    _snake_case							:		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
    _snake_case							:		List[Any]								=  in_proj_weight_cross_attn[:2_56, :]
    _snake_case							:		int								=  in_proj_bias_cross_attn[:2_56]
    _snake_case							:		Union[str, Any]								=  in_proj_weight_cross_attn[2_56:5_12, :]
    _snake_case							:		Optional[int]								=  in_proj_bias_cross_attn[2_56:5_12]
    _snake_case							:		str								=  in_proj_weight_cross_attn[-2_56:, :]
    _snake_case							:		Tuple								=  in_proj_bias_cross_attn[-2_56:]


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case		,					_snake_case							:		int								=  image.size
  _snake_case							:		Optional[Any]								=  max(snake_case__		,   snake_case__    )
  _snake_case							:		Dict								=  8_00 if """detection""" in checkpoint_url else 10_00
  _snake_case							:		Dict								=  target_max_size / current_max_size
  _snake_case							:		Any								=  image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

  return resized_image


def 					UpperCAmelCase__       (snake_case__							:					Any    ):
  """simple docstring"""

  _snake_case							:		int								=  F.to_tensor(snake_case__    )
  _snake_case							:		str								=  F.normalize(snake_case__		,   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 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					Optional[int]		,   snake_case__							:					List[str]    ):
  """simple docstring"""

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

  # load original state dict
  _snake_case							:		List[Any]								=  torch.hub.load_state_dict_from_url(snake_case__		,   map_location="""cpu"""    )
  # rename keys
  for src, dest in rename_keys:
    rename_key(snake_case__		,   snake_case__		,   snake_case__    )
  _snake_case							:		Any								=  rename_backbone_keys(snake_case__    )
  # query, key and value matrices need special treatment
  read_in_q_k_v(snake_case__    )
  # 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							:		int								=  """model."""
  for key in state_dict.copy().keys():
    if not key.startswith("""class_labels_classifier"""    ) and not key.startswith("""bbox_predictor"""    ):
      _snake_case							:		List[Any]								=  state_dict.pop(snake_case__    )
      _snake_case							:		Optional[int]								=  val
    # create HuggingFace model and load state dict
  _snake_case							:		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:
    _snake_case							:		Optional[Any]								=  15
    _snake_case							:		Optional[int]								=  2
    _snake_case							:		str								=  {0: """table""", 1: """table rotated"""}
    _snake_case							:		str								=  idalabel
    _snake_case							:		List[Any]								=  {v: k for k, v in idalabel.items()}
  else:
    _snake_case							:		Optional[Any]								=  1_25
    _snake_case							:		Optional[int]								=  6
    _snake_case							:		Tuple								=  {
        0: """table""",
        1: """table column""",
        2: """table row""",
        3: """table column header""",
        4: """table projected row header""",
        5: """table spanning cell""",
    }
    _snake_case							:		Dict								=  idalabel
    _snake_case							:		Any								=  {v: k for k, v in idalabel.items()}

  _snake_case							:		List[Any]								=  DetrImageProcessor(
      format="""coco_detection"""		,   max_size=8_00 if """detection""" in checkpoint_url else 10_00    )
  _snake_case							:		str								=  TableTransformerForObjectDetection(snake_case__    )
  model.load_state_dict(snake_case__    )
  model.eval()

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

  _snake_case							:		Tuple								=  model(snake_case__    )

  if "detection" in checkpoint_url:
    _snake_case							:		List[str]								=  (1, 15, 3)
    _snake_case							:		Union[str, Any]								=  torch.tensor(
        [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]    )
    _snake_case							:		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:
    _snake_case							:		Tuple								=  (1, 1_25, 7)
    _snake_case							:		Dict								=  torch.tensor(
        [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]    )
    _snake_case							:		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]		,   snake_case__		,   atol=1e-4    )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,   snake_case__		,   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(snake_case__    ).mkdir(exist_ok=snake_case__    )
    model.save_pretrained(snake_case__    )
    image_processor.save_pretrained(snake_case__    )

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


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








from __future__ import annotations





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = None    )      ->       list[list[str]]:
	a										=							word_bank or []
	# create a table
	a										=							len(__lowerCamelCase    ) + 1

	a										=							[]
	for _ in range(__lowerCamelCase    ):
		table.append([]    )
	# seed value
	a										=							[[]]  # because empty string has empty combination

	# iterate through the indices
	for i in range(__lowerCamelCase    ):
		# condition
		if table[i] != []:
			for word in word_bank:
				# slice condition
				if target[i : i + len(__lowerCamelCase    )] == word:
					a										=							[
					    [word, *way] for way in table[i]
					]
					# adds the word to every combination the current position holds
					# now,push that combination to the table[i+len(word)]
					table[i + len(__lowerCamelCase    )] += new_combinations

    # combinations are in reverse order so reverse for better output
	for combination in table[len(__lowerCamelCase    )]:
		combination.reverse()

	return table[len(__lowerCamelCase    )]


if __name__ == "__main__":
		print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
		print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
		print(
		    all_construct(
		        "hexagonosaurus",
		        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
		    )
		)

