



'''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()
lowerCAmelCase: Optional[Any]							=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase: List[str]							=						[]
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 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: Optional[int]					       =					state_dict.pop(lowercase__    )
  a						: List[str]					       =					val
def 				lowerCamelCase__	(						_A    ):
  a						: Any					       =					OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      a						: Union[str, Any]					       =					key.replace('backbone.0.body'			,      'backbone.conv_encoder.model'    )
      a						: Dict					       =					value
    else:
      a						: Union[str, Any]					       =					value

  return new_state_dict
def 				lowerCamelCase__	(						_A    ):
  a						: List[Any]					       =					""""""

  # 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						: str					       =					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						: List[str]					       =					in_proj_weight[:256, :]
    a						: Tuple					       =					in_proj_bias[:256]
    a						: Tuple					       =					in_proj_weight[256:512, :]
    a						: List[Any]					       =					in_proj_bias[256:512]
    a						: int					       =					in_proj_weight[-256:, :]
    a						: Any					       =					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						: 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
    a						: Union[str, Any]					       =					in_proj_weight[:256, :]
    a						: Dict					       =					in_proj_bias[:256]
    a						: Optional[Any]					       =					in_proj_weight[256:512, :]
    a						: List[Any]					       =					in_proj_bias[256:512]
    a						: Any					       =					in_proj_weight[-256:, :]
    a						: Optional[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						: 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
    a						: Optional[int]					       =					in_proj_weight_cross_attn[:256, :]
    a						: str					       =					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						: Any					       =					in_proj_weight_cross_attn[-256:, :]
    a						: List[Any]					       =					in_proj_bias_cross_attn[-256:]
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: Tuple					       =					image.size
  a						: Any					       =					max(lowercase__			,      lowercase__    )
  a						: Optional[Any]					       =					800 if """detection""" in checkpoint_url else 1000
  a						: int					       =					target_max_size / current_max_size
  a						: Optional[Any]					       =					image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

  return resized_image
def 				lowerCamelCase__	(						_A    ):
  a						: List[str]					       =					F.to_tensor(lowercase__    )
  a						: List[str]					       =					F.normalize(lowercase__			,      mean=[0.485, 0.456, 0.406]			,      std=[0.229, 0.224, 0.225]    )
  return image
@torch.no_grad()
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  logger.info('Converting model...'    )

  # load original state dict
  a						: int					       =					torch.hub.load_state_dict_from_url(lowercase__			,      map_location='cpu'    )
  # rename keys
  for src, dest in rename_keys:
    rename_key(lowercase__			,      lowercase__			,      lowercase__    )
  a						: Tuple					       =					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
  a						: Optional[Any]					       =					"""model."""
  for key in state_dict.copy().keys():
    if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
      a						: Union[str, Any]					       =					state_dict.pop(lowercase__    )
      a						: str					       =					val
    # create HuggingFace model and load state dict
  a						: List[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:
    a						: str					       =					15
    a						: Any					       =					2
    a						: Union[str, Any]					       =					{0: """table""", 1: """table rotated"""}
    a						: List[Any]					       =					idalabel
    a						: Dict					       =					{v: k for k, v in idalabel.items()}
  else:
    a						: int					       =					125
    a						: Optional[Any]					       =					6
    a						: Optional[int]					       =					{
        0: """table""",
        1: """table column""",
        2: """table row""",
        3: """table column header""",
        4: """table projected row header""",
        5: """table spanning cell""",
    }
    a						: Optional[int]					       =					idalabel
    a						: List[Any]					       =					{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						: Any					       =					TableTransformerForObjectDetection(lowercase__    )
  model.load_state_dict(lowercase__    )
  model.eval()

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

  a						: List[str]					       =					model(lowercase__    )

  if "detection" in checkpoint_url:
    a						: List[str]					       =					(1, 15, 3)
    a						: Optional[Any]					       =					torch.tensor(
        [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]    )
    a						: Union[str, Any]					       =					torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]    )

  else:
    a						: List[Any]					       =					(1, 125, 7)
    a						: Dict					       =					torch.tensor(
        [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]    )
    a						: Tuple					       =					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]			,      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...'    )
    a						: Dict					       =					(
        """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__":
     lowerCAmelCase: List[str]							=						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.'
     )
     lowerCAmelCase: Tuple							=						parser.parse_args()
     convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''






from math import factorial, pi
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'    )

  a						: Dict					       =					float(_A    )
  a						: List[Any]					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1    ) for r in range(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'    )

  a						: int					       =					float(_A    )
  a						: str					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r    ) for r in range(_A    )    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))