
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()
lowercase__          =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__          =[]
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					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    List[str]  ):






 """simple docstring"""




 __a			:  Tuple		    =  state_dict.pop(lowerCAmelCase__  )
 __a			:  Optional[Any]		    =  val




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ):






 """simple docstring"""




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

 return new_state_dict




def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ):






 """simple docstring"""




 __a			:  Dict		    =  ''''''

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    List[Any]  ):






 """simple docstring"""




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

 return resized_image




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ):






 """simple docstring"""




 __a			:  Any		    =  F.to_tensor(lowerCAmelCase__  )
 __a			:  str		    =  F.normalize(lowerCAmelCase__  ,       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					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    List[Any]  ):






 """simple docstring"""




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

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

 __a			:  Tuple		    =  DetrImageProcessor(
     format='''coco_detection'''  ,       max_size=8_0_0 if '''detection''' in checkpoint_url else 1_0_0_0  )
 __a			:  Optional[Any]		    =  TableTransformerForObjectDetection(lowerCAmelCase__  )
 model.load_state_dict(lowerCAmelCase__  )
 model.eval()

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

 __a			:  Optional[Any]		    =  model(lowerCAmelCase__  )

 if "detection" in checkpoint_url:
  __a			:  Optional[Any]		    =  (1, 1_5, 3)
  __a			:  Optional[int]		    =  torch.tensor(
      [[-6.78_97, -1_6.9_9_8_5, 6.79_37], [-8.01_86, -2_2.2_1_9_2, 6.96_77], [-7.31_17, -2_1.0_7_0_8, 7.40_55]]  )
  __a			:  List[Any]		    =  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:
  __a			:  Union[str, Any]		    =  (1, 1_2_5, 7)
  __a			:  Tuple		    =  torch.tensor(
      [[-1_8.1_4_3_0, -8.32_14, 4.82_74], [-1_8.4_6_8_5, -7.13_61, -4.26_67], [-2_6.3_6_9_3, -9.34_29, -4.99_62]]  )
  __a			:  int		    =  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]  ,       lowerCAmelCase__  ,       atol=1e-4  )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,       lowerCAmelCase__  ,       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(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
  model.save_pretrained(lowerCAmelCase__  )
  image_processor.save_pretrained(lowerCAmelCase__  )

 if push_to_hub:
  # Push model to HF hub
  logger.info('''Pushing model to the hub...'''  )
  __a			:  Tuple		    =  (
      '''microsoft/table-transformer-detection'''
      if '''detection''' in checkpoint_url
      else '''microsoft/table-transformer-structure-recognition'''
  )
  model.push_to_hub(lowerCAmelCase__  )
  image_processor.push_to_hub(lowerCAmelCase__  )


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


from math import pi, sqrt




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):
 if num <= 0:
  raise ValueError('''math domain error'''  )
 if num > 1_71.5:
  raise OverflowError('''math range error'''  )
 elif num - int(lowerCAmelCase__  ) not in (0, 0.5):
  raise NotImplementedError('''num must be an integer or a half-integer'''  )
 elif num == 0.5:
  return sqrt(lowerCAmelCase__  )
 else:
  return 1.0 if num == 1 else (num - 1) * gamma(num - 1  )




def        __UpperCamelCase					(    ):
 assert gamma(0.5  ) == sqrt(lowerCAmelCase__  )
 assert gamma(1  ) == 1.0
 assert gamma(2  ) == 1.0


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      lowercase__          =1.0
      while num:
            lowercase__          =float(input('Gamma of: '))
            print(F"""gamma({num}) = {gamma(num)}""")
            print('\nEnter 0 to exit...')
