


'''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__:       Dict          =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A__:       List[Any]          =			[]
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_   (    _UpperCAmelCase      :      Any       ,_UpperCAmelCase      :      int       ,_UpperCAmelCase      :      List[Any]    )    ->     Union[str, Any]:
       _a       :					Optional[Any]	   =state_dict.pop(_UpperCamelCase    )
       _a       :					Dict	   =val


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      List[Any]    )    ->     Dict:
       _a       :					int	   =OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     _a       :					int	   =key.replace("""backbone.0.body"""       ,"""backbone.conv_encoder.model"""    )
                     _a       :					List[Any]	   =value
              else:
                     _a       :					List[str]	   =value

       return new_state_dict


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      List[str]    )    ->     List[str]:
       _a       :					Union[str, 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       :					List[str]	   =state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"    )
              _a       :					Union[str, 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       :					Optional[int]	   =in_proj_weight[:256, :]
              _a       :					List[Any]	   =in_proj_bias[:256]
              _a       :					Dict	   =in_proj_weight[256:512, :]
              _a       :					Dict	   =in_proj_bias[256:512]
              _a       :					Tuple	   =in_proj_weight[-256:, :]
              _a       :					List[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       :					Union[str, 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[:256, :]
              _a       :					str	   =in_proj_bias[:256]
              _a       :					Optional[int]	   =in_proj_weight[256:512, :]
              _a       :					Optional[Any]	   =in_proj_bias[256:512]
              _a       :					str	   =in_proj_weight[-256:, :]
              _a       :					Optional[int]	   =in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              _a       :					str	   =state_dict.pop(
                  F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"    )
              _a       :					Tuple	   =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       :					Any	   =in_proj_weight_cross_attn[:256, :]
              _a       :					Dict	   =in_proj_bias_cross_attn[:256]
              _a       :					Optional[Any]	   =in_proj_weight_cross_attn[256:512, :]
              _a       :					Any	   =in_proj_bias_cross_attn[256:512]
              _a       :					List[str]	   =in_proj_weight_cross_attn[-256:, :]
              _a       :					Dict	   =in_proj_bias_cross_attn[-256:]


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      Any       ,_UpperCAmelCase      :      List[str]    )    ->     int:
       _a   ,       _a       :					List[Any]	   =image.size
       _a       :					Optional[Any]	   =max(_UpperCamelCase       ,_UpperCamelCase    )
       _a       :					List[Any]	   =800 if """detection""" in checkpoint_url else 1000
       _a       :					Union[str, Any]	   =target_max_size / current_max_size
       _a       :					Optional[int]	   =image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

       return resized_image


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      List[str]    )    ->     Tuple:
       _a       :					List[Any]	   =F.to_tensor(_UpperCamelCase    )
       _a       :					List[str]	   =F.normalize(_UpperCamelCase       ,mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]       ,std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    )
       return image


@torch.no_grad()
def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      List[str]       ,_UpperCAmelCase      :      Dict       ,_UpperCAmelCase      :      List[Any]    )    ->     List[str]:
       logger.info("""Converting model..."""    )

       # load original state dict
       _a       :					int	   =torch.hub.load_state_dict_from_url(_UpperCamelCase       ,map_location="""cpu"""    )
       # rename keys
       for src, dest in rename_keys:
              rename_key(_UpperCamelCase       ,_UpperCamelCase       ,_UpperCamelCase    )
       _a       :					Optional[Any]	   =rename_backbone_keys(_UpperCamelCase    )
       # query, key and value matrices need special treatment
       read_in_q_k_v(_UpperCamelCase    )
       # 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(_UpperCamelCase    )
                     _a       :					Dict	   =val
    # create HuggingFace model and load state dict
       _a       :					Tuple	   =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       :					int	   =15
              _a       :					Dict	   =2
              _a       :					Union[str, Any]	   ={0: """table""", 1: """table rotated"""}
              _a       :					Union[str, Any]	   =idalabel
              _a       :					Optional[Any]	   ={v: k for k, v in idalabel.items()}
       else:
              _a       :					Dict	   =125
              _a       :					List[Any]	   =6
              _a       :					Union[str, Any]	   ={
                  0: """table""",
                  1: """table column""",
                  2: """table row""",
                  3: """table column header""",
                  4: """table projected row header""",
                  5: """table spanning cell""",
              }
              _a       :					Dict	   =idalabel
              _a       :					Any	   ={v: k for k, v in idalabel.items()}

       _a       :					List[str]	   =DetrImageProcessor(
           format="""coco_detection"""       ,max_size=800 if """detection""" in checkpoint_url else 1000    )
       _a       :					Dict	   =TableTransformerForObjectDetection(_UpperCamelCase    )
       model.load_state_dict(_UpperCamelCase    )
       model.eval()

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

       _a       :					List[str]	   =model(_UpperCamelCase    )

       if "detection" in checkpoint_url:
              _a       :					Optional[int]	   =(1, 15, 3)
              _a       :					List[Any]	   =torch.tensor(
                  [[-6.7_8_9_7, -16.9985, 6.7_9_3_7], [-8.0_1_8_6, -22.2192, 6.9_6_7_7], [-7.3_1_1_7, -21.0708, 7.4_0_5_5]]    )
              _a       :					List[str]	   =torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]    )

       else:
              _a       :					Optional[int]	   =(1, 125, 7)
              _a       :					Any	   =torch.tensor(
                  [[-18.1430, -8.3_2_1_4, 4.8_2_7_4], [-18.4685, -7.1_3_6_1, -4.2_6_6_7], [-26.3693, -9.3_4_2_9, -4.9_9_6_2]]    )
              _a       :					Union[str, Any]	   =torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]    )

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :3, :3]       ,_UpperCamelCase       ,atol=1e-4    )
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,_UpperCamelCase       ,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(_UpperCamelCase    ).mkdir(exist_ok=_UpperCamelCase    )
              model.save_pretrained(_UpperCamelCase    )
              image_processor.save_pretrained(_UpperCamelCase    )

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


if __name__ == "__main__":
   A__:       Optional[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.'''
   )
   A__:       int          =			parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










__magic_name__   :  str        =							8.314_4598
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			float:
      """simple docstring"""

      if temperature < 0:
            raise Exception('Temperature cannot be less than 0 K')
      if molar_mass <= 0:
            raise Exception('Molar mass cannot be less than or equal to 0 kg/mol')
      else:
            return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
       import doctest

       # run doctest
       doctest.testmod()

       # example
       __magic_name__   :  List[Any]        =							300
       __magic_name__   :  Union[str, Any]        =							28
       __magic_name__   :  str        =							rms_speed_of_molecule(temperature, molar_mass)
       print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')



