





"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase__				:Any				   =       logging.get_logger(__name__)

lowercase__				:Optional[int]				   =       {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}




class 							snake_case							(			__lowerCamelCase ):

      '''simple docstring'''



      _A     :       Tuple         =       'table-transformer'
      _A     :       int         =       ['past_key_values']
      _A     :       Optional[int]         =       {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(							self       :      Optional[int]						,				__lowercase       :      Optional[Any]=True						,				__lowercase       :      List[Any]=None						,				__lowercase       :      List[Any]=3						,				__lowercase       :      Union[str, Any]=100						,				__lowercase       :      Union[str, Any]=6						,				__lowercase       :      int=2_048						,				__lowercase       :      Union[str, Any]=8						,				__lowercase       :      Union[str, Any]=6						,				__lowercase       :      Optional[int]=2_048						,				__lowercase       :      List[Any]=8						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      int=0.0						,				__lowercase       :      Optional[int]=True						,				__lowercase       :      List[str]="relu"						,				__lowercase       :      int=256						,				__lowercase       :      Tuple=0.1						,				__lowercase       :      str=0.0						,				__lowercase       :      List[Any]=0.0						,				__lowercase       :      List[Any]=0.0_2						,				__lowercase       :      Any=1.0						,				__lowercase       :      str=False						,				__lowercase       :      Any="sine"						,				__lowercase       :      List[str]="resnet50"						,				__lowercase       :      Dict=True						,				__lowercase       :      Union[str, Any]=False						,				__lowercase       :      List[Any]=1						,				__lowercase       :      str=5						,				__lowercase       :      List[Any]=2						,				__lowercase       :      Any=1						,				__lowercase       :      str=1						,				__lowercase       :      int=5						,				__lowercase       :      Union[str, Any]=2						,				__lowercase       :      List[str]=0.1						,				**__lowercase       :      List[str]						,				):


        '''simple docstring'''




        if backbone_config is not None and use_timm_backbone:
          raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )

        if not use_timm_backbone:
          if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
            __UpperCAmelCase    :						Dict       =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
          elif isinstance(SCREAMING_SNAKE_CASE_						,				SCREAMING_SNAKE_CASE_ ):
            __UpperCAmelCase    :						Any       =						backbone_config.get('''model_type''' )
            __UpperCAmelCase    :						Optional[Any]       =						CONFIG_MAPPING[backbone_model_type]
            __UpperCAmelCase    :						Optional[Any]       =						config_class.from_dict(SCREAMING_SNAKE_CASE_ )
          # set timm attributes to None
          __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Union[str, Any]       =						None, None, None

        __UpperCAmelCase    :						Dict       =						use_timm_backbone
        __UpperCAmelCase    :						Dict       =						backbone_config
        __UpperCAmelCase    :						str       =						num_channels
        __UpperCAmelCase    :						List[Any]       =						num_queries
        __UpperCAmelCase    :						List[str]       =						d_model
        __UpperCAmelCase    :						List[Any]       =						encoder_ffn_dim
        __UpperCAmelCase    :						Any       =						encoder_layers
        __UpperCAmelCase    :						Union[str, Any]       =						encoder_attention_heads
        __UpperCAmelCase    :						List[Any]       =						decoder_ffn_dim
        __UpperCAmelCase    :						Optional[Any]       =						decoder_layers
        __UpperCAmelCase    :						Dict       =						decoder_attention_heads
        __UpperCAmelCase    :						str       =						dropout
        __UpperCAmelCase    :						Optional[Any]       =						attention_dropout
        __UpperCAmelCase    :						Dict       =						activation_dropout
        __UpperCAmelCase    :						str       =						activation_function
        __UpperCAmelCase    :						Tuple       =						init_std
        __UpperCAmelCase    :						Tuple       =						init_xavier_std
        __UpperCAmelCase    :						Optional[int]       =						encoder_layerdrop
        __UpperCAmelCase    :						Dict       =						decoder_layerdrop
        __UpperCAmelCase    :						Tuple       =						encoder_layers
        __UpperCAmelCase    :						Union[str, Any]       =						auxiliary_loss
        __UpperCAmelCase    :						Optional[int]       =						position_embedding_type
        __UpperCAmelCase    :						List[str]       =						backbone
        __UpperCAmelCase    :						Optional[int]       =						use_pretrained_backbone
        __UpperCAmelCase    :						Union[str, Any]       =						dilation
        # Hungarian matcher
        __UpperCAmelCase    :						Dict       =						class_cost
        __UpperCAmelCase    :						str       =						bbox_cost
        __UpperCAmelCase    :						Tuple       =						giou_cost
        # Loss coefficients
        __UpperCAmelCase    :						int       =						mask_loss_coefficient
        __UpperCAmelCase    :						Tuple       =						dice_loss_coefficient
        __UpperCAmelCase    :						List[str]       =						bbox_loss_coefficient
        __UpperCAmelCase    :						Dict       =						giou_loss_coefficient
        __UpperCAmelCase    :						Optional[int]       =						eos_coefficient
        super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_						,				**SCREAMING_SNAKE_CASE_ )

      @property
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        return self.encoder_attention_heads







      @property
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        return self.d_model





class 							snake_case							(			__lowerCamelCase ):

      '''simple docstring'''



      _A     :       Optional[int]         =       version.parse('1.11' )

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ('''pixel_mask''', {0: '''batch'''}),
            ] )

      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        return 1e-5







      @property
      def       A_					(							self       :      str ):


        '''simple docstring'''




        return 12






"""simple docstring"""





import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						LxmertConfig.from_json_file(UpperCAmelCase_  )
  print(f'''Building PyTorch model from configuration: {config}'''  )
  __UpperCAmelCase    :						Tuple       =						LxmertForPreTraining(UpperCAmelCase_  )

  # Load weights from tf checkpoint
  load_tf_weights_in_lxmert(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''  )
  torch.save(model.state_dict()  ,			UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:Optional[int]				   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase__				:Union[str, Any]				   =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)