





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


_snake_case  :   Tuple						   =  logging.get_logger(__name__)

_snake_case  :   Union[str, Any]						   =  {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""table-transformer"""
  a_      =			["""past_key_values"""]
  a_      =			{
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }






  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int]=True ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Dict=3 ,  lowerCAmelCase_			:		Optional[int]=1_0_0 ,  lowerCAmelCase_			:		List[Any]=6 ,  lowerCAmelCase_			:		List[Any]=2_0_4_8 ,  lowerCAmelCase_			:		Optional[Any]=8 ,  lowerCAmelCase_			:		Tuple=6 ,  lowerCAmelCase_			:		Union[str, Any]=2_0_4_8 ,  lowerCAmelCase_			:		Optional[Any]=8 ,  lowerCAmelCase_			:		List[Any]=0.0 ,  lowerCAmelCase_			:		Optional[Any]=0.0 ,  lowerCAmelCase_			:		int=True ,  lowerCAmelCase_			:		List[Any]="relu" ,  lowerCAmelCase_			:		str=2_5_6 ,  lowerCAmelCase_			:		Dict=0.1 ,  lowerCAmelCase_			:		Optional[int]=0.0 ,  lowerCAmelCase_			:		List[str]=0.0 ,  lowerCAmelCase_			:		Tuple=0.02 ,  lowerCAmelCase_			:		Union[str, Any]=1.0 ,  lowerCAmelCase_			:		List[Any]=False ,  lowerCAmelCase_			:		Optional[Any]="sine" ,  lowerCAmelCase_			:		Dict="resnet50" ,  lowerCAmelCase_			:		Optional[Any]=True ,  lowerCAmelCase_			:		Optional[int]=False ,  lowerCAmelCase_			:		Union[str, Any]=1 ,  lowerCAmelCase_			:		Any=5 ,  lowerCAmelCase_			:		str=2 ,  lowerCAmelCase_			:		Optional[int]=1 ,  lowerCAmelCase_			:		Dict=1 ,  lowerCAmelCase_			:		Any=5 ,  lowerCAmelCase_			:		Any=2 ,  lowerCAmelCase_			:		str=0.1 ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						Tuple:
     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.'					)
           __lowerCAmelCase							     =     CONFIG_MAPPING['resnet'](out_features=['stage4']					)
        elif isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
           __lowerCAmelCase							     =     backbone_config.get('model_type'					)
           __lowerCAmelCase							     =     CONFIG_MAPPING[backbone_model_type]
           __lowerCAmelCase							     =     config_class.from_dict(lowerCAmelCase_					)
        # set timm attributes to None
        __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     None, None, None

     __lowerCAmelCase							     =     use_timm_backbone
     __lowerCAmelCase							     =     backbone_config
     __lowerCAmelCase							     =     num_channels
     __lowerCAmelCase							     =     num_queries
     __lowerCAmelCase							     =     d_model
     __lowerCAmelCase							     =     encoder_ffn_dim
     __lowerCAmelCase							     =     encoder_layers
     __lowerCAmelCase							     =     encoder_attention_heads
     __lowerCAmelCase							     =     decoder_ffn_dim
     __lowerCAmelCase							     =     decoder_layers
     __lowerCAmelCase							     =     decoder_attention_heads
     __lowerCAmelCase							     =     dropout
     __lowerCAmelCase							     =     attention_dropout
     __lowerCAmelCase							     =     activation_dropout
     __lowerCAmelCase							     =     activation_function
     __lowerCAmelCase							     =     init_std
     __lowerCAmelCase							     =     init_xavier_std
     __lowerCAmelCase							     =     encoder_layerdrop
     __lowerCAmelCase							     =     decoder_layerdrop
     __lowerCAmelCase							     =     encoder_layers
     __lowerCAmelCase							     =     auxiliary_loss
     __lowerCAmelCase							     =     position_embedding_type
     __lowerCAmelCase							     =     backbone
     __lowerCAmelCase							     =     use_pretrained_backbone
     __lowerCAmelCase							     =     dilation
     # Hungarian matcher
     __lowerCAmelCase							     =     class_cost
     __lowerCAmelCase							     =     bbox_cost
     __lowerCAmelCase							     =     giou_cost
     # Loss coefficients
     __lowerCAmelCase							     =     mask_loss_coefficient
     __lowerCAmelCase							     =     dice_loss_coefficient
     __lowerCAmelCase							     =     bbox_loss_coefficient
     __lowerCAmelCase							     =     giou_loss_coefficient
     __lowerCAmelCase							     =     eos_coefficient
     super().__init__(is_encoder_decoder=lowerCAmelCase_ ,  **lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		str					)							->						int:
     return self.encoder_attention_heads






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						int:
     return self.d_model





class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			version.parse("""1.11"""   )






  @property
  def     lowercase		(    self			:		Tuple					)							->						Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ('pixel_mask', {0: 'batch'}),
         ]					)






  @property
  def     lowercase		(    self			:		str					)							->						float:
     return 1e-5






  @property
  def     lowercase		(    self			:		str					)							->						int:
     return 1_2








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # Initialise PyTorch model.
   # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
   # TapasConfig to False.

   # initialize configuration from json file
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WTQ":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     True
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     0.66_4694
      __lowerCAmelCase							     =     0.20_7951
      __lowerCAmelCase							     =     0.12_1194
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     False
      __lowerCAmelCase							     =     0.035_2513

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     False
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     36.4519
      __lowerCAmelCase							     =     0.90_3421
      __lowerCAmelCase							     =     222.088
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     0.76_3141

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     TapasModel(config=lowerCAmelCase_     )
   else:
      raise ValueError(F"""Task {task} not supported."""     )

   print(F"""Building PyTorch model from configuration: {config}"""     )
   # Load weights from tf checkpoint
   load_tf_weights_in_tapas(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

   # Save pytorch-model (weights and configuration)
   print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
   model.save_pretrained(lowerCAmelCase_     )

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
   __lowerCAmelCase							     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',     model_max_length=512     )
   tokenizer.save_pretrained(lowerCAmelCase_     )

   print('Used relative position embeddings:',     model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
     )
     parser.add_argument(
         '--reset_position_index_per_cell',
         default=False,
         action='store_true',
         help='Whether to use relative position embeddings or not. Defaults to True.',
     )
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--tapas_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained TAPAS model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     _snake_case  :   Optional[Any]						   =  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.task,
         args.reset_position_index_per_cell,
         args.tf_checkpoint_path,
         args.tapas_config_file,
         args.pytorch_dump_path,
     )

