


"""simple docstring"""






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        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     TapasConfig.from_json_file(_lowerCamelCase       )
   # set absolute/relative position embeddings parameter
   _lowerCamelCase   :				int							=     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      _lowerCamelCase   :				Tuple							=     TapasForQuestionAnswering(config=_lowerCamelCase       )
   elif task == "WTQ":
      # run_task_main.py hparams
      _lowerCamelCase   :				List[Any]							=     4
      _lowerCamelCase   :				List[str]							=     True
      # hparam_utils.py hparams
      _lowerCamelCase   :				Tuple							=     0.6_6_4_6_9_4
      _lowerCamelCase   :				List[str]							=     0.2_0_7_9_5_1
      _lowerCamelCase   :				Optional[int]							=     0.1_2_1_1_9_4
      _lowerCamelCase   :				int							=     True
      _lowerCamelCase   :				Optional[int]							=     True
      _lowerCamelCase   :				List[str]							=     False
      _lowerCamelCase   :				int							=     0.0_3_5_2_5_1_3

      _lowerCamelCase   :				List[str]							=     TapasForQuestionAnswering(config=_lowerCamelCase       )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      _lowerCamelCase   :				Tuple							=     4
      _lowerCamelCase   :				Union[str, Any]							=     False
      # hparam_utils.py hparams
      _lowerCamelCase   :				Dict							=     3_6.4_5_1_9
      _lowerCamelCase   :				Dict							=     0.9_0_3_4_2_1
      _lowerCamelCase   :				Any							=     2_2_2.0_8_8
      _lowerCamelCase   :				int							=     True
      _lowerCamelCase   :				Optional[int]							=     True
      _lowerCamelCase   :				str							=     True
      _lowerCamelCase   :				Optional[int]							=     0.7_6_3_1_4_1

      _lowerCamelCase   :				Optional[Any]							=     TapasForQuestionAnswering(config=_lowerCamelCase       )
   elif task == "TABFACT":
      _lowerCamelCase   :				Optional[int]							=     TapasForSequenceClassification(config=_lowerCamelCase       )
   elif task == "MLM":
      _lowerCamelCase   :				Optional[int]							=     TapasForMaskedLM(config=_lowerCamelCase       )
   elif task == "INTERMEDIATE_PRETRAINING":
      _lowerCamelCase   :				List[str]							=     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   :				Union[str, Any]							=     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__":
  _lowerCAmelCase							:    str					   =	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.'''
  )
  _lowerCAmelCase							:    int					   =	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,
  )



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :    str											= logging.get_logger(__name__)

__A   :    int											= {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roc_bert'

    def __init__(  self       :    str       ,		lowerCamelCase       :    Optional[Any]=3_05_22       ,		lowerCamelCase       :    List[Any]=7_68       ,		lowerCamelCase       :    int=12       ,		lowerCamelCase       :    str=12       ,		lowerCamelCase       :    Any=30_72       ,		lowerCamelCase       :    List[Any]="gelu"       ,		lowerCamelCase       :    Optional[int]=0.1       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    int=5_12       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    str=1E-12       ,		lowerCamelCase       :    int=True       ,		lowerCamelCase       :    Optional[Any]=0       ,		lowerCamelCase       :    List[Any]="absolute"       ,		lowerCamelCase       :    str=None       ,		lowerCamelCase       :    str=True       ,		lowerCamelCase       :    Tuple=True       ,		lowerCamelCase       :    str=7_68       ,		lowerCamelCase       :    str=9_10       ,		lowerCamelCase       :    Tuple=5_12       ,		lowerCamelCase       :    List[str]=2_48_58       ,		lowerCamelCase       :    Dict=True       ,		**lowerCamelCase       :    Tuple       ,		)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  vocab_size
      lowerCAmelCase_     : Any     =  max_position_embeddings
      lowerCAmelCase_     : Any     =  hidden_size
      lowerCAmelCase_     : int     =  num_hidden_layers
      lowerCAmelCase_     : Tuple     =  num_attention_heads
      lowerCAmelCase_     : List[Any]     =  intermediate_size
      lowerCAmelCase_     : Tuple     =  hidden_act
      lowerCAmelCase_     : List[Any]     =  hidden_dropout_prob
      lowerCAmelCase_     : Dict     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Any     =  initializer_range
      lowerCAmelCase_     : Any     =  type_vocab_size
      lowerCAmelCase_     : Optional[int]     =  layer_norm_eps
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : List[Any]     =  enable_pronunciation
      lowerCAmelCase_     : List[Any]     =  enable_shape
      lowerCAmelCase_     : List[str]     =  pronunciation_embed_dim
      lowerCAmelCase_     : List[str]     =  pronunciation_vocab_size
      lowerCAmelCase_     : Dict     =  shape_embed_dim
      lowerCAmelCase_     : Optional[Any]     =  shape_vocab_size
      lowerCAmelCase_     : List[Any]     =  concat_input
      lowerCAmelCase_     : Any     =  position_embedding_type
      lowerCAmelCase_     : List[str]     =  classifier_dropout
      super().__init__(pad_token_id=lowerCamelCase       ,		**lowerCamelCase						)



