


def 			_lowercase       (				UpperCamelCase__       :					List[Any]			):







 if isinstance(__UpperCAmelCase,				__UpperCAmelCase			):
  raise TypeError('\'float\' object cannot be interpreted as an integer'			)
 if isinstance(__UpperCAmelCase,				__UpperCAmelCase			):
  raise TypeError('\'str\' object cannot be interpreted as an integer'			)

 if num == 0:
  return "0b0"

 __A  :		List[str]       =			False

 if num < 0:
  __A  :		Optional[int]       =			True
  __A  :		Any       =			-num

 __A  :		Tuple       =			[]
 while num > 0:
  binary.insert(0,				num % 2			)
  num >>= 1

 if negative:
  return "-0b" + "".join(str(__UpperCAmelCase			) for e in binary			)

 return "0b" + "".join(str(__UpperCAmelCase			) for e in binary			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()










'''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 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[int]			):
 # 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
 __A  :		Optional[int]       =			TapasConfig.from_json_file(UpperCamelCase__			)
 # set absolute/relative position embeddings parameter
 __A  :		List[str]       =			reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __A  :		Optional[int]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WTQ":
  # run_task_main.py hparams
  __A  :		List[str]       =			4
  __A  :		Any       =			True
  # hparam_utils.py hparams
  __A  :		Any       =			0.664694
  __A  :		Dict       =			0.207951
  __A  :		Optional[Any]       =			0.121194
  __A  :		Any       =			True
  __A  :		Any       =			True
  __A  :		int       =			False
  __A  :		Optional[int]       =			0.0352513

  __A  :		List[Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __A  :		Tuple       =			4
  __A  :		Optional[int]       =			False
  # hparam_utils.py hparams
  __A  :		List[str]       =			36.4519
  __A  :		Union[str, Any]       =			0.903421
  __A  :		List[str]       =			222.088
  __A  :		Optional[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Union[str, Any]       =			True
  __A  :		str       =			0.763141

  __A  :		Union[str, Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "TABFACT":
  __A  :		Union[str, Any]       =			TapasForSequenceClassification(config=UpperCamelCase__			)
 elif task == "MLM":
  __A  :		Any       =			TapasForMaskedLM(config=UpperCamelCase__			)
 elif task == "INTERMEDIATE_PRETRAINING":
  __A  :		List[Any]       =			TapasModel(config=UpperCamelCase__			)
 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(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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

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

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      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.'
 )
 UpperCAmelCase_ :     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,
 )






