


'''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 __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		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
 lowerCamelCase_  				=						TapasConfig.from_json_file(UpperCAmelCase_	)
 # 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=UpperCAmelCase_	)
 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=UpperCAmelCase_	)
 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=UpperCAmelCase_	)
 elif task == "TABFACT":
  lowerCamelCase_  				=						TapasForSequenceClassification(config=UpperCAmelCase_	)
 elif task == "MLM":
  lowerCamelCase_  				=						TapasForMaskedLM(config=UpperCAmelCase_	)
 elif task == "INTERMEDIATE_PRETRAINING":
  lowerCamelCase_  				=						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}'''	)
 lowerCamelCase_  				=						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__":
     a_						:       int      			=				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."""
     )
     a_						:       str      			=				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 __future__ import annotations





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 if b == 0:
  return (1, 0)
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       a % b	)
 lowerCamelCase_  				=						a // b
 return (y, x - k * y)





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						na * na
 lowerCamelCase_  				=						ra * x * na + ra * y * na
 return (n % m + m) % m





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       UpperCAmelCase_	)
 if b < 0:
  lowerCamelCase_  				=						(b % n + n) % n
 return b





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_				,lowerCamelCase_  				=						invert_modulo(UpperCAmelCase_      ,       UpperCAmelCase_	), invert_modulo(UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						na * na
 lowerCamelCase_  				=						ra * x * na + ra * y * na
 return (n % m + m) % m


if __name__ == "__main__":
     from doctest import testmod

     testmod(name="""chinese_remainder_theorem""", verbose=True)
     testmod(name="""chinese_remainder_theorem2""", verbose=True)
     testmod(name="""invert_modulo""", verbose=True)
     testmod(name="""extended_euclid""", verbose=True)



