
"""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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   # 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
   UpperCAmelCase_					:					str			     =    TapasConfig.from_json_file(__lowerCamelCase  )
   # set absolute/relative position embeddings parameter
   UpperCAmelCase_					:					Union[str, Any]			     =    reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      UpperCAmelCase_					:					str			     =    TapasForQuestionAnswering(config=__lowerCamelCase  )
   elif task == "WTQ":
      # run_task_main.py hparams
      UpperCAmelCase_					:					Union[str, Any]			     =    4
      UpperCAmelCase_					:					int			     =    True
      # hparam_utils.py hparams
      UpperCAmelCase_					:					Optional[Any]			     =    0.66_4694
      UpperCAmelCase_					:					Tuple			     =    0.20_7951
      UpperCAmelCase_					:					Dict			     =    0.12_1194
      UpperCAmelCase_					:					Optional[Any]			     =    True
      UpperCAmelCase_					:					Optional[Any]			     =    True
      UpperCAmelCase_					:					int			     =    False
      UpperCAmelCase_					:					str			     =    0.035_2513

      UpperCAmelCase_					:					List[Any]			     =    TapasForQuestionAnswering(config=__lowerCamelCase  )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      UpperCAmelCase_					:					List[str]			     =    4
      UpperCAmelCase_					:					List[str]			     =    False
      # hparam_utils.py hparams
      UpperCAmelCase_					:					List[Any]			     =    36.4519
      UpperCAmelCase_					:					int			     =    0.90_3421
      UpperCAmelCase_					:					Union[str, Any]			     =    222.088
      UpperCAmelCase_					:					List[str]			     =    True
      UpperCAmelCase_					:					str			     =    True
      UpperCAmelCase_					:					str			     =    True
      UpperCAmelCase_					:					Tuple			     =    0.76_3141

      UpperCAmelCase_					:					Dict			     =    TapasForQuestionAnswering(config=__lowerCamelCase  )
   elif task == "TABFACT":
      UpperCAmelCase_					:					List[Any]			     =    TapasForSequenceClassification(config=__lowerCamelCase  )
   elif task == "MLM":
      UpperCAmelCase_					:					Optional[Any]			     =    TapasForMaskedLM(config=__lowerCamelCase  )
   elif task == "INTERMEDIATE_PRETRAINING":
      UpperCAmelCase_					:					int			     =    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}"""  )
   UpperCAmelCase_					:					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__":
    _a		       =							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		       =							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

import math







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    u
   for i in range(1,		__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    temp * (u - i)
   return temp







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    int(input("enter the numbers of values: "  )  )
   UpperCAmelCase_					:					list[list[float]]			     =    []
   for _ in range(__lowerCamelCase  ):
      y.append([]  )
   for i in range(__lowerCamelCase  ):
      for j in range(__lowerCamelCase  ):
         y[i].append(__lowerCamelCase  )
         UpperCAmelCase_					:					Tuple			     =    0

   print("enter the values of parameters in a list: "  )
   UpperCAmelCase_					:					Union[str, Any]			     =    list(map(__lowerCamelCase,		input().split()  )  )

   print("enter the values of corresponding parameters: "  )
   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    float(input()  )

   UpperCAmelCase_					:					Tuple			     =    int(input("enter the value to interpolate: "  )  )
   UpperCAmelCase_					:					Tuple			     =    (value - x[0]) / (x[1] - x[0])

   # for calculating forward difference table

   for i in range(1,		__lowerCamelCase  ):
      for j in range(n - i  ):
         UpperCAmelCase_					:					Union[str, Any]			     =    y[j + 1][i - 1] - y[j][i - 1]

   UpperCAmelCase_					:					Optional[int]			     =    y[0][0]
   for i in range(1,		__lowerCamelCase  ):
      summ += (ucal(__lowerCamelCase,		__lowerCamelCase  ) * y[0][i]) / math.factorial(__lowerCamelCase  )

   print(f"""the value at {value} is {summ}"""  )


if __name__ == "__main__":
    main()


