





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_     (		A__		,				A__		,				A__		,				A__		,				A__    )  ->		Optional[Any]:
       # 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__						:     List[Any]			=    TapasConfig.from_json_file(A__    )
       # set absolute/relative position embeddings parameter
       a__						:     Tuple			=    reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              a__						:     int			=    TapasForQuestionAnswering(config=A__    )
       elif task == "WTQ":
              # run_task_main.py hparams
              a__						:     Union[str, Any]			=    4
              a__						:     str			=    True
              # hparam_utils.py hparams
              a__						:     Optional[int]			=    0.66_46_94
              a__						:     Optional[int]			=    0.20_79_51
              a__						:     Optional[int]			=    0.12_11_94
              a__						:     Optional[Any]			=    True
              a__						:     Union[str, Any]			=    True
              a__						:     str			=    False
              a__						:     Any			=    0.0_35_25_13

              a__						:     List[str]			=    TapasForQuestionAnswering(config=A__    )
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              a__						:     str			=    4
              a__						:     List[Any]			=    False
              # hparam_utils.py hparams
              a__						:     List[str]			=    36.45_19
              a__						:     List[str]			=    0.90_34_21
              a__						:     Optional[Any]			=    222.088
              a__						:     Union[str, Any]			=    True
              a__						:     Optional[Any]			=    True
              a__						:     Optional[Any]			=    True
              a__						:     Any			=    0.76_31_41

              a__						:     List[str]			=    TapasForQuestionAnswering(config=A__    )
       elif task == "TABFACT":
              a__						:     Optional[Any]			=    TapasForSequenceClassification(config=A__    )
       elif task == "MLM":
              a__						:     List[Any]			=    TapasForMaskedLM(config=A__    )
       elif task == "INTERMEDIATE_PRETRAINING":
              a__						:     str			=    TapasModel(config=A__    )
       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(A__		,				A__		,				A__    )

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

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

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


if __name__ == "__main__":
    lowercase				:	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."""
    )
    lowercase				:	List[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,
    )









from __future__ import annotations

from collections.abc import Callable



def A_     (		A__		,				A__		,				A__		,				A__ = 100		,				)  ->		float:
       a__						:     Dict			=    x_start
       a__						:     Any			=    fnc(A__    )
       a__						:     Optional[int]			=    0.0
       for _ in range(A__    ):
              # Approximates small segments of curve as linear and solve
              # for trapezoidal area
              a__						:     Union[str, Any]			=    (x_end - x_start) / steps + xa
              a__						:     str			=    fnc(A__    )
              area += abs(fxa + fxa    ) * (xa - xa) / 2
              # Increment step
              a__						:     Optional[Any]			=    xa
              a__						:     Optional[int]			=    fxa
       return area


if __name__ == "__main__":



    def A_     (		A__    )  ->		List[str]:
           return x**3 + x**2

    print("""f(x) = x^3 + x^2""")
    print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
    lowercase				:	Union[str, Any]      						=  1_0
    while i <= 1_0_0_0_0_0:
        print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
        i *= 1_0


