

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__			:  str			,  A__			:  Optional[Any]			,  A__			:  Union[str, Any]			,  A__			:  List[str]			,  A__			:  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
    SCREAMING_SNAKE_CASE										=   TapasConfig.from_json_file(A__       )
    # set absolute/relative position embeddings parameter
    SCREAMING_SNAKE_CASE										=   reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        SCREAMING_SNAKE_CASE										=   TapasForQuestionAnswering(config=A__       )
    elif task == "WTQ":
        # run_task_main.py hparams
        SCREAMING_SNAKE_CASE										=   4
        SCREAMING_SNAKE_CASE										=   True
        # hparam_utils.py hparams
        SCREAMING_SNAKE_CASE										=   0.6_6_4_6_9_4
        SCREAMING_SNAKE_CASE										=   0.2_0_7_9_5_1
        SCREAMING_SNAKE_CASE										=   0.1_2_1_1_9_4
        SCREAMING_SNAKE_CASE										=   True
        SCREAMING_SNAKE_CASE										=   True
        SCREAMING_SNAKE_CASE										=   False
        SCREAMING_SNAKE_CASE										=   0.0_3_5_2_5_1_3

        SCREAMING_SNAKE_CASE										=   TapasForQuestionAnswering(config=A__       )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        SCREAMING_SNAKE_CASE										=   4
        SCREAMING_SNAKE_CASE										=   False
        # hparam_utils.py hparams
        SCREAMING_SNAKE_CASE										=   3_6.4_5_1_9
        SCREAMING_SNAKE_CASE										=   0.9_0_3_4_2_1
        SCREAMING_SNAKE_CASE										=   2_2_2.0_8_8
        SCREAMING_SNAKE_CASE										=   True
        SCREAMING_SNAKE_CASE										=   True
        SCREAMING_SNAKE_CASE										=   True
        SCREAMING_SNAKE_CASE										=   0.7_6_3_1_4_1

        SCREAMING_SNAKE_CASE										=   TapasForQuestionAnswering(config=A__       )
    elif task == "TABFACT":
        SCREAMING_SNAKE_CASE										=   TapasForSequenceClassification(config=A__       )
    elif task == "MLM":
        SCREAMING_SNAKE_CASE										=   TapasForMaskedLM(config=A__       )
    elif task == "INTERMEDIATE_PRETRAINING":
        SCREAMING_SNAKE_CASE										=   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}"       )
    SCREAMING_SNAKE_CASE										=   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__":
    __A			:					List[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.'
    )
    __A			:					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,
    )


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__A			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d