

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      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  # 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					      =  TapasConfig.from_json_file(__A			)
  # set absolute/relative position embeddings parameter
  UpperCAmelCase					      =  reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    UpperCAmelCase					      =  TapasForQuestionAnswering(config=__A			)
  elif task == "WTQ":
    # run_task_main.py hparams
    UpperCAmelCase					      =  4
    UpperCAmelCase					      =  True
    # hparam_utils.py hparams
    UpperCAmelCase					      =  0.664694
    UpperCAmelCase					      =  0.207951
    UpperCAmelCase					      =  0.121194
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  False
    UpperCAmelCase					      =  0.0352513

    UpperCAmelCase					      =  TapasForQuestionAnswering(config=__A			)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    UpperCAmelCase					      =  4
    UpperCAmelCase					      =  False
    # hparam_utils.py hparams
    UpperCAmelCase					      =  36.4519
    UpperCAmelCase					      =  0.903421
    UpperCAmelCase					      =  222.088
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  0.763141

    UpperCAmelCase					      =  TapasForQuestionAnswering(config=__A			)
  elif task == "TABFACT":
    UpperCAmelCase					      =  TapasForSequenceClassification(config=__A			)
  elif task == "MLM":
    UpperCAmelCase					      =  TapasForMaskedLM(config=__A			)
  elif task == "INTERMEDIATE_PRETRAINING":
    UpperCAmelCase					      =  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}"			)
  UpperCAmelCase					      =  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__":
    lowerCAmelCase__       = 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."
    )
    lowerCAmelCase__       = 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 . import (
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    auto,
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    xlm_roberta,
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    xlnet,
    xmod,
    yolos,
    yoso,
)



