





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]			=    2_22.0_88
              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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase				:	str      						=  {
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[Any]      						=  ["""VisionEncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""TFVisionEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  ["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
    import sys

    lowercase				:	Optional[int]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


