




'''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 						snake_case_							(_a	:							Dict    ,   _a	:							Optional[int]    ,   _a	:							Dict    ,   _a	:							Optional[Any]    ,   _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
    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			       =		0.66_4694
        UpperCAmelCase			       =		0.20_7951
        UpperCAmelCase			       =		0.12_1194
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		False
        UpperCAmelCase			       =		0.035_2513

        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			       =		0.90_3421
        UpperCAmelCase			       =		222.088
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		True
        UpperCAmelCase			       =		0.76_3141

        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[:-1_0] + '''vocab.txt'''    ,   model_max_length=5_1_2     )
    tokenizer.save_pretrained(_a     )

    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,
   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

