


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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   # 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(lowerCAmelCase_   )
   # 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=lowerCAmelCase_   )
   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=lowerCAmelCase_   )
   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=lowerCAmelCase_   )
   elif task == "TABFACT":
      UpperCAmelCase							=     TapasForSequenceClassification(config=lowerCAmelCase_   )
   elif task == "MLM":
      UpperCAmelCase							=     TapasForMaskedLM(config=lowerCAmelCase_   )
   elif task == "INTERMEDIATE_PRETRAINING":
      UpperCAmelCase							=     TapasModel(config=lowerCAmelCase_   )
   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(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

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

   # 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(lowerCAmelCase_   )

   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_torch_available


__a     =			{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			[
     """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """IBertForMaskedLM""",
     """IBertForMultipleChoice""",
     """IBertForQuestionAnswering""",
     """IBertForSequenceClassification""",
     """IBertForTokenClassification""",
     """IBertModel""",
     """IBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ibert import (
      IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      IBertForMaskedLM,
      IBertForMultipleChoice,
      IBertForQuestionAnswering,
      IBertForSequenceClassification,
      IBertForTokenClassification,
      IBertModel,
      IBertPreTrainedModel,
  )

else:
 import sys

 __a     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



