
'''simple docstring'''
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      List[str]            =  TaConfig.from_json_file(__SCREAMING_SNAKE_CASE    )
    print(f'Building PyTorch model from configuration: {config}'    )
    snake_case_	:      str            =  TaForConditionalGeneration(__SCREAMING_SNAKE_CASE    )

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    )

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'    )
    model.save_pretrained(__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  a_     =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained T5 model. \nThis 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.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_     =  {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  ["SplinterTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  [
      "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SplinterForQuestionAnswering",
      "SplinterForPreTraining",
      "SplinterLayer",
      "SplinterModel",
      "SplinterPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
  from .tokenization_splinter import SplinterTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_splinter_fast import SplinterTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )


else:
  import sys

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




