

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase     :   int						     =  logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      bool       =  field(default=__magic_name__   ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''})
  SCREAMING_SNAKE_CASE    :      bool       =  field(
      default=__magic_name__   ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''})
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  field(
      default=__magic_name__   ,     metadata={
          '''help''': (
              '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
              '''to the `max_length` value of the model configuration.'''
          )
      }   ,     )
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  field(
      default=__magic_name__   ,     metadata={
          '''help''': (
              '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
              '''to the `num_beams` value of the model configuration.'''
          )
      }   ,     )
  SCREAMING_SNAKE_CASE    :      Optional[Union[str, Path, GenerationConfig]]       =  field(
      default=__magic_name__   ,     metadata={
          '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
      }   ,     )

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     super().to_dict()
        for k, v in d.items():
              if isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    SCREAMING_SNAKE_CASE_				:  str           =     v.to_dict()
        return d









import argparse

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


logging.set_verbosity_info()

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     TaConfig.from_json_file(a  )
      print(f"Building PyTorch model from configuration: {config}"  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     TaForConditionalGeneration(a  )

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

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


if __name__ == "__main__":
    lowerCAmelCase     :   int						     =  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.'
    )
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






