






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Tuple		  =			CanineConfig()
  SCREAMING_SNAKE_CASE				:					Dict		  =			CanineModel(__UpperCAmelCase )
  model.eval()

  print(F'''Building PyTorch model from configuration: {config}''' )

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(__UpperCAmelCase  ,   __UpperCAmelCase  ,   __UpperCAmelCase )

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

  # Save tokenizer files
  SCREAMING_SNAKE_CASE				:					Dict		  =			CanineTokenizer()
  print(F'''Save tokenizer files to {pytorch_dump_path}''' )
  tokenizer.save_pretrained(__UpperCAmelCase )


if __name__ == "__main__":
   snake_case			 =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
   )
   parser.add_argument(
       """--pytorch_dump_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to a folder where the PyTorch model will be placed.""",
   )
   snake_case			 =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)











from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 							a_  (		datasets.BuilderConfig				):
						UpperCamelCase__      :						Optional[Callable]				       =None
						UpperCamelCase__      :						Optional[dict]				       =None
						UpperCamelCase__      :						Optional[datasets.Features]				       =None

						def 	__a       (    self	:List[str])		->					List[str]:
												assert self.generator is not None, "generator must be specified"

												if self.gen_kwargs is None:
																		UpperCAmelCase_     							=     {}







class 							a_  (		datasets.GeneratorBasedBuilder				):
						UpperCamelCase__      :						List[str]				       =GeneratorConfig

						def 	__a       (    self	:List[str])		->					List[Any]:
												return datasets.DatasetInfo(features=self.config.features)

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:List[str])		->					Optional[Any]:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs=self.config.gen_kwargs)]




						def 	__a       (    self	:Optional[int]   ,						**_lowercase	:str)		->					Union[str, Any]:
												for idx, ex in enumerate(self.config.generator(**_lowercase)):
																		yield idx, ex





