



import argparse

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


logging.set_verbosity_info()







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     CanineConfig()
						UpperCAmelCase_     							=     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
						UpperCAmelCase_     							=     CanineTokenizer()
						print(f"Save tokenizer files to {pytorch_dump_path}"	)
						tokenizer.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							UpperCamelCase_	       =   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.",
							)
							UpperCamelCase_	       =   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





