from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def    __magic_name__(	__UpperCAmelCase=10		)    ->     Any:



      '''simple docstring'''

      for _ in tqdm(range(0			,    args.n_examples			,    __UpperCAmelCase		)		):
            yield [next(__UpperCAmelCase		)[args.text_column] for _ in range(__UpperCAmelCase		)]


# Configuration
snake_case__						      =			HfArgumentParser(TokenizerTrainingArguments)
snake_case__						      =			parser.parse_args()

# Base tokenizer
snake_case__						      =			AutoTokenizer.from_pretrained(args.base_tokenizer)
snake_case__						      =			list(bytes_to_unicode().values())

# Load dataset
snake_case__						      =			load_dataset(args.dataset_name, split='train', streaming=True)
snake_case__						      =			iter(dataset)


# Training and saving
snake_case__						      =			tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)




new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
import argparse

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


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					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
      _lowerCamelCase           =					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)