"""simple docstring"""







import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_SCREAMING_SNAKE_CASE	:		Tuple   =   logging.getLogger(__name__)



def       A__    (	)  ->							int:
   __lowerCamelCase    		=					argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'						)
   parser.add_argument(
       '--dataset_name'      , type=lowercase_      , default='wikitext'      , help='Name of the training. Explore datasets at: hf.co/datasets.'      , )
   parser.add_argument(
       '--dataset_config'      , type=lowercase_      , default='wikitext-103-raw-v1'      , help='Configuration name of the dataset.'						)
   parser.add_argument(
       '--batch_size'      , type=lowercase_      , default=1000      , help='Batch size during training.'      , )
   parser.add_argument(
       '--vocab_size'      , type=lowercase_      , default=10048      , help='Size of the desired vocabulary.'      , )
   parser.add_argument(
       '--limit'      , default=lowercase_      , type=lowercase_      , help='Limit the number of shards (used for debugging).'      , )
   parser.add_argument(
       '--export_to_hub'      , action='store_true'      , )

   __lowerCamelCase    		=					parser.parse_args()
   return args



def       A__    (	lowercase_     :	Tuple						)  ->							int:
   __lowerCamelCase    		=					datasets.load_dataset(args.dataset_name      , args.dataset_config      , split='train'						)

   if args.limit is not None:
      __lowerCamelCase    		=					min(len(lowercase_						)      , args.limit						)
      __lowerCamelCase    		=					dataset.select(range(lowercase_						)						)
      logger.info(F'''Limiting the dataset to {args.limit} entries.'''						)

   def batch_iterator():
      for i in range(0      , len(lowercase_						)      , args.batch_size						):
         yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
   __lowerCamelCase    		=					Tokenizer(Unigram()						)
   __lowerCamelCase    		=					normalizers.Sequence([normalizers.Replace('``'      , '"'						), normalizers.Replace('\'\''      , '"'						)]						)
   __lowerCamelCase    		=					pre_tokenizers.Metaspace()

   # Prepare the trainer.
   __lowerCamelCase    		=					UnigramTrainer(
       unk_token='<unk>'      , special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']      , vocab_size=args.vocab_size      , )

   logger.info('Training the tokenizer.'						)
   tokenizer.train_from_iterator(batch_iterator()      , trainer=lowercase_						)
   logger.info('Tokenizer training complete!'						)

   __lowerCamelCase    		=					tokenizer.token_to_id('[CLS]'						)
   __lowerCamelCase    		=					tokenizer.token_to_id('[SEP]'						)
   __lowerCamelCase    		=					processors.TemplateProcessing(
       single='[CLS]:0 $A:0 [SEP]:0'      , pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'      , special_tokens=[
           ('[CLS]', cls_token_id),
           ('[SEP]', sep_token_id),
       ]      , )
   __lowerCamelCase    		=					decoders.Metaspace()

   if args.export_to_hub:
      logger.info('Exporting the trained tokenzier to Hub.'						)
      __lowerCamelCase    		=					AlbertTokenizerFast(tokenizer_object=lowercase_						)
      new_tokenizer.push_to_hub('unigram-tokenizer-dataset'						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Tuple   =   parse_args()
     main(args)














import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

							# Load weights from tf checkpoint
							load_tf_weights_in_electra(
							    UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	discriminator_or_generator=UpperCAmelCase     )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'     )
							torch.save(model.state_dict()    ,	UpperCAmelCase     )


if __name__ == "__main__":
						__lowerCamelCase						= 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 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.'
						)
						parser.add_argument(
						    '--discriminator_or_generator',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
						        '\'generator\'.'
						    ),
						)
						__lowerCamelCase						= parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
						)