

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


snake_case_    						=				logging.getLogger(__name__)

def 			snake_case__    (					):



					'''simple docstring'''





					lowercase__					:     Any   = argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'    )
					parser.add_argument(
					    '--dataset_name'			,  type=SCREAMING_SNAKE_CASE_			,  default='wikitext'			,  help='Name of the training. Explore datasets at: hf.co/datasets.'			,  )
					parser.add_argument(
					    '--dataset_config'			,  type=SCREAMING_SNAKE_CASE_			,  default='wikitext-103-raw-v1'			,  help='Configuration name of the dataset.'    )
					parser.add_argument(
					    '--batch_size'			,  type=SCREAMING_SNAKE_CASE_			,  default=1_000			,  help='Batch size during training.'			,  )
					parser.add_argument(
					    '--vocab_size'			,  type=SCREAMING_SNAKE_CASE_			,  default=10_048			,  help='Size of the desired vocabulary.'			,  )
					parser.add_argument(
					    '--limit'			,  default=SCREAMING_SNAKE_CASE_			,  type=SCREAMING_SNAKE_CASE_			,  help='Limit the number of shards (used for debugging).'			,  )
					parser.add_argument(
					    '--export_to_hub'			,  action='store_true'			,  )

					lowercase__					:     List[Any]   = parser.parse_args()
					return args

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int    ):



					'''simple docstring'''





					lowercase__					:     List[str]   = datasets.load_dataset(args.dataset_name			,  args.dataset_config			,  split='train'    )

					if args.limit is not None:
										lowercase__					:     List[str]   = min(len(SCREAMING_SNAKE_CASE_    )			,  args.limit    )
										lowercase__					:     Dict   = dataset.select(range(SCREAMING_SNAKE_CASE_    )    )
										logger.info(f"""Limiting the dataset to {args.limit} entries."""    )

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

    # Prepare the tokenizer.
					lowercase__					:     Tuple   = Tokenizer(Unigram()    )
					lowercase__					:     Optional[Any]   = normalizers.Sequence([normalizers.Replace('``'			,  '"'    ), normalizers.Replace('\'\''			,  '"'    )]    )
					lowercase__					:     List[str]   = pre_tokenizers.Metaspace()

					# Prepare the trainer.
					lowercase__					:     Dict   = 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=SCREAMING_SNAKE_CASE_    )
					logger.info('Tokenizer training complete!'    )

					lowercase__					:     Union[str, Any]   = tokenizer.token_to_id('[CLS]'    )
					lowercase__					:     Any   = tokenizer.token_to_id('[SEP]'    )
					lowercase__					:     Any   = 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),
					    ]			,  )
					lowercase__					:     Any   = decoders.Metaspace()

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


if __name__ == "__main__":
							snake_case_    						=				parse_args()
							main(args)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''GPTJForCausalLM''',
           '''GPTJForQuestionAnswering''',
           '''GPTJForSequenceClassification''',
           '''GPTJModel''',
           '''GPTJPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''TFGPTJForCausalLM''',
           '''TFGPTJForQuestionAnswering''',
           '''TFGPTJForSequenceClassification''',
           '''TFGPTJModel''',
           '''TFGPTJPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''FlaxGPTJForCausalLM''',
           '''FlaxGPTJModel''',
           '''FlaxGPTJPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptj import (
                  GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTJForCausalLM,
                  GPTJForQuestionAnswering,
                  GPTJForSequenceClassification,
                  GPTJModel,
                  GPTJPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_gptj import (
                  TFGPTJForCausalLM,
                  TFGPTJForQuestionAnswering,
                  TFGPTJForSequenceClassification,
                  TFGPTJModel,
                  TFGPTJPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
       import sys

       snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

