"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}




class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase							):
      a__								=						"""convnext"""



      def __init__(		self       ,     lowercase=3       ,     lowercase=4       ,     lowercase=4       ,     lowercase=None       ,     lowercase=None       ,     lowercase="gelu"       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=1e-6       ,     lowercase=0.0       ,     lowercase=2_24       ,     lowercase=None       ,     lowercase=None       ,     **lowercase       ,     )      ->     Any:





        '''simple docstring'''





        super().__init__(**lowercase)

        a__:	List[str]     				=      num_channels
        a__:	Tuple     				=      patch_size
        a__:	str     				=      num_stages
        a__:	Any     				=      [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
        a__:	Any     				=      [3, 3, 9, 3] if depths is None else depths
        a__:	List[Any]     				=      hidden_act
        a__:	Dict     				=      initializer_range
        a__:	int     				=      layer_norm_eps
        a__:	Optional[int]     				=      layer_scale_init_value
        a__:	Optional[int]     				=      drop_path_rate
        a__:	Union[str, Any]     				=      image_size
        a__:	List[Any]     				=      ['stem'] + [f'stage{idx}' for idx in range(1       ,     len(self.depths) + 1)]
        a__				,       a__:	Optional[int]     				=      get_aligned_output_features_output_indices(
            out_features=lowercase       ,     out_indices=lowercase       ,     stage_names=self.stage_names)







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						version.parse("""1.11"""							)



      @property
      def     lowerCamelCase_  (		self)      ->     Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ])



      @property
      def     lowerCamelCase_  (		self)      ->     float:





        '''simple docstring'''





        return 1e-5









"""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


lowerCAmelCase__        =      logging.getLogger(__name__)


def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						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"	,						)

		UpperCamelCase =						parser.parse_args()
		return args


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						datasets.load_dataset(args.dataset_name	,						args.dataset_config	,						split="train"     )

		if args.limit is not None:
				UpperCamelCase =						min(len(_SCREAMING_SNAKE_CASE     )	,						args.limit     )
				UpperCamelCase =						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.
		UpperCamelCase =						Tokenizer(Unigram()     )
		UpperCamelCase =						normalizers.Sequence([normalizers.Replace("``"	,						"\""     ), normalizers.Replace("''"	,						"\""     )]     )
		UpperCamelCase =						pre_tokenizers.Metaspace()

		# Prepare the trainer.
		UpperCamelCase =						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!"     )

		UpperCamelCase =						tokenizer.token_to_id("[CLS]"     )
		UpperCamelCase =						tokenizer.token_to_id("[SEP]"     )
		UpperCamelCase =						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),
		    ]	,						)
		UpperCamelCase =						decoders.Metaspace()

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


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






