
"""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


_lowercase							 =							logging.getLogger(__name__)



def     lowerCAmelCase__ (		)	->List[str]:
			__lowercase						      =					argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."			)
			parser.add_argument(
			    "--dataset_name" ,      type=__magic_name__ ,      default="wikitext" ,      help="Name of the training. Explore datasets at: hf.co/datasets." ,      )
			parser.add_argument(
			    "--dataset_config" ,      type=__magic_name__ ,      default="wikitext-103-raw-v1" ,      help="Configuration name of the dataset."			)
			parser.add_argument(
			    "--batch_size" ,      type=__magic_name__ ,      default=1_0_0_0 ,      help="Batch size during training." ,      )
			parser.add_argument(
			    "--vocab_size" ,      type=__magic_name__ ,      default=1_0_0_4_8 ,      help="Size of the desired vocabulary." ,      )
			parser.add_argument(
			    "--limit" ,      default=__magic_name__ ,      type=__magic_name__ ,      help="Limit the number of shards (used for debugging)." ,      )
			parser.add_argument(
			    "--export_to_hub" ,      action="store_true" ,      )

			__lowercase						      =					parser.parse_args()
			return args



def     lowerCAmelCase__ (		__magic_name__			)	->Union[str, Any]:
			__lowercase						      =					datasets.load_dataset(args.dataset_name ,      args.dataset_config ,      split="train"			)

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

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

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

			# Prepare the trainer.
			__lowercase						      =					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=__magic_name__			)
			logger.info("Tokenizer training complete!"			)

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

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


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







"""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 				__a    (   __a				,      __a					):



							'''simple docstring'''







							_lowerCamelCase				: List[Any]					      =      """convnext"""
							def __init__(    self     ,						_lowerCamelCase=3     ,						_lowerCamelCase=4     ,						_lowerCamelCase=4     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						_lowerCamelCase="gelu"     ,						_lowerCamelCase=0.02     ,						_lowerCamelCase=1e-12     ,						_lowerCamelCase=1e-6     ,						_lowerCamelCase=0.0     ,						_lowerCamelCase=224     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						**_lowerCamelCase     ,						) ->       List[str]:




										'''simple docstring'''
										super().__init__(**_lowerCamelCase					)

										__lowercase						      =					num_channels
										__lowercase						      =					patch_size
										__lowercase						      =					num_stages
										__lowercase						      =					[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
										__lowercase						      =					[3, 3, 9, 3] if depths is None else depths
										__lowercase						      =					hidden_act
										__lowercase						      =					initializer_range
										__lowercase						      =					layer_norm_eps
										__lowercase						      =					layer_scale_init_value
										__lowercase						      =					drop_path_rate
										__lowercase						      =					image_size
										__lowercase						      =					["stem"] + [f'''stage{idx}''' for idx in range(1     ,						len(self.depths					) + 1					)]
										__lowercase      ,				__lowercase						      =					get_aligned_output_features_output_indices(
										    out_features=_lowerCamelCase     ,						out_indices=_lowerCamelCase     ,						stage_names=self.stage_names					)





class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: str					      =      version.parse("""1.11"""					)
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Mapping[str, Mapping[int, str]]:




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





							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       float:




										'''simple docstring'''
										return 1e-5





