
"""simple docstring"""




from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if is_torch_available():
										import torch

										if isinstance(__snake_case,	torch.Tensor     ):
															return True

					return isinstance(__snake_case,	np.ndarray     )




class 	UpperCamelCase      (			_UpperCAmelCase ):

				"""simple docstring"""



				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      fields(self     )

									# Safety and consistency checks
									if not len(_lowerCAmelCase     ):
														raise ValueError(f'''{self.__class__.__name__} has no fields.'''     )

									SCREAMING_SNAKE_CASE           =      getattr(self						,      class_fields[0].name     )
									SCREAMING_SNAKE_CASE           =      all(getattr(self						,      field.name     ) is None for field in class_fields[1:]     )

									if other_fields_are_none and isinstance(_lowerCAmelCase						,      _lowerCAmelCase     ):
														for key, value in first_field.items():
																			SCREAMING_SNAKE_CASE           =      value
									else:
														for field in class_fields:
																			SCREAMING_SNAKE_CASE           =      getattr(self						,      field.name     )
																			if v is not None:
																								SCREAMING_SNAKE_CASE           =      v


				def __delitem__(							self						,      *lowercase__						,      **lowercase__     )       ->       List[str]:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''     )


				def 				A   (							self						,      *lowercase__						,      **lowercase__     )       ->       str:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''     )


				def 				A   (							self						,      *lowercase__						,      **lowercase__     )       ->       str:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''     )


				def 				A   (							self						,      *lowercase__						,      **lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''     )


				def __getitem__(							self						,      lowercase__     )       ->       Tuple:
									"""simple docstring"""







									if isinstance(_lowerCAmelCase						,      _lowerCAmelCase     ):
														SCREAMING_SNAKE_CASE           =      dict(self.items()     )
														return inner_dict[k]
									else:
														return self.to_tuple()[k]


				def __setattr__(							self						,      lowercase__						,      lowercase__     )       ->       int:
									"""simple docstring"""







									if name in self.keys() and value is not None:
														# Don't call self.__setitem__ to avoid recursion errors
														super().__setitem__(_lowerCAmelCase						,      _lowerCAmelCase     )
									super().__setattr__(_lowerCAmelCase						,      _lowerCAmelCase     )


				def __setitem__(							self						,      lowercase__						,      lowercase__     )       ->       Optional[int]:
									"""simple docstring"""







									super().__setitem__(_lowerCAmelCase						,      _lowerCAmelCase     )
									# Don't call self.__setattr__ to avoid recursion errors
									super().__setattr__(_lowerCAmelCase						,      _lowerCAmelCase     )




				def 				A   (							self     )       ->       Tuple[Any]:
									"""simple docstring"""







									return tuple(self[k] for k in self.keys()     )




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


UpperCamelCase			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

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

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

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

						# Prepare the trainer.
						__magic_name__					=      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=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      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),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

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


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

