



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  {
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}


class 						lowerCamelCase__			(		_UpperCAmelCase ):
						_lowerCAmelCase             =			"ernie"


						def __init__(			self    :	int   ,							_a    :	Dict=3_0_5_2_2   ,							_a    :	List[str]=7_6_8   ,							_a    :	int=1_2   ,							_a    :	Optional[Any]=1_2   ,							_a    :	List[str]=3_0_7_2   ,							_a    :	Optional[Any]="gelu"   ,							_a    :	Any=0.1   ,							_a    :	Any=0.1   ,							_a    :	Dict=5_1_2   ,							_a    :	Union[str, Any]=2   ,							_a    :	Union[str, Any]=3   ,							_a    :	List[str]=False   ,							_a    :	Union[str, Any]=0.0_2   ,							_a    :	Tuple=1e-12   ,							_a    :	Tuple=0   ,							_a    :	Any="absolute"   ,							_a    :	str=True   ,							_a    :	Dict=None   ,							**_a    :	Union[str, Any]   ,							):
													super().__init__(pad_token_id=_lowerCAmelCase   ,							**_lowerCAmelCase      )

													a__:							Optional[int]									=vocab_size
													a__:							Any									=hidden_size
													a__:							Optional[Any]									=num_hidden_layers
													a__:							str									=num_attention_heads
													a__:							Union[str, Any]									=hidden_act
													a__:							List[str]									=intermediate_size
													a__:							Dict									=hidden_dropout_prob
													a__:							List[str]									=attention_probs_dropout_prob
													a__:							Optional[int]									=max_position_embeddings
													a__:							Dict									=type_vocab_size
													a__:							int									=task_type_vocab_size
													a__:							Optional[Any]									=use_task_id
													a__:							int									=initializer_range
													a__:							str									=layer_norm_eps
													a__:							Dict									=position_embedding_type
													a__:							Any									=use_cache
													a__:							Union[str, Any]									=classifier_dropout


class 						lowerCamelCase__			(		_UpperCAmelCase ):


						@property
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):





													if self.task == "multiple-choice":
																				a__:							int									={0: "batch", 1: "choice", 2: "sequence"}
													else:
																				a__:							Any									={0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("input_ids", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													        ("token_type_ids", dynamic_axis),
													        ("task_type_ids", dynamic_axis),
													    ]      )









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)

