'''simple docstring'''





from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int=10  )   ->				Tuple:







     """simple docstring"""
     for _ in tqdm(range(0				,	args.n_examples				,	snake_case  )  ):
          yield [next(snake_case  )[args.text_column] for _ in range(snake_case  )]


# Configuration
UpperCamelCase       :     Union[str, Any]			=    HfArgumentParser(TokenizerTrainingArguments)
UpperCamelCase       :     Optional[int]			=    parser.parse_args()

# Base tokenizer
UpperCamelCase       :     Optional[int]			=    AutoTokenizer.from_pretrained(args.base_tokenizer)
UpperCamelCase       :     List[str]			=    list(bytes_to_unicode().values())

# Load dataset
UpperCamelCase       :     Union[str, Any]			=    load_dataset(args.dataset_name, split="""train""", streaming=True)
UpperCamelCase       :     Union[str, Any]			=    iter(dataset)


# Training and saving
UpperCamelCase       :     List[Any]			=    tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)


'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Optional[int]			=    {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			"cpmant"





    def __init__(					self     :						Tuple							,							UpperCAmelCase_     :						int = 3_0_7_2_0							,							UpperCAmelCase_     :						int = 4_0_9_6							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 1_2_8							,							UpperCAmelCase_     :						int = 1_0_2_4_0							,							UpperCAmelCase_     :						int = 4_8							,							UpperCAmelCase_     :						int = 0.0							,							UpperCAmelCase_     :						int = 5_1_2							,							UpperCAmelCase_     :						int = 2_0_4_8							,							UpperCAmelCase_     :						int = 1e-6							,							UpperCAmelCase_     :						float = 1.0							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						bool = True							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         a					:							Union[str, Any]							= prompt_types
         a					:							List[str]							= prompt_length
         a					:							List[Any]							= segment_types
         a					:							Any							= hidden_size
         a					:							Optional[int]							= num_attention_heads
         a					:							Union[str, Any]							= dim_head
         a					:							int							= dim_ff
         a					:							Union[str, Any]							= num_hidden_layers
         a					:							List[Any]							= position_bias_num_buckets
         a					:							Optional[int]							= position_bias_max_distance
         a					:							str							= dropout_p
         a					:							List[str]							= eps
         a					:							Dict							= use_cache
         a					:							Tuple							= vocab_size
         a					:							Optional[Any]							= init_std

