



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


__snake_case    :		int  =logging.get_logger(__name__)

__snake_case    :		Tuple  ={
    'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""gpt_neox_japanese"""



		def __init__(self		,__lowerCamelCase=3_20_00		,__lowerCamelCase=25_60		,__lowerCamelCase=32		,__lowerCamelCase=32		,__lowerCamelCase=4		,__lowerCamelCase="gelu"		,__lowerCamelCase=1.00		,__lowerCamelCase=1_00_00		,__lowerCamelCase=20_48		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-5		,__lowerCamelCase=True		,__lowerCamelCase=3_19_96		,__lowerCamelCase=3_19_99		,__lowerCamelCase=0.1		,__lowerCamelCase=0.0		,**__lowerCamelCase		,)					->      Optional[Any]:

						"""simple docstring"""






						super().__init__(bos_token_id=__lowerCamelCase		,eos_token_id=__lowerCamelCase		,**__lowerCamelCase			)
						lowerCAmelCase__					:    Dict												=					vocab_size
						lowerCAmelCase__					:    List[Any]												=					max_position_embeddings
						lowerCAmelCase__					:    List[Any]												=					hidden_size
						lowerCAmelCase__					:    List[str]												=					num_hidden_layers
						lowerCAmelCase__					:    List[Any]												=					num_attention_heads
						lowerCAmelCase__					:    Optional[int]												=					intermediate_multiple_size
						lowerCAmelCase__					:    int												=					hidden_act
						lowerCAmelCase__					:    int												=					rotary_pct
						lowerCAmelCase__					:    List[Any]												=					rotary_emb_base
						lowerCAmelCase__					:    Optional[int]												=					initializer_range
						lowerCAmelCase__					:    Dict												=					layer_norm_eps
						lowerCAmelCase__					:    Optional[Any]												=					use_cache
						lowerCAmelCase__					:    Dict												=					attention_dropout
						lowerCAmelCase__					:    List[str]												=					hidden_dropout






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[str]  ={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

