



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


__snake_case    :		Any  =logging.get_logger(__name__)

# TODO: upload to AWS
__snake_case    :		Any  ={
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}



class 	lowerCamelCase__      (							__lowerCamelCase):




		'''simple docstring'''



		snake_case_     ='retribert'



		def __init__(self		,__lowerCamelCase=3_05_22		,__lowerCamelCase=7_68		,__lowerCamelCase=8		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=5_12		,__lowerCamelCase=2		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-12		,__lowerCamelCase=True		,__lowerCamelCase=1_28		,__lowerCamelCase=0		,**__lowerCamelCase		,)					->      Tuple:

						"""simple docstring"""






						super().__init__(pad_token_id=UpperCamelCase_		,**UpperCamelCase_			)

						lowerCAmelCase__					:    int												=					vocab_size
						lowerCAmelCase__					:    Union[str, Any]												=					hidden_size
						lowerCAmelCase__					:    Tuple												=					num_hidden_layers
						lowerCAmelCase__					:    str												=					num_attention_heads
						lowerCAmelCase__					:    int												=					hidden_act
						lowerCAmelCase__					:    Any												=					intermediate_size
						lowerCAmelCase__					:    Optional[Any]												=					hidden_dropout_prob
						lowerCAmelCase__					:    Any												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    List[Any]												=					max_position_embeddings
						lowerCAmelCase__					:    Union[str, Any]												=					type_vocab_size
						lowerCAmelCase__					:    List[Any]												=					initializer_range
						lowerCAmelCase__					:    Optional[Any]												=					layer_norm_eps
						lowerCAmelCase__					:    List[Any]												=					share_encoders
						lowerCAmelCase__					:    str												=					projection_dim






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)

