
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=_A     )
class      UpperCamelCase					(		_A     ):


   '''simple docstring'''







   lowercase					:	str									=field(default="""question-answering-extractive"""						,      metadata={"""include_in_asdict_even_if_is_default""": True}     )
   lowercase					:	List[Any]									=Features({"""question""": Value("""string"""     ), """context""": Value("""string"""     )}     )
   lowercase					:	Dict									=Features(
       {
           """answers""": Sequence(
               {
                   """text""": Value("""string"""     ),
                   """answer_start""": Value("""int32"""     ),
               }     )
       }     )
   lowercase					:	Tuple									="""question"""
   lowercase					:	Optional[Any]									="""context"""
   lowercase					:	Tuple									="""answers"""
   @property
   def 		UpperCamelCase  (     self		):
        return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "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)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

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

