



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

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




@dataclass(frozen=lowerCAmelCase_     )
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	field(default="""text-classification"""				,     metadata={"""include_in_asdict_even_if_is_default""": True}     )
     _SCREAMING_SNAKE_CASE						=	Features({"""text""": Value("""string"""     )}     )
     _SCREAMING_SNAKE_CASE						=	Features({"""labels""": ClassLabel}     )
     _SCREAMING_SNAKE_CASE						=	"text"
     _SCREAMING_SNAKE_CASE						=	"labels"






     def    lowercase_   (       self  :  List[str], _snake_case  :  Optional[Any]					)   ->Any:
            if self.label_column not in features:
                   raise ValueError(F'''Column {self.label_column} is not present in features.'''					)
            if not isinstance(features[self.label_column], _snake_case					):
                   raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''					)
            snake_case__							:	List[str]   	=	copy.deepcopy(self					)
            snake_case__							:	Tuple   	=	self.label_schema.copy()
            snake_case__							:	Union[str, Any]   	=	features[self.label_column]
            snake_case__							:	Any   	=	label_schema
            return task_template






     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Dict[str, str]:
            return {
                self.text_column: "text",
                self.label_column: "labels",
            }











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


a_   :Optional[int]        =      logging.get_logger(__name__)

a_   :Dict        =      {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""openai-gpt"""
     _SCREAMING_SNAKE_CASE						=	{
         """max_position_embeddings""": """n_positions""",
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }






     def __init__(       self  :  Optional[int], _snake_case  :  Dict=4_0_4_7_8, _snake_case  :  str=5_1_2, _snake_case  :  int=7_6_8, _snake_case  :  Tuple=1_2, _snake_case  :  Any=1_2, _snake_case  :  str="gelu", _snake_case  :  List[str]=0.1, _snake_case  :  Any=0.1, _snake_case  :  Dict=0.1, _snake_case  :  int=1e-5, _snake_case  :  Optional[Any]=0.0_2, _snake_case  :  List[Any]="cls_index", _snake_case  :  Any=True, _snake_case  :  Any=None, _snake_case  :  int=True, _snake_case  :  Optional[Any]=0.1, **_snake_case  :  List[Any], )   ->Optional[int]:
            snake_case__							:	int   	=	vocab_size
            snake_case__							:	Dict   	=	n_positions
            snake_case__							:	str   	=	n_embd
            snake_case__							:	str   	=	n_layer
            snake_case__							:	List[Any]   	=	n_head
            snake_case__							:	List[Any]   	=	afn
            snake_case__							:	Optional[Any]   	=	resid_pdrop
            snake_case__							:	List[str]   	=	embd_pdrop
            snake_case__							:	List[Any]   	=	attn_pdrop
            snake_case__							:	Optional[int]   	=	layer_norm_epsilon
            snake_case__							:	str   	=	initializer_range
            snake_case__							:	List[str]   	=	summary_type
            snake_case__							:	Optional[int]   	=	summary_use_proj
            snake_case__							:	List[str]   	=	summary_activation
            snake_case__							:	Optional[Any]   	=	summary_first_dropout
            snake_case__							:	int   	=	summary_proj_to_labels
            super().__init__(**_snake_case					)






