

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


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

_UpperCAmelCase						:  Any									=			{
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Any			     =     '''xglm'''
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     ['''past_key_values''']

				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     {
				    '''num_attention_heads''': '''attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''num_hidden_layers''': '''num_layers''',
				}




				def __init__(   self ,  snake_case=25_6008 ,  snake_case=2048 ,  snake_case=1024 ,  snake_case=4096 ,  snake_case=24 ,  snake_case=16 ,  snake_case="gelu" ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=0.0 ,  snake_case=0.0 ,  snake_case=0.02 ,  snake_case=True ,  snake_case=True ,  snake_case=2 ,  snake_case=1 ,  snake_case=0 ,  snake_case=2 ,  **snake_case ,  ):
											snake_case_     							=       vocab_size
											snake_case_     							=       max_position_embeddings
											snake_case_     							=       d_model
											snake_case_     							=       ffn_dim
											snake_case_     							=       num_layers
											snake_case_     							=       attention_heads
											snake_case_     							=       activation_function
											snake_case_     							=       dropout
											snake_case_     							=       attention_dropout
											snake_case_     							=       activation_dropout
											snake_case_     							=       layerdrop
											snake_case_     							=       init_std
											snake_case_     							=       scale_embedding  # scale factor will be sqrt(d_model) if True
											snake_case_     							=       use_cache

											super().__init__(
											    pad_token_id=snake_case ,  bos_token_id=snake_case ,  eos_token_id=snake_case ,  decoder_start_token_id=snake_case ,  **snake_case ,  )







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

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







@dataclass(frozen=lowercase_     )
class 					lowercase							(			lowercase_     ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default='''text-classification'''      ,    metadata={'''include_in_asdict_even_if_is_default''': True}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''text''': Value('''string'''     )}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''labels''': ClassLabel}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     "text"
				__SCREAMING_SNAKE_CASE   :					str			     =     "labels"




				def        a				(   self ,  snake_case			):
											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_     							=       copy.deepcopy(self			)
											snake_case_     							=       self.label_schema.copy()
											snake_case_     							=       features[self.label_column]
											snake_case_     							=       label_schema
											return task_template




				@property
				def        a				(   self			):
											return {
											    self.text_column: "text",
											    self.label_column: "labels",
											}




