



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a      =				logging.get_logger(__name__)

__a      =				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}
class 				lowerCamelCase  (			_SCREAMING_SNAKE_CASE				):






					'''simple docstring'''





					_A		:			List[Any]						=     """roberta"""






					def __init__(       self:							Union[str, Any]						,     snake_case:							str=50_265						,     snake_case:							Any=768						,     snake_case:							Union[str, Any]=12						,     snake_case:							List[str]=12						,     snake_case:							int=3_072						,     snake_case:							str="gelu"						,     snake_case:							List[str]=0.1						,     snake_case:							List[str]=0.1						,     snake_case:							Tuple=512						,     snake_case:							Any=2						,     snake_case:							Optional[int]=0.0_2						,     snake_case:							Optional[Any]=1E-12						,     snake_case:							Dict=1						,     snake_case:							Any=0						,     snake_case:							Optional[Any]=2						,     snake_case:							str="absolute"						,     snake_case:							Optional[Any]=True						,     snake_case:							Optional[int]=None						,     **snake_case:							List[str]						,     )   ->	Tuple:
											super().__init__(pad_token_id=snake_case						,     bos_token_id=snake_case						,     eos_token_id=snake_case						,     **snake_case						)

											snake_case_ :List[Any]						=			vocab_size
											snake_case_ :Tuple						=			hidden_size
											snake_case_ :Any						=			num_hidden_layers
											snake_case_ :str						=			num_attention_heads
											snake_case_ :str						=			hidden_act
											snake_case_ :List[str]						=			intermediate_size
											snake_case_ :Dict						=			hidden_dropout_prob
											snake_case_ :int						=			attention_probs_dropout_prob
											snake_case_ :str						=			max_position_embeddings
											snake_case_ :Optional[int]						=			type_vocab_size
											snake_case_ :Dict						=			initializer_range
											snake_case_ :int						=			layer_norm_eps
											snake_case_ :List[Any]						=			position_embedding_type
											snake_case_ :Any						=			use_cache
											snake_case_ :Optional[Any]						=			classifier_dropout

class 				lowerCamelCase  (			_SCREAMING_SNAKE_CASE				):






					'''simple docstring'''





					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																	snake_case_ :Union[str, Any]						=			{0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																	snake_case_ :Tuple						=			{0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]						)




'''simple docstring'''






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


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



