
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__      :    Dict    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Dict    							= {
    '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_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Union[str, Any]					       =   '''roberta'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=5_02_65   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=5_12   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=0   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__="absolute"   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Tuple:



											"""simple docstring"""




											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__   ,       bos_token_id=SCREAMING_SNAKE_CASE__   ,       eos_token_id=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )

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






class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				@property
				def 	__magic_name__    (self    )					->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE__						:       Any           =   {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]    )





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


A   :     str       =				logging.get_logger(__name__)

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


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


