






from collections import OrderedDict
from typing import Mapping

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


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

lowercase  :							Optional[int]			  =    {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   List[Any]						=					'ibert'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=3_0522		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=512		,				_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=False		,				_SCREAMING_SNAKE_CASE="none"		,				**_SCREAMING_SNAKE_CASE		,				) ->				Tuple:
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

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




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
