

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


A_       : str         =			logging.get_logger(__name__)

A_       : str         =			{
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Optional[int]  							=       '''dpr'''






					def __init__( self    ,       A__=3_0522    ,       A__=768    ,       A__=12    ,       A__=12    ,       A__=3072    ,       A__="gelu"    ,       A__=0.1    ,       A__=0.1    ,       A__=512    ,       A__=2    ,       A__=0.0_2    ,       A__=1e-12    ,       A__=0    ,       A__="absolute"    ,       A__ = 0    ,       **A__    ,       ):
						super().__init__(pad_token_id=A__    ,       **A__			)

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_       : Optional[int]         =			{'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : str         =			[
	    'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'IBertForMaskedLM',
	    'IBertForMultipleChoice',
	    'IBertForQuestionAnswering',
	    'IBertForSequenceClassification',
	    'IBertForTokenClassification',
	    'IBertModel',
	    'IBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

	A_       : Union[str, Any]         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



