


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


__lowercase	:						List[str]       =	logging.get_logger(__name__)

__lowercase	:						Dict       =	{
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "distilbert"
	A_				=    {
	    "hidden_size": "dim",
	    "num_attention_heads": "n_heads",
	    "num_hidden_layers": "n_layers",
	}




	def __init__(   self						,				__a=3_0522						,				__a=512						,				__a=False						,				__a=6						,				__a=12						,				__a=768						,				__a=4 * 768						,				__a=0.1						,				__a=0.1						,				__a="gelu"						,				__a=0.02						,				__a=0.1						,				__a=0.2						,				__a=0						,				**__a						,				):

						'''simple docstring'''





						__a			:       Any					    =  vocab_size
						__a			:       List[str]					    =  max_position_embeddings
						__a			:       Optional[Any]					    =  sinusoidal_pos_embds
						__a			:       int					    =  n_layers
						__a			:       Optional[Any]					    =  n_heads
						__a			:       Optional[int]					    =  dim
						__a			:       Optional[int]					    =  hidden_dim
						__a			:       Optional[Any]					    =  dropout
						__a			:       Tuple					    =  attention_dropout
						__a			:       Dict					    =  activation
						__a			:       List[str]					    =  initializer_range
						__a			:       Optional[Any]					    =  qa_dropout
						__a			:       Optional[int]					    =  seq_classif_dropout
						super().__init__(**__a						,				pad_token_id=__a							)





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__a			:       Dict					    =  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__a			:       Optional[int]					    =  {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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase       :     Any   =     {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Dict   =     ["""ElectraTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Tuple   =     [
        """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ElectraForCausalLM""",
        """ElectraForMaskedLM""",
        """ElectraForMultipleChoice""",
        """ElectraForPreTraining""",
        """ElectraForQuestionAnswering""",
        """ElectraForSequenceClassification""",
        """ElectraForTokenClassification""",
        """ElectraModel""",
        """ElectraPreTrainedModel""",
        """load_tf_weights_in_electra""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[Any]   =     [
        """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFElectraForMaskedLM""",
        """TFElectraForMultipleChoice""",
        """TFElectraForPreTraining""",
        """TFElectraForQuestionAnswering""",
        """TFElectraForSequenceClassification""",
        """TFElectraForTokenClassification""",
        """TFElectraModel""",
        """TFElectraPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[str]   =     [
        """FlaxElectraForCausalLM""",
        """FlaxElectraForMaskedLM""",
        """FlaxElectraForMultipleChoice""",
        """FlaxElectraForPreTraining""",
        """FlaxElectraForQuestionAnswering""",
        """FlaxElectraForSequenceClassification""",
        """FlaxElectraForTokenClassification""",
        """FlaxElectraModel""",
        """FlaxElectraPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

    __lowerCamelCase       :     Any   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
