





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a   	=    {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    ["DebertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    [
        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaForMaskedLM",
        "DebertaForQuestionAnswering",
        "DebertaForSequenceClassification",
        "DebertaForTokenClassification",
        "DebertaModel",
        "DebertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    [
        "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaForMaskedLM",
        "TFDebertaForQuestionAnswering",
        "TFDebertaForSequenceClassification",
        "TFDebertaForTokenClassification",
        "TFDebertaModel",
        "TFDebertaPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    a   	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












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


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   """biogpt"""





     def __init__(     self				,							_UpperCamelCase=42384				,							_UpperCamelCase=1024				,							_UpperCamelCase=24				,							_UpperCamelCase=16				,							_UpperCamelCase=4096				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=1024				,							_UpperCamelCase=0.02				,							_UpperCamelCase=1e-12				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=0.0				,							_UpperCamelCase=0.0				,							_UpperCamelCase=1				,							_UpperCamelCase=0				,							_UpperCamelCase=2				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   layer_norm_eps
        _UpperCAmelCase           =   scale_embedding
        _UpperCAmelCase           =   use_cache
        _UpperCAmelCase           =   layerdrop
        _UpperCAmelCase           =   activation_dropout
        super().__init__(pad_token_id=_UpperCamelCase				,							bos_token_id=_UpperCamelCase				,							eos_token_id=_UpperCamelCase				,							**_UpperCamelCase					)