






from typing import TYPE_CHECKING

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


a		:       Union[str, 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:
       a		:       Optional[int]			     = ["ElectraTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Optional[Any]			     = [
           "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:
       a		:       Union[str, 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:
       a		:       Tuple			     = [
           "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

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










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


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

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


