


from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE     :			Union[str, Any]        =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :			str        =							{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class        A     (		lowerCamelCase_	):







     '''simple docstring'''







     lowerCamelCase :						Any            =   'roberta-prelayernorm'

     def __init__(				self    :							List[Any]			,				_UpperCamelCase    :							int=50_265			,				_UpperCamelCase    :							List[Any]=768			,				_UpperCamelCase    :							Optional[Any]=12			,				_UpperCamelCase    :							Union[str, Any]=12			,				_UpperCamelCase    :							Optional[Any]=3_072			,				_UpperCamelCase    :							Optional[int]="gelu"			,				_UpperCamelCase    :							List[Any]=0.1			,				_UpperCamelCase    :							Tuple=0.1			,				_UpperCamelCase    :							Any=512			,				_UpperCamelCase    :							Tuple=2			,				_UpperCamelCase    :							int=0.0_2			,				_UpperCamelCase    :							Any=1e-12			,				_UpperCamelCase    :							int=1			,				_UpperCamelCase    :							Any=0			,				_UpperCamelCase    :							Any=2			,				_UpperCamelCase    :							Optional[Any]="absolute"			,				_UpperCamelCase    :							str=True			,				_UpperCamelCase    :							List[Any]=None			,				**_UpperCamelCase    :							List[str]			,				):
      super().__init__(pad_token_id=_UpperCamelCase			,				bos_token_id=_UpperCamelCase			,				eos_token_id=_UpperCamelCase			,				**_UpperCamelCase)

      _lowercase:       Any						=				vocab_size
      _lowercase:       Any						=				hidden_size
      _lowercase:       Dict						=				num_hidden_layers
      _lowercase:       Any						=				num_attention_heads
      _lowercase:       List[str]						=				hidden_act
      _lowercase:       str						=				intermediate_size
      _lowercase:       str						=				hidden_dropout_prob
      _lowercase:       str						=				attention_probs_dropout_prob
      _lowercase:       Optional[Any]						=				max_position_embeddings
      _lowercase:       Any						=				type_vocab_size
      _lowercase:       Dict						=				initializer_range
      _lowercase:       List[Any]						=				layer_norm_eps
      _lowercase:       List[str]						=				position_embedding_type
      _lowercase:       Any						=				use_cache
      _lowercase:       Dict						=				classifier_dropout






class        A     (		lowerCamelCase_	):







     '''simple docstring'''







     @property
     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      if self.task == "multiple-choice":
       _lowercase:       Optional[Any]						=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
       _lowercase:       Any						=				{0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ])



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

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



