from collections import OrderedDict
from typing import Mapping

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


A__           =							logging.get_logger(__name__)

A__           =							{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'data2vec-text'







							def __init__(				self     :    str	,					__snake_case     :    Optional[Any]=30522	,					__snake_case     :    str=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    List[str]=12	,					__snake_case     :    Optional[Any]=3072	,					__snake_case     :    Any="gelu"	,					__snake_case     :    str=0.1	,					__snake_case     :    List[str]=0.1	,					__snake_case     :    Optional[int]=512	,					__snake_case     :    Tuple=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    List[Any]=1e-1_2	,					__snake_case     :    str=1	,					__snake_case     :    Tuple=0	,					__snake_case     :    int=2	,					__snake_case     :    Tuple="absolute"	,					__snake_case     :    List[Any]=True	,					__snake_case     :    int=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

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


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Tuple		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Dict						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:Union[str, Any]						  =     {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_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__)



