



"""simple docstring"""
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     =    {
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      Any						=					"""rembert"""






					def __init__(	self:							List[Any]						,					snake_case:							Union[str, Any]=250_300						,					snake_case:							Optional[int]=1_152						,					snake_case:							Any=32						,					snake_case:							Tuple=18						,					snake_case:							List[Any]=256						,					snake_case:							str=1_664						,					snake_case:							Optional[Any]=4_608						,					snake_case:							List[str]="gelu"						,					snake_case:							List[str]=0.0						,					snake_case:							Optional[int]=0.0						,					snake_case:							Tuple=0.1						,					snake_case:							int=512						,					snake_case:							Dict=2						,					snake_case:							Tuple=0.0_2						,					snake_case:							Optional[int]=1E-12						,					snake_case:							Optional[Any]=True						,					snake_case:							Tuple=0						,					snake_case:							Any=312						,					snake_case:							List[Any]=313						,					**snake_case:							int						,					)       ->     Any:
											super().__init__(pad_token_id=snake_case						,					bos_token_id=snake_case						,					eos_token_id=snake_case						,					**snake_case						)

											snake_case_			:int						=      vocab_size
											snake_case_			:str						=      input_embedding_size
											snake_case_			:int						=      output_embedding_size
											snake_case_			:str						=      max_position_embeddings
											snake_case_			:List[Any]						=      hidden_size
											snake_case_			:Tuple						=      num_hidden_layers
											snake_case_			:Dict						=      num_attention_heads
											snake_case_			:Optional[Any]						=      intermediate_size
											snake_case_			:Tuple						=      hidden_act
											snake_case_			:Optional[Any]						=      hidden_dropout_prob
											snake_case_			:List[str]						=      attention_probs_dropout_prob
											snake_case_			:List[Any]						=      classifier_dropout_prob
											snake_case_			:List[str]						=      initializer_range
											snake_case_			:List[Any]						=      type_vocab_size
											snake_case_			:Any						=      layer_norm_eps
											snake_case_			:int						=      use_cache
											snake_case_			:str						=      False

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					@property
					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																	snake_case_			:Union[str, Any]						=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																	snake_case_			:Optional[int]						=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											        ("""token_type_ids""", dynamic_axis),
											    ]						)






					@property
					def        lowerCAmelCase_    (	self:							str						)       ->     float:
											return 1E-4





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__a     =    {
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    ["XLMProphetNetTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    [
						    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLMProphetNetDecoder",
						    "XLMProphetNetEncoder",
						    "XLMProphetNetForCausalLM",
						    "XLMProphetNetForConditionalGeneration",
						    "XLMProphetNetModel",
						    "XLMProphetNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_prophetnet import (
												    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMProphetNetDecoder,
												    XLMProphetNetEncoder,
												    XLMProphetNetForCausalLM,
												    XLMProphetNetForConditionalGeneration,
												    XLMProphetNetModel,
												    XLMProphetNetPreTrainedModel,
												)

else:
						import sys

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