


"""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       (			_a ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      'rembert'






							def __init__(						self :     Union[str, Any]		, lowerCamelCase_ :     str=250300		, lowerCamelCase_ :     List[Any]=1152		, lowerCamelCase_ :     Any=32		, lowerCamelCase_ :     str=18		, lowerCamelCase_ :     str=256		, lowerCamelCase_ :     List[Any]=1664		, lowerCamelCase_ :     str=4608		, lowerCamelCase_ :     str="gelu"		, lowerCamelCase_ :     Any=0.0		, lowerCamelCase_ :     Dict=0.0		, lowerCamelCase_ :     Union[str, Any]=0.1		, lowerCamelCase_ :     Dict=512		, lowerCamelCase_ :     List[Any]=2		, lowerCamelCase_ :     Any=0.02		, lowerCamelCase_ :     Optional[Any]=1E-12		, lowerCamelCase_ :     Tuple=True		, lowerCamelCase_ :     List[Any]=0		, lowerCamelCase_ :     Optional[int]=312		, lowerCamelCase_ :     Any=313		, **lowerCamelCase_ :     Tuple		, ):
													super().__init__(pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, **lowerCamelCase_				)

													UpperCAmelCase__		=    vocab_size
													UpperCAmelCase__		=    input_embedding_size
													UpperCAmelCase__		=    output_embedding_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__		=    classifier_dropout_prob
													UpperCAmelCase__		=    initializer_range
													UpperCAmelCase__		=    type_vocab_size
													UpperCAmelCase__		=    layer_norm_eps
													UpperCAmelCase__		=    use_cache
													UpperCAmelCase__		=    False




class 							lowerCamelCase       (			_a ):


							"""simple docstring"""






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






							@property
							def 					lowerCAmelCase     (						self :     str				):
													return 1E-4



"""simple docstring"""







from typing import TYPE_CHECKING

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


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

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