


from collections import OrderedDict
from typing import Mapping

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


snake_case__     							= logging.get_logger(__name__)

snake_case__     							= {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class     snake_case__  (      _a  ):
				__lowerCAmelCase		=      'rembert'
				def __init__(  self     ,      _UpperCAmelCase=25_0300     ,      _UpperCAmelCase=1152     ,      _UpperCAmelCase=32     ,      _UpperCAmelCase=18     ,      _UpperCAmelCase=256     ,      _UpperCAmelCase=1664     ,      _UpperCAmelCase=4608     ,      _UpperCAmelCase="gelu"     ,      _UpperCAmelCase=0.0     ,      _UpperCAmelCase=0.0     ,      _UpperCAmelCase=0.1     ,      _UpperCAmelCase=512     ,      _UpperCAmelCase=2     ,      _UpperCAmelCase=0.02     ,      _UpperCAmelCase=1E-1_2     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=0     ,      _UpperCAmelCase=312     ,      _UpperCAmelCase=313     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										super().__init__(pad_token_id=_UpperCAmelCase     ,      bos_token_id=_UpperCAmelCase     ,      eos_token_id=_UpperCAmelCase     ,      **_UpperCAmelCase      )

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






class     snake_case__  (      _a  ):
				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										if self.task == "multiple-choice":
																__magic_name__				:							Union[str, Any]				=      {0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																__magic_name__				:							Union[str, Any]				=      {0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										        ('token_type_ids', dynamic_axis),
										    ]      )





				@property
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return 1E-4






from typing import TYPE_CHECKING

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


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

			snake_case__     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


