


"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


lowercase  		=			logging.get_logger(__name__)

lowercase  		=			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Dict				  =  MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							__magic_name__					:     List[Any]				  =  '''mobilebert'''




							def __init__(						self	,					lowerCamelCase__=30_522	,					lowerCamelCase__=512	,					lowerCamelCase__=24	,					lowerCamelCase__=4	,					lowerCamelCase__=512	,					lowerCamelCase__="relu"	,					lowerCamelCase__=0.0	,					lowerCamelCase__=0.1	,					lowerCamelCase__=512	,					lowerCamelCase__=2	,					lowerCamelCase__=0.02	,					lowerCamelCase__=1E-12	,					lowerCamelCase__=0	,					lowerCamelCase__=128	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=128	,					lowerCamelCase__=False	,					lowerCamelCase__=True	,					lowerCamelCase__=4	,					lowerCamelCase__="no_norm"	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					**lowerCamelCase__	,					)   ->	str:







								'''simple docstring'''




								super().__init__(pad_token_id=lowerCamelCase__	,					**lowerCamelCase__)

								snake_case__  :						List[Any]		=      vocab_size
								snake_case__  :						Any		=      hidden_size
								snake_case__  :						int		=      num_hidden_layers
								snake_case__  :						Optional[Any]		=      num_attention_heads
								snake_case__  :						List[Any]		=      hidden_act
								snake_case__  :						Optional[int]		=      intermediate_size
								snake_case__  :						Optional[int]		=      hidden_dropout_prob
								snake_case__  :						List[str]		=      attention_probs_dropout_prob
								snake_case__  :						Tuple		=      max_position_embeddings
								snake_case__  :						Dict		=      type_vocab_size
								snake_case__  :						Optional[Any]		=      initializer_range
								snake_case__  :						Any		=      layer_norm_eps
								snake_case__  :						Optional[Any]		=      embedding_size
								snake_case__  :						Any		=      trigram_input
								snake_case__  :						Optional[Any]		=      use_bottleneck
								snake_case__  :						Union[str, Any]		=      intra_bottleneck_size
								snake_case__  :						Tuple		=      use_bottleneck_attention
								snake_case__  :						str		=      key_query_shared_bottleneck
								snake_case__  :						Tuple		=      num_feedforward_networks
								snake_case__  :						Optional[Any]		=      normalization_type
								snake_case__  :						List[Any]		=      classifier_activation

								if self.use_bottleneck:
									snake_case__  :						Tuple		=      intra_bottleneck_size
								else:
									snake_case__  :						Union[str, Any]		=      hidden_size

								snake_case__  :						Dict		=      classifier_dropout


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							@property
							def        UpperCAmelCase (						self)   ->	Mapping[str, Mapping[int, str]]:







								'''simple docstring'''




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









"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """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:
					lowercase  		=			[
					    """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

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





