"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase     					=     logging.get_logger(__name__)

UpperCamelCase     					=     {
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					List[Any]						      =							"""rembert"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=2_5_0_3_0_0       ,     _SCREAMING_SNAKE_CASE=1_1_5_2       ,     _SCREAMING_SNAKE_CASE=3_2       ,     _SCREAMING_SNAKE_CASE=1_8       ,     _SCREAMING_SNAKE_CASE=2_5_6       ,     _SCREAMING_SNAKE_CASE=1_6_6_4       ,     _SCREAMING_SNAKE_CASE=4_6_0_8       ,     _SCREAMING_SNAKE_CASE="gelu"       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=0.0       ,     _SCREAMING_SNAKE_CASE=0.1       ,     _SCREAMING_SNAKE_CASE=5_1_2       ,     _SCREAMING_SNAKE_CASE=2       ,     _SCREAMING_SNAKE_CASE=0.0_2       ,     _SCREAMING_SNAKE_CASE=1E-12       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=0       ,     _SCREAMING_SNAKE_CASE=3_1_2       ,     _SCREAMING_SNAKE_CASE=3_1_3       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Tuple:
							super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       ,     bos_token_id=_SCREAMING_SNAKE_CASE       ,     eos_token_id=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )

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




class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""







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







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





"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

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




