



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


snake_case_			:     Any  	=  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  ['MobileViTFeatureExtractor']
					snake_case_			:     Any  	=  ['MobileViTImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[Any]  	=  [
					    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MobileViTForImageClassification',
					    'MobileViTForSemanticSegmentation',
					    'MobileViTModel',
					    'MobileViTPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     int  	=  [
					    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFMobileViTForImageClassification',
					    'TFMobileViTForSemanticSegmentation',
					    'TFMobileViTModel',
					    'TFMobileViTPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilevit import MobileViTFeatureExtractor
										from .image_processing_mobilevit import MobileViTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevit import (
										    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTForImageClassification,
										    MobileViTForSemanticSegmentation,
										    MobileViTModel,
										    MobileViTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mobilevit import (
										    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFMobileViTForImageClassification,
										    TFMobileViTForSemanticSegmentation,
										    TFMobileViTModel,
										    TFMobileViTPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Union[str, Any]  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


snake_case_			:     Optional[int]  	=  logging.get_logger(__name__)

snake_case_			:     List[Any]  	=  {
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """xlm-roberta-xl"""





	def __init__(			self :    Optional[int]			,lowerCamelCase__ :    Optional[Any]=250880			,lowerCamelCase__ :    Tuple=2560			,lowerCamelCase__ :    Union[str, Any]=36			,lowerCamelCase__ :    List[str]=32			,lowerCamelCase__ :    Optional[Any]=10240			,lowerCamelCase__ :    Tuple="gelu"			,lowerCamelCase__ :    int=0.1			,lowerCamelCase__ :    int=0.1			,lowerCamelCase__ :    Optional[int]=514			,lowerCamelCase__ :    List[str]=1			,lowerCamelCase__ :    Dict=0.0_2			,lowerCamelCase__ :    Any=1E-05			,lowerCamelCase__ :    Union[str, Any]=1			,lowerCamelCase__ :    str=0			,lowerCamelCase__ :    Tuple=2			,lowerCamelCase__ :    Union[str, Any]="absolute"			,lowerCamelCase__ :    Optional[Any]=True			,lowerCamelCase__ :    List[str]=None			,**lowerCamelCase__ :    Dict			,):







							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__			,bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,**lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       hidden_size
							_UpperCamelCase :		str			    =       num_hidden_layers
							_UpperCamelCase :		str			    =       num_attention_heads
							_UpperCamelCase :		Any			    =       hidden_act
							_UpperCamelCase :		Dict			    =       intermediate_size
							_UpperCamelCase :		Optional[int]			    =       hidden_dropout_prob
							_UpperCamelCase :		Any			    =       attention_probs_dropout_prob
							_UpperCamelCase :		List[Any]			    =       max_position_embeddings
							_UpperCamelCase :		str			    =       type_vocab_size
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		Optional[int]			    =       layer_norm_eps
							_UpperCamelCase :		Optional[int]			    =       position_embedding_type
							_UpperCamelCase :		Optional[Any]			    =       use_cache
							_UpperCamelCase :		Optional[Any]			    =       classifier_dropout






class 							lowercase__     (							lowercase       ):





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







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


