


'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase  :		Any		     =	{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Tuple		     =	['DeiTFeatureExtractor']
				_lowerCAmelCase  :		Union[str, Any]		     =	['DeiTImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Optional[int]		     =	[
				    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DeiTForImageClassification',
				    'DeiTForImageClassificationWithTeacher',
				    'DeiTForMaskedImageModeling',
				    'DeiTModel',
				    'DeiTPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		List[str]		     =	[
				    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDeiTForImageClassification',
				    'TFDeiTForImageClassificationWithTeacher',
				    'TFDeiTForMaskedImageModeling',
				    'TFDeiTModel',
				    'TFDeiTPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_deit import DeiTFeatureExtractor
								from .image_processing_deit import DeiTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deit import (
								    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DeiTForImageClassification,
								    DeiTForImageClassificationWithTeacher,
								    DeiTForMaskedImageModeling,
								    DeiTModel,
								    DeiTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deit import (
								    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDeiTForImageClassification,
								    TFDeiTForImageClassificationWithTeacher,
								    TFDeiTForMaskedImageModeling,
								    TFDeiTModel,
								    TFDeiTPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase  :		Optional[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


_lowerCAmelCase  :		Any		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[str]		     =	{
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					List[str]			      = """xlm-roberta"""



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=1 ,					snake_case__=0 ,					snake_case__=2 ,					snake_case__="absolute" ,					snake_case__=True ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					**snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= hidden_size
		lowerCAmelCase					:    Optional[Any]      							= num_hidden_layers
		lowerCAmelCase					:    Any      							= num_attention_heads
		lowerCAmelCase					:    Optional[int]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= intermediate_size
		lowerCAmelCase					:    Dict      							= hidden_dropout_prob
		lowerCAmelCase					:    Union[str, Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= max_position_embeddings
		lowerCAmelCase					:    Optional[int]      							= type_vocab_size
		lowerCAmelCase					:    int      							= initializer_range
		lowerCAmelCase					:    List[Any]      							= layer_norm_eps
		lowerCAmelCase					:    Union[str, Any]      							= position_embedding_type
		lowerCAmelCase					:    Union[str, Any]      							= use_cache
		lowerCAmelCase					:    List[str]      							= classifier_dropout



class  lowerCAmelCase				(				a							):



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