






from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__		:							Any 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							Tuple 				=				{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'data2vec-text'




     def __init__(				self	,						_lowerCAmelCase=30522	,						_lowerCAmelCase=768	,						_lowerCAmelCase=12	,						_lowerCAmelCase=12	,						_lowerCAmelCase=3072	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=2	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-12	,						_lowerCAmelCase=1	,						_lowerCAmelCase=0	,						_lowerCAmelCase=2	,						_lowerCAmelCase="absolute"	,						_lowerCAmelCase=True	,						_lowerCAmelCase=None	,						**_lowerCAmelCase	,						):
          super().__init__(pad_token_id=_lowerCAmelCase	,						bos_token_id=_lowerCAmelCase	,						eos_token_id=_lowerCAmelCase	,						**_lowerCAmelCase		)

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







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     @property
     def   __UpperCAmelCase  (				self		):
          if self.task == "multiple-choice":
               UpperCAmelCase__      :  List[str]				      =			{0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
               UpperCAmelCase__      :  Tuple				      =			{0: """batch""", 1: """sequence"""}
          return OrderedDict(
              [
                  ("""input_ids""", dynamic_axis),
                  ("""attention_mask""", dynamic_axis),
              ]		)











"""simple docstring"""







from typing import TYPE_CHECKING

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


A		=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['ViTFeatureExtractor']
			A		=						['ViTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ViTForImageClassification',
			    'ViTForMaskedImageModeling',
			    'ViTModel',
			    'ViTPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'TFViTForImageClassification',
			    'TFViTModel',
			    'TFViTPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'FlaxViTForImageClassification',
			    'FlaxViTModel',
			    'FlaxViTPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_vit import ViTFeatureExtractor
						from .image_processing_vit import ViTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit import (
						    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTForImageClassification,
						    ViTForMaskedImageModeling,
						    ViTModel,
						    ViTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
			import sys

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



