



"""simple docstring"""

from typing import TYPE_CHECKING

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


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

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						str												=			['''ViTFeatureExtractor''']
     lowercase__					:						Optional[int]												=			['''ViTImageProcessor''']

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''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

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










"""simple docstring"""

from statistics import mean, stdev




def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Optional[int]       =			min(_a					)
    snake_case_				:  str       =			max(_a					)
    # normalize data
    return [round((x - x_min) / (x_max - x_min)	,    _a					) for x in data]






def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Any       =			mean(_a					)
    snake_case_				:  str       =			stdev(_a					)
    # standardize data
    return [round((x - mu) / (sigma)	,    _a					) for x in data]





