






"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_		:			Any				   =					{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_		:			Optional[Any]				   =					[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

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








"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
          return resize(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(A	,mean=A	,std=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       make_list_of_images(A					)

          if not valid_images(A					):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

          if do_resize and size is None:
                raise ValueError("""Size must be specified if do_resize is True."""					)

          if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

          # All transformations expect numpy arrays.
          UpperCAmelCase							=       [to_numpy_array(A					) for image in images]

          if do_resize:
                UpperCAmelCase							=       [self.resize(image=A	,size=A	,resample=A					) for image in images]

          if do_rescale:
                UpperCAmelCase							=       [self.rescale(image=A	,scale=A					) for image in images]

          if do_normalize:
                UpperCAmelCase							=       [self.normalize(image=A	,mean=A	,std=A					) for image in images]

          UpperCAmelCase							=       [to_channel_dimension_format(A	,A					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=A	,tensor_type=A					)



