



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 (
    center_crop,
    get_resize_output_image_size,
    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


_lowercase						  =		logging.get_logger(__name__)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				['''pixel_values''']




     def __init__(				self ,						a__ = True ,						a__ = None ,						a__ = PILImageResampling.BILINEAR ,						a__ = True ,						a__ = None ,						a__ = True ,						a__ = 1 / 2_5_5 ,						a__ = True ,						a__ = None ,						a__ = None ,						**a__ ,						):
         super().__init__(**UpperCamelCase_)
         A__		 =					size if size is not None else {'shortest_edge': 2_5_6}
         A__		 =					get_size_dict(UpperCamelCase_ ,						default_to_square=UpperCamelCase_)
         A__		 =					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         A__		 =					get_size_dict(UpperCamelCase_)
         A__		 =					do_resize
         A__		 =					size
         A__		 =					resample
         A__		 =					do_center_crop
         A__		 =					crop_size
         A__		 =					do_rescale
         A__		 =					rescale_factor
         A__		 =					do_normalize
         A__		 =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A__		 =					image_std if image_std is not None else IMAGENET_STANDARD_STD




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = PILImageResampling.BICUBIC ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(UpperCamelCase_ ,						default_to_square=UpperCamelCase_)
         if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
         A__		 =					get_resize_output_image_size(UpperCamelCase_ ,						size=size['''shortest_edge'''] ,						default_to_square=UpperCamelCase_)
         return resize(UpperCamelCase_ ,						size=UpperCamelCase_ ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(UpperCamelCase_)
         return center_crop(UpperCamelCase_ ,						size=(size['''height'''], size['''width''']) ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						**a__):
         return rescale(UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         return normalize(UpperCamelCase_ ,						mean=UpperCamelCase_ ,						std=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_)




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = ChannelDimension.FIRST ,						**a__ ,						):
         A__		 =					do_resize if do_resize is not None else self.do_resize
         A__		 =					size if size is not None else self.size
         A__		 =					get_size_dict(UpperCamelCase_ ,						default_to_square=UpperCamelCase_)
         A__		 =					resample if resample is not None else self.resample
         A__		 =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A__		 =					crop_size if crop_size is not None else self.crop_size
         A__		 =					get_size_dict(UpperCamelCase_)
         A__		 =					do_rescale if do_rescale is not None else self.do_rescale
         A__		 =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A__		 =					do_normalize if do_normalize is not None else self.do_normalize
         A__		 =					image_mean if image_mean is not None else self.image_mean
         A__		 =					image_std if image_std is not None else self.image_std

         A__		 =					make_list_of_images(UpperCamelCase_)

         if not valid_images(UpperCamelCase_):
             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_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.''')

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

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

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

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

         if do_center_crop:
             A__		 =					[self.center_crop(image=UpperCamelCase_ ,						size=UpperCamelCase_) for image in images]

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

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

         A__		 =					[to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_) for image in images]

         A__		 =					{'pixel_values': images}
         return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_)








from typing import TYPE_CHECKING

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


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileViTForImageClassification",
       "MobileViTForSemanticSegmentation",
       "MobileViTModel",
       "MobileViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "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

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
