
"""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 center_crop, 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, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Union[str, Any]   			=      ['pixel_values']







       def __init__(       self				,lowerCamelCase_ = True				,lowerCamelCase_ = None				,lowerCamelCase_ = PIL.Image.NEAREST				,lowerCamelCase_ = False				,lowerCamelCase_ = None				,lowerCamelCase_ = 1 / 255				,lowerCamelCase_ = False				,lowerCamelCase_ = True				,lowerCamelCase_ = True				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = True				,**lowerCamelCase_				,):



          '''simple docstring'''

          super().__init__(**lowerCamelCase_		)
          _UpperCAmelCase 						=       size if size is not None else {"height": 346, "width": 346}
          _UpperCAmelCase 						=       get_size_dict(lowerCamelCase_		)
          _UpperCAmelCase 						=       crop_size if crop_size is not None else {"height": 289, "width": 289}
          _UpperCAmelCase 						=       get_size_dict(lowerCamelCase_				,param_name="crop_size"		)

          _UpperCAmelCase 						=       do_resize
          _UpperCAmelCase 						=       size
          _UpperCAmelCase 						=       resample
          _UpperCAmelCase 						=       do_center_crop
          _UpperCAmelCase 						=       crop_size
          _UpperCAmelCase 						=       do_rescale
          _UpperCAmelCase 						=       rescale_factor
          _UpperCAmelCase 						=       rescale_offset
          _UpperCAmelCase 						=       do_normalize
          _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
          _UpperCAmelCase 						=       include_top







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_ = PIL.Image.NEAREST				,lowerCamelCase_ = None				,**lowerCamelCase_				,):



          '''simple docstring'''

          _UpperCAmelCase 						=       get_size_dict(lowerCamelCase_		)
          if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
          return resize(
              lowerCamelCase_				,size=(size["height"], size["width"])				,resample=lowerCamelCase_				,data_format=lowerCamelCase_				,**lowerCamelCase_		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_ = None				,**lowerCamelCase_				,):



          '''simple docstring'''

          _UpperCAmelCase 						=       get_size_dict(lowerCamelCase_		)
          if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
          return center_crop(lowerCamelCase_				,size=(size["height"], size["width"])				,data_format=lowerCamelCase_				,**lowerCamelCase_		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_ = True				,lowerCamelCase_ = None				,**lowerCamelCase_				,):



          '''simple docstring'''

          if offset:
             _UpperCAmelCase 						=       (image - 1_2_7.5) * scale
             if data_format is not None:
                _UpperCAmelCase 						=       to_channel_dimension_format(lowerCamelCase_				,lowerCamelCase_		)
             _UpperCAmelCase 						=       rescaled_image.astype(np.floataa		)
          else:
             _UpperCAmelCase 						=       rescale(lowerCamelCase_				,scale=lowerCamelCase_				,data_format=lowerCamelCase_				,**lowerCamelCase_		)
          return rescaled_image







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_ = None				,**lowerCamelCase_				,):



          '''simple docstring'''

          return normalize(lowerCamelCase_				,mean=lowerCamelCase_				,std=lowerCamelCase_				,data_format=lowerCamelCase_				,**lowerCamelCase_		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_=None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = ChannelDimension.FIRST				,**lowerCamelCase_				,):



          '''simple docstring'''

          _UpperCAmelCase 						=       do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase 						=       resample if resample is not None else self.resample
          _UpperCAmelCase 						=       do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase 						=       do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase 						=       rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase 						=       rescale_offset if rescale_offset is not None else self.rescale_offset
          _UpperCAmelCase 						=       do_normalize if do_normalize is not None else self.do_normalize
          _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 						=       include_top if include_top is not None else self.include_top

          _UpperCAmelCase 						=       size if size is not None else self.size
          _UpperCAmelCase 						=       get_size_dict(lowerCamelCase_		)
          _UpperCAmelCase 						=       crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase 						=       get_size_dict(lowerCamelCase_				,param_name="crop_size"		)

          _UpperCAmelCase 						=       make_list_of_images(lowerCamelCase_		)

          if not valid_images(lowerCamelCase_		):
             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 or resample is None:
             raise ValueError("Size and resample 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.
          _UpperCAmelCase 						=       [to_numpy_array(lowerCamelCase_		) for image in images]

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

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

          if do_rescale:
             _UpperCAmelCase 						=       [self.rescale(image=lowerCamelCase_				,scale=lowerCamelCase_				,offset=lowerCamelCase_		) for image in images]

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

          if include_top:
             _UpperCAmelCase 						=       [self.normalize(image=lowerCamelCase_				,mean=[0, 0, 0]				,std=lowerCamelCase_		) for image in images]

          _UpperCAmelCase 						=       [to_channel_dimension_format(lowerCamelCase_				,lowerCamelCase_		) for image in images]

          _UpperCAmelCase 						=       {"pixel_values": images}
          return BatchFeature(data=lowerCamelCase_				,tensor_type=lowerCamelCase_		)



"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

