"""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


UpperCAmelCase__     						=  logging.get_logger(__name__)


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     int   							=							['pixel_values']


    def __init__(    self		:     Union[str, Any]      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Dict[str, int] = None      , __lowerCAmelCase		:     PILImageResampling = PIL.Image.BICUBIC      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Dict[str, int] = None      , __lowerCAmelCase		:     Union[int, float] = 1 / 255      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , **__lowerCAmelCase		:     int      , ):
      super().__init__(**__lowerCAmelCase    )
      _UpperCAmelCase         =					size if size is not None else {"""height""": 256, """width""": 256}
      _UpperCAmelCase         =					get_size_dict(__lowerCAmelCase    )
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      _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         =					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


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     np.ndarray      , __lowerCAmelCase		:     Dict[str, int]      , __lowerCAmelCase		:     PILImageResampling = PIL.Image.BICUBIC      , __lowerCAmelCase		:     Optional[Union[str, ChannelDimension]] = None      , **__lowerCAmelCase		:     int      , ):
      _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    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     np.ndarray      , __lowerCAmelCase		:     Dict[str, int]      , __lowerCAmelCase		:     Optional[Union[str, ChannelDimension]] = None      , **__lowerCAmelCase		:     Tuple      , ):
      _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    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     np.ndarray      , __lowerCAmelCase		:     Union[int, float]      , __lowerCAmelCase		:     Optional[Union[str, ChannelDimension]] = None      , **__lowerCAmelCase		:     int      , ):
      return rescale(__lowerCAmelCase      , scale=__lowerCAmelCase      , data_format=__lowerCAmelCase      , **__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Any      , __lowerCAmelCase		:     np.ndarray      , __lowerCAmelCase		:     Union[float, List[float]]      , __lowerCAmelCase		:     Union[float, List[float]]      , __lowerCAmelCase		:     Optional[Union[str, ChannelDimension]] = None      , **__lowerCAmelCase		:     Optional[Any]      , ):
      return normalize(__lowerCAmelCase      , mean=__lowerCAmelCase      , std=__lowerCAmelCase      , data_format=__lowerCAmelCase      , **__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     ImageInput      , __lowerCAmelCase		:     bool = None      , __lowerCAmelCase		:     Dict[str, int] = None      , __lowerCAmelCase		:     Union[str, Any]=None      , __lowerCAmelCase		:     bool = None      , __lowerCAmelCase		:     Dict[str, int] = None      , __lowerCAmelCase		:     bool = None      , __lowerCAmelCase		:     float = None      , __lowerCAmelCase		:     bool = None      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , __lowerCAmelCase		:     Optional[Union[str, TensorType]] = None      , __lowerCAmelCase		:     ChannelDimension = ChannelDimension.FIRST      , **__lowerCAmelCase		:     Optional[int]      , ):
      _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         =					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         =					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    ) for image in images]

      if do_normalize:
        _UpperCAmelCase         =					[self.normalize(image=__lowerCAmelCase      , mean=__lowerCAmelCase      , 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


warnings.warn(
    """memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
    """`from accelerate import find_executable_batch_size` to avoid this warning.""",
    FutureWarning,
)
