'''simple docstring'''

from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_ : str						  =      logging.get_logger(__name__)





class 							SCREAMING_SNAKE_CASE__						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BICUBIC   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Union[int, float] = 1 / 255   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN   ,		__lowerCAmelCase				:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					None:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      size if size is not None else {"shortest_edge": 224}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="crop_size")

         lowercase_     =      do_resize
         lowercase_     =      size
         lowercase_     =      resample
         lowercase_     =      do_center_crop
         lowercase_     =      crop_size
         lowercase_     =      do_rescale
         lowercase_     =      rescale_factor
         lowercase_     =      do_normalize
         lowercase_     =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         lowercase_     =      image_std if image_std is not None else IMAGENET_DEFAULT_STD


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BICUBIC   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	int   ,		)      ->					np.ndarray:
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
               lowercase_     =      int((256 / 224) * size["shortest_edge"])
               lowercase_     =      get_resize_output_image_size(__lowerCAmelCase   ,		size=__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
               lowercase_     =      {"height": output_size[0], "width": output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
               raise ValueError(
                   F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}')
         return resize(
             __lowerCAmelCase   ,		size=(size_dict["height"], size_dict["width"])   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Any   ,		)      ->					np.ndarray:
         lowercase_     =      get_size_dict(__lowerCAmelCase)
         if "height" not in size or "width" not in size:
               raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}')
         return center_crop(__lowerCAmelCase   ,		size=(size["height"], size["width"])   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Union[int, float]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					np.ndarray:
         return rescale(__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


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


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	ImageInput   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Dict[str, int]] = None   ,		__lowerCAmelCase				:	PILImageResampling = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Dict[str, int]] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[float] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[float, Iterable[float]]] = None   ,		__lowerCAmelCase				:	Optional[Union[float, Iterable[float]]] = None   ,		__lowerCAmelCase				:	Optional[TensorType] = None   ,		__lowerCAmelCase				:	ChannelDimension = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	Tuple   ,		)      ->					BatchFeature:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      resample if resample is not None else self.resample
         lowercase_     =      do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase_     =      do_normalize if do_normalize is not None else self.do_normalize
         lowercase_     =      image_mean if image_mean is not None else self.image_mean
         lowercase_     =      image_std if image_std is not None else self.image_std

         lowercase_     =      size if size is not None else self.size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      crop_size if crop_size is not None else self.crop_size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="crop_size")

         lowercase_     =      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:
               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.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for image in images]

         if do_resize:
               lowercase_     =      [self.resize(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         if do_center_crop:
               lowercase_     =      [self.center_crop(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         if do_normalize:
               lowercase_     =      [self.normalize(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)







'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	List[str]=PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					None:
         lowercase_     =      do_resize
         lowercase_     =      do_rescale
         lowercase_     =      size_divisor
         lowercase_     =      resample
         super().__init__(**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	Any)      ->					np.ndarray:
         lowercase_ ,							lowercase_     =      get_image_size(__lowerCAmelCase)
         # Rounds the height and width down to the closest multiple of size_divisor
         lowercase_     =      height // size_divisor * size_divisor
         lowercase_     =      width // size_divisor * size_divisor
         lowercase_     =      resize(__lowerCAmelCase   ,		(new_h, new_w)   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)
         return image


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	float   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	int)      ->					np.ndarray:
         return rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	str=None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[TensorType, str]] = None   ,		__lowerCAmelCase				:	ChannelDimension = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	List[str]   ,		)      ->					BatchFeature:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      size_divisor if size_divisor is not None else self.size_divisor
         lowercase_     =      resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
               raise ValueError("size_divisor is required for resizing")

         lowercase_     =      make_list_of_images(__lowerCAmelCase)

         if not valid_images(__lowerCAmelCase):
               raise ValueError("Invalid image(s)")

         # All transformations expect numpy arrays.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for img in images]

         if do_resize:
               lowercase_     =      [self.resize(__lowerCAmelCase   ,		size_divisor=__lowerCAmelCase   ,		resample=__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(__lowerCAmelCase   ,		scale=1 / 255) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)






