



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


__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : List[Any]       					=  ["pixel_values"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = 1 / 255,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		None:
          super().__init__(**SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     size if size is not None else {'shortest_edge': 256}
          UpperCamelCase   :  Optional[int]						     =     get_size_dict(SCREAMING_SNAKE_CASE_,      default_to_square=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     crop_size if crop_size is not None else {'height': 224, 'width': 224}
          UpperCamelCase   :  Dict						     =     get_size_dict(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     do_resize
          UpperCamelCase   :  Optional[int]						     =     size
          UpperCamelCase   :  List[str]						     =     resample
          UpperCamelCase   :  Union[str, Any]						     =     do_center_crop
          UpperCamelCase   :  List[str]						     =     crop_size
          UpperCamelCase   :  Optional[Any]						     =     do_rescale
          UpperCamelCase   :  str						     =     rescale_factor
          UpperCamelCase   :  List[str]						     =     do_normalize
          UpperCamelCase   :  Any						     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase   :  int						     =     image_std if image_std is not None else IMAGENET_STANDARD_STD
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          UpperCamelCase   :  List[str]						     =     get_size_dict(SCREAMING_SNAKE_CASE_,      default_to_square=SCREAMING_SNAKE_CASE_	)
          if "shortest_edge" not in size:
             raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
          UpperCamelCase   :  Union[str, Any]						     =     get_resize_output_image_size(SCREAMING_SNAKE_CASE_,      size=size['shortest_edge'],      default_to_square=SCREAMING_SNAKE_CASE_	)
          return resize(SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_,      resample=SCREAMING_SNAKE_CASE_,      data_format=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          UpperCamelCase   :  Union[str, Any]						     =     get_size_dict(SCREAMING_SNAKE_CASE_	)
          return center_crop(SCREAMING_SNAKE_CASE_,      size=(size['height'], size['width']),      data_format=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_	)					->		np.ndarray:
          return rescale(SCREAMING_SNAKE_CASE_,      scale=SCREAMING_SNAKE_CASE_,      data_format=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          return normalize(SCREAMING_SNAKE_CASE_,      mean=SCREAMING_SNAKE_CASE_,      std=SCREAMING_SNAKE_CASE_,      data_format=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,      **SCREAMING_SNAKE_CASE_,      )					->		Any:
          UpperCamelCase   :  List[str]						     =     do_resize if do_resize is not None else self.do_resize
          UpperCamelCase   :  Any						     =     size if size is not None else self.size
          UpperCamelCase   :  Optional[Any]						     =     get_size_dict(SCREAMING_SNAKE_CASE_,      default_to_square=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     resample if resample is not None else self.resample
          UpperCamelCase   :  Dict						     =     do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase   :  List[str]						     =     crop_size if crop_size is not None else self.crop_size
          UpperCamelCase   :  Optional[Any]						     =     get_size_dict(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase   :  str						     =     rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase   :  Optional[Any]						     =     do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase   :  int						     =     image_mean if image_mean is not None else self.image_mean
          UpperCamelCase   :  List[str]						     =     image_std if image_std is not None else self.image_std

          UpperCamelCase   :  List[Any]						     =     make_list_of_images(SCREAMING_SNAKE_CASE_	)

          if not valid_images(SCREAMING_SNAKE_CASE_	):
             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.
          UpperCamelCase   :  Tuple						     =     [to_numpy_array(SCREAMING_SNAKE_CASE_	) for image in images]

          if do_resize:
             UpperCamelCase   :  Optional[int]						     =     [self.resize(image=SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_,      resample=SCREAMING_SNAKE_CASE_	) for image in images]

          if do_center_crop:
             UpperCamelCase   :  Any						     =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_	) for image in images]

          if do_rescale:
             UpperCamelCase   :  str						     =     [self.rescale(image=SCREAMING_SNAKE_CASE_,      scale=SCREAMING_SNAKE_CASE_	) for image in images]

          if do_normalize:
             UpperCamelCase   :  Optional[int]						     =     [self.normalize(image=SCREAMING_SNAKE_CASE_,      mean=SCREAMING_SNAKE_CASE_,      std=SCREAMING_SNAKE_CASE_	) for image in images]

          UpperCamelCase   :  Union[str, Any]						     =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) for image in images]

          UpperCamelCase   :  Union[str, Any]						     =     {'pixel_values': images}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_,      tensor_type=SCREAMING_SNAKE_CASE_	)








from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     lowerCAmelCase_		(  a__							):
       @staticmethod
       @abstractmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		int:
          raise NotImplementedError()






       @abstractmethod
       def  snake_case_	(      self	)					->		str:
          raise NotImplementedError()



