


'''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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a_   :      str				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		["""pixel_values"""]
 def __init__(	self ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = PILImageResampling.BICUBIC ,			__magic_name__ = True ,			__magic_name__ = True ,			__magic_name__ = 1 / 2_55 ,			__magic_name__ = None ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      super().__init__(**__magic_name__  )
      _a      =							size if size is not None else {'height': 2_24, 'width': 2_24}
      _a      =							get_size_dict(__magic_name__  )
      _a      =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      _a      =							get_size_dict(__magic_name__ ,			default_to_square=__magic_name__ ,			param_name='crop_size'  )

      _a      =							do_resize
      _a      =							do_rescale
      _a      =							do_normalize
      _a      =							do_center_crop
      _a      =							crop_size
      _a      =							size
      _a      =							resample
      _a      =							rescale_factor
      _a      =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _a      =							image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = PILImageResampling.BILINEAR ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							np.ndarray:
      _a      =							get_size_dict(__magic_name__  )

      if "shortest_edge" in size:
           _a      =							get_resize_output_image_size(__magic_name__ ,			size=size['shortest_edge'] ,			default_to_square=__magic_name__  )
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
           _a      =							(size['height'], size['width'])
      else:
           raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'  )
      return resize(__magic_name__ ,			size=__magic_name__ ,			resample=__magic_name__ ,			data_format=__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							np.ndarray:
      _a      =							get_size_dict(__magic_name__  )
      if "height" not in size or "width" not in size:
           raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'  )
      return center_crop(__magic_name__ ,			size=(size['height'], size['width']) ,			data_format=__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = None ,			**__magic_name__  )   ->							np.ndarray:
      return rescale(__magic_name__ ,			scale=__magic_name__ ,			data_format=__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							np.ndarray:
      return normalize(__magic_name__ ,			mean=__magic_name__ ,			std=__magic_name__ ,			data_format=__magic_name__ ,			**__magic_name__  )




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = ChannelDimension.FIRST ,			**__magic_name__ ,			)   ->							BatchFeature:
      _a      =							do_resize if do_resize is not None else self.do_resize
      _a      =							do_rescale if do_rescale is not None else self.do_rescale
      _a      =							do_normalize if do_normalize is not None else self.do_normalize
      _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(__magic_name__ ,			param_name='crop_size' ,			default_to_square=__magic_name__  )
      _a      =							resample if resample is not None else self.resample
      _a      =							rescale_factor if rescale_factor is not None else self.rescale_factor
      _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      =							size if size is not None else self.size
      _a      =							get_size_dict(__magic_name__  )

      if not is_batched(__magic_name__  ):
           _a      =							[images]

      if not valid_images(__magic_name__  ):
           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.'  )

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

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

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

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

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

      _a      =							[to_channel_dimension_format(__magic_name__ ,			__magic_name__  ) for image in images]

      _a      =							{'pixel_values': images}
      return BatchFeature(data=__magic_name__ ,			tensor_type=__magic_name__  )





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

