'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCAmelCase_			:			str    	=   logging.get_logger(__name__)


if is_vision_available():
       import PIL





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


   def __init__( self					:						List[Any] ,		__lowerCAmelCase					:						Optional[Any] = True ,		__lowerCAmelCase					:						List[str] = None ,		__lowerCAmelCase					:						Optional[int] = PILImageResampling.BILINEAR ,		__lowerCAmelCase					:						Any = True ,		__lowerCAmelCase					:						Any = False ,		__lowerCAmelCase					:						Optional[Any] = True ,		__lowerCAmelCase					:						Union[str, Any] = True ,		__lowerCAmelCase					:						Union[str, Any] = 1 / 255 ,		__lowerCAmelCase					:						Tuple = True ,		__lowerCAmelCase					:						Optional[int] = None ,		__lowerCAmelCase					:						List[Any] = None ,		**__lowerCAmelCase					:						Optional[Any] ,		)   ->       None:
         super().__init__(**__UpperCAmelCase)

         lowercase_     			=   size if size is not None else {"height": 2560, "width": 1920}
         if isinstance(__UpperCAmelCase ,		(tuple, list)):
               # The previous feature extractor size parameter was in (width, height) format
               lowercase_     			=   size[::-1]
         lowercase_     			=   get_size_dict(__UpperCAmelCase)

         lowercase_     			=   do_resize
         lowercase_     			=   size
         lowercase_     			=   resample
         lowercase_     			=   do_thumbnail
         lowercase_     			=   do_align_long_axis
         lowercase_     			=   do_pad
         lowercase_     			=   do_rescale
         lowercase_     			=   rescale_factor
         lowercase_     			=   do_normalize
         lowercase_     			=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase_     			=   image_std if image_std is not None else IMAGENET_STANDARD_STD


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Dict = None)   ->       np.ndarray:
         lowercase_			,				lowercase_     			=   get_image_size(__UpperCAmelCase)
         lowercase_			,				lowercase_     			=   size["height"], size["width"]

         if (output_width < output_height and input_width > input_height) or (
             output_width > output_height and input_width < input_height
         ):
               lowercase_     			=   np.rotaa(__UpperCAmelCase ,		3)

         if data_format is not None:
               lowercase_     			=   to_channel_dimension_format(__UpperCAmelCase ,		__UpperCAmelCase)

         return image


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						List[str] = False ,		__lowerCAmelCase					:						Any = None ,		)   ->       np.ndarray:
         lowercase_			,				lowercase_     			=   size["height"], size["width"]
         lowercase_			,				lowercase_     			=   get_image_size(__UpperCAmelCase)

         lowercase_     			=   output_width - input_width
         lowercase_     			=   output_height - input_height

         if random_padding:
               lowercase_     			=   np.random.randint(low=0 ,		high=delta_height + 1)
               lowercase_     			=   np.random.randint(low=0 ,		high=delta_width + 1)
         else:
               lowercase_     			=   delta_height // 2
               lowercase_     			=   delta_width // 2

         lowercase_     			=   delta_height - pad_top
         lowercase_     			=   delta_width - pad_left

         lowercase_     			=   ((pad_top, pad_bottom), (pad_left, pad_right))
         return pad(__UpperCAmelCase ,		__UpperCAmelCase ,		data_format=__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						List[str] ,		*__lowerCAmelCase					:						List[Any] ,		**__lowerCAmelCase					:						Optional[Any])   ->       List[Any]:
         logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead.")
         return self.pad_image(*__UpperCAmelCase ,		**__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Union[str, Any] = PILImageResampling.BICUBIC ,		__lowerCAmelCase					:						Dict = None ,		**__lowerCAmelCase					:						Any ,		)   ->       np.ndarray:
         lowercase_			,				lowercase_     			=   get_image_size(__UpperCAmelCase)
         lowercase_			,				lowercase_     			=   size["height"], size["width"]

         # We always resize to the smallest of either the input or output size.
         lowercase_     			=   min(__UpperCAmelCase ,		__UpperCAmelCase)
         lowercase_     			=   min(__UpperCAmelCase ,		__UpperCAmelCase)

         if height == input_height and width == input_width:
               return image

         if input_height > input_width:
               lowercase_     			=   int(input_width * height / input_height)
         elif input_width > input_height:
               lowercase_     			=   int(input_height * width / input_width)

         return resize(
             __UpperCAmelCase ,		size=(height, width) ,		resample=__UpperCAmelCase ,		reducing_gap=2.0 ,		data_format=__UpperCAmelCase ,		**__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						int = PILImageResampling.BICUBIC ,		__lowerCAmelCase					:						Optional[Any] = None ,		**__lowerCAmelCase					:						List[str] ,		)   ->       np.ndarray:
         lowercase_     			=   get_size_dict(__UpperCAmelCase)
         lowercase_     			=   min(size["height"] ,		size["width"])
         lowercase_     			=   get_resize_output_image_size(__UpperCAmelCase ,		size=__UpperCAmelCase ,		default_to_square=__UpperCAmelCase)
         lowercase_     			=   resize(__UpperCAmelCase ,		size=__UpperCAmelCase ,		resample=__UpperCAmelCase ,		data_format=__UpperCAmelCase ,		**__UpperCAmelCase)
         return resized_image


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						Any = None ,		**__lowerCAmelCase					:						List[str] ,		)   ->       Optional[Any]:
         return rescale(__UpperCAmelCase ,		scale=__UpperCAmelCase ,		data_format=__UpperCAmelCase ,		**__UpperCAmelCase)


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


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Union[str, Any] = None ,		__lowerCAmelCase					:						Optional[int] = None ,		__lowerCAmelCase					:						str = None ,		__lowerCAmelCase					:						Dict = None ,		__lowerCAmelCase					:						List[Any] = None ,		__lowerCAmelCase					:						str = None ,		__lowerCAmelCase					:						Optional[int] = False ,		__lowerCAmelCase					:						int = None ,		__lowerCAmelCase					:						Dict = None ,		__lowerCAmelCase					:						Optional[Any] = None ,		__lowerCAmelCase					:						Dict = None ,		__lowerCAmelCase					:						int = None ,		__lowerCAmelCase					:						Optional[int] = None ,		__lowerCAmelCase					:						Tuple = ChannelDimension.FIRST ,		**__lowerCAmelCase					:						Any ,		)   ->       PIL.Image.Image:
         lowercase_     			=   do_resize if do_resize is not None else self.do_resize
         lowercase_     			=   size if size is not None else self.size
         if isinstance(__UpperCAmelCase ,		(tuple, list)):
               # Previous feature extractor had size in (width, height) format
               lowercase_     			=   size[::-1]
         lowercase_     			=   get_size_dict(__UpperCAmelCase)
         lowercase_     			=   resample if resample is not None else self.resample
         lowercase_     			=   do_thumbnail if do_thumbnail is not None else self.do_thumbnail
         lowercase_     			=   do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
         lowercase_     			=   do_pad if do_pad is not None else self.do_pad
         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_     			=   make_list_of_images(__UpperCAmelCase)

         if not valid_images(__UpperCAmelCase):
               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_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True.")

         if do_pad and size is None:
               raise ValueError("Size must be specified if do_pad 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(__UpperCAmelCase) for image in images]

         if do_align_long_axis:
               lowercase_     			=   [self.align_long_axis(__UpperCAmelCase ,		size=__UpperCAmelCase) for image in images]

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

         if do_thumbnail:
               lowercase_     			=   [self.thumbnail(image=__UpperCAmelCase ,		size=__UpperCAmelCase) for image in images]

         if do_pad:
               lowercase_     			=   [self.pad_image(image=__UpperCAmelCase ,		size=__UpperCAmelCase ,		random_padding=__UpperCAmelCase) for image in images]

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

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

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

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










"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

     return "".join(chr(ord(_lowerCAmelCase							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
   from doctest import testmod

   testmod()