



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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_lowercase	  =		logging.get_logger(__name__)



class     _UpperCAmelCase			(       _snake_case     ):
     UpperCamelCase__		    =				['''pixel_values''']




     def __init__(				self ,						a__ = True ,						a__ = None ,						a__ = 0.9 ,						a__ = PILImageResampling.BICUBIC ,						a__ = True ,						a__ = None ,						a__ = 1 / 2_5_5 ,						a__ = True ,						a__ = True ,						a__ = None ,						a__ = None ,						**a__ ,						):
         super().__init__(**lowerCAmelCase__)
         A__		 =					size if size is not None else {'''shortest_edge''': 2_2_4}
         A__		 =					get_size_dict(lowerCAmelCase__ ,						default_to_square=lowerCAmelCase__)
         A__		 =					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         A__		 =					get_size_dict(lowerCAmelCase__ ,						param_name='''crop_size''')

         A__		 =					do_resize
         A__		 =					size
         A__		 =					crop_pct
         A__		 =					resample
         A__		 =					do_center_crop
         A__		 =					crop_size
         A__		 =					do_rescale
         A__		 =					rescale_factor
         A__		 =					do_normalize
         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 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						a__ = PILImageResampling.BICUBIC ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(lowerCAmelCase__ ,						default_to_square=lowerCAmelCase__)
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(F"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
         if crop_pct is not None:
             if "shortest_edge" in size:
                 A__		 =					int(size['''shortest_edge'''] / crop_pct)
             elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                     A__		 =					int(size['''height'''] / crop_pct)
                 else:
                     A__		 =					(int(size['''height'''] / crop_pct), int(size['''width'''] / crop_pct))
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(lowerCAmelCase__))

             A__		 =					get_resize_output_image_size(lowerCAmelCase__ ,						size=lowerCAmelCase__ ,						default_to_square=lowerCAmelCase__)
         else:
             if "shortest_edge" in size:
                 A__		 =					get_resize_output_image_size(lowerCAmelCase__ ,						size=size['''shortest_edge'''] ,						default_to_square=lowerCAmelCase__)
             elif "height" in size and "width" in size:
                 A__		 =					(size['''height'''], size['''width'''])
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(lowerCAmelCase__))

         return resize(lowerCAmelCase__ ,						size=lowerCAmelCase__ ,						resample=lowerCAmelCase__ ,						data_format=lowerCAmelCase__ ,						**lowerCAmelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(lowerCAmelCase__)
         if "height" not in size or "width" not in size:
             raise ValueError(F"size must contain 'height' and 'width' as keys. Got {size.keys()}")
         return center_crop(lowerCAmelCase__ ,						size=(size['''height'''], size['''width''']) ,						data_format=lowerCAmelCase__ ,						**lowerCAmelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         return rescale(lowerCAmelCase__ ,						scale=lowerCAmelCase__ ,						data_format=lowerCAmelCase__ ,						**lowerCAmelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         return normalize(lowerCAmelCase__ ,						mean=lowerCAmelCase__ ,						std=lowerCAmelCase__ ,						data_format=lowerCAmelCase__ ,						**lowerCAmelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = ChannelDimension.FIRST ,						**a__ ,						):
         A__		 =					do_resize if do_resize is not None else self.do_resize
         A__		 =					crop_pct if crop_pct is not None else self.crop_pct
         A__		 =					resample if resample is not None else self.resample
         A__		 =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A__		 =					do_rescale if do_rescale is not None else self.do_rescale
         A__		 =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A__		 =					do_normalize if do_normalize is not None else self.do_normalize
         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(lowerCAmelCase__ ,						default_to_square=lowerCAmelCase__)
         A__		 =					crop_size if crop_size is not None else self.crop_size
         A__		 =					get_size_dict(lowerCAmelCase__ ,						param_name='''crop_size''')

         A__		 =					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_pct is None:
             raise ValueError('''Crop_pct 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.
         A__		 =					[to_numpy_array(lowerCAmelCase__) for image in images]

         if do_resize:
             A__		 =					[self.resize(image=lowerCAmelCase__ ,						size=lowerCAmelCase__ ,						crop_pct=lowerCAmelCase__ ,						resample=lowerCAmelCase__) for image in images]

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

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

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

         A__		 =					[to_channel_dimension_format(lowerCAmelCase__ ,						lowerCAmelCase__) for image in images]

         A__		 =					{'''pixel_values''': images}
         return BatchFeature(data=lowerCAmelCase__ ,						tensor_type=lowerCAmelCase__)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       float ,       UpperCamelCase_				:       int	)->  float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase_	) ,       UpperCamelCase_	)
    return number - int(UpperCamelCase_	)


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))
