
'''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, 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, is_vision_available, logging


if is_vision_available():
						import PIL


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Any										=  ['''pixel_values''']




						def __init__(   self       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = PIL.Image.BICUBIC       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = 1 / 255       ,  _lowercase = True       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(**_lowercase )
												_lowerCAmelCase    		=					size if size is not None else {"""height""": 256, """width""": 256}
												_lowerCAmelCase    		=					get_size_dict(_lowercase )
												_lowerCAmelCase    		=					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
												_lowerCAmelCase    		=					get_size_dict(_lowercase       ,  param_name="""crop_size""" )

												_lowerCAmelCase    		=					do_resize
												_lowerCAmelCase    		=					size
												_lowerCAmelCase    		=					resample
												_lowerCAmelCase    		=					do_center_crop
												_lowerCAmelCase    		=					crop_size
												_lowerCAmelCase    		=					do_rescale
												_lowerCAmelCase    		=					rescale_factor
												_lowerCAmelCase    		=					do_normalize
												_lowerCAmelCase    		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_lowerCAmelCase    		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = PIL.Image.BICUBIC       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					get_size_dict(_lowercase )
												if "height" not in size or "width" not in size:
																		raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
												return resize(
												    _lowercase       ,  size=(size["""height"""], size["""width"""])       ,  resample=_lowercase       ,  data_format=_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					get_size_dict(_lowercase )
												if "height" not in size or "width" not in size:
																		raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
												return center_crop(_lowercase       ,  size=(size["""height"""], size["""width"""])       ,  data_format=_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												return rescale(_lowercase       ,  scale=_lowercase       ,  data_format=_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												return normalize(_lowercase       ,  mean=_lowercase       ,  std=_lowercase       ,  data_format=_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase=None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = ChannelDimension.FIRST       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					do_resize if do_resize is not None else self.do_resize
												_lowerCAmelCase    		=					resample if resample is not None else self.resample
												_lowerCAmelCase    		=					do_center_crop if do_center_crop is not None else self.do_center_crop
												_lowerCAmelCase    		=					do_rescale if do_rescale is not None else self.do_rescale
												_lowerCAmelCase    		=					rescale_factor if rescale_factor is not None else self.rescale_factor
												_lowerCAmelCase    		=					do_normalize if do_normalize is not None else self.do_normalize
												_lowerCAmelCase    		=					image_mean if image_mean is not None else self.image_mean
												_lowerCAmelCase    		=					image_std if image_std is not None else self.image_std

												_lowerCAmelCase    		=					size if size is not None else self.size
												_lowerCAmelCase    		=					get_size_dict(_lowercase )
												_lowerCAmelCase    		=					crop_size if crop_size is not None else self.crop_size
												_lowerCAmelCase    		=					get_size_dict(_lowercase       ,  param_name="""crop_size""" )

												_lowerCAmelCase    		=					make_list_of_images(_lowercase )

												if not valid_images(_lowercase ):
																		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_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.
												_lowerCAmelCase    		=					[to_numpy_array(_lowercase ) for image in images]

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

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

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

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

												_lowerCAmelCase    		=					[to_channel_dimension_format(_lowercase       ,  _lowercase ) for image in images]

												_lowerCAmelCase    		=					{"""pixel_values""": images}
												return BatchFeature(data=_lowercase       ,  tensor_type=_lowercase )








def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     if digit_amount > 0:
          return round(number - int(snake_case__	)						,snake_case__	)
     return number - int(snake_case__	)


if __name__ == "__main__":
     print(decimal_isolate(1.5_3, 0))
     print(decimal_isolate(3_5.3_4_5, 1))
     print(decimal_isolate(3_5.3_4_5, 2))
     print(decimal_isolate(3_5.3_4_5, 3))
     print(decimal_isolate(-1_4.7_8_9, 3))
     print(decimal_isolate(0, 2))
     print(decimal_isolate(-1_4.1_2_3, 1))
     print(decimal_isolate(-1_4.1_2_3, 2))
     print(decimal_isolate(-1_4.1_2_3, 3))


