




def      A      (      _SCREAMING_SNAKE_CASE    )	-> int:
       assert (
           isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) and number_of_steps > 0
       ), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
       if number_of_steps == 1:
              return 1
       lowerCamelCase ,      lowerCamelCase   :      Dict			 =					1, 1
       for _ in range(number_of_steps - 1    ):
              lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					current + previous, current
       return current


if __name__ == "__main__":
       import doctest

       doctest.testmod()







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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase  							=						logging.get_logger(__name__)


if is_vision_available():
      import PIL



class   snake_case_       (		__A       ):
   __A	:		str         =				["pixel_values"]






   def __init__(      self				:     int	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[int, float] = 1 / 2_55	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = True	,						**lowercase_				:     Union[str, Any]	,						)					->						None:
       super().__init__(**lowercase_  )
       lowercase__ :   Tuple    					=				size if size is not None else {"shortest_edge": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       lowercase__ :   List[str]    					=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_	,						param_name="crop_size"  )

       lowercase__ :   Dict    					=				do_resize
       lowercase__ :   List[Any]    					=				size
       lowercase__ :   int    					=				resample
       lowercase__ :   Union[str, Any]    					=				do_center_crop
       lowercase__ :   Optional[int]    					=				crop_size
       lowercase__ :   List[str]    					=				do_rescale
       lowercase__ :   int    					=				rescale_factor
       lowercase__ :   List[Any]    					=				do_normalize
       lowercase__ :   Union[str, Any]    					=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowercase__ :   str    					=				image_std if image_std is not None else OPENAI_CLIP_STD
       lowercase__ :   Dict    					=				do_convert_rgb






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Union[str, Any]	,						)					->						np.ndarray:
       lowercase__ :   str    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
       lowercase__ :   Dict    					=				get_resize_output_image_size(lowercase_	,						size=size["shortest_edge"]	,						default_to_square=lowercase_  )
       return resize(lowercase_	,						size=lowercase_	,						resample=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     int	,						)					->						np.ndarray:
       lowercase__ :   Optional[Any]    					=				get_size_dict(lowercase_  )
       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(lowercase_	,						size=(size["height"], size["width"])	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[int, float]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Optional[Any]	,						)					->						Any:
       return rescale(lowercase_	,						scale=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     str	,						)					->						np.ndarray:
       return normalize(lowercase_	,						mean=lowercase_	,						std=lowercase_	,						data_format=lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     ImageInput	,						lowercase_				:     bool = None	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = None	,						lowercase_				:     bool = None	,						lowercase_				:     int = None	,						lowercase_				:     bool = None	,						lowercase_				:     float = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						lowercase_				:     Optional[ChannelDimension] = ChannelDimension.FIRST	,						**lowercase_				:     Union[str, Any]	,						)					->						PIL.Image.Image:
       lowercase__ :   int    					=				do_resize if do_resize is not None else self.do_resize
       lowercase__ :   Dict    					=				size if size is not None else self.size
       lowercase__ :   List[Any]    					=				get_size_dict(lowercase_	,						param_name="size"	,						default_to_square=lowercase_  )
       lowercase__ :   Dict    					=				resample if resample is not None else self.resample
       lowercase__ :   int    					=				do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__ :   Dict    					=				crop_size if crop_size is not None else self.crop_size
       lowercase__ :   List[str]    					=				get_size_dict(lowercase_	,						param_name="crop_size"	,						default_to_square=lowercase_  )
       lowercase__ :   int    					=				do_rescale if do_rescale is not None else self.do_rescale
       lowercase__ :   Dict    					=				rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__ :   Union[str, Any]    					=				do_normalize if do_normalize is not None else self.do_normalize
       lowercase__ :   int    					=				image_mean if image_mean is not None else self.image_mean
       lowercase__ :   List[str]    					=				image_std if image_std is not None else self.image_std
       lowercase__ :   Union[str, Any]    					=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase__ :   Union[str, Any]    					=				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:
           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."  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
           lowercase__ :   Dict    					=				[convert_to_rgb(lowercase_  ) for image in images]

       # All transformations expect numpy arrays.
       lowercase__ :   Optional[Any]    					=				[to_numpy_array(lowercase_  ) for image in images]

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

       if do_center_crop:
           lowercase__ :   int    					=				[self.center_crop(image=lowercase_	,						size=lowercase_  ) for image in images]

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

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

       lowercase__ :   Optional[Any]    					=				[to_channel_dimension_format(lowercase_	,						lowercase_  ) for image in images]

       lowercase__ :   List[str]    					=				{"pixel_values": images}
       return BatchFeature(data=lowercase_	,						tensor_type=lowercase_  )



