


"""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


__snake_case   			=    logging.get_logger(__name__)







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[Any]							  =  ['''pixel_values''']







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

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)
       snake_case    :      Tuple      					=    size if size is not None else {"height": 256, "width": 256}
       snake_case    :      Union[str, Any]      					=    get_size_dict(UpperCamelCase__						)
       snake_case    :      str      					=    crop_size if crop_size is not None else {"height": 224, "width": 224}
       snake_case    :      int      					=    get_size_dict(UpperCamelCase__						,	param_name="crop_size"						)

       snake_case    :      Dict      					=    do_resize
       snake_case    :      int      					=    size
       snake_case    :      Dict      					=    resample
       snake_case    :      Optional[int]      					=    do_center_crop
       snake_case    :      List[str]      					=    crop_size
       snake_case    :      Dict      					=    do_rescale
       snake_case    :      Union[str, Any]      					=    rescale_factor
       snake_case    :      Tuple      					=    do_normalize
       snake_case    :      Tuple      					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case    :      Any      					=    image_std if image_std is not None else IMAGENET_STANDARD_STD







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = PIL.Image.BICUBIC						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		np.ndarray:

       '''simple docstring'''




       snake_case    :      Dict      					=    get_size_dict(UpperCamelCase__						)
       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(
           UpperCamelCase__						,	size=(size["height"], size["width"])						,	resample=UpperCamelCase__						,	data_format=UpperCamelCase__						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		np.ndarray:

       '''simple docstring'''




       snake_case    :      int      					=    get_size_dict(UpperCamelCase__						)
       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(UpperCamelCase__						,	size=(size["height"], size["width"])						,	data_format=UpperCamelCase__						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		Tuple:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	**UpperCamelCase__						,	)	->		np.ndarray:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__=None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = ChannelDimension.FIRST						,	**UpperCamelCase__						,	)	->		PIL.Image.Image:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    do_resize if do_resize is not None else self.do_resize
       snake_case    :      Optional[Any]      					=    resample if resample is not None else self.resample
       snake_case    :      List[str]      					=    do_center_crop if do_center_crop is not None else self.do_center_crop
       snake_case    :      int      					=    do_rescale if do_rescale is not None else self.do_rescale
       snake_case    :      List[Any]      					=    rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case    :      List[Any]      					=    do_normalize if do_normalize is not None else self.do_normalize
       snake_case    :      List[str]      					=    image_mean if image_mean is not None else self.image_mean
       snake_case    :      List[str]      					=    image_std if image_std is not None else self.image_std

       snake_case    :      Dict      					=    size if size is not None else self.size
       snake_case    :      int      					=    get_size_dict(UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    crop_size if crop_size is not None else self.crop_size
       snake_case    :      int      					=    get_size_dict(UpperCamelCase__						,	param_name="crop_size"						)

       snake_case    :      Optional[Any]      					=    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 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.
       snake_case    :      Dict      					=    [to_numpy_array(UpperCamelCase__						) for image in images]

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

       if do_center_crop:
           snake_case    :      str      					=    [self.center_crop(image=UpperCamelCase__						,	size=UpperCamelCase__						) for image in images]

       if do_rescale:
           snake_case    :      Optional[Any]      					=    [self.rescale(image=UpperCamelCase__						,	scale=UpperCamelCase__						) for image in images]

       if do_normalize:
           snake_case    :      Dict      					=    [self.normalize(image=UpperCamelCase__						,	mean=UpperCamelCase__						,	std=UpperCamelCase__						) for image in images]

       snake_case    :      Optional[int]      					=    [to_channel_dimension_format(UpperCamelCase__						,	UpperCamelCase__						) for image in images]

       snake_case    :      int      					=    {"pixel_values": images}
       return BatchFeature(data=UpperCamelCase__						,	tensor_type=UpperCamelCase__						)









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    if index == r:
        for j in range(lowercase ):
            print(data[j]						,							end=" " )
        print(" " )
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    snake_case    :      Union[str, Any]      					=    arr[i]
    combination_util(lowercase						,							lowercase						,							lowercase						,							index + 1						,							lowercase						,							i + 1 )
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							i + 1 )
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(lowercase						,							lowercase						,							lowercase						,							0						,							lowercase						,							0 )


if __name__ == "__main__":
  # Driver code to check the function above
  __snake_case   			=    [10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu





