
'''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,
    get_resize_output_image_size,
    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, logging


__snake_case:	Optional[int]										=      logging.get_logger(__name__)


class 			_UpperCAmelCase			(			SCREAMING_SNAKE_CASE__     ):

     """simple docstring"""

     a_     						=       ["pixel_values"]


     def __init__(   self				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = PILImageResampling.BILINEAR				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = 1 / 2_55				,		lowerCAmelCase_ = True				,		lowerCAmelCase_ = None				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       super().__init__(**lowerCAmelCase_			)
       a_   :						Dict   =				size if size is not None else {"""shortest_edge""": 2_56}
       a_   :						Tuple   =				get_size_dict(lowerCAmelCase_				,		default_to_square=lowerCAmelCase_			)
       a_   :						Dict   =				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
       a_   :						Optional[Any]   =				get_size_dict(lowerCAmelCase_			)
       a_   :						Optional[int]   =				do_resize
       a_   :						List[str]   =				size
       a_   :						Optional[int]   =				resample
       a_   :						str   =				do_center_crop
       a_   :						str   =				crop_size
       a_   :						Optional[Any]   =				do_rescale
       a_   :						Tuple   =				rescale_factor
       a_   :						Optional[Any]   =				do_normalize
       a_   :						Optional[Any]   =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       a_   :						List[Any]   =				image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = PILImageResampling.BICUBIC				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       a_   :						Union[str, Any]   =				get_size_dict(lowerCAmelCase_				,		default_to_square=lowerCAmelCase_			)
       if "shortest_edge" not in size:
         raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
       a_   :						List[Any]   =				get_resize_output_image_size(lowerCAmelCase_				,		size=size["""shortest_edge"""]				,		default_to_square=lowerCAmelCase_			)
       return resize(lowerCAmelCase_				,		size=lowerCAmelCase_				,		resample=lowerCAmelCase_				,		data_format=lowerCAmelCase_				,		**lowerCAmelCase_			)


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

       a_   :						str   =				get_size_dict(lowerCAmelCase_			)
       return center_crop(lowerCAmelCase_				,		size=(size["""height"""], size["""width"""])				,		data_format=lowerCAmelCase_				,		**lowerCAmelCase_			)


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_			):

       '''simple docstring'''

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


     def 				_lowerCAmelCase	(   self				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_				,		lowerCAmelCase_ = None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

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






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

       '''simple docstring'''

       a_   :						str   =				do_resize if do_resize is not None else self.do_resize
       a_   :						Optional[int]   =				size if size is not None else self.size
       a_   :						Tuple   =				get_size_dict(lowerCAmelCase_				,		default_to_square=lowerCAmelCase_			)
       a_   :						Tuple   =				resample if resample is not None else self.resample
       a_   :						Any   =				do_center_crop if do_center_crop is not None else self.do_center_crop
       a_   :						Dict   =				crop_size if crop_size is not None else self.crop_size
       a_   :						Optional[Any]   =				get_size_dict(lowerCAmelCase_			)
       a_   :						Optional[int]   =				do_rescale if do_rescale is not None else self.do_rescale
       a_   :						Optional[int]   =				rescale_factor if rescale_factor is not None else self.rescale_factor
       a_   :						List[Any]   =				do_normalize if do_normalize is not None else self.do_normalize
       a_   :						str   =				image_mean if image_mean is not None else self.image_mean
       a_   :						Optional[Any]   =				image_std if image_std is not None else self.image_std

       a_   :						Optional[Any]   =				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:
         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."""			)

       # All transformations expect numpy arrays.
       a_   :						str   =				[to_numpy_array(lowerCAmelCase_			) for image in images]

       if do_resize:
         a_   :						int   =				[self.resize(image=lowerCAmelCase_				,		size=lowerCAmelCase_				,		resample=lowerCAmelCase_			) for image in images]

       if do_center_crop:
         a_   :						Dict   =				[self.center_crop(image=lowerCAmelCase_				,		size=lowerCAmelCase_			) for image in images]

       if do_rescale:
         a_   :						Any   =				[self.rescale(image=lowerCAmelCase_				,		scale=lowerCAmelCase_			) for image in images]

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

       a_   :						Union[str, Any]   =				[to_channel_dimension_format(lowerCAmelCase_				,		lowerCAmelCase_			) for image in images]

       a_   :						int   =				{"""pixel_values""": images}
       return BatchFeature(data=lowerCAmelCase_				,		tensor_type=lowerCAmelCase_			)








def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if index == r:
						for j in range(SCREAMING_SNAKE_CASE ):
									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
			SCREAMING_SNAKE_CASE_								=						arr[i]
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    0       ,    SCREAMING_SNAKE_CASE       ,    0 )


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