


from typing import Dict, Iterable, 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, logging


_lowerCamelCase     :		List[Any]								=       logging.get_logger(__name__)


class     UpperCamelCase_   (			_a					):

    '''simple docstring'''






    UpperCAmelCase__							  =				["""pixel_values"""]


    def __init__(      self				:     Dict ,			UpperCAmelCase__				:     int = True ,			UpperCAmelCase__				:     List[str] = None ,			UpperCAmelCase__				:     List[Any] = PILImageResampling.BICUBIC ,			UpperCAmelCase__				:     Any = True ,			UpperCAmelCase__				:     Union[str, Any] = None ,			UpperCAmelCase__				:     Optional[int] = True ,			UpperCAmelCase__				:     List[str] = 1 / 255 ,			UpperCAmelCase__				:     Optional[Any] = True ,			UpperCAmelCase__				:     int = IMAGENET_DEFAULT_MEAN ,			UpperCAmelCase__				:     Any = IMAGENET_DEFAULT_STD ,			**UpperCAmelCase__				:     List[Any] ,			)						->None:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__							=   size if size is not None else {'shortest_edge': 224}
           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   crop_size if crop_size is not None else {'height': 224, 'width': 224}
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')

           A__							=   do_resize
           A__							=   size
           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 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Optional[int] = PILImageResampling.BICUBIC ,			UpperCAmelCase__				:     str = None ,			**UpperCAmelCase__				:     str ,			)						->np.ndarray:





           '''simple docstring'''

           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                  A__							=   int((256 / 224) * size['''shortest_edge'''])
                  A__							=   get_resize_output_image_size(UpperCAmelCase__ ,			size=UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
                  A__							=   {'height': output_size[0], 'width': output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                  raise ValueError(
                      f"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}""")
           return resize(
               UpperCAmelCase__ ,			size=(size_dict['''height'''], size_dict['''width''']) ,			resample=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Optional[Any] = None ,			**UpperCAmelCase__				:     Any ,			)						->np.ndarray:





           '''simple docstring'''

           A__							=   get_size_dict(UpperCAmelCase__)
           if "height" not in size or "width" not in size:
                  raise ValueError(f"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}""")
           return center_crop(UpperCAmelCase__ ,			size=(size['''height'''], size['''width''']) ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Any = None ,			**UpperCAmelCase__				:     Any ,			)						->np.ndarray:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Optional[Any] = None ,			**UpperCAmelCase__				:     Optional[int] ,			)						->np.ndarray:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Tuple = None ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     List[str] = None ,			UpperCAmelCase__				:     List[Any] = None ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     Optional[Any] = None ,			UpperCAmelCase__				:     Tuple = None ,			UpperCAmelCase__				:     List[str] = None ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     Dict = None ,			UpperCAmelCase__				:     Optional[Any] = None ,			UpperCAmelCase__				:     List[str] = ChannelDimension.FIRST ,			**UpperCAmelCase__				:     Dict ,			)						->BatchFeature:





           '''simple docstring'''

           A__							=   do_resize if do_resize is not None else self.do_resize
           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(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   crop_size if crop_size is not None else self.crop_size
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')

           A__							=   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:
                  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__							=   [to_numpy_array(UpperCAmelCase__) for image in images]

           if do_resize:
                  A__							=   [self.resize(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__) for image in images]

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

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

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

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

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








from __future__ import annotations

from typing import Any







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       create_state_space_tree(lowercase_			,    []			,    0						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       if index == len(lowercase_						):
              print(lowercase_						)
              return

       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.append(sequence[index]						)
       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.pop()


if __name__ == "__main__":
  _lowerCamelCase     :		list[Any]								=       [3, 1, 2, 4]
  generate_all_subsequences(seq)

  seq.clear()
  seq.extend(["""A""", """B""", """C"""])
  generate_all_subsequences(seq)


