





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


snake_case__					:							int													=     logging.get_logger(__name__)


if is_vision_available():
       import PIL





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = ["""pixel_values"""]




   def __init__(self	:List[str]    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:PILImageResampling = PILImageResampling.BICUBIC    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Union[int, float] = 1 / 255    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:bool = True    ,   **_UpperCamelCase	:str    ,   )->					None:
         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    ,   default_to_square=_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 OPENAI_CLIP_MEAN
         __A          =				image_std if image_std is not None else OPENAI_CLIP_STD
         __A          =				do_convert_rgb




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




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Dict[str, int]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:List[str]    ,   )->					np.ndarray:
         __A          =				get_size_dict(_UpperCamelCase )
         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(_UpperCamelCase    ,   size=(size['''height'''], size['''width'''])    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




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




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






   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:ImageInput    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:PILImageResampling = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:int = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:float = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, List[float]]] = None    ,   _UpperCamelCase	:bool = None    ,   _UpperCamelCase	:Optional[Union[str, TensorType]] = None    ,   _UpperCamelCase	:Optional[ChannelDimension] = ChannelDimension.FIRST    ,   **_UpperCamelCase	:Optional[Any]    ,   )->					PIL.Image.Image:
         __A          =				do_resize if do_resize is not None else self.do_resize
         __A          =				size if size is not None else self.size
         __A          =				get_size_dict(_UpperCamelCase    ,   param_name='''size'''    ,   default_to_square=_UpperCamelCase )
         __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          =				crop_size if crop_size is not None else self.crop_size
         __A          =				get_size_dict(_UpperCamelCase    ,   param_name='''crop_size'''    ,   default_to_square=_UpperCamelCase )
         __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          =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __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.''' )

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

         # All transformations expect numpy arrays.
         __A          =				[to_numpy_array(_UpperCamelCase ) for image in images]

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

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

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

         if do_normalize:
               __A          =				[self.normalize(image=_UpperCamelCase    ,   mean=_UpperCamelCase    ,   std=_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



def _a				(	lowerCamelCase:   list[int | str] )	->						None:







      '''simple docstring'''






      create_state_space_tree(lowerCamelCase					,				[]					,				0					,				[0 for i in range(len(lowerCamelCase ) )] )



def _a				(	lowerCamelCase:   list[int | str]					,				lowerCamelCase:   list[int | str]					,				lowerCamelCase:   int					,				lowerCamelCase:   list[int]					,				)	->						None:







      '''simple docstring'''






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

      for i in range(len(lowerCamelCase ) ):
            if not index_used[i]:
                  current_sequence.append(sequence[i] )
                  __A          =				True
                  create_state_space_tree(lowerCamelCase					,				lowerCamelCase					,				index + 1					,				lowerCamelCase )
                  current_sequence.pop()
                  __A          =				False
snake_case__					:							list[int | str]													=     [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case__					:							list[int | str]													=     ["A", "B", "C"]







generate_all_permutations(sequence_a)





