'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_     :						Tuple    							=logging.get_logger(__name__)


class     __UpperCAmelCase       ( __a		):
  __A :				int											= ['pixel_values']





  def __init__(	self    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = PILImageResampling.BICUBIC    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = 1 / 255    ,     _lowerCamelCase = None    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )
     lowerCAmelCase_			=      size if size is not None else {'''height''': 224, '''width''': 224}
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )
     lowerCAmelCase_			=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase    ,     param_name='''crop_size''' )

     lowerCAmelCase_			=      do_resize
     lowerCAmelCase_			=      do_rescale
     lowerCAmelCase_			=      do_normalize
     lowerCAmelCase_			=      do_center_crop
     lowerCAmelCase_			=      crop_size
     lowerCAmelCase_			=      size
     lowerCAmelCase_			=      resample
     lowerCAmelCase_			=      rescale_factor
     lowerCAmelCase_			=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     lowerCAmelCase_			=      image_std if image_std is not None else IMAGENET_DEFAULT_STD





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = PILImageResampling.BILINEAR    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )

     if "shortest_edge" in size:
        lowerCAmelCase_			=      get_resize_output_image_size(_lowerCamelCase    ,     size=size['''shortest_edge''']    ,     default_to_square=_lowerCamelCase )
        # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
     elif "height" in size and "width" in size:
        lowerCAmelCase_			=      (size['''height'''], size['''width'''])
     else:
        raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
     return resize(_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )
     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(_lowerCamelCase    ,     size=(size['''height'''], size['''width'''])    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase ):
     return rescale(_lowerCamelCase    ,     scale=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     return normalize(_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )







  def 						UpperCAmelCase_	(	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    ,     ):
     lowerCAmelCase_			=      do_resize if do_resize is not None else self.do_resize
     lowerCAmelCase_			=      do_rescale if do_rescale is not None else self.do_rescale
     lowerCAmelCase_			=      do_normalize if do_normalize is not None else self.do_normalize
     lowerCAmelCase_			=      do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCAmelCase_			=      crop_size if crop_size is not None else self.crop_size
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     param_name='''crop_size'''    ,     default_to_square=_lowerCamelCase )
     lowerCAmelCase_			=      resample if resample is not None else self.resample
     lowerCAmelCase_			=      rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCAmelCase_			=      image_mean if image_mean is not None else self.image_mean
     lowerCAmelCase_			=      image_std if image_std is not None else self.image_std

     lowerCAmelCase_			=      size if size is not None else self.size
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )

     if not is_batched(_lowerCamelCase ):
        lowerCAmelCase_			=      [images]

     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.''' )

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

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

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

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

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

     lowerCAmelCase_			=      [to_channel_dimension_format(_lowerCamelCase    ,     _lowerCamelCase ) for image in images]

     lowerCAmelCase_			=      {'''pixel_values''': images}
     return BatchFeature(data=_lowerCamelCase    ,     tensor_type=_lowerCamelCase )






'''simple docstring'''

from __future__ import annotations



def snake_case_      (					__snake_case  :							list[int | str])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0	,   [0 for i in range(len(__snake_case))])



def snake_case_      (					__snake_case  :							list[int | str]	,   __snake_case  :							list[int | str]	,   __snake_case  :							int	,   __snake_case  :							list[int]	,   )			->					None:

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

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

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






generate_all_permutations(sequence_a)





