




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            = logging.get_logger(__name__)


if is_vision_available():
		import PIL







class 		lowercase							(     UpperCamelCase__	):
					_a											=		["pixel_values"]

					def __init__(						self	,  _a = True	,  _a = None	,  _a = PILImageResampling.BICUBIC	,  _a = True	,  _a = None	,  _a = True	,  _a = 1 / 255	,  _a = True	,  _a = None	,  _a = None	,  _a = True	,  **_a	,  )		->							None:
											super().__init__(**_a       )
											_A							:  Optional[Any]    =		size if size is not None else {"""shortest_edge""": 224}
											_A							:  Optional[Any]    =		get_size_dict(_a	,  default_to_square=_a       )
											_A							:  List[Any]    =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											_A							:  List[str]    =		get_size_dict(_a	,  default_to_square=_a	,  param_name="""crop_size"""       )

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

					def       a__			(						self	,  _a	,  _a	,  _a = PILImageResampling.BICUBIC	,  _a = None	,  **_a	,  )		->							np.ndarray:
											_A							:  Tuple    =		get_size_dict(_a	,  default_to_square=_a       )
											if "shortest_edge" not in size:
																	raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
											_A							:  Optional[int]    =		get_resize_output_image_size(_a	,  size=size["""shortest_edge"""]	,  default_to_square=_a       )
											return resize(_a	,  size=_a	,  resample=_a	,  data_format=_a	,  **_a       )

					def       a__			(						self	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							np.ndarray:
											_A							:  Tuple    =		get_size_dict(_a       )
											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(_a	,  size=(size["""height"""], size["""width"""])	,  data_format=_a	,  **_a       )

					def       a__			(						self	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							Optional[int]:
											return rescale(_a	,  scale=_a	,  data_format=_a	,  **_a       )

					def       a__			(						self	,  _a	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							np.ndarray:
											return normalize(_a	,  mean=_a	,  std=_a	,  data_format=_a	,  **_a       )



					def       a__			(						self	,  _a	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = ChannelDimension.FIRST	,  **_a	,  )		->							PIL.Image.Image:
											_A							:  Optional[int]    =		do_resize if do_resize is not None else self.do_resize
											_A							:  Dict    =		size if size is not None else self.size
											_A							:  List[str]    =		get_size_dict(_a	,  param_name="""size"""	,  default_to_square=_a       )
											_A							:  List[str]    =		resample if resample is not None else self.resample
											_A							:  Optional[int]    =		do_center_crop if do_center_crop is not None else self.do_center_crop
											_A							:  int    =		crop_size if crop_size is not None else self.crop_size
											_A							:  List[Any]    =		get_size_dict(_a	,  param_name="""crop_size"""	,  default_to_square=_a       )
											_A							:  Tuple    =		do_rescale if do_rescale is not None else self.do_rescale
											_A							:  str    =		rescale_factor if rescale_factor is not None else self.rescale_factor
											_A							:  Tuple    =		do_normalize if do_normalize is not None else self.do_normalize
											_A							:  Any    =		image_mean if image_mean is not None else self.image_mean
											_A							:  List[str]    =		image_std if image_std is not None else self.image_std
											_A							:  List[str]    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											_A							:  str    =		make_list_of_images(_a       )

											if not valid_images(_a       ):
																	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							:  Dict    =		[convert_to_rgb(_a       ) for image in images]

											# All transformations expect numpy arrays.
											_A							:  int    =		[to_numpy_array(_a       ) for image in images]

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

											if do_center_crop:
																	_A							:  Optional[Any]    =		[self.center_crop(image=_a	,  size=_a       ) for image in images]

											if do_rescale:
																	_A							:  Union[str, Any]    =		[self.rescale(image=_a	,  scale=_a       ) for image in images]

											if do_normalize:
																	_A							:  int    =		[self.normalize(image=_a	,  mean=_a	,  std=_a       ) for image in images]

											_A							:  int    =		[to_channel_dimension_format(_a	,  _a       ) for image in images]

											_A							:  Optional[Any]    =		{"""pixel_values""": images}
											return BatchFeature(data=_a	,  tensor_type=_a       )







import operator







def        lowerCAmelCase_			(  snake_case_,snake_case_ = False,snake_case_ = None  ):
      _A							:  str    =		operator.lt if reverse else operator.gt
      _A							:  Optional[Any]    =		solution or []

      if not arr:
            return solution

      _A							:  Dict    =		[arr.pop(0  )]
      for i, item in enumerate(snake_case_  ):
            if _operator(snake_case_,sublist[-1]  ):
                  sublist.append(snake_case_  )
                  arr.pop(snake_case_  )

    #  merging sublist into solution list
      if not solution:
            solution.extend(snake_case_  )
      else:
            while sublist:
                  _A							:  Union[str, Any]    =		sublist.pop(0  )
                  for i, xx in enumerate(snake_case_  ):
                        if not _operator(snake_case_,snake_case_  ):
                              solution.insert(snake_case_,snake_case_  )
                              break
                  else:
                        solution.append(snake_case_  )

      strand_sort(snake_case_,snake_case_,snake_case_  )
      return solution


if __name__ == "__main__":
  assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
  assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]

