


'''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, is_vision_available, logging


if is_vision_available():
		import PIL


__A		=logging.get_logger(__name__)


class 						_snake_case    (   __UpperCAmelCase		):
					lowerCAmelCase					:Optional[Any]            =    ['''pixel_values''']




					def __init__(					self							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = PILImageResampling.BILINEAR							,							_lowerCamelCase = True							,							_lowerCamelCase = 1 / 255							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								super().__init__(**__SCREAMING_SNAKE_CASE)
								UpperCAmelCase__     :    str         =   size if size is not None else {"""shortest_edge""": 384}
								UpperCAmelCase__     :    Dict         =   get_size_dict(__SCREAMING_SNAKE_CASE							,							default_to_square=__SCREAMING_SNAKE_CASE)

								UpperCAmelCase__     :    List[Any]         =   do_resize
								UpperCAmelCase__     :    Union[str, Any]         =   size
								# Default value set here for backwards compatibility where the value in config is None
								UpperCAmelCase__     :    Optional[int]         =   crop_pct if crop_pct is not None else 224 / 256
								UpperCAmelCase__     :    str         =   resample
								UpperCAmelCase__     :    Tuple         =   do_rescale
								UpperCAmelCase__     :    Optional[int]         =   rescale_factor
								UpperCAmelCase__     :    List[str]         =   do_normalize
								UpperCAmelCase__     :    Tuple         =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase__     :    str         =   image_std if image_std is not None else IMAGENET_STANDARD_STD




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__     :    Any         =   get_size_dict(__SCREAMING_SNAKE_CASE							,							default_to_square=__SCREAMING_SNAKE_CASE)
								if "shortest_edge" not in size:
											raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''')
								UpperCAmelCase__     :    str         =   size["""shortest_edge"""]

								if shortest_edge < 384:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											UpperCAmelCase__     :    Any         =   int(shortest_edge / crop_pct)
											UpperCAmelCase__     :    List[Any]         =   get_resize_output_image_size(__SCREAMING_SNAKE_CASE							,							size=__SCREAMING_SNAKE_CASE							,							default_to_square=__SCREAMING_SNAKE_CASE)
											UpperCAmelCase__     :    Optional[Any]         =   resize(image=__SCREAMING_SNAKE_CASE							,							size=__SCREAMING_SNAKE_CASE							,							resample=__SCREAMING_SNAKE_CASE							,							data_format=__SCREAMING_SNAKE_CASE							,							**__SCREAMING_SNAKE_CASE)
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=__SCREAMING_SNAKE_CASE							,							size=(shortest_edge, shortest_edge)							,							data_format=__SCREAMING_SNAKE_CASE							,							**__SCREAMING_SNAKE_CASE)
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    __SCREAMING_SNAKE_CASE							,							size=(shortest_edge, shortest_edge)							,							resample=__SCREAMING_SNAKE_CASE							,							data_format=__SCREAMING_SNAKE_CASE							,							**__SCREAMING_SNAKE_CASE)




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




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




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = ChannelDimension.FIRST							,							**_lowerCamelCase							,							):
								UpperCAmelCase__     :    Any         =   do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase__     :    str         =   crop_pct if crop_pct is not None else self.crop_pct
								UpperCAmelCase__     :    Dict         =   resample if resample is not None else self.resample
								UpperCAmelCase__     :    Any         =   do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase__     :    List[Any]         =   rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase__     :    Union[str, Any]         =   do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase__     :    Any         =   image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase__     :    Any         =   image_std if image_std is not None else self.image_std

								UpperCAmelCase__     :    Dict         =   size if size is not None else self.size
								UpperCAmelCase__     :    List[Any]         =   get_size_dict(__SCREAMING_SNAKE_CASE							,							default_to_square=__SCREAMING_SNAKE_CASE)

								UpperCAmelCase__     :    List[str]         =   make_list_of_images(__SCREAMING_SNAKE_CASE)

								if not valid_images(__SCREAMING_SNAKE_CASE):
											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 or resample is None:
											raise ValueError("""Size and resample must be specified if do_resize is True.""")

								if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
											raise ValueError("""crop_pct must be specified if size < 384.""")

								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.
								UpperCAmelCase__     :    List[str]         =   [to_numpy_array(__SCREAMING_SNAKE_CASE) for image in images]

								if do_resize:
											UpperCAmelCase__     :    Union[str, Any]         =   [self.resize(image=__SCREAMING_SNAKE_CASE							,							size=__SCREAMING_SNAKE_CASE							,							crop_pct=__SCREAMING_SNAKE_CASE							,							resample=__SCREAMING_SNAKE_CASE) for image in images]

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

								if do_normalize:
											UpperCAmelCase__     :    List[Any]         =   [self.normalize(image=__SCREAMING_SNAKE_CASE							,							mean=__SCREAMING_SNAKE_CASE							,							std=__SCREAMING_SNAKE_CASE) for image in images]

								UpperCAmelCase__     :    Tuple         =   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE							,							__SCREAMING_SNAKE_CASE) for image in images]

								UpperCAmelCase__     :    Tuple         =   {"""pixel_values""": images}
								return BatchFeature(data=__SCREAMING_SNAKE_CASE							,							tensor_type=__SCREAMING_SNAKE_CASE)




from __future__ import annotations

from typing import Any





def      __snake_case	(			_UpperCAmelCase				):
    if not postfix_notation:
        return 0

    __a						       =				{'''+''', '''-''', '''*''', '''/'''}
    __a						       =				[]

    for token in postfix_notation:
        if token in operations:
            __a       ,   __a						       =				stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(_UpperCAmelCase				)				)

    return stack.pop()


if __name__ == "__main__":
 import doctest

 doctest.testmod()





