


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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


lowercase_           =	logging.get_logger(__name__)


class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      __snake_case					       =   ["""pixel_values"""]




      def __init__(				self:       Dict						,      a:       bool = True						,      a:       Dict[str, int] = None						,      a:       bool = True						,      a:       Dict[str, int] = None						,      a:       PILImageResampling = PILImageResampling.BICUBIC						,      a:       bool = True						,      a:       Union[int, float] = 1 / 255						,      a:       bool = True						,      a:       Optional[Union[float, List[float]]] = None						,      a:       Optional[Union[float, List[float]]] = None						,      **a:       int						,      ):
       super().__init__(**snake_case__				)
       __lowerCamelCase		:							Union[str, Any]							=						crop_size if crop_size is not None else {"height": 256, "width": 256}
       __lowerCamelCase		:							List[Any]							=						get_size_dict(snake_case__						,      param_name='crop_size'				)
       __lowerCamelCase		:							Union[str, Any]							=						size if size is not None else {"height": 224, "width": 224}
       __lowerCamelCase		:							List[str]							=						get_size_dict(snake_case__				)

       __lowerCamelCase		:							Dict							=						do_center_crop
       __lowerCamelCase		:							Optional[Any]							=						crop_size
       __lowerCamelCase		:							Tuple							=						do_resize
       __lowerCamelCase		:							Union[str, Any]							=						size
       __lowerCamelCase		:							int							=						resample
       __lowerCamelCase		:							Union[str, Any]							=						do_rescale
       __lowerCamelCase		:							str							=						rescale_factor
       __lowerCamelCase		:							Tuple							=						do_normalize
       __lowerCamelCase		:							Tuple							=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       __lowerCamelCase		:							Any							=						image_std if image_std is not None else IMAGENET_DEFAULT_STD




      def      _snake_case	(				self:       str						,      a:       np.ndarray						,      a:       Dict[str, int]						,      a:       Optional[int] = None						,      a:       Optional[Union[str, ChannelDimension]] = None						,      **a:       List[str]						,      ):
       __lowerCamelCase		:							List[Any]							=						self.size if size is None else size
       __lowerCamelCase		:							Dict							=						get_size_dict(snake_case__				)
       __lowerCamelCase		:							int							=						get_size_dict(snake_case__						,      param_name='crop_size'				)

       __lowerCamelCase		:							Optional[Any]							=						get_image_size(snake_case__				)
       __lowerCamelCase		:							List[str]							=						min(snake_case__						,      snake_case__				)
       __lowerCamelCase		:							List[str]							=						(size["height"] / crop_size["height"]) * min_dim
       __lowerCamelCase		:							Dict							=						(size["width"] / crop_size["width"]) * min_dim
       return center_crop(snake_case__						,      size=(cropped_height, cropped_width)						,      data_format=snake_case__						,      **snake_case__				)




      def      _snake_case	(				self:       Optional[Any]						,      a:       np.ndarray						,      a:       Dict[str, int]						,      a:       PILImageResampling = PIL.Image.BICUBIC						,      a:       Optional[Union[str, ChannelDimension]] = None						,      **a:       List[str]						,      ):
       __lowerCamelCase		:							Optional[Any]							=						get_size_dict(snake_case__				)
       if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'				)
       return resize(
           snake_case__						,      size=(size['height'], size['width'])						,      resample=snake_case__						,      data_format=snake_case__						,      **snake_case__				)




      def      _snake_case	(				self:       Any						,      a:       np.ndarray						,      a:       Union[int, float]						,      a:       Optional[Union[str, ChannelDimension]] = None						,      **a:       int						,      ):
       return rescale(snake_case__						,      scale=snake_case__						,      data_format=snake_case__						,      **snake_case__				)




      def      _snake_case	(				self:       List[Any]						,      a:       np.ndarray						,      a:       Union[float, List[float]]						,      a:       Union[float, List[float]]						,      a:       Optional[Union[str, ChannelDimension]] = None						,      **a:       Any						,      ):
       return normalize(snake_case__						,      mean=snake_case__						,      std=snake_case__						,      data_format=snake_case__						,      **snake_case__				)




      def      _snake_case	(				self:       Tuple						,      a:       ImageInput						,      a:       Optional[bool] = None						,      a:       Optional[Dict[str, int]] = None						,      a:       Optional[bool] = None						,      a:       Optional[Dict[str, int]] = None						,      a:       PILImageResampling = None						,      a:       Optional[bool] = None						,      a:       Optional[float] = None						,      a:       Optional[bool] = None						,      a:       Optional[Union[float, List[float]]] = None						,      a:       Optional[Union[float, List[float]]] = None						,      a:       Optional[Union[str, TensorType]] = None						,      a:       ChannelDimension = ChannelDimension.FIRST						,      **a:       Optional[Any]						,      ):
       __lowerCamelCase		:							List[str]							=						do_center_crop if do_center_crop is not None else self.do_center_crop
       __lowerCamelCase		:							Optional[int]							=						crop_size if crop_size is not None else self.crop_size
       __lowerCamelCase		:							List[str]							=						get_size_dict(snake_case__						,      param_name='crop_size'				)
       __lowerCamelCase		:							List[Any]							=						do_resize if do_resize is not None else self.do_resize
       __lowerCamelCase		:							Any							=						size if size is not None else self.size
       __lowerCamelCase		:							str							=						get_size_dict(snake_case__				)
       __lowerCamelCase		:							Union[str, Any]							=						resample if resample is not None else self.resample
       __lowerCamelCase		:							Union[str, Any]							=						do_rescale if do_rescale is not None else self.do_rescale
       __lowerCamelCase		:							Any							=						rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowerCamelCase		:							Optional[int]							=						do_normalize if do_normalize is not None else self.do_normalize
       __lowerCamelCase		:							Dict							=						image_mean if image_mean is not None else self.image_mean
       __lowerCamelCase		:							Union[str, Any]							=						image_std if image_std is not None else self.image_std

       __lowerCamelCase		:							Optional[Any]							=						make_list_of_images(snake_case__				)

       if not valid_images(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_center_crop and crop_size is None:
        raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'				)

       if do_resize and size is None:
        raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'				)

       # All transformations expect numpy arrays.
       __lowerCamelCase		:							Optional[int]							=						[to_numpy_array(snake_case__				) for image in images]

       if do_center_crop:
        __lowerCamelCase		:							Union[str, Any]							=						[self.center_crop(snake_case__						,      snake_case__						,      size=snake_case__				) for image in images]

       if do_resize:
        __lowerCamelCase		:							Optional[int]							=						[self.resize(image=snake_case__						,      size=snake_case__						,      resample=snake_case__				) for image in images]

       if do_rescale:
        __lowerCamelCase		:							int							=						[self.rescale(image=snake_case__						,      scale=snake_case__				) for image in images]

       if do_normalize:
        __lowerCamelCase		:							Optional[int]							=						[self.normalize(image=snake_case__						,      mean=snake_case__						,      std=snake_case__				) for image in images]

       __lowerCamelCase		:							str							=						[to_channel_dimension_format(snake_case__						,      snake_case__				) for image in images]

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









"""simple docstring"""



import requests
from bsa import BeautifulSoup


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      int       , snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				(
         "https://www.horoscope.com/us/horoscopes/general/"
         f'horoscope-general-daily-{day}.aspx?sign={zodiac_sign}'
     )
     UpperCAmelCase__		:  List[str]				=				BeautifulSoup(requests.get(snake_case							).content       , "html.parser"							)
     return soup.find("div"       , class_="main-horoscope"							).p.text


if __name__ == "__main__":
 print("""Daily Horoscope. \n""")
 print(
     """enter your Zodiac sign number:\n""",
     """1. Aries\n""",
     """2. Taurus\n""",
     """3. Gemini\n""",
     """4. Cancer\n""",
     """5. Leo\n""",
     """6. Virgo\n""",
     """7. Libra\n""",
     """8. Scorpio\n""",
     """9. Sagittarius\n""",
     """10. Capricorn\n""",
     """11. Aquarius\n""",
     """12. Pisces\n""",
 )
 _lowerCAmelCase     :     List[str]            =	int(input("""number> """).strip())
 print("""choose some day:\n""", """yesterday\n""", """today\n""", """tomorrow\n""")
 _lowerCAmelCase     :     int            =	input("""enter the day> """)
 _lowerCAmelCase     :     Any            =	horoscope(zodiac_sign, day)
 print(horoscope_text)


