






"""simple docstring"""



import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =CONFIG_NAME
   @register_to_config
   def __init__(						self	:     Union[str, Any]				,       snake_case__	:     bool = True				,       snake_case__	:     int = 8				,       snake_case__	:     str = "lanczos"				,       snake_case__	:     bool = True				,       snake_case__	:     bool = False				,       ):


        '''simple docstring'''
        super().__init__()
   @staticmethod
   def 	__a				(						snake_case__	:     np.ndarray				):


        '''simple docstring'''
        if images.ndim == 3:
             UpperCAmelCase__		:  List[Any]				=				images[None, ...]
        UpperCAmelCase__		:  Tuple				=				(images * 2_5_5).round().astype("uint8"				)
        if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             UpperCAmelCase__		:  str				=				[Image.fromarray(image.squeeze()				,       mode="L"				) for image in images]
        else:
             UpperCAmelCase__		:  Tuple				=				[Image.fromarray(snake_case__				) for image in images]

        return pil_images
   @staticmethod
   def 	__a				(						snake_case__	:     Union[List[PIL.Image.Image], PIL.Image.Image]				):


        '''simple docstring'''
        if not isinstance(snake_case__				,       snake_case__				):
             UpperCAmelCase__		:  str				=				[images]
        UpperCAmelCase__		:  Optional[Any]				=				[np.array(snake_case__				).astype(np.floataa				) / 255.0 for image in images]
        UpperCAmelCase__		:  Dict				=				np.stack(snake_case__				,       axis=0				)

        return images
   @staticmethod
   def 	__a				(						snake_case__	:     np.ndarray				):


        '''simple docstring'''
        if images.ndim == 3:
             UpperCAmelCase__		:  Optional[Any]				=				images[..., None]

        UpperCAmelCase__		:  Any				=				torch.from_numpy(images.transpose(0				,       3				,       1				,       2				)				)
        return images
   @staticmethod
   def 	__a				(						snake_case__	:     torch.FloatTensor				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				images.cpu().permute(0				,       2				,       3				,       1				).float().numpy()
        return images
   @staticmethod
   def 	__a				(						snake_case__	:     Union[str, Any]				):


        '''simple docstring'''
        return 2.0 * images - 1.0
   @staticmethod
   def 	__a				(						snake_case__	:     Dict				):


        '''simple docstring'''
        return (images / 2 + 0.5).clamp(0				,       1				)
   @staticmethod
   def 	__a				(						snake_case__	:     PIL.Image.Image				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				image.convert("RGB"				)
        return image
   def 	__a				(						self	:     Tuple				,       snake_case__	:     PIL.Image.Image				,       snake_case__	:     Optional[int] = None				,       snake_case__	:     Optional[int] = None				,       ):


        '''simple docstring'''
        if height is None:
             UpperCAmelCase__		:  Tuple				=				image.height
        if width is None:
             UpperCAmelCase__		:  Union[str, Any]				=				image.width

        UpperCAmelCase__				,							UpperCAmelCase__		:  Any				=				(
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        UpperCAmelCase__		:  Optional[int]				=				image.resize((width, height)				,       resample=PIL_INTERPOLATION[self.config.resample]				)
        return image
   def 	__a				(						self	:     int				,       snake_case__	:     Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]				,       snake_case__	:     Optional[int] = None				,       snake_case__	:     Optional[int] = None				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				(PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(snake_case__				,       snake_case__				):
             UpperCAmelCase__		:  str				=				[image]
        elif not (isinstance(snake_case__				,       snake_case__				) and all(isinstance(snake_case__				,       snake_case__				) for i in image				)):
             raise ValueError(
                 f'Input is in incorrect format: {[type(snake_case__				) for i in image]}. Currently, we only support {", ".join(snake_case__				)}'				)

        if isinstance(image[0]				,       PIL.Image.Image				):
             if self.config.do_convert_rgb:
                  UpperCAmelCase__		:  Dict				=				[self.convert_to_rgb(snake_case__				) for i in image]
             if self.config.do_resize:
                  UpperCAmelCase__		:  Optional[Any]				=				[self.resize(snake_case__				,       snake_case__				,       snake_case__				) for i in image]
             UpperCAmelCase__		:  int				=				self.pil_to_numpy(snake_case__				)  # to np
             UpperCAmelCase__		:  Optional[Any]				=				self.numpy_to_pt(snake_case__				)  # to pt

        elif isinstance(image[0]				,       np.ndarray				):
             UpperCAmelCase__		:  Union[str, Any]				=				np.concatenate(snake_case__				,       axis=0				) if image[0].ndim == 4 else np.stack(snake_case__				,       axis=0				)
             UpperCAmelCase__		:  Tuple				=				self.numpy_to_pt(snake_case__				)
             UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[Any]				=				image.shape
             if self.config.do_resize and (
                 height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
             ):
                  raise ValueError(
                      f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
                      f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'				)

        elif isinstance(image[0]				,       torch.Tensor				):
             UpperCAmelCase__		:  Optional[int]				=				torch.cat(snake_case__				,       axis=0				) if image[0].ndim == 4 else torch.stack(snake_case__				,       axis=0				)
             UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  Dict				=				image.shape

             # don't need any preprocess if the image is latents
             if channel == 4:
                  return image

             if self.config.do_resize and (
                 height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
             ):
                  raise ValueError(
                      f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
                      f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'				)

        # expected range [0,1], normalize to [-1,1]
        UpperCAmelCase__		:  List[str]				=				self.config.do_normalize
        if image.min() < 0:
             warnings.warn(
                 "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
                 f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'				,       snake_case__				,       )
             UpperCAmelCase__		:  Union[str, Any]				=				False

        if do_normalize:
             UpperCAmelCase__		:  Union[str, Any]				=				self.normalize(snake_case__				)

        return image




   def 	__a				(						self	:     List[str]				,       snake_case__	:     torch.FloatTensor				,       snake_case__	:     str = "pil"				,       snake_case__	:     Optional[List[bool]] = None				,       ):


        '''simple docstring'''
        if not isinstance(snake_case__				,       torch.Tensor				):
             raise ValueError(
                 f'Input for postprocessing is in incorrect format: {type(snake_case__				)}. We only support pytorch tensor'				)
        if output_type not in ["latent", "pt", "np", "pil"]:
             UpperCAmelCase__		:  str				=				(
                 f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                 "`pil`, `np`, `pt`, `latent`"
             )
             deprecate("Unsupported output_type"				,       "1.0.0"				,       snake_case__				,       standard_warn=snake_case__				)
             UpperCAmelCase__		:  Optional[int]				=				"np"

        if output_type == "latent":
             return image

        if do_denormalize is None:
             UpperCAmelCase__		:  Any				=				[self.config.do_normalize] * image.shape[0]

        UpperCAmelCase__		:  Dict				=				torch.stack(
            [self.denormalize(image[i]				) if do_denormalize[i] else image[i] for i in range(image.shape[0]				)]				)

        if output_type == "pt":
             return image

        UpperCAmelCase__		:  Optional[Any]				=				self.pt_to_numpy(snake_case__				)

        if output_type == "np":
             return image

        if output_type == "pil":
             return self.numpy_to_pil(snake_case__				)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =CONFIG_NAME
   @register_to_config
   def __init__(						self	:     List[Any]				,       snake_case__	:     bool = True				,       snake_case__	:     int = 8				,       snake_case__	:     str = "lanczos"				,       snake_case__	:     bool = True				,       ):


        '''simple docstring'''
        super().__init__()
   @staticmethod
   def 	__a				(						snake_case__	:     Optional[Any]				):


        '''simple docstring'''
        if images.ndim == 3:
             UpperCAmelCase__		:  str				=				images[None, ...]
        UpperCAmelCase__		:  Union[str, Any]				=				(images * 2_5_5).round().astype("uint8"				)
        if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             UpperCAmelCase__		:  Optional[Any]				=				[Image.fromarray(image.squeeze()				,       mode="L"				) for image in images]
        else:
             UpperCAmelCase__		:  Optional[Any]				=				[Image.fromarray(image[:, :, :3]				) for image in images]

        return pil_images
   @staticmethod
   def 	__a				(						snake_case__	:     str				):


        '''simple docstring'''
        return image[:, :, 1] * 2**8 + image[:, :, 2]
   def 	__a				(						self	:     str				,       snake_case__	:     Union[str, Any]				):


        '''simple docstring'''
        if images.ndim == 3:
             UpperCAmelCase__		:  List[Any]				=				images[None, ...]
        UpperCAmelCase__		:  Dict				=				images[:, :, :, 3:]
        if images.shape[-1] == 6:
             UpperCAmelCase__		:  List[Any]				=				(images_depth * 2_5_5).round().astype("uint8"				)
             UpperCAmelCase__		:  List[str]				=				[
                 Image.fromarray(self.rgblike_to_depthmap(snake_case__				)				,       mode="I;16"				) for image_depth in images_depth
             ]
        elif images.shape[-1] == 4:
             UpperCAmelCase__		:  Union[str, Any]				=				(images_depth * 6_5535.0).astype(np.uintaa				)
             UpperCAmelCase__		:  Optional[int]				=				[Image.fromarray(snake_case__				,       mode="I;16"				) for image_depth in images_depth]
        else:
             raise Exception("Not supported"				)

        return pil_images




   def 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     torch.FloatTensor				,       snake_case__	:     str = "pil"				,       snake_case__	:     Optional[List[bool]] = None				,       ):


        '''simple docstring'''
        if not isinstance(snake_case__				,       torch.Tensor				):
             raise ValueError(
                 f'Input for postprocessing is in incorrect format: {type(snake_case__				)}. We only support pytorch tensor'				)
        if output_type not in ["latent", "pt", "np", "pil"]:
             UpperCAmelCase__		:  List[str]				=				(
                 f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
                 "`pil`, `np`, `pt`, `latent`"
             )
             deprecate("Unsupported output_type"				,       "1.0.0"				,       snake_case__				,       standard_warn=snake_case__				)
             UpperCAmelCase__		:  List[str]				=				"np"

        if do_denormalize is None:
             UpperCAmelCase__		:  Optional[int]				=				[self.config.do_normalize] * image.shape[0]

        UpperCAmelCase__		:  Union[str, Any]				=				torch.stack(
            [self.denormalize(image[i]				) if do_denormalize[i] else image[i] for i in range(image.shape[0]				)]				)

        UpperCAmelCase__		:  Optional[int]				=				self.pt_to_numpy(snake_case__				)

        if output_type == "np":
             if image.shape[-1] == 6:
                  UpperCAmelCase__		:  int				=				np.stack([self.rgblike_to_depthmap(im[:, :, 3:]				) for im in image]				,       axis=0				)
             else:
                  UpperCAmelCase__		:  Dict				=				image[:, :, :, 3:]
             return image[:, :, :, :3], image_depth

        if output_type == "pil":
             return self.numpy_to_pil(snake_case__				), self.numpy_to_depth(snake_case__				)
        else:
             raise Exception(f'This type {output_type} is not supported'				)










"""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)


