






"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ViltImageProcessor






class 						snake_case  (  unittest.TestCase       ):





    def __init__(							self	:					List[str]				,		A	:					Optional[int]				,		A	:					Union[str, Any]=7				,		A	:					str=3				,		A	:					Dict=1_8				,		A	:					Optional[int]=3_0				,		A	:					Tuple=4_0_0				,		A	:					str=True				,		A	:					Any=None				,		A	:					Dict=2				,		A	:					Union[str, Any]=True				,		A	:					List[Any]=[0.5, 0.5, 0.5]				,		A	:					Tuple=[0.5, 0.5, 0.5]				,		):


      '''simple docstring'''




      a     :			Tuple				=   size if size is not None else {'shortest_edge': 3_0}
      a     :			Optional[Any]				=   parent
      a     :			Union[str, Any]				=   batch_size
      a     :			Optional[Any]				=   num_channels
      a     :			List[str]				=   image_size
      a     :			Optional[Any]				=   min_resolution
      a     :			Union[str, Any]				=   max_resolution
      a     :			Tuple				=   do_resize
      a     :			Optional[int]				=   size
      a     :			Any				=   size_divisor
      a     :			str				=   do_normalize
      a     :			Tuple				=   image_mean
      a     :			Dict				=   image_std





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }





    def 				lowerCamelCase__      (							self	:					Optional[int]				,		A	:					List[Any]				,		A	:					Optional[Any]=False			):


      '''simple docstring'''




      if not batched:
        a     :			List[Any]				=   self.size['shortest_edge']
        a     :			Dict				=   image_inputs[0]
        if isinstance(A				,		Image.Image			):
          a,     a     :			Union[str, Any]				=   image.size
        else:
          a,     a     :			List[Any]				=   image.shape[1], image.shape[2]
        a     :			Optional[int]				=   size / min(A				,		A			)
        if h < w:
          a,     a     :			Union[str, Any]				=   size, scale * w
        else:
          a,     a     :			int				=   scale * h, size

        a     :			Optional[int]				=   int((1_3_3_3 / 8_0_0) * size			)
        if max(A				,		A			) > max_size:
          a     :			Dict				=   max_size / max(A				,		A			)
          a     :			List[Any]				=   newh * scale
          a     :			List[Any]				=   neww * scale

        a,     a     :			Optional[int]				=   int(newh + 0.5			), int(neww + 0.5			)
        a,     a     :			Optional[Any]				=   (
            newh // self.size_divisor * self.size_divisor,
            neww // self.size_divisor * self.size_divisor,
        )

      else:
        a     :			Optional[Any]				=   []
        for image in image_inputs:
          a,     a     :			List[str]				=   self.get_expected_values([image]			)
          expected_values.append((expected_height, expected_width)			)
        a     :			Union[str, Any]				=   max(A				,		key=lambda A			: item[0]			)[0]
        a     :			List[str]				=   max(A				,		key=lambda A			: item[1]			)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class 						snake_case  (  UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     ViltImageProcessor if is_vision_available() else None





    def 				lowerCamelCase__      (							self	:					Union[str, Any]			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   ViltImageProcessingTester(self			)





    @property
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A				,		'image_mean'			)			)
      self.assertTrue(hasattr(A				,		'image_std'			)			)
      self.assertTrue(hasattr(A				,		'do_normalize'			)			)
      self.assertTrue(hasattr(A				,		'do_resize'			)			)
      self.assertTrue(hasattr(A				,		'size'			)			)
      self.assertTrue(hasattr(A				,		'size_divisor'			)			)





    def 				lowerCamelCase__      (							self	:					Union[str, Any]			):


      '''simple docstring'''




      a     :			Dict				=   self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size				,		{'shortest_edge': 3_0}			)

      a     :			Union[str, Any]				=   self.image_processing_class.from_dict(self.image_processor_dict				,		size=4_2			)
      self.assertEqual(image_processor.size				,		{'shortest_edge': 4_2}			)





    def 				lowerCamelCase__      (							self	:					Union[str, Any]			):


      '''simple docstring'''




      pass





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			Optional[int]				=   self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      a     :			Dict				=   prepare_image_inputs(self.image_processor_tester				,		equal_resolution=A			)
      for image in image_inputs:
        self.assertIsInstance(A				,		Image.Image			)

      # Test not batched input
      a     :			int				=   image_processing(image_inputs[0]				,		return_tensors='pt'			).pixel_values

      a,     a     :			int				=   self.image_processor_tester.get_expected_values(A			)
      self.assertEqual(
          encoded_images.shape				,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,		)

      # Test batched
      a     :			List[Any]				=   image_processing(A				,		return_tensors='pt'			).pixel_values

      a,     a     :			Optional[int]				=   self.image_processor_tester.get_expected_values(A				,		batched=A			)
      self.assertEqual(
          encoded_images.shape				,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )				,		)





    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			Any				=   self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      a     :			List[str]				=   prepare_image_inputs(self.image_processor_tester				,		equal_resolution=A				,		numpify=A			)
      for image in image_inputs:
        self.assertIsInstance(A				,		np.ndarray			)

      # Test not batched input
      a     :			str				=   image_processing(image_inputs[0]				,		return_tensors='pt'			).pixel_values

      a,     a     :			str				=   self.image_processor_tester.get_expected_values(A			)
      self.assertEqual(
          encoded_images.shape				,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,		)

      # Test batched
      a     :			List[str]				=   image_processing(A				,		return_tensors='pt'			).pixel_values

      a,     a     :			Optional[int]				=   self.image_processor_tester.get_expected_values(A				,		batched=A			)
      self.assertEqual(
          encoded_images.shape				,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )				,		)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			int				=   self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      a     :			List[Any]				=   prepare_image_inputs(self.image_processor_tester				,		equal_resolution=A				,		torchify=A			)
      for image in image_inputs:
        self.assertIsInstance(A				,		torch.Tensor			)

      # Test not batched input
      a     :			int				=   image_processing(image_inputs[0]				,		return_tensors='pt'			).pixel_values

      a,     a     :			Optional[int]				=   self.image_processor_tester.get_expected_values(A			)
      self.assertEqual(
          encoded_images.shape				,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,		)

      # Test batched
      a     :			Union[str, Any]				=   image_processing(A				,		return_tensors='pt'			).pixel_values

      a,     a     :			List[Any]				=   self.image_processor_tester.get_expected_values(A				,		batched=A			)
      self.assertEqual(
          encoded_images.shape				,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )				,		)









"""simple docstring"""







import requests
from bsa import BeautifulSoup


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase	):
		_lowercase							:      Dict			=					(
		    """https://www.horoscope.com/us/horoscopes/general/"""
		    F"""horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"""
		)
		_lowercase							:      Any			=					BeautifulSoup(requests.get(__UpperCAmelCase	).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""",
		)
		UpperCAmelCase:   Union[str, Any]       			=							int(input("""number> """).strip())
		print("""choose some day:\n""", """yesterday\n""", """today\n""", """tomorrow\n""")
		UpperCAmelCase:   List[Any]       			=							input("""enter the day> """)
		UpperCAmelCase:   List[Any]       			=							horoscope(zodiac_sign, day)
		print(horoscope_text)



