

"""simple docstring"""





from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import (
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				)


_SCREAMING_SNAKE_CASE							:	int										=			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			Dict[str, Any]
_SCREAMING_SNAKE_CASE							:	Dict										=			List[Prediction]

@add_end_docstrings(snake_case__     )
class      __a (   snake_case__     ):

			"""simple docstring"""

			def __init__(				self  :       str    ,	*lowercase_  :       Tuple    ,	**lowercase_  :       Any    ):
								super().__init__(*lowercase_    ,	**lowercase_    )

								if self.framework == "tf":
													raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''    )

								requires_backends(self    ,	'''vision'''    )
								self.check_model_type(
								    dict(
								        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()    )    )






			def  _lowerCAmelCase      (				self  :       int    ,	**lowercase_  :       Dict    ):
								UpperCamelCase__		:     str								={}
								UpperCamelCase__		:     Optional[Any]								={}
								if "subtask" in kwargs:
													UpperCamelCase__		:     Dict								=kwargs['''subtask''']
													UpperCamelCase__		:     int								=kwargs['''subtask''']
								if "threshold" in kwargs:
													UpperCamelCase__		:     int								=kwargs['''threshold''']
								if "mask_threshold" in kwargs:
													UpperCamelCase__		:     Tuple								=kwargs['''mask_threshold''']
								if "overlap_mask_area_threshold" in kwargs:
													UpperCamelCase__		:     Optional[int]								=kwargs['''overlap_mask_area_threshold''']

								return preprocess_kwargs, {}, postprocess_kwargs






			def __call__(				self  :       Dict    ,	lowercase_  :       Tuple    ,	**lowercase_  :       Optional[Any]    ):
								return super().__call__(lowercase_    ,	**lowercase_    )






			def  _lowerCAmelCase      (				self  :       Union[str, Any]    ,	lowercase_  :       Optional[int]    ,	lowercase_  :       int=None    ):
								UpperCamelCase__		:     Optional[int]								=load_image(lowercase_    )
								UpperCamelCase__		:     List[str]								=[(image.height, image.width)]
								if self.model.config.__class__.__name__ == "OneFormerConfig":
													if subtask is None:
																		UpperCamelCase__		:     List[str]								={}
													else:
																		UpperCamelCase__		:     Tuple								={'''task_inputs''': [subtask]}
													UpperCamelCase__		:     Union[str, Any]								=self.image_processor(images=[image]    ,	return_tensors='''pt'''    ,	**lowercase_    )
													UpperCamelCase__		:     Optional[int]								=self.tokenizer(
													    inputs['''task_inputs''']    ,	padding='''max_length'''    ,	max_length=self.model.config.task_seq_len    ,	return_tensors=self.framework    ,	)['''input_ids''']
								else:
													UpperCamelCase__		:     Dict								=self.image_processor(images=[image]    ,	return_tensors='''pt'''    )
								UpperCamelCase__		:     Any								=target_size
								return inputs






			def  _lowerCAmelCase      (				self  :       str    ,	lowercase_  :       int    ):
								UpperCamelCase__		:     Dict								=model_inputs.pop('''target_size'''    )
								UpperCamelCase__		:     List[str]								=self.model(**lowercase_    )
								UpperCamelCase__		:     List[Any]								=target_size
								return model_outputs






			def  _lowerCAmelCase      (				self  :       Any    ,	lowercase_  :       List[str]    ,	lowercase_  :       Optional[int]=None    ,	lowercase_  :       Optional[Any]=0.9    ,	lowercase_  :       List[str]=0.5    ,	lowercase_  :       List[Any]=0.5    ):
								UpperCamelCase__		:     List[Any]								=None
								if subtask in {"panoptic", None} and hasattr(self.image_processor    ,	'''post_process_panoptic_segmentation'''    ):
													UpperCamelCase__		:     str								=self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor    ,	'''post_process_instance_segmentation'''    ):
													UpperCamelCase__		:     Optional[int]								=self.image_processor.post_process_instance_segmentation

								if fn is not None:
													UpperCamelCase__		:     int								=fn(
													    lowercase_    ,	threshold=lowercase_    ,	mask_threshold=lowercase_    ,	overlap_mask_area_threshold=lowercase_    ,	target_sizes=model_outputs['''target_size''']    ,	)[0]

													UpperCamelCase__		:     Union[str, Any]								=[]
													UpperCamelCase__		:     int								=outputs['''segmentation''']

													for segment in outputs["segments_info"]:
																		UpperCamelCase__		:     Any								=(segmentation == segment['''id''']) * 255
																		UpperCamelCase__		:     Optional[Any]								=Image.fromarray(mask.numpy().astype(np.uinta    )    ,	mode='''L'''    )
																		UpperCamelCase__		:     str								=self.model.config.idalabel[segment['''label_id''']]
																		UpperCamelCase__		:     List[Any]								=segment['''score''']
																		annotation.append({'''score''': score, '''label''': label, '''mask''': mask}    )

								elif subtask in {"semantic", None} and hasattr(self.image_processor    ,	'''post_process_semantic_segmentation'''    ):
													UpperCamelCase__		:     Any								=self.image_processor.post_process_semantic_segmentation(
													    lowercase_    ,	target_sizes=model_outputs['''target_size''']    )[0]

													UpperCamelCase__		:     Optional[int]								=[]
													UpperCamelCase__		:     List[str]								=outputs.numpy()
													UpperCamelCase__		:     str								=np.unique(lowercase_    )

													for label in labels:
																		UpperCamelCase__		:     Optional[int]								=(segmentation == label) * 255
																		UpperCamelCase__		:     Dict								=Image.fromarray(mask.astype(np.uinta    )    ,	mode='''L'''    )
																		UpperCamelCase__		:     List[Any]								=self.model.config.idalabel[label]
																		annotation.append({'''score''': None, '''label''': label, '''mask''': mask}    )
								else:
													raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model    )}'''    )
								return annotation





"""simple docstring"""





import requests
from bsa import BeautifulSoup



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=(
					    '''https://www.horoscope.com/us/horoscopes/general/'''
					    F'''horoscope-general-daily-{day}.aspx?sign={zodiac_sign}'''
					)
					UpperCamelCase__		:     List[str]								=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""",
				)
				_SCREAMING_SNAKE_CASE							:	Any										=			int(input("""number> """).strip())
				print("""choose some day:\n""", """yesterday\n""", """today\n""", """tomorrow\n""")
				_SCREAMING_SNAKE_CASE							:	List[str]										=			input("""enter the day> """)
				_SCREAMING_SNAKE_CASE							:	Dict										=			horoscope(zodiac_sign, day)
				print(horoscope_text)


