





"""simple docstring"""


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


a__       :  str       		=					logging.get_logger(__name__)

a__       :  int       		=					OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

a__       :  Any       		=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 				a__      (	_UpperCAmelCase						)     ->	Tuple:

 '''simple docstring'''






 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   snake_case__     				=					model_type_to_module_name(_UpperCAmelCase						)

   snake_case__     				=					importlib.import_module(f".{module_name}"						,  "transformers.models"						)
   try:
    return getattr(_UpperCAmelCase						,  _UpperCAmelCase						)
   except AttributeError:
    continue

 for processor in PROCESSOR_MAPPING._extra_content.values():
  if getattr(_UpperCAmelCase						,  "__name__"						,  _UpperCAmelCase						) == class_name:
   return processor

  # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
  # init and we return the proper dummy to get an appropriate error message.
 snake_case__     				=					importlib.import_module("transformers"						)
 if hasattr(_UpperCAmelCase						,  _UpperCAmelCase						):
  return getattr(_UpperCAmelCase						,  _UpperCAmelCase						)

 return None



class   lowercase_					:

       def __init__(      self  ):






        '''simple docstring'''

        raise EnvironmentError(
            "AutoProcessor is designed to be instantiated "
            "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."  )

       @classmethod
       @replace_list_option_in_docstrings(snake_case_  )
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					kwargs.pop("config" ,       snake_case_  )
        snake_case__     				=					kwargs.pop("trust_remote_code" ,       snake_case_  )
        snake_case__     				=					True

        snake_case__     				=					None
        snake_case__     				=					None

        # First, let's see if we have a preprocessor config.
        # Filter the kwargs for `get_file_from_repo`.
        snake_case__     				=					{
            key: kwargs[key] for key in inspect.signature(snake_case_  ).parameters.keys() if key in kwargs
        }
        # Let's start by checking whether the processor class is saved in an image processor
        snake_case__     				=					get_file_from_repo(
            snake_case_ ,       snake_case_ ,       **snake_case_  )
        if preprocessor_config_file is not None:
         snake_case__     				=					ImageProcessingMixin.get_image_processor_dict(snake_case_ ,       **snake_case_  )
         snake_case__     				=					config_dict.get("processor_class" ,       snake_case_  )
         if "AutoProcessor" in config_dict.get("auto_map" ,       {}  ):
          snake_case__     				=					config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
        if preprocessor_config_file is not None and processor_class is None:
         snake_case__     				=					FeatureExtractionMixin.get_feature_extractor_dict(snake_case_ ,       **snake_case_  )
         snake_case__     				=					config_dict.get("processor_class" ,       snake_case_  )
         if "AutoProcessor" in config_dict.get("auto_map" ,       {}  ):
          snake_case__     				=					config_dict['auto_map']['AutoProcessor']

        if processor_class is None:
         # Next, let's check whether the processor class is saved in a tokenizer
         snake_case__     				=					get_file_from_repo(
             snake_case_ ,       snake_case_ ,       **snake_case_  )
         if tokenizer_config_file is not None:
          with open(snake_case_ ,       encoding="utf-8"  ) as reader:
           snake_case__     				=					json.load(snake_case_  )

          snake_case__     				=					config_dict.get("processor_class" ,       snake_case_  )
          if "AutoProcessor" in config_dict.get("auto_map" ,       {}  ):
           snake_case__     				=					config_dict['auto_map']['AutoProcessor']

        if processor_class is None:
         # Otherwise, load config, if it can be loaded.
         if not isinstance(snake_case_ ,       snake_case_  ):
          snake_case__     				=					AutoConfig.from_pretrained(
              snake_case_ ,       trust_remote_code=snake_case_ ,       **snake_case_  )

         # And check if the config contains the processor class.
         snake_case__     				=					getattr(snake_case_ ,       "processor_class" ,       snake_case_  )
         if hasattr(snake_case_ ,       "auto_map"  ) and "AutoProcessor" in config.auto_map:
          snake_case__     				=					config.auto_map['AutoProcessor']

        if processor_class is not None:
         snake_case__     				=					processor_class_from_name(snake_case_  )

        snake_case__     				=					processor_auto_map is not None
        snake_case__     				=					processor_class is not None or type(snake_case_  ) in PROCESSOR_MAPPING
        snake_case__     				=					resolve_trust_remote_code(
            snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_  )

        if has_remote_code and trust_remote_code:
         snake_case__     				=					get_class_from_dynamic_module(
             snake_case_ ,       snake_case_ ,       **snake_case_  )
         snake_case__     				=					kwargs.pop("code_revision" ,       snake_case_  )
         if os.path.isdir(snake_case_  ):
          processor_class.register_for_auto_class()
         return processor_class.from_pretrained(
             snake_case_ ,       trust_remote_code=snake_case_ ,       **snake_case_  )
        elif processor_class is not None:
         return processor_class.from_pretrained(
             snake_case_ ,       trust_remote_code=snake_case_ ,       **snake_case_  )
        # Last try: we use the PROCESSOR_MAPPING.
        elif type(snake_case_  ) in PROCESSOR_MAPPING:
         return PROCESSOR_MAPPING[type(snake_case_  )].from_pretrained(snake_case_ ,       **snake_case_  )

        # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
        # tokenizer.
        try:
         return AutoTokenizer.from_pretrained(
             snake_case_ ,       trust_remote_code=snake_case_ ,       **snake_case_  )
        except Exception:
         try:
          return AutoImageProcessor.from_pretrained(
              snake_case_ ,       trust_remote_code=snake_case_ ,       **snake_case_  )
         except Exception:
          pass

         try:
          return AutoFeatureExtractor.from_pretrained(
              snake_case_ ,       trust_remote_code=snake_case_ ,       **snake_case_  )
         except Exception:
          pass

        raise ValueError(
            F"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
            "tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"
            "the files of at least one of those processing classes."  )

       @staticmethod
       def 		_SCREAMING_SNAKE_CASE(      __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        PROCESSOR_MAPPING.register(snake_case_ ,       snake_case_  )








"""simple docstring"""




import requests
from bsa import BeautifulSoup





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





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