

"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE							:	Union[str, Any]										=			["""GPT2TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE							:	Optional[int]										=			[
				    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """GPT2DoubleHeadsModel""",
				    """GPT2ForQuestionAnswering""",
				    """GPT2ForSequenceClassification""",
				    """GPT2ForTokenClassification""",
				    """GPT2LMHeadModel""",
				    """GPT2Model""",
				    """GPT2PreTrainedModel""",
				    """load_tf_weights_in_gpt2""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE							:	Any										=			[
				    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFGPT2DoubleHeadsModel""",
				    """TFGPT2ForSequenceClassification""",
				    """TFGPT2LMHeadModel""",
				    """TFGPT2MainLayer""",
				    """TFGPT2Model""",
				    """TFGPT2PreTrainedModel""",
				]

try:
				if not is_keras_nlp_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE							:	Any										=			["""TFGPT2Tokenizer"""]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE							:	Optional[int]										=			["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
				from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
				from .tokenization_gpta import GPTaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_fast import GPTaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_gpta import (
								    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GPTaDoubleHeadsModel,
								    GPTaForQuestionAnswering,
								    GPTaForSequenceClassification,
								    GPTaForTokenClassification,
								    GPTaLMHeadModel,
								    GPTaModel,
								    GPTaPreTrainedModel,
								    load_tf_weights_in_gpta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_gpta import (
								    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFGPTaDoubleHeadsModel,
								    TFGPTaForSequenceClassification,
								    TFGPTaLMHeadModel,
								    TFGPTaMainLayer,
								    TFGPTaModel,
								    TFGPTaPreTrainedModel,
								)

				try:
								if not is_keras_nlp_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_gpta_tf import TFGPTaTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
				import sys

				_SCREAMING_SNAKE_CASE							:	Tuple										=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""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)


