


'''simple docstring'''




import requests
from bsa import BeautifulSoup





def 							a_       (						lowercase							,						lowercase    )			->str:






    '''simple docstring'''

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









'''simple docstring'''




import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase  :      Any      		=				get_tests_dir("""fixtures/spiece.model""")





@require_sentencepiece
@require_tokenizers
class _snake_case   ( a__    ,   unittest.TestCase   ):
							"""simple docstring"""





							__a												=     ErnieMTokenizer
							__a												=     False
							__a												=     True
							__a												=     False
							__a												=     False







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:



											"""simple docstring"""







											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCamelCase		:      Optional[int]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
											tokenizer.save_pretrained(self.tmpdirname	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[int]											=					"this is a test"
											__lowerCamelCase		:      List[str]											=					"this is a test"
											return input_text, output_text







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Any											=					"<pad>"
											__lowerCamelCase		:      Any											=					0

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A	)			,				__A	)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A	)			,				__A	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:      Union[str, Any]											=					list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]			,				"<pad>"	)
											self.assertEqual(vocab_keys[1]			,				"<unk>"	)
											self.assertEqual(vocab_keys[-1]			,				"▁eloquent"	)
											self.assertEqual(len(__A	)			,				30000	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







											self.assertEqual(self.get_tokenizer().vocab_size			,				30000	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											if not self.test_rust_tokenizer:
															return

											__lowerCamelCase		:      Union[str, Any]											=					self.get_tokenizer()
											__lowerCamelCase		:      Optional[Any]											=					self.get_rust_tokenizer()

											__lowerCamelCase		:      Dict											=					"I was born in 92000, and this is falsé."

											__lowerCamelCase		:      str											=					tokenizer.tokenize(__A	)
											__lowerCamelCase		:      str											=					rust_tokenizer.tokenize(__A	)
											self.assertListEqual(__A			,				__A	)

											__lowerCamelCase		:      Tuple											=					tokenizer.encode(__A			,				add_special_tokens=__A	)
											__lowerCamelCase		:      Tuple											=					rust_tokenizer.encode(__A			,				add_special_tokens=__A	)
											self.assertListEqual(__A			,				__A	)

											__lowerCamelCase		:      Optional[int]											=					self.get_rust_tokenizer()
											__lowerCamelCase		:      Dict											=					tokenizer.encode(__A	)
											__lowerCamelCase		:      List[str]											=					rust_tokenizer.encode(__A	)
											self.assertListEqual(__A			,				__A	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:      Tuple											=					ErnieMTokenizer(__A			,				do_lower_case=__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)

											__lowerCamelCase		:      Optional[Any]											=					tokenizer.tokenize("This is a test"	)
											self.assertListEqual(__A			,				["▁this", "▁is", "▁a", "▁test"]	)

											self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)			,				[48, 25, 21, 1289]	)

											__lowerCamelCase		:      Any											=					tokenizer.tokenize("I was born in 92000, and this is falsé."	)
											# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
											self.assertListEqual(
											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]	)
											__lowerCamelCase		:      Optional[int]											=					tokenizer.convert_tokens_to_ids(__A	)
											self.assertListEqual(__A			,				[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]	)

											__lowerCamelCase		:      Dict											=					tokenizer.convert_ids_to_tokens(__A	)
											self.assertListEqual(
											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,				)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Union[str, Any]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)

											__lowerCamelCase		:      int											=					tokenizer.encode("sequence builders"	)
											__lowerCamelCase		:      List[Any]											=					tokenizer.encode("multi-sequence build"	)

											__lowerCamelCase		:      Tuple											=					tokenizer.build_inputs_with_special_tokens(__A	)
											__lowerCamelCase		:      Optional[int]											=					tokenizer.build_inputs_with_special_tokens(__A			,				__A	)

											assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
											assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
											    tokenizer.sep_token_id
											] + text_a + [tokenizer.sep_token_id]







							@slow
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											__lowerCamelCase		:      Tuple											=					{"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=__A			,				model_name="susnato/ernie-m-base_pytorch"			,				sequences=[
											        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
											        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
											        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
											        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
											        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
											        "conditioning on both left and right context in all layers.",
											        "The quick brown fox jumps over the lazy dog.",
											    ]			,				)





