
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


A		:   List[Any]         =    get_tests_dir('''fixtures/spiece.model''')



@require_sentencepiece
@require_tokenizers
class       a_   ( _a				, unittest.TestCase						):
			a  : Optional[int]				      =       ErnieMTokenizer
			a  : Optional[int]				      =       False
			a  : Optional[Any]				      =       True
			a  : Optional[Any]				      =       False
			a  : int				      =       False






			def       UpperCamelCase_							(			self     ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_lowercase									=				ErnieMTokenizer(__UpperCamelCase							, unk_token="""<unk>"""							, pad_token="""<pad>"""     )
									tokenizer.save_pretrained(self.tmpdirname     )






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									_lowercase									=				"""this is a test"""
									_lowercase									=				"""this is a test"""
									return input_text, output_text






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				"""<pad>"""
									_lowercase									=				0

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






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				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(__UpperCamelCase     )							, 30_000     )






			def       UpperCamelCase_							(			self     ):
									self.assertEqual(self.get_tokenizer().vocab_size							, 30_000     )






			def       UpperCamelCase_							(			self     ):
									if not self.test_rust_tokenizer:
															return

									_lowercase									=				self.get_tokenizer()
									_lowercase									=				self.get_rust_tokenizer()

									_lowercase									=				"""I was born in 92000, and this is falsé."""

									_lowercase									=				tokenizer.tokenize(__UpperCamelCase     )
									_lowercase									=				rust_tokenizer.tokenize(__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase     )

									_lowercase									=				tokenizer.encode(__UpperCamelCase							, add_special_tokens=__UpperCamelCase     )
									_lowercase									=				rust_tokenizer.encode(__UpperCamelCase							, add_special_tokens=__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase     )

									_lowercase									=				self.get_rust_tokenizer()
									_lowercase									=				tokenizer.encode(__UpperCamelCase     )
									_lowercase									=				rust_tokenizer.encode(__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, __UpperCamelCase     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ErnieMTokenizer(__UpperCamelCase							, do_lower_case=__UpperCamelCase							, unk_token="""<unk>"""							, pad_token="""<pad>"""     )

									_lowercase									=				tokenizer.tokenize("""This is a test"""     )
									self.assertListEqual(__UpperCamelCase							, ["""▁this""", """▁is""", """▁a""", """▁test"""]     )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase     )							, [48, 25, 21, 1_289]     )

									_lowercase									=				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(
									    __UpperCamelCase							, ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]     )
									_lowercase									=				tokenizer.convert_tokens_to_ids(__UpperCamelCase     )
									self.assertListEqual(__UpperCamelCase							, [31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]     )

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






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ErnieMTokenizer(__UpperCamelCase							, unk_token="""<unk>"""							, pad_token="""<pad>"""     )

									_lowercase									=				tokenizer.encode("""sequence builders"""     )
									_lowercase									=				tokenizer.encode("""multi-sequence build"""     )

									_lowercase									=				tokenizer.build_inputs_with_special_tokens(__UpperCamelCase     )
									_lowercase									=				tokenizer.build_inputs_with_special_tokens(__UpperCamelCase							, __UpperCamelCase     )

									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       UpperCamelCase_							(			self     ):
									# fmt: off
									_lowercase									=				{"""input_ids""": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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=__UpperCamelCase							, 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.""",
									    ]							, )

from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A		:   Union[str, Any]         =    '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  str		,      SCREAMING_SNAKE_CASE_  :  str					)     ->    list | None:

						try:
												# Has user provided both inputs?
												if not drug_name or not zip_code:
																		return None

												_lowercase									=				BASE_URL.format(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_					)
												_lowercase									=				get(SCREAMING_SNAKE_CASE_					)

												# Is the response ok?
												response.raise_for_status()

												# Scrape the data using bs4
												_lowercase									=				BeautifulSoup(response.text		,      """html.parser"""					)

												# This list will store the name and price.
												_lowercase									=				[]

												# Fetch all the grids that contains the items.
												_lowercase									=				soup.find_all("""div"""		,      {"""class""": """grid-x pharmCard"""}					)
												if grid_list and len(SCREAMING_SNAKE_CASE_					) > 0:
																		for grid in grid_list:
																								# Get the pharmacy price.
																								_lowercase									=				grid.find("""p"""		,      {"""class""": """list-title"""}					).text

																								# Get price of the drug.
																								_lowercase									=				grid.find("""span"""		,      {"""p""", """price price-large"""}					).text

																								pharmacy_price_list.append(
																								    {
																								        """pharmacy_name""": pharmacy_name,
																								        """price""": price,
																								    }					)

												return pharmacy_price_list

						except (HTTPError, exceptions.RequestException, ValueError):
												return None


if __name__ == "__main__":
		# Enter a drug name and a zip code
		A		:   Union[str, Any]         =    input('''Enter drug name: ''').strip()
		A		:   Dict         =    input('''Enter zip code: ''').strip()

		A		:   list | None         =    fetch_pharmacy_and_price_list(
		    drug_name, zip_code
		)

		if pharmacy_price_list:
				print(F'\nSearch results for {drug_name} at location {zip_code}:')
				for pharmacy_price in pharmacy_price_list:
						A		:   List[str]         =    pharmacy_price['''pharmacy_name''']
						A		:   Dict         =    pharmacy_price['''price''']

						print(F'Pharmacy: {name} Price: {price}')
		else:
				print(F'No results found for {drug_name}')