



import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


A__						:						Optional[Any]          =      '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

A__						:						Optional[Any]          =      '''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

A__						:						Union[str, Any]          =      '''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION		)
class        __snake_case		(  datasets.Metric		):




							def      UpperCAmelCase__    ( self  : Tuple):
									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,       citation=_CITATION       ,       inputs_description=_KWARGS_DESCRIPTION       ,       features=datasets.Features(
									        {
									            '''predictions''': {
									                '''id''': datasets.Value('''string'''),
									                '''prediction_text''': datasets.Value('''string'''),
									                '''no_answer_probability''': datasets.Value('''float32'''),
									            },
									            '''references''': {
									                '''id''': datasets.Value('''string'''),
									                '''answers''': datasets.features.Sequence(
									                    {'''text''': datasets.Value('''string'''), '''answer_start''': datasets.Value('''int32''')}),
									            },
									        })       ,       codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']       ,       reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']       ,       )







							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : Dict       ,       A_  : List[str]       ,       A_  : Tuple=1.0):
									lowerCAmelCase_    :  Optional[Any]             =				{p['''id''']: p['''no_answer_probability'''] for p in predictions}
									lowerCAmelCase_    :  int             =				[{'''paragraphs''': [{'''qas''': references}]}]
									lowerCAmelCase_    :  Any             =				{p['''id''']: p['''prediction_text'''] for p in predictions}

									lowerCAmelCase_    :  Optional[int]             =				make_qid_to_has_ans(A_)  # maps qid to True/False
									lowerCAmelCase_    :  List[str]             =				[k for k, v in qid_to_has_ans.items() if v]
									lowerCAmelCase_    :  Optional[int]             =				[k for k, v in qid_to_has_ans.items() if not v]

									lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				get_raw_scores(A_       ,       A_)
									lowerCAmelCase_    :  int             =				apply_no_ans_threshold(A_       ,       A_       ,       A_       ,       A_)
									lowerCAmelCase_    :  List[str]             =				apply_no_ans_threshold(A_       ,       A_       ,       A_       ,       A_)
									lowerCAmelCase_    :  Union[str, Any]             =				make_eval_dict(A_       ,       A_)

									if has_ans_qids:
											lowerCAmelCase_    :  Tuple             =				make_eval_dict(A_       ,       A_       ,       qid_list=A_)
											merge_eval(A_       ,       A_       ,       '''HasAns''')
									if no_ans_qids:
											lowerCAmelCase_    :  Any             =				make_eval_dict(A_       ,       A_       ,       qid_list=A_)
											merge_eval(A_       ,       A_       ,       '''NoAns''')
									find_all_best_thresh(A_       ,       A_       ,       A_       ,       A_       ,       A_       ,       A_)
									return dict(A_)










from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A__						:						int          =      '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	str				):

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

				lowerCAmelCase_    :  Union[str, Any]             =				BASE_URL.format(__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Optional[int]             =				get(__UpperCamelCase				)

				# Is the response ok?
				response.raise_for_status()

				# Scrape the data using bs4
				lowerCAmelCase_    :  Optional[int]             =				BeautifulSoup(response.text  ,'''html.parser'''				)

				# This list will store the name and price.
				lowerCAmelCase_    :  List[Any]             =				[]

				# Fetch all the grids that contains the items.
				lowerCAmelCase_    :  Union[str, Any]             =				soup.find_all('''div'''  ,{'''class''': '''grid-x pharmCard'''}				)
				if grid_list and len(__UpperCamelCase				) > 0:
						for grid in grid_list:
								# Get the pharmacy price.
								lowerCAmelCase_    :  int             =				grid.find('''p'''  ,{'''class''': '''list-title'''}				).text

								# Get price of the drug.
								lowerCAmelCase_    :  List[Any]             =				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__						:						Dict          =      input('''Enter drug name: ''').strip()
						A__						:						int          =      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__						:						Union[str, Any]          =      pharmacy_price['''pharmacy_name''']
																		A__						:						Tuple          =      pharmacy_price['''price''']

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





