






"""simple docstring"""







import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def _a (		):







      """simple docstring"""






      def filter_log(_snake_case							):
            return False if "This IS expected if you are initializing" in record.msg else True

      UpperCAmelCase							=       datasets.utils.logging.get_logger("""transformers.modeling_utils"""							)
      logger.addFilter(_snake_case							)
      try:
            yield
      finally:
            logger.removeFilter(_snake_case							)


_UpperCamelCase     			=  """\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
"""

_UpperCamelCase     			=  """\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project's README at https://github.com/Tiiiger/bert_score#readme for more information.
"""

_UpperCamelCase     			=  """
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. 'en').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bertscore = datasets.load_metric(\"bertscore\")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")
    >>> print([round(v, 2) for v in results[\"f1\"]])
    [1.0, 1.0]
"""







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






    def 				_UpperCamelCase							( self					):
          return datasets.MetricInfo(
              description=_DESCRIPTION	,citation=_CITATION	,homepage="""https://github.com/Tiiiger/bert_score"""	,inputs_description=_KWARGS_DESCRIPTION	,features=datasets.Features(
                  {
                      """predictions""": datasets.Value("""string"""	,id="""sequence"""					),
                      """references""": datasets.Sequence(datasets.Value("""string"""	,id="""sequence"""					)	,id="""references"""					),
                  }					)	,codebase_urls=["""https://github.com/Tiiiger/bert_score"""]	,reference_urls=[
                  """https://github.com/Tiiiger/bert_score""",
                  """https://arxiv.org/abs/1904.09675""",
              ]	,)






    def 				_UpperCamelCase							( self	,A	,A	,A=None	,A=None	,A=None	,A=False	,A=False	,A=None	,A=64	,A=4	,A=False	,A=False	,A=None	,A=False	,):
          UpperCAmelCase							=       bert_score.utils.get_hash
          UpperCAmelCase							=       bert_score.BERTScorer

          if version.parse(bert_score.__version__					) >= version.parse("""0.3.10"""					):
                UpperCAmelCase							=       functools.partial(A	,use_fast_tokenizer=A					)
                UpperCAmelCase							=       functools.partial(A	,use_fast_tokenizer=A					)
          elif use_fast_tokenizer:
                raise ImportWarning(
                    """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
                    """You can install it with `pip install \"bert-score>=0.3.10\"`."""					)

          if model_type is None:
                assert lang is not None, "either lang or model_type should be specified"
                UpperCAmelCase							=       bert_score.utils.langamodel[lang.lower()]

          if num_layers is None:
                UpperCAmelCase							=       bert_score.utils.modelalayers[model_type]

          UpperCAmelCase							=       get_hash(
              model=A	,num_layers=A	,idf=A	,rescale_with_baseline=A	,use_custom_baseline=baseline_path is not None	,)

          with filter_logging_context():
                if not hasattr(self	,"""cached_bertscorer"""					) or self.cached_bertscorer.hash != hashcode:
                      UpperCAmelCase							=       scorer(
                          model_type=A	,num_layers=A	,batch_size=A	,nthreads=A	,all_layers=A	,idf=A	,device=A	,lang=A	,rescale_with_baseline=A	,baseline_path=A	,)

          ((UpperCAmelCase)			,					(UpperCAmelCase)			,					(UpperCAmelCase))							=       self.cached_bertscorer.score(
              cands=A	,refs=A	,verbose=A	,batch_size=A	,)
          UpperCAmelCase							=       {
              """precision""": P.tolist(),
              """recall""": R.tolist(),
              """f1""": F.tolist(),
              """hashcode""": hashcode,
          }
          return output_dict






    def 				_UpperCamelCase							( self	,A=None	,A=None	,**A					):
          # References can be strings or lists of strings
          # Let's change strings to lists of strings with one element
          if references is not None:
                UpperCAmelCase							=       [[ref] if isinstance(A	,A					) else ref for ref in references]
          super().add_batch(predictions=A	,references=A	,**A					)






    def 				_UpperCamelCase							( self	,A=None	,A=None	,**A					):
          # References can be strings or lists of strings
          # Let's change strings to lists of strings with one element
          if isinstance(A	,A					):
                UpperCAmelCase							=       [reference]
          super().add(prediction=A	,reference=A	,**A					)











"""simple docstring"""







from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

_UpperCamelCase     			=  """https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true"""

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






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

            UpperCAmelCase							=       BASE_URL.format(_snake_case      ,       _snake_case							)
            UpperCAmelCase							=       get(_snake_case							)

            # Is the response ok?
            response.raise_for_status()

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

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

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

                        # Get price of the drug.
                        UpperCAmelCase							=       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
  _UpperCamelCase     			=  input("""Enter drug name: """).strip()
  _UpperCamelCase     			=  input("""Enter zip code: """).strip()

  _UpperCamelCase     			=  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:
      _UpperCamelCase     			=  pharmacy_price["""pharmacy_name"""]
      _UpperCamelCase     			=  pharmacy_price["""price"""]

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



