






'''simple docstring'''




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCamelCase_							      =      logging.get_logger(__name__)

@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		42
   snake_case					       =		42
   snake_case					       =		None
   snake_case					       =		None

   def 		lowerCAmelCase				(    self				:			Any	):





         '''simple docstring'''





         return json.dumps(dataclasses.asdict(self	)   ,   indent=2	) + "\n"





@dataclass(frozen=snake_case_		)
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		42
   snake_case					       =		None
   snake_case					       =		None
   snake_case					       =		None

   def 		lowerCAmelCase				(    self				:			Any	):





         '''simple docstring'''





         return json.dumps(dataclasses.asdict(self	)	) + "\n"





class      _UpperCAmelCase    :




   """simple docstring"""

   def 		lowerCAmelCase				(    self				:			List[Any]   ,   __UpperCAmelCase				:			str	):





         '''simple docstring'''





         raise NotImplementedError()

   def 		lowerCAmelCase				(    self				:			str   ,   __UpperCAmelCase				:			Optional[Any]	):





         '''simple docstring'''





         raise NotImplementedError()

   def 		lowerCAmelCase				(    self				:			Optional[Any]   ,   __UpperCAmelCase				:			Optional[int]	):





         '''simple docstring'''





         raise NotImplementedError()

   def 		lowerCAmelCase				(    self				:			str   ,   __UpperCAmelCase				:			Union[str, Any]	):





         '''simple docstring'''





         raise NotImplementedError()

   def 		lowerCAmelCase				(    self				:			List[str]	):





         '''simple docstring'''





         raise NotImplementedError()

   def 		lowerCAmelCase				(    self				:			Dict   ,   __UpperCAmelCase				:			str	):





         '''simple docstring'''





         if len(self.get_labels()	) > 1:
               _A      =			self.get_labels()[int(example.label	)]
         return example

   @classmethod
   def 		lowerCAmelCase				(    cls				:			Union[str, Any]   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			Any=None	):





         '''simple docstring'''





         with open(__UpperCAmelCase   ,   "r"   ,   encoding="utf-8-sig"	) as f:
               return list(csv.reader(__UpperCAmelCase   ,   delimiter="\t"   ,   quotechar=__UpperCAmelCase	)	)





class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(    self				:			Dict   ,   __UpperCAmelCase				:			Optional[Any]=None   ,   __UpperCAmelCase				:			List[Any]=None   ,   __UpperCAmelCase				:			str="classification"   ,   __UpperCAmelCase				:			Union[str, Any]=False	):





         '''simple docstring'''





         _A      =			[] if labels is None else labels
         _A      =			[] if examples is None else examples
         _A      =			mode
         _A      =			verbose

   def __len__(    self				:			List[str]	):





         '''simple docstring'''





         return len(self.examples	)

   def __getitem__(    self				:			str   ,   __UpperCAmelCase				:			str	):





         '''simple docstring'''





         if isinstance(__UpperCAmelCase   ,   __UpperCAmelCase	):
               return SingleSentenceClassificationProcessor(labels=self.labels   ,   examples=self.examples[idx]	)
         return self.examples[idx]

   @classmethod
   def 		lowerCAmelCase				(    cls				:			int   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Tuple=""   ,   __UpperCAmelCase				:			Dict=0   ,   __UpperCAmelCase				:			Tuple=1   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			Any=False   ,   **__UpperCAmelCase				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			cls(**__UpperCAmelCase	)
         processor.add_examples_from_csv(
             __UpperCAmelCase   ,   split_name=__UpperCAmelCase   ,   column_label=__UpperCAmelCase   ,   column_text=__UpperCAmelCase   ,   column_id=__UpperCAmelCase   ,   skip_first_row=__UpperCAmelCase   ,   overwrite_labels=__UpperCAmelCase   ,   overwrite_examples=__UpperCAmelCase   ,   )
         return processor

   @classmethod
   def 		lowerCAmelCase				(    cls				:			int   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Any=None   ,   **__UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			cls(**__UpperCAmelCase	)
         processor.add_examples(__UpperCAmelCase   ,   labels=__UpperCAmelCase	)
         return processor

   def 		lowerCAmelCase				(    self				:			Tuple   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Optional[int]=""   ,   __UpperCAmelCase				:			str=0   ,   __UpperCAmelCase				:			Optional[int]=1   ,   __UpperCAmelCase				:			Union[str, Any]=None   ,   __UpperCAmelCase				:			List[Any]=False   ,   __UpperCAmelCase				:			List[str]=False   ,   __UpperCAmelCase				:			Optional[int]=False   ,   ):





         '''simple docstring'''





         _A      =			self._read_tsv(__UpperCAmelCase	)
         if skip_first_row:
               _A      =			lines[1:]
         _A      =			[]
         _A      =			[]
         _A      =			[]
         for i, line in enumerate(__UpperCAmelCase	):
               texts.append(line[column_text]	)
               labels.append(line[column_label]	)
               if column_id is not None:
                     ids.append(line[column_id]	)
               else:
                     _A      =			f'''{split_name}-{i}''' if split_name else str(__UpperCAmelCase	)
                     ids.append(__UpperCAmelCase	)

         return self.add_examples(
             __UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase   ,   overwrite_labels=__UpperCAmelCase   ,   overwrite_examples=__UpperCAmelCase	)

   def 		lowerCAmelCase				(    self				:			str   ,   __UpperCAmelCase				:			Tuple   ,   __UpperCAmelCase				:			int=None   ,   __UpperCAmelCase				:			Optional[Any]=None   ,   __UpperCAmelCase				:			Optional[int]=False   ,   __UpperCAmelCase				:			Optional[int]=False	):





         '''simple docstring'''





         if labels is not None and len(__UpperCAmelCase	) != len(__UpperCAmelCase	):
               raise ValueError(
                   f'''Text and labels have mismatched lengths {len(__UpperCAmelCase	)} and {len(__UpperCAmelCase	)}'''	)
         if ids is not None and len(__UpperCAmelCase	) != len(__UpperCAmelCase	):
               raise ValueError(f'''Text and ids have mismatched lengths {len(__UpperCAmelCase	)} and {len(__UpperCAmelCase	)}'''	)
         if ids is None:
               _A      =			[None] * len(__UpperCAmelCase	)
         if labels is None:
               _A      =			[None] * len(__UpperCAmelCase	)
         _A      =			[]
         _A      =			set()
         for text_or_text_and_label, label, guid in zip(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase	):
               if isinstance(__UpperCAmelCase   ,   (tuple, list)	) and label is None:
                     _A  ,			_A      =			text_or_text_and_label
               else:
                     _A      =			text_or_text_and_label
               added_labels.add(__UpperCAmelCase	)
               examples.append(InputExample(guid=__UpperCAmelCase   ,   text_a=__UpperCAmelCase   ,   text_b=__UpperCAmelCase   ,   label=__UpperCAmelCase	)	)

         # Update examples
         if overwrite_examples:
               _A      =			examples
         else:
               self.examples.extend(__UpperCAmelCase	)

         # Update labels
         if overwrite_labels:
               _A      =			list(__UpperCAmelCase	)
         else:
               _A      =			list(set(self.labels	).union(__UpperCAmelCase	)	)

         return self.examples

   def 		lowerCAmelCase				(    self				:			Optional[Any]   ,   __UpperCAmelCase				:			Tuple   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			List[Any]=False   ,   __UpperCAmelCase				:			List[str]=0   ,   __UpperCAmelCase				:			Optional[Any]=True   ,   __UpperCAmelCase				:			str=None   ,   ):





         '''simple docstring'''





         if max_length is None:
               _A      =			tokenizer.max_len

         _A      =			{label: i for i, label in enumerate(self.labels	)}

         _A      =			[]
         for ex_index, example in enumerate(self.examples	):
               if ex_index % 10000 == 0:
                     logger.info(f'''Tokenizing example {ex_index}'''	)

               _A      =			tokenizer.encode(
                   example.text_a   ,   add_special_tokens=__UpperCAmelCase   ,   max_length=min(__UpperCAmelCase   ,   tokenizer.max_len	)   ,   )
               all_input_ids.append(__UpperCAmelCase	)

         _A      =			max(len(__UpperCAmelCase	) for input_ids in all_input_ids	)

         _A      =			[]
         for ex_index, (input_ids, example) in enumerate(zip(__UpperCAmelCase   ,   self.examples	)	):
               if ex_index % 10000 == 0:
                     logger.info(f'''Writing example {ex_index}/{len(self.examples	)}'''	)
               # The mask has 1 for real tokens and 0 for padding tokens. Only real
               # tokens are attended to.
               _A      =			[1 if mask_padding_with_zero else 0] * len(__UpperCAmelCase	)

               # Zero-pad up to the sequence length.
               _A      =			batch_length - len(__UpperCAmelCase	)
               if pad_on_left:
                     _A      =			([pad_token] * padding_length) + input_ids
                     _A      =			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
               else:
                     _A      =			input_ids + ([pad_token] * padding_length)
                     _A      =			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

               if len(__UpperCAmelCase	) != batch_length:
                     raise ValueError(f'''Error with input length {len(__UpperCAmelCase	)} vs {batch_length}'''	)
               if len(__UpperCAmelCase	) != batch_length:
                     raise ValueError(f'''Error with input length {len(__UpperCAmelCase	)} vs {batch_length}'''	)

               if self.mode == "classification":
                     _A      =			label_map[example.label]
               elif self.mode == "regression":
                     _A      =			float(example.label	)
               else:
                     raise ValueError(self.mode	)

               if ex_index < 5 and self.verbose:
                     logger.info("*** Example ***"	)
                     logger.info(f'''guid: {example.guid}'''	)
                     logger.info(f'''input_ids: {' '.join([str(__UpperCAmelCase	) for x in input_ids]	)}'''	)
                     logger.info(f'''attention_mask: {' '.join([str(__UpperCAmelCase	) for x in attention_mask]	)}'''	)
                     logger.info(f'''label: {example.label} (id = {label})'''	)

               features.append(InputFeatures(input_ids=__UpperCAmelCase   ,   attention_mask=__UpperCAmelCase   ,   label=__UpperCAmelCase	)	)

         if return_tensors is None:
               return features
         elif return_tensors == "tf":
               if not is_tf_available():
                     raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"	)
               import tensorflow as tf

               def gen():
                     for ex in features:
                           yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

               _A      =			tf.data.Dataset.from_generator(
                   __UpperCAmelCase   ,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)   ,   ({"input_ids": tf.TensorShape([None]	), "attention_mask": tf.TensorShape([None]	)}, tf.TensorShape([]	))   ,   )
               return dataset
         elif return_tensors == "pt":
               if not is_torch_available():
                     raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"	)
               import torch
               from torch.utils.data import TensorDataset

               _A      =			torch.tensor([f.input_ids for f in features]   ,   dtype=torch.long	)
               _A      =			torch.tensor([f.attention_mask for f in features]   ,   dtype=torch.long	)
               if self.mode == "classification":
                     _A      =			torch.tensor([f.label for f in features]   ,   dtype=torch.long	)
               elif self.mode == "regression":
                     _A      =			torch.tensor([f.label for f in features]   ,   dtype=torch.float	)

               _A      =			TensorDataset(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase	)
               return dataset
         else:
               raise ValueError("return_tensors should be one of 'tf' or 'pt'"	)













'''simple docstring'''




from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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

def       __lowercase		(    __lowercase     ,						__lowercase ) ->		list | None:

      '''simple docstring'''


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

            _A      =			BASE_URL.format(__lowercase     ,						__lowercase )
            _A      =			get(__lowercase )

            # Is the response ok?
            response.raise_for_status()

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

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

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

                        # Get price of the drug.
                        _A      =			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
    lowerCamelCase_							      =      input('''Enter drug name: ''').strip()
    lowerCamelCase_							      =      input('''Enter zip code: ''').strip()

    lowerCamelCase_							      =      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:
            lowerCamelCase_							      =      pharmacy_price['''pharmacy_name''']
            lowerCamelCase_							      =      pharmacy_price['''price''']

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





