


"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.mpnet.modeling_tf_mpnet import (
        TFMPNetForMaskedLM,
        TFMPNetForMultipleChoice,
        TFMPNetForQuestionAnswering,
        TFMPNetForSequenceClassification,
        TFMPNetForTokenClassification,
        TFMPNetModel,
    )







class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      int ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      str=13 ,				__lowerCamelCase							:      str=7 ,				__lowerCamelCase							:      str=True ,				__lowerCamelCase							:      List[Any]=True ,				__lowerCamelCase							:      Dict=False ,				__lowerCamelCase							:      str=True ,				__lowerCamelCase							:      Any=99 ,				__lowerCamelCase							:      Optional[int]=64 ,				__lowerCamelCase							:      Any=2 ,				__lowerCamelCase							:      Dict=4 ,				__lowerCamelCase							:      List[Any]=64 ,				__lowerCamelCase							:      Tuple="gelu" ,				__lowerCamelCase							:      str=0.1 ,				__lowerCamelCase							:      List[Any]=0.1 ,				__lowerCamelCase							:      List[str]=512 ,				__lowerCamelCase							:      Optional[Any]=16 ,				__lowerCamelCase							:      Optional[Any]=2 ,				__lowerCamelCase							:      str=0.0_2 ,				__lowerCamelCase							:      Any=3 ,				__lowerCamelCase							:      int=4 ,				__lowerCamelCase							:      Union[str, Any]=None ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  parent
             lowerCamelCase__		:    Tuple   =  batch_size
             lowerCamelCase__		:    Union[str, Any]   =  seq_length
             lowerCamelCase__		:    Dict   =  is_training
             lowerCamelCase__		:    Optional[Any]   =  use_input_mask
             lowerCamelCase__		:    Union[str, Any]   =  use_token_type_ids
             lowerCamelCase__		:    List[Any]   =  use_labels
             lowerCamelCase__		:    Optional[int]   =  vocab_size
             lowerCamelCase__		:    Dict   =  hidden_size
             lowerCamelCase__		:    Optional[int]   =  num_hidden_layers
             lowerCamelCase__		:    Dict   =  num_attention_heads
             lowerCamelCase__		:    Optional[Any]   =  intermediate_size
             lowerCamelCase__		:    Dict   =  hidden_act
             lowerCamelCase__		:    str   =  hidden_dropout_prob
             lowerCamelCase__		:    int   =  attention_probs_dropout_prob
             lowerCamelCase__		:    Dict   =  max_position_embeddings
             lowerCamelCase__		:    List[str]   =  type_vocab_size
             lowerCamelCase__		:    List[str]   =  type_sequence_label_size
             lowerCamelCase__		:    Any   =  initializer_range
             lowerCamelCase__		:    int   =  num_labels
             lowerCamelCase__		:    str   =  num_choices
             lowerCamelCase__		:    Optional[Any]   =  scope





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

             lowerCamelCase__		:    str   =  None
             if self.use_input_mask:
                   lowerCamelCase__		:    str   =  random_attention_mask([self.batch_size, self.seq_length]	)

             lowerCamelCase__		:    Tuple   =  None
             lowerCamelCase__		:    int   =  None
             lowerCamelCase__		:    Tuple   =  None
             if self.use_labels:
                   lowerCamelCase__		:    List[Any]   =  ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
                   lowerCamelCase__		:    int   =  ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
                   lowerCamelCase__		:    Optional[int]   =  ids_tensor([self.batch_size] ,				self.num_choices	)

             lowerCamelCase__		:    Dict   =  MPNetConfig(
                 vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				initializer_range=self.initializer_range ,				)
             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





       def 						lowerCAmelCase					(       self							:      int ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  TFMPNetModel(config=__lowerCamelCase	)
             lowerCamelCase__		:    Tuple   =  {"input_ids": input_ids, "attention_mask": input_mask}
             lowerCamelCase__		:    str   =  model(__lowerCamelCase	)
             lowerCamelCase__		:    Dict   =  [input_ids, input_mask]
             lowerCamelCase__		:    Union[str, Any]   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)





       def 						lowerCAmelCase					(       self							:      Optional[int] ,				__lowerCamelCase							:      Optional[int] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  TFMPNetForMaskedLM(config=__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  {"input_ids": input_ids, "attention_mask": input_mask}
             lowerCamelCase__		:    List[Any]   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  TFMPNetForQuestionAnswering(config=__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
             }
             lowerCamelCase__		:    int   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)	)
             self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)	)





       def 						lowerCAmelCase					(       self							:      Optional[int] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  self.num_labels
             lowerCamelCase__		:    str   =  TFMPNetForSequenceClassification(__lowerCamelCase	)
             lowerCamelCase__		:    Dict   =  {"input_ids": input_ids, "attention_mask": input_mask}
             lowerCamelCase__		:    List[str]   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)





       def 						lowerCAmelCase					(       self							:      Optional[Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[str]   =  self.num_choices
             lowerCamelCase__		:    Optional[int]   =  TFMPNetForMultipleChoice(__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  tf.tile(tf.expand_dims(__lowerCamelCase ,				1	) ,				(1, self.num_choices, 1)	)
             lowerCamelCase__		:    Dict   =  tf.tile(tf.expand_dims(__lowerCamelCase ,				1	) ,				(1, self.num_choices, 1)	)
             lowerCamelCase__		:    List[Any]   =  {
                 "input_ids": multiple_choice_inputs_ids,
                 "attention_mask": multiple_choice_input_mask,
             }
             lowerCamelCase__		:    Any   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)	)





       def 						lowerCAmelCase					(       self							:      Union[str, Any] ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.num_labels
             lowerCamelCase__		:    Optional[Any]   =  TFMPNetForTokenClassification(__lowerCamelCase	)
             lowerCamelCase__		:    List[str]   =  {"input_ids": input_ids, "attention_mask": input_mask}
             lowerCamelCase__		:    Union[str, Any]   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)	)





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  self.prepare_config_and_inputs()
             (lowerCamelCase__)		:    int   =  config_and_inputs
             lowerCamelCase__		:    Dict   =  {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict







@require_tf
class       _lowercase			(							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      (
           (
               TFMPNetForMaskedLM,
               TFMPNetForMultipleChoice,
               TFMPNetForQuestionAnswering,
               TFMPNetForSequenceClassification,
               TFMPNetForTokenClassification,
               TFMPNetModel,
           )
           if is_tf_available()
           else ()
       )
       A__ 						=      (
           {
               "feature-extraction": TFMPNetModel,
               "fill-mask": TFMPNetForMaskedLM,
               "question-answering": TFMPNetForQuestionAnswering,
               "text-classification": TFMPNetForSequenceClassification,
               "token-classification": TFMPNetForTokenClassification,
               "zero-shot": TFMPNetForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       A__ 						=      False
       A__ 						=      False





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  TFMPNetModelTester(self	)
             lowerCamelCase__		:    Any   =  ConfigTester(self ,				config_class=__lowerCamelCase ,				hidden_size=37	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase	)





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


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase	)





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


             '''simple docstring'''



             for model_name in ["microsoft/mpnet-base"]:
                   lowerCamelCase__		:    Optional[Any]   =  TFMPNetModel.from_pretrained(__lowerCamelCase	)
                   self.assertIsNotNone(__lowerCamelCase	)







@require_tf
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  TFMPNetModel.from_pretrained("microsoft/mpnet-base"	)
             lowerCamelCase__		:    int   =  tf.constant([[0, 1, 2, 3, 4, 5]]	)
             lowerCamelCase__		:    Any   =  model(__lowerCamelCase	)[0]

             lowerCamelCase__		:    Optional[int]   =  [1, 6, 768]
             self.assertEqual(output.shape ,				__lowerCamelCase	)

             lowerCamelCase__		:    Union[str, Any]   =  tf.constant(
                 [
                     [
                         [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
                         [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
                         [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
                     ]
                 ]	)
             tf.debugging.assert_near(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1E-4	)






from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A				:   List[str]		= "https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true"

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




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

            lowerCamelCase__		:    List[str]   =  BASE_URL.format(_A   , _A						)
            lowerCamelCase__		:    int   =  get(_A						)

            # Is the response ok?
            response.raise_for_status()

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

            # This list will store the name and price.
            lowerCamelCase__		:    int   =  []

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

                        # Get price of the drug.
                        lowerCamelCase__		:    Union[str, 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				:   Any		= input("Enter drug name: ").strip()
    A				:   Union[str, Any]		= 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				:   Dict		= pharmacy_price["pharmacy_name"]
            A				:   Optional[int]		= pharmacy_price["price"]

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


