


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, 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.openai.modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTModel,
    )







class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      List[Any] ,				__lowerCamelCase							:      Union[str, Any] ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  parent
             lowerCamelCase__		:    Optional[Any]   =  13
             lowerCamelCase__		:    Tuple   =  7
             lowerCamelCase__		:    Union[str, Any]   =  True
             lowerCamelCase__		:    Tuple   =  True
             lowerCamelCase__		:    Any   =  True
             lowerCamelCase__		:    Tuple   =  True
             lowerCamelCase__		:    List[str]   =  True
             lowerCamelCase__		:    Dict   =  99
             lowerCamelCase__		:    str   =  32
             lowerCamelCase__		:    int   =  2
             lowerCamelCase__		:    Tuple   =  4
             lowerCamelCase__		:    Optional[Any]   =  37
             lowerCamelCase__		:    Optional[int]   =  "gelu"
             lowerCamelCase__		:    Union[str, Any]   =  0.1
             lowerCamelCase__		:    List[str]   =  0.1
             lowerCamelCase__		:    int   =  512
             lowerCamelCase__		:    Any   =  16
             lowerCamelCase__		:    Any   =  2
             lowerCamelCase__		:    Any   =  0.0_2
             lowerCamelCase__		:    List[str]   =  3
             lowerCamelCase__		:    str   =  4
             lowerCamelCase__		:    Any   =  None
             lowerCamelCase__		:    Union[str, Any]   =  self.vocab_size - 1





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



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

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

             lowerCamelCase__		:    int   =  None
             if self.use_token_type_ids:
                   lowerCamelCase__		:    str   =  ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

             lowerCamelCase__		:    Any   =  None
             if self.use_mc_token_ids:
                   lowerCamelCase__		:    Union[str, Any]   =  ids_tensor([self.batch_size, self.num_choices] ,				self.seq_length	)

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

             lowerCamelCase__		:    str   =  OpenAIGPTConfig(
                 vocab_size=self.vocab_size ,				n_embd=self.hidden_size ,				n_layer=self.num_hidden_layers ,				n_head=self.num_attention_heads ,				n_positions=self.max_position_embeddings ,				pad_token_id=self.pad_token_id ,				)

             lowerCamelCase__		:    List[str]   =  ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,				2	)

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
                 token_type_ids,
                 mc_token_ids,
                 sequence_labels,
                 token_labels,
                 choice_labels,
             )





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


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  TFOpenAIGPTModel(config=__lowerCamelCase	)
             lowerCamelCase__		:    str   =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
             lowerCamelCase__		:    Any   =  model(__lowerCamelCase	)

             lowerCamelCase__		:    Optional[Any]   =  [input_ids, input_mask]
             lowerCamelCase__		:    Any   =  model(__lowerCamelCase	)

             lowerCamelCase__		:    Optional[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							:      Union[str, Any] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      Any ,				*__lowerCamelCase							:      Any	):


             '''simple docstring'''



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





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


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  TFOpenAIGPTDoubleHeadsModel(config=__lowerCamelCase	)

             lowerCamelCase__		:    Union[str, Any]   =  tf.tile(tf.expand_dims(__lowerCamelCase ,				1	) ,				(1, self.num_choices, 1)	)
             lowerCamelCase__		:    Any   =  tf.tile(tf.expand_dims(__lowerCamelCase ,				1	) ,				(1, self.num_choices, 1)	)
             lowerCamelCase__		:    int   =  tf.tile(tf.expand_dims(__lowerCamelCase ,				1	) ,				(1, self.num_choices, 1)	)

             lowerCamelCase__		:    Any   =  {
                 "input_ids": multiple_choice_inputs_ids,
                 "mc_token_ids": mc_token_ids,
                 "attention_mask": multiple_choice_input_mask,
                 "token_type_ids": multiple_choice_token_type_ids,
             }
             lowerCamelCase__		:    Union[str, Any]   =  model(__lowerCamelCase	)
             self.parent.assertEqual(
                 result.logits.shape ,				(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)	)
             self.parent.assertEqual(result.mc_logits.shape ,				(self.batch_size, self.num_choices)	)





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


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  self.num_labels
             lowerCamelCase__		:    Tuple   =  ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
             lowerCamelCase__		:    Optional[int]   =  {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
                 "labels": sequence_labels,
             }
             lowerCamelCase__		:    Optional[Any]   =  TFOpenAIGPTForSequenceClassification(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.prepare_config_and_inputs()

             (
                 (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       (
                 lowerCamelCase__
             )   ,       
             )		:    List[Any]   =  config_and_inputs

             lowerCamelCase__		:    Optional[Any]   =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict







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


       """simple docstring"""




       A__ 						=      (
           (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
           if is_tf_available()
           else ()
       )
       A__ 						=      (
           (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       A__ 						=      (
           {
               "feature-extraction": TFOpenAIGPTModel,
               "text-classification": TFOpenAIGPTForSequenceClassification,
               "text-generation": TFOpenAIGPTLMHeadModel,
               "zero-shot": TFOpenAIGPTForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       A__ 						=      False
       A__ 						=      False





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


             '''simple docstring'''



             if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                   # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                   # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                   # tiny config could not be created.
                   return True

             return False





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  TFOpenAIGPTModelTester(self	)
             lowerCamelCase__		:    Dict   =  ConfigTester(self ,				config_class=__lowerCamelCase ,				n_embd=37	)





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



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





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


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



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





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


             '''simple docstring'''



             for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:    str   =  TFOpenAIGPTModel.from_pretrained(__lowerCamelCase	)
                   self.assertIsNotNone(__lowerCamelCase	)







@require_tf
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"	)
             lowerCamelCase__		:    Union[str, Any]   =  tf.convert_to_tensor([[481, 4735, 544]] ,				dtype=tf.intaa	)  # the president is
             lowerCamelCase__		:    Union[str, Any]   =  [
                 481,
                 4735,
                 544,
                 246,
                 963,
                 870,
                 762,
                 239,
                 244,
                 40477,
                 244,
                 249,
                 719,
                 881,
                 487,
                 544,
                 240,
                 244,
                 603,
                 481,
             ]  # the president is a very good man. " \n " i\'m sure he is, " said the

             lowerCamelCase__		:    int   =  model.generate(__lowerCamelCase ,				do_sample=__lowerCamelCase	)
             self.assertListEqual(output_ids[0].numpy().tolist() ,				__lowerCamelCase	)






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}')


