






"""simple docstring"""







import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_UpperCamelCase     			=  [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
  import run_clm
  import run_image_classification
  import run_mlm
  import run_ner
  import run_qa as run_squad
  import run_summarization
  import run_swag
  import run_text_classification
  import run_translation


logging.basicConfig(level=logging.DEBUG)

_UpperCamelCase     			=  logging.getLogger()

def _a (		):







      """simple docstring"""






      UpperCAmelCase							=       argparse.ArgumentParser()
      parser.add_argument("""-f"""							)
      UpperCAmelCase							=       parser.parse_args()
      return args.f

def _a (		_snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       {}
      UpperCAmelCase							=       os.path.join(_snake_case      ,       """all_results.json"""							)
      if os.path.exists(_snake_case							):
            with open(_snake_case      ,       """r"""							) as f:
                  UpperCAmelCase							=       json.load(_snake_case							)
      else:
            raise ValueError(F'''can\'t find {path}'''							)
      return results

def _a (		):







      """simple docstring"""






      return bool(tf.config.list_physical_devices("""GPU"""							)							)


_UpperCamelCase     			=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 					lowerCamelCase__					(       snake_case ):






    @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""					)
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

          if is_cuda_available():
                testargs.append("""--fp16"""					)

          with patch.object(A	,"""argv"""	,A					):
                run_text_classification.main()
                # Reset the mixed precision policy so we don't break other tests
                tf.keras.mixed_precision.set_global_policy("""float32"""					)
                UpperCAmelCase							=       get_results(A					)
                self.assertGreaterEqual(result["""eval_accuracy"""]	,0.75					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

          if len(tf.config.list_physical_devices("""GPU"""					)					) > 1:
                # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                return

          with patch.object(A	,"""argv"""	,A					):
                run_clm.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertLess(result["""eval_perplexity"""]	,100					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

          with patch.object(A	,"""argv"""	,A					):
                run_mlm.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertLess(result["""eval_perplexity"""]	,42					)






    def 				_UpperCamelCase							( self					):
          # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
          UpperCAmelCase							=       7 if get_gpu_count() > 1 else 2

          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

          with patch.object(A	,"""argv"""	,A					):
                run_ner.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertGreaterEqual(result["""accuracy"""]	,0.75					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

          with patch.object(A	,"""argv"""	,A					):
                run_squad.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertGreaterEqual(result["""f1"""]	,30					)
                self.assertGreaterEqual(result["""exact"""]	,30					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

          with patch.object(A	,"""argv"""	,A					):
                run_swag.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertGreaterEqual(result["""val_accuracy"""]	,0.8					)






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

          with patch.object(A	,"""argv"""	,A					):
                run_summarization.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertGreaterEqual(result["""rouge1"""]	,10					)
                self.assertGreaterEqual(result["""rouge2"""]	,2					)
                self.assertGreaterEqual(result["""rougeL"""]	,7					)
                self.assertGreaterEqual(result["""rougeLsum"""]	,7					)






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

          with patch.object(A	,"""argv"""	,A					):
                run_translation.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertGreaterEqual(result["""bleu"""]	,30					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

          with patch.object(A	,"""argv"""	,A					):
                run_image_classification.main()
                UpperCAmelCase							=       get_results(A					)
                self.assertGreaterEqual(result["""accuracy"""]	,0.7					)











"""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}""")



