






"""simple docstring"""







import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
  from tests.trainer.test_trainer import (  # noqa
      RegressionModelConfig,
      RegressionPreTrainedModel,
      get_regression_trainer,
  )


set_seed(42)

_UpperCamelCase     			=  get_tests_dir("""fixtures""")
_UpperCamelCase     			=  os.path.join(dirname(get_tests_dir()))
_UpperCamelCase     			=  dirname(os.path.abspath(__file__))

# default torch.distributed port
_UpperCamelCase     			=  """10999"""

_UpperCamelCase     			=  """t5-small"""

# *** Working Models ***
_UpperCamelCase     			=  """hf-internal-testing/tiny-albert"""
_UpperCamelCase     			=  """sshleifer/bart-tiny-random"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-bert"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-bigbird_pegasus"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-big_bird"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-blenderbot"""
_UpperCamelCase     			=  """bigscience/bigscience-small-testing"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-deberta"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-deberta-v2"""
_UpperCamelCase     			=  """sshleifer/tiny-distilbert-base-cased"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-electra"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-flaubert"""
_UpperCamelCase     			=  """stas/tiny-wmt19-en-de"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-funnel"""
_UpperCamelCase     			=  """sshleifer/tiny-gpt2"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-gptj"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-gpt_neo"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-layoutlm"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-led"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-longformer"""
_UpperCamelCase     			=  """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
_UpperCamelCase     			=  """sshleifer/tiny-marian-en-de"""
_UpperCamelCase     			=  """sshleifer/tiny-mbart"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-mobilebert"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-mpnet"""
_UpperCamelCase     			=  """stas/pegasus-cnn_dailymail-tiny-random"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-prophetnet"""
_UpperCamelCase     			=  """sshleifer/tiny-distilroberta-base"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-squeezebert"""
_UpperCamelCase     			=  """patrickvonplaten/t5-tiny-random"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-t5-v1.1"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-vit"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-xlm-roberta"""
_UpperCamelCase     			=  """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-mt5"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-camembert"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-convbert"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-layoutlmv2"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-ctrl"""
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
_UpperCamelCase     			=  """"""
# - "luke" Entities classes - no plan to make such example
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_UpperCamelCase     			=  """hf-internal-testing/tiny-random-tapas"""

def _a (		_snake_case=False							):







      """simple docstring"""






      UpperCAmelCase							=       min(2      ,       get_gpu_count()							) if distributed else 1
      UpperCAmelCase							=       os.environ.get("""DS_TEST_PORT"""      ,       _snake_case							)
      return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()

def _a (		):







      """simple docstring"""






      UpperCAmelCase							=       F'''{FIXTURE_DIRECTORY}/tests_samples'''
      UpperCAmelCase							=       F'''{data_dir_samples}/wmt_en_ro'''
      UpperCAmelCase							=       F'''{data_dir_samples}/xsum'''
      UpperCAmelCase							=       """
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

      # try to cover as many models as possible once (it's enough to run on one task per model)
      # but need a tiny model for each
      #
      # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
      UpperCAmelCase							=       {
          """trans""": [
              """bart""",
              """fsmt""",
              """m2m_100""",
              """marian""",
              """mbart""",
              """t5""",
              """t5_v1""",
              # "mt5", missing model files
          ],
          """sum""": [
              """pegasus""",
          ],
          """clm""": [
              """big_bird""",
              """bigbird_pegasus""",
              """blenderbot""",
              """bloom""",
              """gpt2""",
              """gpt_neo""",
              """gptj""",
              """xlm-roberta""",
              """prophetnet""",
              # "camembert", missing model files
          ],
          """mlm""": [
              """albert""",
              """deberta""",
              """deberta-v2""",
              """distilbert""",
              """electra""",
              """flaubert""",
              """funnel""",
              """layoutlm""",
              # "reformer", # multiple issues with either mlm/qa/clas
          ],
          """qa""": [
              """led""",
              """longformer""",
              """mobilebert""",
              """mpnet""",
              """roberta""",
              """squeezebert""",
              # "convbert", # missing tokenizer files
              # "layoutlmv2", missing model files
          ],
          """clas""": [
              """bert""",
              """xlnet""",
              # "hubert", # missing tokenizer files
              # "ibert", # multiple issues with either mlm/qa/clas
              # "transfo-xl", # tokenizer issues as ctrl
              # "ctrl", # tokenizer issues
              # "openai-gpt", missing model files
              # "tapas", multiple issues
          ],
          """img_clas""": [
              """vit""",
          ],
      }

      UpperCAmelCase							=       F'''{ROOT_DIRECTORY}/examples/pytorch'''

      UpperCAmelCase							=       {
          """trans""": F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
          """sum""": F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
          """clm""": F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
          """mlm""": F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
          """qa""": F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
          """clas""": F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
          """img_clas""": F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
      }

      UpperCAmelCase							=       get_launcher(distributed=_snake_case							)

      UpperCAmelCase							=       {}
      for task, args in tasks.items():
            UpperCAmelCase							=       args.split()
            for model in tasksamodels[task]:
                  UpperCAmelCase							=       globals()[F'''{model.upper().replace('-'      ,       '_'							)}_TINY''']
                  UpperCAmelCase							=       F'''--model_name_or_path {model_name}'''.split()
                  UpperCAmelCase							=       launcher + args + args_model + args_main

                  # # generation special case
                  # if task == "gen":
                  #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
                  #     args_model += f"--model_type {model}".split()
                  #     cmds[f"{task}_{model}"] = launcher + args + args_model
                  # else:

      return cmds


_UpperCamelCase     			=  make_task_cmds()

_UpperCamelCase     			=  """zero2"""
_UpperCamelCase     			=  """zero3"""

_UpperCamelCase     			=  [ZEROa, ZEROa]

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       parameterized.to_safe_name("""_""".join(str(_snake_case							) for x in param.args							)							)
      return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_UpperCamelCase     			=  list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 					lowerCamelCase__					(       snake_case ):






    def 				_UpperCamelCase							( self	,A	,A					):
          # return a ready to run train cmd
          if task not in task_cmds:
                raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''					)

          UpperCAmelCase							=       task_cmds[task]
          UpperCAmelCase							=       F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

          UpperCAmelCase							=       self.get_auto_remove_tmp_dir()
          UpperCAmelCase							=       F'''--output_dir {output_dir}'''.split()

          cmd += args_ds + args_out

          return cmd, output_dir






    @parameterized.expand(A	,name_func=A					)
    def 				_UpperCamelCase							( self	,A	,A					):
          # testing the ability to do a run followed by recovery of full fp32 weights

          UpperCAmelCase			,					UpperCAmelCase							=       self.get_task_cmd(A	,A					)

          # 1. generate the checkpoint
          cmd += "--save_steps 1".split()
          # keep for quick debug
          # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
          execute_subprocess_async(A	,env=self.get_env()					)

          # 2. test that the fp32 weights get reconsolidated
          UpperCAmelCase							=       F'''{output_dir}/checkpoint-1'''
          UpperCAmelCase							=       F'''{chkpt_dir}/out.bin'''
          UpperCAmelCase							=       F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
          # keep for quick debug
          # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
          subprocess.check_call(A	,shell=A					)
          assert os.path.exists(A					), F'''{recovered_model_path} was not found'''

          # possibly could also test that the resulting saved model is usable but given that we use
          # random models we won't know if it's any good











"""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}""")



