



import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
	from comet_ml import OfflineExperiment

if is_tensorboard_available():
	import struct

	import tensorboard.compat.proto.event_pba as event_pba

A__     				=	logging.getLogger(__name__)





@require_tensorboard
class  __lowerCAmelCase			(					unittest.TestCase	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """test_project_with_config"""
									with tempfile.TemporaryDirectory() as dirpath:
														_lowerCAmelCase      =   Accelerator(log_with="""tensorboard"""						,    project_dir=_snake_case		)
														_lowerCAmelCase      =   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
														accelerator.init_trackers(_snake_case						,    _snake_case		)
														accelerator.end_training()
														for child in Path(F'{dirpath}/{project_name}'		).glob("""*/**"""		):
																			_lowerCAmelCase      =   list(filter(lambda _snake_case		: x.is_file()						,    child.iterdir()		)		)[0]
														self.assertNotEqual(str(_snake_case		)						,    """"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """test_project_with_log"""
									with tempfile.TemporaryDirectory() as dirpath:
														_lowerCAmelCase      =   Accelerator(log_with="""tensorboard"""						,    project_dir=_snake_case		)
														accelerator.init_trackers(_snake_case		)
														_lowerCAmelCase      =   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
														accelerator.log(_snake_case						,    step=0		)
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														_lowerCAmelCase      =   list(filter(lambda _snake_case		: x.is_file()						,    Path(F'{dirpath}/{project_name}'		).iterdir()		)		)[0]
														self.assertNotEqual(str(_snake_case		)						,    """"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """test_project_with_log"""
									with tempfile.TemporaryDirectory() as dirpath:
														_lowerCAmelCase      =   Accelerator(log_with="""tensorboard"""						,    project_dir=_snake_case		)
														accelerator.init_trackers(_snake_case		)
														_lowerCAmelCase      =   {"""tensor""": torch.tensor(1		)}
														accelerator.log(_snake_case						,    step=0		)
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														_lowerCAmelCase      =   list(filter(lambda _snake_case		: x.is_file()						,    Path(F'{dirpath}/{project_name}'		).iterdir()		)		)[0]
														# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
														with open(_snake_case						,    """rb"""		) as f:
																			_lowerCAmelCase      =   f.read()
														_lowerCAmelCase      =   False
														while data:
																			_lowerCAmelCase      =   struct.unpack("""Q"""						,    data[:8]		)

																			_lowerCAmelCase      =   data[12 : 12 + int(header[0]		)]  # 8+4
																			_lowerCAmelCase      =   data[12 + int(header[0]		) + 4 :]
																			_lowerCAmelCase      =   event_pba.Event()

																			event.ParseFromString(_snake_case		)
																			if event.HasField("""summary"""		):
																								for value in event.summary.value:
																													if value.simple_value == 1.0 and value.tag == "tensor":
																																		_lowerCAmelCase      =   True
														self.assertTrue(_snake_case						,    """Converted tensor was not found in the log file!"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									with self.assertRaisesRegex(_snake_case						,    """Logging with `tensorboard` requires a `logging_dir`"""		):
														_lowerCAmelCase      =   Accelerator(log_with="""tensorboard"""		)
									with tempfile.TemporaryDirectory() as dirpath:
														_lowerCAmelCase      =   Accelerator(log_with="""tensorboard"""						,    project_dir=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   ProjectConfiguration(total_limit=30		)
									with tempfile.TemporaryDirectory() as dirpath:
														_lowerCAmelCase      =   Accelerator(log_with="""tensorboard"""						,    project_dir=_snake_case						,    project_config=_snake_case		)





@require_wandb
@mock.patch.dict(os.environ	,						{'''WANDB_MODE''': '''offline'''}	)
class  __lowerCAmelCase			(					lowerCamelCase__	,						lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									super().setUp()
									# wandb let's us override where logs are stored to via the WANDB_DIR env var
									self.add_mocks(mock.patch.dict(os.environ						,    {"""WANDB_DIR""": self.tmpdir}		)		)




				@staticmethod
				def 							snake_case       (							_snake_case						,    _snake_case						,    _snake_case = True		):



									"""simple docstring"""





									_lowerCAmelCase      =   RF'{section} ([\S\s]*?)\n\n'
									if record:
														_lowerCAmelCase      =   RF'Record: {pattern}'
									_lowerCAmelCase      =   re.findall(_snake_case						,    _snake_case		)[0]
									# A config
									if section == "config" or section == "history":
														_lowerCAmelCase      =   re.findall(R"""\"([a-zA-Z0-9_.,]+)"""						,    _snake_case		)
														return {key: val for key, val in zip(cleaned_record[0::2]						,    cleaned_record[1::2]		)}
									# Everything else
									else:
														return dict(re.findall(R"""(\w+): \"([^\s]+)\""""						,    _snake_case		)		)




				@skip
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """test_project_with_config"""
									_lowerCAmelCase      =   Accelerator(log_with="""wandb"""		)
									_lowerCAmelCase      =   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
									_lowerCAmelCase      =   {"""wandb""": {"""tags""": ["""my_tag"""]}}
									accelerator.init_trackers(_snake_case						,    _snake_case						,    _snake_case		)
									_lowerCAmelCase      =   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
									accelerator.log(_snake_case						,    step=0		)
									accelerator.end_training()
									# The latest offline log is stored at wandb/latest-run/*.wandb
									for child in Path(F'{self.tmpdir}/wandb/latest-run'		).glob("""*"""		):
														if child.is_file() and child.suffix == ".wandb":
																			_lowerCAmelCase      =   subprocess.check_output(
																			    ["""wandb""", """sync""", """--view""", """--verbose""", str(_snake_case		)]						,    env=os.environ.copy()		).decode("""utf8"""						,    """ignore"""		)
																			break

        # Check HPS through careful parsing and cleaning
									_lowerCAmelCase      =   self.parse_log(_snake_case						,    """config"""		)
									self.assertEqual(logged_items["""num_iterations"""]						,    """12"""		)
									self.assertEqual(logged_items["""learning_rate"""]						,    """0.01"""		)
									self.assertEqual(logged_items["""some_boolean"""]						,    """false"""		)
									self.assertEqual(logged_items["""some_string"""]						,    """some_value"""		)
									self.assertEqual(logged_items["""some_string"""]						,    """some_value"""		)

									# Run tags
									_lowerCAmelCase      =   self.parse_log(_snake_case						,    """run"""						,    _snake_case		)
									self.assertEqual(logged_items["""tags"""]						,    """my_tag"""		)

									# Actual logging
									_lowerCAmelCase      =   self.parse_log(_snake_case						,    """history"""		)
									self.assertEqual(logged_items["""total_loss"""]						,    """0.1"""		)
									self.assertEqual(logged_items["""iteration"""]						,    """1"""		)
									self.assertEqual(logged_items["""my_text"""]						,    """some_value"""		)
									self.assertEqual(logged_items["""_step"""]						,    """0"""		)


def 		_UpperCAmelCase    (				self     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   run_name
					_lowerCAmelCase      =   OfflineExperiment(project_name=snake_case     ,						offline_directory=snake_case      )
					logger.info(F'Initialized offline CometML project {self.run_name}'      )
					logger.info("""Make sure to log any initial configurations with `self.store_init_configuration` before training!"""      )





@require_comet_ml
@mock.patch.object(lowerCamelCase__	,						'''__init__'''	,						lowerCamelCase__	)
class  __lowerCAmelCase			(					unittest.TestCase	):




				@staticmethod
				def 							snake_case       (							_snake_case						,    _snake_case						,    _snake_case = False		):



									"""simple docstring"""





									for log in log_list:
														_lowerCAmelCase      =   json.loads(_snake_case		)["""payload"""]
														if is_param and "param" in j.keys():
																			if j["param"]["paramName"] == key:
																								return j["param"]["paramValue"]
														if "log_other" in j.keys():
																			if j["log_other"]["key"] == key:
																								return j["log_other"]["val"]
														if "metric" in j.keys():
																			if j["metric"]["metricName"] == key:
																								return j["metric"]["metricValue"]




				def 							snake_case       (							self		):



									"""simple docstring"""





									with tempfile.TemporaryDirectory() as d:
														_lowerCAmelCase      =   CometMLTracker("""test_project_with_config"""						,    _snake_case		)
														_lowerCAmelCase      =   Accelerator(log_with=_snake_case		)
														_lowerCAmelCase      =   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
														accelerator.init_trackers(_snake_case						,    _snake_case		)
														accelerator.end_training()
														_lowerCAmelCase      =   os.listdir(_snake_case		)[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														_lowerCAmelCase      =   os.path.join(_snake_case						,    _snake_case		)
														_lowerCAmelCase      =   zipfile.ZipFile(_snake_case						,    """r"""		)
														_lowerCAmelCase      =   archive.open("""messages.json"""		).read().decode("""utf-8"""		)
									_lowerCAmelCase      =   log.split("""\n"""		)[:-1]
									self.assertEqual(self.get_value_from_key(_snake_case						,    """num_iterations"""						,    _snake_case		)						,    12		)
									self.assertEqual(self.get_value_from_key(_snake_case						,    """learning_rate"""						,    _snake_case		)						,    0.01		)
									self.assertEqual(self.get_value_from_key(_snake_case						,    """some_boolean"""						,    _snake_case		)						,    _snake_case		)
									self.assertEqual(self.get_value_from_key(_snake_case						,    """some_string"""						,    _snake_case		)						,    """some_value"""		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									with tempfile.TemporaryDirectory() as d:
														_lowerCAmelCase      =   CometMLTracker("""test_project_with_config"""						,    _snake_case		)
														_lowerCAmelCase      =   Accelerator(log_with=_snake_case		)
														accelerator.init_trackers(_snake_case		)
														_lowerCAmelCase      =   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
														accelerator.log(_snake_case						,    step=0		)
														accelerator.end_training()
														_lowerCAmelCase      =   os.listdir(_snake_case		)[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														_lowerCAmelCase      =   os.path.join(_snake_case						,    _snake_case		)
														_lowerCAmelCase      =   zipfile.ZipFile(_snake_case						,    """r"""		)
														_lowerCAmelCase      =   archive.open("""messages.json"""		).read().decode("""utf-8"""		)
									_lowerCAmelCase      =   log.split("""\n"""		)[:-1]
									self.assertEqual(self.get_value_from_key(_snake_case						,    """curr_step"""						,    _snake_case		)						,    0		)
									self.assertEqual(self.get_value_from_key(_snake_case						,    """total_loss"""		)						,    0.1		)
									self.assertEqual(self.get_value_from_key(_snake_case						,    """iteration"""		)						,    1		)
									self.assertEqual(self.get_value_from_key(_snake_case						,    """my_text"""		)						,    """some_value"""		)





class  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    [
				    '''total_loss''',
				    '''iteration''',
				    '''my_text''',
				    '''learning_rate''',
				    '''num_iterations''',
				    '''some_boolean''',
				    '''some_string''',
				]

				__lowerCamelCase			=    '''my_custom_tracker'''
				__lowerCamelCase			=    False




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   open(F'{dir}/log.csv'						,    """w+"""		)
									_lowerCAmelCase      =   csv.DictWriter(self.f						,    fieldnames=self._col_names		)
									self.writer.writeheader()




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									return self.writer




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									logger.info("""Call init"""		)
									self.writer.writerow(_snake_case		)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									logger.info("""Call log"""		)
									self.writer.writerow(_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									self.f.close()





class  __lowerCAmelCase			(					unittest.TestCase	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									with tempfile.TemporaryDirectory() as d:
														_lowerCAmelCase      =   MyCustomTracker(_snake_case		)
														_lowerCAmelCase      =   Accelerator(log_with=_snake_case		)
														_lowerCAmelCase      =   {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
														accelerator.init_trackers("""Some name"""						,    _snake_case		)
														accelerator.end_training()
														with open(F'{d}/log.csv'						,    """r"""		) as f:
																			_lowerCAmelCase      =   csv.DictReader(_snake_case		)
																			_lowerCAmelCase      =   next(_snake_case		)
																			_lowerCAmelCase      =   {
																			    """total_loss""": """""",
																			    """iteration""": """""",
																			    """my_text""": """""",
																			    """learning_rate""": """0.01""",
																			    """num_iterations""": """12""",
																			    """some_boolean""": """False""",
																			    """some_string""": """some_value""",
																			}
																			self.assertDictEqual(_snake_case						,    _snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									with tempfile.TemporaryDirectory() as d:
														_lowerCAmelCase      =   MyCustomTracker(_snake_case		)
														_lowerCAmelCase      =   Accelerator(log_with=_snake_case		)
														accelerator.init_trackers("""Some name"""		)
														_lowerCAmelCase      =   {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
														accelerator.log(_snake_case						,    step=0		)
														accelerator.end_training()
														with open(F'{d}/log.csv'						,    """r"""		) as f:
																			_lowerCAmelCase      =   csv.DictReader(_snake_case		)
																			_lowerCAmelCase      =   next(_snake_case		)
																			_lowerCAmelCase      =   {
																			    """total_loss""": """0.1""",
																			    """iteration""": """1""",
																			    """my_text""": """some_value""",
																			    """learning_rate""": """""",
																			    """num_iterations""": """""",
																			    """some_boolean""": """""",
																			    """some_string""": """""",
																			}
																			self.assertDictEqual(_snake_case						,    _snake_case		)








from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

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

										_lowerCAmelCase      =   BASE_URL.format(snake_case     ,						snake_case      )
										_lowerCAmelCase      =   get(snake_case      )

										# Is the response ok?
										response.raise_for_status()

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

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

										# Fetch all the grids that contains the items.
										_lowerCAmelCase      =   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.
																				_lowerCAmelCase      =   grid.find("""p"""     ,						{"""class""": """list-title"""}      ).text

																				# Get price of the drug.
																				_lowerCAmelCase      =   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__     				=	input("""Enter drug name: """).strip()
	A__     				=	input("""Enter zip code: """).strip()

	A__     				=	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__     				=	pharmacy_price["""pharmacy_name"""]
			A__     				=	pharmacy_price["""price"""]

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



