


'''simple docstring'''







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__    :   Optional[int]								=			logging.getLogger(__name__)
@require_tensorboard
class    lowercase_	(       unittest.TestCase  ):


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


    def   __a				(	self       ):
          UpperCamelCase__      =							"test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
                UpperCamelCase__      =							Accelerator(log_with="tensorboard"    ,   project_dir=__UpperCamelCase       )
                accelerator.init_trackers(__UpperCamelCase       )
                UpperCamelCase__      =							{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
                accelerator.log(__UpperCamelCase    ,   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
                UpperCamelCase__      =							list(filter(lambda a       : x.is_file()    ,   Path(f'''{dirpath}/{project_name}'''       ).iterdir()       )       )[0]
                self.assertNotEqual(str(__UpperCamelCase       )    ,   ""       )


    def   __a				(	self       ):
          UpperCamelCase__      =							"test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
                UpperCamelCase__      =							Accelerator(log_with="tensorboard"    ,   project_dir=__UpperCamelCase       )
                accelerator.init_trackers(__UpperCamelCase       )
                UpperCamelCase__      =							{"tensor": torch.tensor(1       )}
                accelerator.log(__UpperCamelCase    ,   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
                UpperCamelCase__      =							list(filter(lambda a       : 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(__UpperCamelCase    ,   "rb"       ) as f:
                      UpperCamelCase__      =							f.read()
                UpperCamelCase__      =							False
                while data:
                      UpperCamelCase__      =							struct.unpack("Q"    ,   data[:8]       )

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

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


    def   __a				(	self       ):
          with self.assertRaisesRegex(__UpperCamelCase    ,   "Logging with `tensorboard` requires a `logging_dir`"       ):
                UpperCamelCase__      =							Accelerator(log_with="tensorboard"       )
          with tempfile.TemporaryDirectory() as dirpath:
                UpperCamelCase__      =							Accelerator(log_with="tensorboard"    ,   project_dir=__UpperCamelCase       )


    def   __a				(	self       ):
          UpperCamelCase__      =							ProjectConfiguration(total_limit=30       )
          with tempfile.TemporaryDirectory() as dirpath:
                UpperCamelCase__      =							Accelerator(log_with="tensorboard"    ,   project_dir=__UpperCamelCase    ,   project_config=__UpperCamelCase       )

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


    def   __a				(	self       ):
          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   __a				(	a    ,   a    ,   a = True       ):
          UpperCamelCase__      =							rf'''{section} ([\S\s]*?)\n\n'''
          if record:
                UpperCamelCase__      =							rf'''Record: {pattern}'''
          UpperCamelCase__      =							re.findall(__UpperCamelCase    ,   __UpperCamelCase       )[0]
          # A config
          if section == "config" or section == "history":
                UpperCamelCase__      =							re.findall(r"\"([a-zA-Z0-9_.,]+)"    ,   __UpperCamelCase       )
                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]+)\""    ,   __UpperCamelCase       )       )


    @skip
    def   __a				(	self       ):
          UpperCamelCase__      =							"test_project_with_config"
          UpperCamelCase__      =							Accelerator(log_with="wandb"       )
          UpperCamelCase__      =							{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          UpperCamelCase__      =							{"wandb": {"tags": ["my_tag"]}}
          accelerator.init_trackers(__UpperCamelCase    ,   __UpperCamelCase    ,   __UpperCamelCase       )
          UpperCamelCase__      =							{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
          accelerator.log(__UpperCamelCase    ,   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":
                      UpperCamelCase__      =							subprocess.check_output(
                          ["wandb", "sync", "--view", "--verbose", str(__UpperCamelCase       )]    ,   env=os.environ.copy()       ).decode("utf8"    ,   "ignore"       )
                      break

        # Check HPS through careful parsing and cleaning
          UpperCamelCase__      =							self.parse_log(__UpperCamelCase    ,   "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
          UpperCamelCase__      =							self.parse_log(__UpperCamelCase    ,   "run"    ,   __UpperCamelCase       )
          self.assertEqual(logged_items["tags"]    ,   "my_tag"       )

          # Actual logging
          UpperCamelCase__      =							self.parse_log(__UpperCamelCase    ,   "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 ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							run_name
      UpperCamelCase__      =							OfflineExperiment(project_name=__lowerCamelCase ,     offline_directory=__lowerCamelCase       )
      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    lowercase_	(       unittest.TestCase  ):


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


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

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

    __UpperCAmelCase       =      'my_custom_tracker'
    __UpperCAmelCase       =      False


    def __init__(	self    ,   a       ):
          UpperCamelCase__      =							open(f'''{dir}/log.csv'''    ,   "w+"       )
          UpperCamelCase__      =							csv.DictWriter(self.f    ,   fieldnames=self._col_names       )
          self.writer.writeheader()


    @property
    def   __a				(	self       ):
          return self.writer


    def   __a				(	self    ,   a       ):
          logger.info("Call init"       )
          self.writer.writerow(__UpperCamelCase       )


    def   __a				(	self    ,   a    ,   a       ):
          logger.info("Call log"       )
          self.writer.writerow(__UpperCamelCase       )


    def   __a				(	self       ):
          self.f.close()

class    lowercase_	(       unittest.TestCase  ):


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


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









'''simple docstring'''

from __future__ import annotations






def __UpperCamelCase (      __lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	)   ->  list[float | int]:







     '''simple docstring'''






     if not all((nth_term, start_term_a, common_ratio_r)					):
          return []
     _a            =		[]
     _a            =		1
     _a            =		common_ratio_r
     for _ in range(int(__lowerCamelCase					)					):
          if not series:
               series.append(__lowerCamelCase					)
          else:
               power += 1
               series.append(float(start_term_a * multiple					)					)
               _a            =		pow(float(__lowerCamelCase					)   ,	__lowerCamelCase					)
     return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowercase__					  =		float(input("Enter the last number (n term) of the Geometric Series"))
   lowercase__					  =		float(input("Enter the starting term (a) of the Geometric Series"))
   lowercase__					  =		float(
       input("Enter the common ratio between two terms (r) of the Geometric Series")
   )
   print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
   print(geometric_series(nth_term, start_term_a, common_ratio_r))

