





"""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

lowerCamelCase_			    =	logging.getLogger(__name__)






@require_tensorboard
class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						"test_project_with_log"
             with tempfile.TemporaryDirectory() as dirpath:
                   __SCREAMING_SNAKE_CASE     :Any	       =						Accelerator(log_with='''tensorboard'''	,project_dir=lowerCamelCase__							)
                   accelerator.init_trackers(lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
                   accelerator.log(lowerCamelCase__	,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
                   __SCREAMING_SNAKE_CASE     :int	       =						list(filter(lambda SCREAMING_SNAKE_CASE__							: x.is_file()	,Path(f'''{dirpath}/{project_name}'''							).iterdir()							)							)[0]
                   self.assertNotEqual(str(lowerCamelCase__							)	,''''''							)




       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						"test_project_with_log"
             with tempfile.TemporaryDirectory() as dirpath:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						Accelerator(log_with='''tensorboard'''	,project_dir=lowerCamelCase__							)
                   accelerator.init_trackers(lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :Dict	       =						{"tensor": torch.tensor(1							)}
                   accelerator.log(lowerCamelCase__	,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
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						list(filter(lambda SCREAMING_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(lowerCamelCase__	,'''rb'''							) as f:
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						f.read()
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						False
                   while data:
                         __SCREAMING_SNAKE_CASE     :List[Any]	       =						struct.unpack('''Q'''	,data[:8]							)

                         __SCREAMING_SNAKE_CASE     :Any	       =						data[12 : 12 + int(header[0]							)]  # 8+4
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						data[12 + int(header[0]							) + 4 :]
                         __SCREAMING_SNAKE_CASE     :Dict	       =						event_pba.Event()

                         event.ParseFromString(lowerCamelCase__							)
                         if event.HasField('''summary'''							):
                               for value in event.summary.value:
                                     if value.simple_value == 1.0 and value.tag == "tensor":
                                           __SCREAMING_SNAKE_CASE     :List[Any]	       =						True
                   self.assertTrue(lowerCamelCase__	,'''Converted tensor was not found in the log file!'''							)




       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             with self.assertRaisesRegex(lowerCamelCase__	,'''Logging with `tensorboard` requires a `logging_dir`'''							):
                   __SCREAMING_SNAKE_CASE     :str	       =						Accelerator(log_with='''tensorboard'''							)
             with tempfile.TemporaryDirectory() as dirpath:
                   __SCREAMING_SNAKE_CASE     :int	       =						Accelerator(log_with='''tensorboard'''	,project_dir=lowerCamelCase__							)




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						ProjectConfiguration(total_limit=30							)
             with tempfile.TemporaryDirectory() as dirpath:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						Accelerator(log_with='''tensorboard'''	,project_dir=lowerCamelCase__	,project_config=lowerCamelCase__							)






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




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """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 					_UpperCamelCase  (		SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = True							)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						Rf'''{section} ([\S\s]*?)\n\n'''
             if record:
                   __SCREAMING_SNAKE_CASE     :Any	       =						Rf'''Record: {pattern}'''
             __SCREAMING_SNAKE_CASE     :Dict	       =						re.findall(lowerCamelCase__	,lowerCamelCase__							)[0]
             # A config
             if section == "config" or section == "history":
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						re.findall(R'''\"([a-zA-Z0-9_.,]+)'''	,lowerCamelCase__							)
                   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]+)\"'''	,lowerCamelCase__							)							)




       @skip
       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						"test_project_with_config"
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						Accelerator(log_with='''wandb'''							)
             __SCREAMING_SNAKE_CASE     :int	       =						{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						{"wandb": {"tags": ["my_tag"]}}
             accelerator.init_trackers(lowerCamelCase__	,lowerCamelCase__	,lowerCamelCase__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
             accelerator.log(lowerCamelCase__	,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":
                         __SCREAMING_SNAKE_CASE     :Any	       =						subprocess.check_output(
                             ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(lowerCamelCase__							)]	,env=os.environ.copy()							).decode('''utf8'''	,'''ignore'''							)
                         break

        # Check HPS through careful parsing and cleaning
             __SCREAMING_SNAKE_CASE     :int	       =						self.parse_log(lowerCamelCase__	,'''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
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.parse_log(lowerCamelCase__	,'''run'''	,lowerCamelCase__							)
             self.assertEqual(logged_items['''tags''']	,'''my_tag'''							)

             # Actual logging
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.parse_log(lowerCamelCase__	,'''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   __lowerCamelCase     (							self  :	List[Any]				,     a_  :	int				,     a_  :	Optional[int]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Dict	       =						run_name
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						OfflineExperiment(project_name=a_				,     offline_directory=a_							)
      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(_a					,			'''__init__'''					,			_a						)
class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




       @staticmethod
       def 					_UpperCamelCase  (		SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = False							)    ->   Optional[int]:





             """simple docstring"""






             for log in log_list:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						json.loads(lowerCamelCase__							)["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 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             with tempfile.TemporaryDirectory() as d:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						CometMLTracker('''test_project_with_config'''	,lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						Accelerator(log_with=lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
                   accelerator.init_trackers(lowerCamelCase__	,lowerCamelCase__							)
                   accelerator.end_training()
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						os.listdir(lowerCamelCase__							)[0]  # Comet is nice, it's just a zip file here
                   # We parse the raw logs
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						os.path.join(lowerCamelCase__	,lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						zipfile.ZipFile(lowerCamelCase__	,'''r'''							)
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						archive.open('''messages.json'''							).read().decode('''utf-8'''							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						log.split('''\n'''							)[:-1]
             self.assertEqual(self.get_value_from_key(lowerCamelCase__	,'''num_iterations'''	,lowerCamelCase__							)	,12							)
             self.assertEqual(self.get_value_from_key(lowerCamelCase__	,'''learning_rate'''	,lowerCamelCase__							)	,0.0_1							)
             self.assertEqual(self.get_value_from_key(lowerCamelCase__	,'''some_boolean'''	,lowerCamelCase__							)	,lowerCamelCase__							)
             self.assertEqual(self.get_value_from_key(lowerCamelCase__	,'''some_string'''	,lowerCamelCase__							)	,'''some_value'''							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






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






class       _SCREAMING_SNAKE_CASE(     _a						):
       SCREAMING_SNAKE_CASE_     :   Tuple					=						[
           '''total_loss''',
           '''iteration''',
           '''my_text''',
           '''learning_rate''',
           '''num_iterations''',
           '''some_boolean''',
           '''some_string''',
       ]

       SCREAMING_SNAKE_CASE_     :   List[str]					=						'''my_custom_tracker'''
       SCREAMING_SNAKE_CASE_     :   Tuple					=						False




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						open(f'''{dir}/log.csv'''	,'''w+'''							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						csv.DictWriter(self.f	,fieldnames=self._col_names							)
             self.writer.writeheader()




       @property
       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             return self.writer




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             logger.info('''Call init'''							)
             self.writer.writerow(lowerCamelCase__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             logger.info('''Call log'''							)
             self.writer.writerow(lowerCamelCase__							)




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             self.f.close()






class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             with tempfile.TemporaryDirectory() as d:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						MyCustomTracker(lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						Accelerator(log_with=lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
                   accelerator.init_trackers('''Some name'''	,lowerCamelCase__							)
                   accelerator.end_training()
                   with open(f'''{d}/log.csv'''	,'''r'''							) as f:
                         __SCREAMING_SNAKE_CASE     :Dict	       =						csv.DictReader(lowerCamelCase__							)
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						next(lowerCamelCase__							)
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						{
                             "total_loss": "",
                             "iteration": "",
                             "my_text": "",
                             "learning_rate": "0.01",
                             "num_iterations": "12",
                             "some_boolean": "False",
                             "some_string": "some_value",
                         }
                         self.assertDictEqual(lowerCamelCase__	,lowerCamelCase__							)




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             with tempfile.TemporaryDirectory() as d:
                   __SCREAMING_SNAKE_CASE     :str	       =						MyCustomTracker(lowerCamelCase__							)
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						Accelerator(log_with=lowerCamelCase__							)
                   accelerator.init_trackers('''Some name'''							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
                   accelerator.log(lowerCamelCase__	,step=0							)
                   accelerator.end_training()
                   with open(f'''{d}/log.csv'''	,'''r'''							) as f:
                         __SCREAMING_SNAKE_CASE     :Dict	       =						csv.DictReader(lowerCamelCase__							)
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						next(lowerCamelCase__							)
                         __SCREAMING_SNAKE_CASE     :List[Any]	       =						{
                             "total_loss": "0.1",
                             "iteration": "1",
                             "my_text": "some_value",
                             "learning_rate": "",
                             "num_iterations": "",
                             "some_boolean": "",
                             "some_string": "",
                         }
                         self.assertDictEqual(lowerCamelCase__	,lowerCamelCase__							)






"""simple docstring"""
import math
from collections.abc import Generator



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)							))
      for i in (n for n in numbers if n > 1):
            # only need to check for factors up to sqrt(i)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						int(math.sqrt(a_							)							) + 1
            for j in range(2				,     a_							):
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							a_  :	int							)    ->   Generator[int, None, None]:
      __SCREAMING_SNAKE_CASE     :Generator	       =						(i for i in range(1				,     (max_n + 1)				,     2							))
      # It's useless to test even numbers as they will not be prime
      if max_n > 2:
            yield 2  # Because 2 will not be tested, it's necessary to yield it now
      for i in (n for n in numbers if n > 1):
            __SCREAMING_SNAKE_CASE     :int	       =						int(math.sqrt(a_							)							) + 1
            for j in range(3				,     a_				,     2							):
                  # As we removed the even numbers, we don't need them now
                  if (i % j) == 0:
                        break
            else:
                  yield i



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      from timeit import timeit

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''from __main__ import slow_primes, primes, fast_primes'''
      print(timeit('''slow_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)
      print(timeit('''fast_primes(1_000_000_000_000)'''				,     setup=a_				,     number=1_00_00_00							)							)


if __name__ == "__main__":
  lowerCamelCase_          =	int(input("Calculate primes up to:\n>> ").strip())
  for ret in primes(number):
    print(ret)
  benchmark()