




'''simple docstring'''

from __future__ import annotations

# Define the Gravitational Constant G and the function
a__         =     6.6743E-11  # unit of G : m^3 * kg^-1 * s^-2




def    snake_case__     (  a       ,      a       ,      a       ,      a	)			->       dict[str, float]:





			'''simple docstring'''






			snake_case__          =						mass_a * mass_a

			if (force, mass_a, mass_a, distance).count(0	) != 1:
						raise ValueError("""One and only one argument must be 0"""	)
			if force < 0:
						raise ValueError("""Gravitational force can not be negative"""	)
			if distance < 0:
						raise ValueError("""Distance can not be negative"""	)
			if mass_a < 0 or mass_a < 0:
						raise ValueError("""Mass can not be negative"""	)
			if force == 0:
						snake_case__          =						GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
						return {"force": force}
			elif mass_a == 0:
						snake_case__          =						(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_1": mass_a}
			elif mass_a == 0:
						snake_case__          =						(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_2": mass_a}
			elif distance == 0:
						snake_case__          =						(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
						return {"distance": distance}
			raise ValueError("""One and only one argument must be 0"""	)


# Run doctest
if __name__ == "__main__":
		import doctest

		doctest.testmod()

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

UpperCAmelCase__						:	int	     = logging.getLogger(__name__)






@require_tensorboard
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      _A:    int						     =			'''test_project_with_config'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         _A:    Any						     =			{'''num_iterations''': 1_2, '''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('''*/**'''	):
            _A:    int						     =			list(filter(lambda lowerCAmelCase_	: x.is_file()     ,     child.iterdir()	)	)[0]
         self.assertNotEqual(str(lowerCAmelCase_	)     ,     ''''''	)


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         accelerator.init_trackers(lowerCAmelCase_	)
         _A:    str						     =			{'''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
         _A:    Optional[int]						     =			list(filter(lambda lowerCAmelCase_	: x.is_file()     ,     Path(F"""{dirpath}/{project_name}"""	).iterdir()	)	)[0]
         self.assertNotEqual(str(lowerCAmelCase_	)     ,     ''''''	)


   def 					__magic_name__							(   self	:				Union[str, Any]	):

      """simple docstring"""
      _A:    Tuple						     =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    int						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         accelerator.init_trackers(lowerCAmelCase_	)
         _A:    Optional[int]						     =			{'''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
         _A:    Optional[int]						     =			list(filter(lambda lowerCAmelCase_	: 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:
            _A:    Tuple						     =			f.read()
         _A:    Optional[Any]						     =			False
         while data:
            _A:    str						     =			struct.unpack('''Q'''     ,     data[:8]	)

            _A:    List[Any]						     =			data[1_2 : 1_2 + int(header[0]	)]  # 8+4
            _A:    Any						     =			data[1_2 + int(header[0]	) + 4 :]
            _A:    Any						     =			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":
                     _A:    int						     =			True
         self.assertTrue(lowerCAmelCase_     ,     '''Converted tensor was not found in the log file!'''	)


   def 					__magic_name__							(   self	:				Union[str, Any]	):

      """simple docstring"""
      with self.assertRaisesRegex(lowerCAmelCase_     ,     '''Logging with `tensorboard` requires a `logging_dir`'''	):
         _A:    Optional[int]						     =			Accelerator(log_with='''tensorboard'''	)
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				str	):

      """simple docstring"""
      _A:    Dict						     =			ProjectConfiguration(total_limit=3_0	)
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    str						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_     ,     project_config=lowerCAmelCase_	)






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





   '''simple docstring'''


   def 					__magic_name__							(   self	:				List[Any]	):

      """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 					__magic_name__							(   lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = True	):

      """simple docstring"""
      _A:    List[Any]						     =			RF"""{section} ([\S\s]*?)\n\n"""
      if record:
         _A:    Tuple						     =			RF"""Record: {pattern}"""
      _A:    int						     =			re.findall(lowerCAmelCase_     ,     lowerCAmelCase_	)[0]
      # A config
      if section == "config" or section == "history":
         _A:    Dict						     =			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 					__magic_name__							(   self	:				Dict	):

      """simple docstring"""
      _A:    int						     =			'''test_project_with_config'''
      _A:    List[str]						     =			Accelerator(log_with='''wandb'''	)
      _A:    List[str]						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
      _A:    Optional[Any]						     =			{'''wandb''': {'''tags''': ['''my_tag''']}}
      accelerator.init_trackers(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Union[str, Any]						     =			{'''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":
            _A:    Optional[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
      _A:    Optional[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
      _A:    Optional[int]						     =			self.parse_log(lowerCAmelCase_     ,     '''run'''     ,     lowerCAmelCase_	)
      self.assertEqual(logged_items['''tags''']     ,     '''my_tag'''	)

      # Actual logging
      _A:    Tuple						     =			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						,						a						,						a ) ->  int:
   _A:    List[Any]						     =			run_name
   _A:    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(SCREAMING_SNAKE_CASE__				,				'''__init__'''				,				SCREAMING_SNAKE_CASE__  )
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   @staticmethod
   def 					__magic_name__							(   lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      for log in log_list:
         _A:    Optional[Any]						     =			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 					__magic_name__							(   self	:				Optional[int]	):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
         _A:    int						     =			CometMLTracker('''test_project_with_config'''     ,     lowerCAmelCase_	)
         _A:    List[Any]						     =			Accelerator(log_with=lowerCAmelCase_	)
         _A:    Any						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
         accelerator.init_trackers(lowerCAmelCase_     ,     lowerCAmelCase_	)
         accelerator.end_training()
         _A:    List[Any]						     =			os.listdir(lowerCAmelCase_	)[0]  # Comet is nice, it's just a zip file here
         # We parse the raw logs
         _A:    int						     =			os.path.join(lowerCAmelCase_     ,     lowerCAmelCase_	)
         _A:    Tuple						     =			zipfile.ZipFile(lowerCAmelCase_     ,     '''r'''	)
         _A:    Tuple						     =			archive.open('''messages.json'''	).read().decode('''utf-8'''	)
      _A:    Tuple						     =			log.split('''\n'''	)[:-1]
      self.assertEqual(self.get_value_from_key(lowerCAmelCase_     ,     '''num_iterations'''     ,     lowerCAmelCase_	)     ,     1_2	)
      self.assertEqual(self.get_value_from_key(lowerCAmelCase_     ,     '''learning_rate'''     ,     lowerCAmelCase_	)     ,     0.01	)
      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 					__magic_name__							(   self	:				int	):

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





   '''simple docstring'''
   __UpperCamelCase						:       str												=       [
       '''total_loss''',
       '''iteration''',
       '''my_text''',
       '''learning_rate''',
       '''num_iterations''',
       '''some_boolean''',
       '''some_string''',
   ]

   __UpperCamelCase						:       Optional[int]												=       '''my_custom_tracker'''
   __UpperCamelCase						:       Optional[int]												=       False


   def __init__(   self	:				Dict     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Optional[int]						     =			open(F"""{dir}/log.csv"""     ,     '''w+'''	)
      _A:    str						     =			csv.DictWriter(self.f     ,     fieldnames=self._col_names	)
      self.writer.writeheader()


   @property
   def 					__magic_name__							(   self	:				int	):

      """simple docstring"""
      return self.writer


   def 					__magic_name__							(   self	:				List[Any]     ,     lowerCAmelCase_	:				dict	):

      """simple docstring"""
      logger.info('''Call init'''	)
      self.writer.writerow(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				dict     ,     lowerCAmelCase_	:				Optional[int]	):

      """simple docstring"""
      logger.info('''Call log'''	)
      self.writer.writerow(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      self.f.close()






class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
         _A:    str						     =			MyCustomTracker(lowerCAmelCase_	)
         _A:    Optional[int]						     =			Accelerator(log_with=lowerCAmelCase_	)
         _A:    List[str]						     =			{'''num_iterations''': 1_2, '''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:
            _A:    str						     =			csv.DictReader(lowerCAmelCase_	)
            _A:    int						     =			next(lowerCAmelCase_	)
            _A:    int						     =			{
                '''total_loss''': '''''',
                '''iteration''': '''''',
                '''my_text''': '''''',
                '''learning_rate''': '''0.01''',
                '''num_iterations''': '''12''',
                '''some_boolean''': '''False''',
                '''some_string''': '''some_value''',
            }
            self.assertDictEqual(lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
         _A:    Any						     =			MyCustomTracker(lowerCAmelCase_	)
         _A:    Any						     =			Accelerator(log_with=lowerCAmelCase_	)
         accelerator.init_trackers('''Some name'''	)
         _A:    Optional[int]						     =			{'''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:
            _A:    List[str]						     =			csv.DictReader(lowerCAmelCase_	)
            _A:    List[str]						     =			next(lowerCAmelCase_	)
            _A:    List[Any]						     =			{
                '''total_loss''': '''0.1''',
                '''iteration''': '''1''',
                '''my_text''': '''some_value''',
                '''learning_rate''': '''''',
                '''num_iterations''': '''''',
                '''some_boolean''': '''''',
                '''some_string''': '''''',
            }
            self.assertDictEqual(lowerCAmelCase_     ,     lowerCAmelCase_	)



