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      :				Any     			=logging.getLogger(__name__)




@require_tensorboard
class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    'test_project_with_config'
          with tempfile.TemporaryDirectory() as dirpath:
              _lowerCamelCase  :		Tuple			    =    Accelerator(log_with="""tensorboard"""     ,	project_dir=__lowerCamelCase)
              _lowerCamelCase  :		Optional[int]			    =    {'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("""*/**"""):
                  _lowerCamelCase  :		List[str]			    =    list(filter(lambda _UpperCamelCase: x.is_file()     ,	child.iterdir()))[0]
              self.assertNotEqual(str(__lowerCamelCase)     ,	"""""")



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Optional[int]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->str:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    'test_project_with_log'
          with tempfile.TemporaryDirectory() as dirpath:
              _lowerCamelCase  :		Any			    =    Accelerator(log_with="""tensorboard"""     ,	project_dir=__lowerCamelCase)
              accelerator.init_trackers(__lowerCamelCase)
              _lowerCamelCase  :		Tuple			    =    {'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
              _lowerCamelCase  :		Union[str, Any]			    =    list(filter(lambda _UpperCamelCase: 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:
                  _lowerCamelCase  :		Any			    =    f.read()
              _lowerCamelCase  :		List[str]			    =    False
              while data:
                  _lowerCamelCase  :		str			    =    struct.unpack("""Q"""     ,	data[:8])

                  _lowerCamelCase  :		List[str]			    =    data[12 : 12 + int(header[0])]  # 8+4
                  _lowerCamelCase  :		List[str]			    =    data[12 + int(header[0]) + 4 :]
                  _lowerCamelCase  :		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":
                              _lowerCamelCase  :		Union[str, Any]			    =    True
              self.assertTrue(__lowerCamelCase     ,	"""Converted tensor was not found in the log file!""")



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->int:


          """simple docstring"""


          with self.assertRaisesRegex(__lowerCamelCase     ,	"""Logging with `tensorboard` requires a `logging_dir`"""):
              _lowerCamelCase  :		List[str]			    =    Accelerator(log_with="""tensorboard""")
          with tempfile.TemporaryDirectory() as dirpath:
              _lowerCamelCase  :		Optional[int]			    =    Accelerator(log_with="""tensorboard"""     ,	project_dir=__lowerCamelCase)




      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    ProjectConfiguration(total_limit=30)
          with tempfile.TemporaryDirectory() as dirpath:
              _lowerCamelCase  :		Any			    =    Accelerator(log_with="""tensorboard"""     ,	project_dir=__lowerCamelCase     ,	project_config=__lowerCamelCase)





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





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Optional[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 						_SCREAMING_SNAKE_CASE     (       _UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					Dict     ,	_UpperCamelCase  :					Tuple = True)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    RF"""{section} ([\S\s]*?)\n\n"""
          if record:
              _lowerCamelCase  :		Union[str, Any]			    =    RF"""Record: {pattern}"""
          _lowerCamelCase  :		Union[str, Any]			    =    re.findall(__lowerCamelCase     ,	__lowerCamelCase)[0]
          # A config
          if section == "config" or section == "history":
              _lowerCamelCase  :		int			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


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

        # Check HPS through careful parsing and cleaning
          _lowerCamelCase  :		Union[str, Any]			    =    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
          _lowerCamelCase  :		Union[str, Any]			    =    self.parse_log(__lowerCamelCase     ,	"""run"""     ,	__lowerCamelCase)
          self.assertEqual(logged_items["""tags"""]     ,	"""my_tag""")

          # Actual logging
          _lowerCamelCase  :		str			    =    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     A__	(       self ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Any			    =    run_name
    _lowerCamelCase  :		Dict			    =    OfflineExperiment(project_name=_SCREAMING_SNAKE_CASE ,			offline_directory=_SCREAMING_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(A_			,	'__init__'			,	A_		)
class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      @staticmethod
      def 						_SCREAMING_SNAKE_CASE     (       _UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					List[Any]     ,	_UpperCamelCase  :					Tuple = False)					->int:


          """simple docstring"""


          for log in log_list:
              _lowerCamelCase  :		List[str]			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->str:


          """simple docstring"""


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


          """simple docstring"""


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





      '''simple docstring'''






      _snake_case     		=							[
          'total_loss',
          'iteration',
          'my_text',
          'learning_rate',
          'num_iterations',
          'some_boolean',
          'some_string',
      ]

      _snake_case     		=							'my_custom_tracker'
      _snake_case     		=							False



      def __init__(       self  :					str     ,	_UpperCamelCase  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


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



      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Optional[int]:


          """simple docstring"""


          return self.writer



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					Any)					->Dict:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	_UpperCamelCase  :					Optional[Any]     ,	_UpperCamelCase  :					int)					->str:


          """simple docstring"""


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




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->List[Any]:


          """simple docstring"""


          self.f.close()





class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Any:


          """simple docstring"""


          with tempfile.TemporaryDirectory() as d:
              _lowerCamelCase  :		Optional[Any]			    =    MyCustomTracker(__lowerCamelCase)
              _lowerCamelCase  :		Dict			    =    Accelerator(log_with=__lowerCamelCase)
              _lowerCamelCase  :		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:
                  _lowerCamelCase  :		Optional[Any]			    =    csv.DictReader(__lowerCamelCase)
                  _lowerCamelCase  :		List[str]			    =    next(__lowerCamelCase)
                  _lowerCamelCase  :		Optional[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 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Dict:


          """simple docstring"""


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





"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     tuple[int, int]:







     '''simple docstring'''


     if len(_SCREAMING_SNAKE_CASE						) == 1:
          return a[0], a[0]
     else:
          # split A into half.
          __A					:					List[str]								=		a[: len(_SCREAMING_SNAKE_CASE						) // 2]
          __A					:					Optional[Any]								=		a[len(_SCREAMING_SNAKE_CASE						) // 2 :]

          # 2 sub problems, 1/2 of original size.
          __A						,		__A					:					int								=		max_difference(_SCREAMING_SNAKE_CASE						)
          __A						,		__A					:					str								=		max_difference(_SCREAMING_SNAKE_CASE						)

          # get min of first and max of second
          # linear time
          __A					:					str								=		min(_SCREAMING_SNAKE_CASE						)
          __A					:					Optional[int]								=		max(_SCREAMING_SNAKE_CASE						)

          # 3 cases, either (small1, big1),
          # (min_first, max_second), (small2, big2)
          # constant comparisons
          if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
               return smalla, biga
          elif biga - smalla > max_second - min_first:
               return smalla, biga
          else:
               return min_first, max_second


if __name__ == "__main__":
       import doctest

       doctest.testmod()



