
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__				:      List[str]    		=     logging.getLogger(__name__)






@require_tensorboard
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Dict				      = '''test_project_with_config'''
           with tempfile.TemporaryDirectory() as dirpath:
               SCREAMING_SNAKE_CASE				:				Any				      = Accelerator(log_with='''tensorboard'''				,       project_dir=_lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				str				      = {'''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				:				int				      = list(filter(lambda _lowerCamelCase   : x.is_file()				,       child.iterdir()   )   )[0]
               self.assertNotEqual(str(_lowerCamelCase   )				,       ''''''   )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = '''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				:				List[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
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = list(filter(lambda _lowerCamelCase   : x.is_file()				,       Path(F"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
               self.assertNotEqual(str(_lowerCamelCase   )				,       ''''''   )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''test_project_with_log'''
           with tempfile.TemporaryDirectory() as dirpath:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = Accelerator(log_with='''tensorboard'''				,       project_dir=_lowerCamelCase   )
               accelerator.init_trackers(_lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {'''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				:				Dict				      = 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:
                   SCREAMING_SNAKE_CASE				:				str				      = f.read()
               SCREAMING_SNAKE_CASE				:				Dict				      = False
               while data:
                   SCREAMING_SNAKE_CASE				:				Any				      = struct.unpack('''Q'''				,       data[:8]   )

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






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           with self.assertRaisesRegex(_lowerCamelCase				,       '''Logging with `tensorboard` requires a `logging_dir`'''   ):
               SCREAMING_SNAKE_CASE				:				Dict				      = Accelerator(log_with='''tensorboard'''   )
           with tempfile.TemporaryDirectory() as dirpath:
               SCREAMING_SNAKE_CASE				:				str				      = Accelerator(log_with='''tensorboard'''				,       project_dir=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = ProjectConfiguration(total_limit=30   )
           with tempfile.TemporaryDirectory() as dirpath:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = Accelerator(log_with='''tensorboard'''				,       project_dir=_lowerCamelCase				,       project_config=_lowerCamelCase   )






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



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->Any:
           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 	__lowerCAmelCase     (		_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase = True   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = RF"""{section} ([\S\s]*?)\n\n"""
           if record:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = RF"""Record: {pattern}"""
           SCREAMING_SNAKE_CASE				:				Any				      = re.findall(_lowerCamelCase				,       _lowerCamelCase   )[0]
           # A config
           if section == "config" or section == "history":
               SCREAMING_SNAKE_CASE				:				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 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				str				      = '''test_project_with_config'''
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = Accelerator(log_with='''wandb'''   )
           SCREAMING_SNAKE_CASE				:				Any				      = {'''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				:				List[str]				      = {'''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				:				Union[str, 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				:				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
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.parse_log(_lowerCamelCase				,       '''run'''				,       _lowerCamelCase   )
           self.assertEqual(logged_items['''tags''']				,       '''my_tag'''   )

           # Actual logging
           SCREAMING_SNAKE_CASE				:				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 				UpperCAmelCase_(    self ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = run_name
    SCREAMING_SNAKE_CASE				:				Tuple				      = 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     a_       (			unittest.TestCase							):



       """simple docstring"""






       @staticmethod
       def 	__lowerCAmelCase     (		_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase = False   )					->str:
           for log in log_list:
               SCREAMING_SNAKE_CASE				:				Tuple				      = 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 	__lowerCAmelCase     (		self   )					->Tuple:
           with tempfile.TemporaryDirectory() as d:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = CometMLTracker('''test_project_with_config'''				,       _lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = Accelerator(log_with=_lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Tuple				      = {'''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				:				Union[str, Any]				      = os.listdir(_lowerCamelCase   )[0]  # Comet is nice, it's just a zip file here
               # We parse the raw logs
               SCREAMING_SNAKE_CASE				:				Tuple				      = os.path.join(_lowerCamelCase				,       _lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = zipfile.ZipFile(_lowerCamelCase				,       '''r'''   )
               SCREAMING_SNAKE_CASE				:				str				      = archive.open('''messages.json'''   ).read().decode('''utf-8'''   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = 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 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           with tempfile.TemporaryDirectory() as d:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = CometMLTracker('''test_project_with_config'''				,       _lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Tuple				      = Accelerator(log_with=_lowerCamelCase   )
               accelerator.init_trackers(_lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
               accelerator.log(_lowerCamelCase				,       step=0   )
               accelerator.end_training()
               SCREAMING_SNAKE_CASE				:				Tuple				      = os.listdir(_lowerCamelCase   )[0]  # Comet is nice, it's just a zip file here
               # We parse the raw logs
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = os.path.join(_lowerCamelCase				,       _lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				int				      = zipfile.ZipFile(_lowerCamelCase				,       '''r'''   )
               SCREAMING_SNAKE_CASE				:				str				      = archive.open('''messages.json'''   ).read().decode('''utf-8'''   )
           SCREAMING_SNAKE_CASE				:				str				      = 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     a_       (			a__							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Tuple						=  [
           'total_loss',
           'iteration',
           'my_text',
           'learning_rate',
           'num_iterations',
           'some_boolean',
           'some_string',
       ]

       __SCREAMING_SNAKE_CASE       :					Dict						=  'my_custom_tracker'
       __SCREAMING_SNAKE_CASE       :					Optional[Any]						=  False






       def __init__(		self				,       _lowerCamelCase   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = open(F"""{dir}/log.csv"""				,       '''w+'''   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = csv.DictWriter(self.f				,       fieldnames=self._col_names   )
           self.writer.writeheader()






       @property
       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           return self.writer






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Any:
           logger.info('''Call init'''   )
           self.writer.writerow(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Dict:
           logger.info('''Call log'''   )
           self.writer.writerow(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           self.f.close()






class     a_       (			unittest.TestCase							):



       """simple docstring"""






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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				:      List[Any]    		=     {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      List[str]    		=     [
          '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DecisionTransformerGPT2Model''',
          '''DecisionTransformerGPT2PreTrainedModel''',
          '''DecisionTransformerModel''',
          '''DecisionTransformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DecisionTransformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_decision_transformer import (
                DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DecisionTransformerGPTaModel,
                DecisionTransformerGPTaPreTrainedModel,
                DecisionTransformerModel,
                DecisionTransformerPreTrainedModel,
            )


else:
      import sys

      a__				:      str    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
