
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__       :   int   						=					logging.getLogger(__name__)





@require_tensorboard
class 						lowercase       (  unittest.TestCase					):
					def lowercase_			(		self    ):
								"""simple docstring"""







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







								lowerCAmelCase__ :    Optional[Any]				      =			'''test_project_with_log'''
								with tempfile.TemporaryDirectory() as dirpath:
											lowerCAmelCase__ :    Dict				      =			Accelerator(log_with='''tensorboard''' ,	project_dir=SCREAMING_SNAKE_CASE__    )
											accelerator.init_trackers(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
											accelerator.log(SCREAMING_SNAKE_CASE__ ,	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 SCREAMING_SNAKE_CASE__    : x.is_file() ,	Path(f'''{dirpath}/{project_name}'''    ).iterdir()    )    )[0]
											self.assertNotEqual(str(SCREAMING_SNAKE_CASE__    ) ,	''''''    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			'''test_project_with_log'''
								with tempfile.TemporaryDirectory() as dirpath:
											lowerCAmelCase__ :    Dict				      =			Accelerator(log_with='''tensorboard''' ,	project_dir=SCREAMING_SNAKE_CASE__    )
											accelerator.init_trackers(SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    str				      =			{'''tensor''': torch.tensor(1    )}
											accelerator.log(SCREAMING_SNAKE_CASE__ ,	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 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(SCREAMING_SNAKE_CASE__ ,	'''rb'''    ) as f:
														lowerCAmelCase__ :    Union[str, Any]				      =			f.read()
											lowerCAmelCase__ :    str				      =			False
											while data:
														lowerCAmelCase__ :    int				      =			struct.unpack('''Q''' ,	data[:8]    )

														lowerCAmelCase__ :    Union[str, Any]				      =			data[12 : 12 + int(header[0]    )]  # 8+4
														lowerCAmelCase__ :    int				      =			data[12 + int(header[0]    ) + 4 :]
														lowerCAmelCase__ :    Any				      =			event_pba.Event()

														event.ParseFromString(SCREAMING_SNAKE_CASE__    )
														if event.HasField('''summary'''    ):
																	for value in event.summary.value:
																				if value.simple_value == 1.0 and value.tag == "tensor":
																							lowerCAmelCase__ :    int				      =			True
											self.assertTrue(SCREAMING_SNAKE_CASE__ ,	'''Converted tensor was not found in the log file!'''    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__ ,	'''Logging with `tensorboard` requires a `logging_dir`'''    ):
											lowerCAmelCase__ :    List[str]				      =			Accelerator(log_with='''tensorboard'''    )
								with tempfile.TemporaryDirectory() as dirpath:
											lowerCAmelCase__ :    Optional[Any]				      =			Accelerator(log_with='''tensorboard''' ,	project_dir=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			ProjectConfiguration(total_limit=30    )
								with tempfile.TemporaryDirectory() as dirpath:
											lowerCAmelCase__ :    Optional[Any]				      =			Accelerator(log_with='''tensorboard''' ,	project_dir=SCREAMING_SNAKE_CASE__ ,	project_config=SCREAMING_SNAKE_CASE__    )





@require_wandb
@mock.patch.dict(os.environ	,		{"""WANDB_MODE""": """offline"""}					)
class 						lowercase       (  __UpperCamelCase	,		__UpperCamelCase					):
					def lowercase_			(		self    ):
								"""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 lowercase_			(		SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ = True    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			Rf'''{section} ([\S\s]*?)\n\n'''
								if record:
											lowerCAmelCase__ :    str				      =			Rf'''Record: {pattern}'''
								lowerCAmelCase__ :    Optional[int]				      =			re.findall(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )[0]
								# A config
								if section == "config" or section == "history":
											lowerCAmelCase__ :    Optional[Any]				      =			re.findall(R'''"([a-zA-Z0-9_.,]+)''' ,	SCREAMING_SNAKE_CASE__    )
											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]+)"''' ,	SCREAMING_SNAKE_CASE__    )    )
					@skip
					def lowercase_			(		self    ):
								"""simple docstring"""







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

        # Check HPS through careful parsing and cleaning
								lowerCAmelCase__ :    List[Any]				      =			self.parse_log(SCREAMING_SNAKE_CASE__ ,	'''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__ :    List[str]				      =			self.parse_log(SCREAMING_SNAKE_CASE__ ,	'''run''' ,	SCREAMING_SNAKE_CASE__    )
								self.assertEqual(logged_items['''tags'''] ,	'''my_tag'''    )

								# Actual logging
								lowerCAmelCase__ :    List[Any]				      =			self.parse_log(SCREAMING_SNAKE_CASE__ ,	'''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       :  Optional[Any]	,__UpperCamelCase       :  str	,__UpperCamelCase       :  str    ):
			lowerCAmelCase__ :    Any				      =			run_name
			lowerCAmelCase__ :    Union[str, Any]				      =			OfflineExperiment(project_name=__UpperCamelCase	,offline_directory=__UpperCamelCase    )
			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(__UpperCamelCase	,		"""__init__"""	,		__UpperCamelCase					)
class 						lowercase       (  unittest.TestCase					):
					@staticmethod
					def lowercase_			(		SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ = False    ):
								"""simple docstring"""







								for log in log_list:
											lowerCAmelCase__ :    Any				      =			json.loads(SCREAMING_SNAKE_CASE__    )['''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 lowercase_			(		self    ):
								"""simple docstring"""







								with tempfile.TemporaryDirectory() as d:
											lowerCAmelCase__ :    Optional[int]				      =			CometMLTracker('''test_project_with_config''' ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    int				      =			Accelerator(log_with=SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
											accelerator.init_trackers(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											accelerator.end_training()
											lowerCAmelCase__ :    Any				      =			os.listdir(SCREAMING_SNAKE_CASE__    )[0]  # Comet is nice, it's just a zip file here
											# We parse the raw logs
											lowerCAmelCase__ :    Tuple				      =			os.path.join(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Optional[Any]				      =			zipfile.ZipFile(SCREAMING_SNAKE_CASE__ ,	'''r'''    )
											lowerCAmelCase__ :    Dict				      =			archive.open('''messages.json'''    ).read().decode('''utf-8'''    )
								lowerCAmelCase__ :    Dict				      =			log.split('''\n'''    )[:-1]
								self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE__ ,	'''num_iterations''' ,	SCREAMING_SNAKE_CASE__    ) ,	12    )
								self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE__ ,	'''learning_rate''' ,	SCREAMING_SNAKE_CASE__    ) ,	0.01    )
								self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE__ ,	'''some_boolean''' ,	SCREAMING_SNAKE_CASE__    ) ,	SCREAMING_SNAKE_CASE__    )
								self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE__ ,	'''some_string''' ,	SCREAMING_SNAKE_CASE__    ) ,	'''some_value'''    )
					def lowercase_			(		self    ):
								"""simple docstring"""







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





class 						lowercase       (  __UpperCamelCase					):
					__a					=    [
					    """total_loss""",
					    """iteration""",
					    """my_text""",
					    """learning_rate""",
					    """num_iterations""",
					    """some_boolean""",
					    """some_string""",
					]

					__a					=    """my_custom_tracker"""
					__a					=    False
					def __init__(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			open(f'''{dir}/log.csv''' ,	'''w+'''    )
								lowerCAmelCase__ :    List[str]				      =			csv.DictWriter(self.f ,	fieldnames=self._col_names    )
								self.writer.writeheader()
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return self.writer
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								logger.info('''Call init'''    )
								self.writer.writerow(SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								logger.info('''Call log'''    )
								self.writer.writerow(SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								self.f.close()





class 						lowercase       (  unittest.TestCase					):
					def lowercase_			(		self    ):
								"""simple docstring"""







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







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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCvtForImageClassification""",
							    """TFCvtModel""",
							    """TFCvtPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cvt import (
														    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CvtForImageClassification,
														    CvtModel,
														    CvtPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_cvt import (
														    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCvtForImageClassification,
														    TFCvtModel,
														    TFCvtPreTrainedModel,
														)


else:
							import sys

							A__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



