
"""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

A_						       =				logging.getLogger(__name__)




@require_tensorboard
class    __lowerCamelCase     (   unittest.TestCase ):




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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     '''test_project_with_log'''
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCamelCase_ 		=     Accelerator(log_with='''tensorboard'''       ,	project_dir=UpperCAmelCase      )
										accelerator.init_trackers(UpperCAmelCase      )
										lowerCamelCase_ 		=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
										accelerator.log(UpperCAmelCase       ,	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_ 		=     list(filter(lambda UpperCAmelCase      : x.is_file()       ,	Path(f"{dirpath}/{project_name}"      ).iterdir()      )      )[0]
										self.assertNotEqual(str(UpperCAmelCase      )       ,	''''''      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     '''test_project_with_log'''
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCamelCase_ 		=     Accelerator(log_with='''tensorboard'''       ,	project_dir=UpperCAmelCase      )
										accelerator.init_trackers(UpperCAmelCase      )
										lowerCamelCase_ 		=     {'''tensor''': torch.tensor(1      )}
										accelerator.log(UpperCAmelCase       ,	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_ 		=     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(UpperCAmelCase       ,	'''rb'''      ) as f:
													lowerCamelCase_ 		=     f.read()
										lowerCamelCase_ 		=     False
										while data:
													lowerCamelCase_ 		=     struct.unpack('''Q'''       ,	data[:8]      )

													lowerCamelCase_ 		=     data[12 : 12 + int(header[0]      )]  # 8+4
													lowerCamelCase_ 		=     data[12 + int(header[0]      ) + 4 :]
													lowerCamelCase_ 		=     event_pba.Event()

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




				def 					UpperCAmelCase__	(							self      ):
							with self.assertRaisesRegex(UpperCAmelCase       ,	'''Logging with `tensorboard` requires a `logging_dir`'''      ):
										lowerCamelCase_ 		=     Accelerator(log_with='''tensorboard'''      )
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCamelCase_ 		=     Accelerator(log_with='''tensorboard'''       ,	project_dir=UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     ProjectConfiguration(total_limit=30      )
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCamelCase_ 		=     Accelerator(log_with='''tensorboard'''       ,	project_dir=UpperCAmelCase       ,	project_config=UpperCAmelCase      )




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




				def 					UpperCAmelCase__	(							self      ):
							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__	(							UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = True      ):
							lowerCamelCase_ 		=     rf"{section} ([\S\s]*?)\n\n"
							if record:
										lowerCamelCase_ 		=     rf"Record: {pattern}"
							lowerCamelCase_ 		=     re.findall(UpperCAmelCase       ,	UpperCAmelCase      )[0]
							# A config
							if section == "config" or section == "history":
										lowerCamelCase_ 		=     re.findall(r'''"([a-zA-Z0-9_.,]+)'''       ,	UpperCAmelCase      )
										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]+)"'''       ,	UpperCAmelCase      )      )





				@skip
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     '''test_project_with_config'''
							lowerCamelCase_ 		=     Accelerator(log_with='''wandb'''      )
							lowerCamelCase_ 		=     {'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
							lowerCamelCase_ 		=     {'''wandb''': {'''tags''': ['''my_tag''']}}
							accelerator.init_trackers(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      )
							lowerCamelCase_ 		=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
							accelerator.log(UpperCAmelCase       ,	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_ 		=     subprocess.check_output(
													    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(UpperCAmelCase      )]       ,	env=os.environ.copy()      ).decode('''utf8'''       ,	'''ignore'''      )
													break

        # Check HPS through careful parsing and cleaning
							lowerCamelCase_ 		=     self.parse_log(UpperCAmelCase       ,	'''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_ 		=     self.parse_log(UpperCAmelCase       ,	'''run'''       ,	UpperCAmelCase      )
							self.assertEqual(logged_items['''tags''']       ,	'''my_tag'''      )

							# Actual logging
							lowerCamelCase_ 		=     self.parse_log(UpperCAmelCase       ,	'''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    lowercase	(  self  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     run_name
			lowerCamelCase_ 		=     OfflineExperiment(project_name=lowerCAmelCase__  ,offline_directory=lowerCAmelCase__	)
			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(lowerCAmelCase	,  '__init__'	,  lowerCAmelCase )
class    __lowerCamelCase     (   unittest.TestCase ):




				@staticmethod
				def 					UpperCAmelCase__	(							UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = False      ):
							for log in log_list:
										lowerCamelCase_ 		=     json.loads(UpperCAmelCase      )['''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      ):
							with tempfile.TemporaryDirectory() as d:
										lowerCamelCase_ 		=     CometMLTracker('''test_project_with_config'''       ,	UpperCAmelCase      )
										lowerCamelCase_ 		=     Accelerator(log_with=UpperCAmelCase      )
										lowerCamelCase_ 		=     {'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
										accelerator.init_trackers(UpperCAmelCase       ,	UpperCAmelCase      )
										accelerator.end_training()
										lowerCamelCase_ 		=     os.listdir(UpperCAmelCase      )[0]  # Comet is nice, it's just a zip file here
										# We parse the raw logs
										lowerCamelCase_ 		=     os.path.join(UpperCAmelCase       ,	UpperCAmelCase      )
										lowerCamelCase_ 		=     zipfile.ZipFile(UpperCAmelCase       ,	'''r'''      )
										lowerCamelCase_ 		=     archive.open('''messages.json'''      ).read().decode('''utf-8'''      )
							lowerCamelCase_ 		=     log.split('''\n'''      )[:-1]
							self.assertEqual(self.get_value_from_key(UpperCAmelCase       ,	'''num_iterations'''       ,	UpperCAmelCase      )       ,	12      )
							self.assertEqual(self.get_value_from_key(UpperCAmelCase       ,	'''learning_rate'''       ,	UpperCAmelCase      )       ,	0.0_1      )
							self.assertEqual(self.get_value_from_key(UpperCAmelCase       ,	'''some_boolean'''       ,	UpperCAmelCase      )       ,	UpperCAmelCase      )
							self.assertEqual(self.get_value_from_key(UpperCAmelCase       ,	'''some_string'''       ,	UpperCAmelCase      )       ,	'''some_value'''      )





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




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			int									=						[
				    'total_loss',
				    'iteration',
				    'my_text',
				    'learning_rate',
				    'num_iterations',
				    'some_boolean',
				    'some_string',
				]

				a__:			List[Any]									=						'my_custom_tracker'
				a__:			Any									=						False




				def __init__(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     open(f"{dir}/log.csv"       ,	'''w+'''      )
							lowerCamelCase_ 		=     csv.DictWriter(self.f       ,	fieldnames=self._col_names      )
							self.writer.writeheader()




				@property
				def 					UpperCAmelCase__	(							self      ):
							return self.writer




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							logger.info('''Call init'''      )
							self.writer.writerow(UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							logger.info('''Call log'''      )
							self.writer.writerow(UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							self.f.close()




class    __lowerCamelCase     (   unittest.TestCase ):




				def 					UpperCAmelCase__	(							self      ):
							with tempfile.TemporaryDirectory() as d:
										lowerCamelCase_ 		=     MyCustomTracker(UpperCAmelCase      )
										lowerCamelCase_ 		=     Accelerator(log_with=UpperCAmelCase      )
										lowerCamelCase_ 		=     {'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
										accelerator.init_trackers('''Some name'''       ,	UpperCAmelCase      )
										accelerator.end_training()
										with open(f"{d}/log.csv"       ,	'''r'''      ) as f:
													lowerCamelCase_ 		=     csv.DictReader(UpperCAmelCase      )
													lowerCamelCase_ 		=     next(UpperCAmelCase      )
													lowerCamelCase_ 		=     {
													    '''total_loss''': '''''',
													    '''iteration''': '''''',
													    '''my_text''': '''''',
													    '''learning_rate''': '''0.01''',
													    '''num_iterations''': '''12''',
													    '''some_boolean''': '''False''',
													    '''some_string''': '''some_value''',
													}
													self.assertDictEqual(UpperCAmelCase       ,	UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							with tempfile.TemporaryDirectory() as d:
										lowerCamelCase_ 		=     MyCustomTracker(UpperCAmelCase      )
										lowerCamelCase_ 		=     Accelerator(log_with=UpperCAmelCase      )
										accelerator.init_trackers('''Some name'''      )
										lowerCamelCase_ 		=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
										accelerator.log(UpperCAmelCase       ,	step=0      )
										accelerator.end_training()
										with open(f"{d}/log.csv"       ,	'''r'''      ) as f:
													lowerCamelCase_ 		=     csv.DictReader(UpperCAmelCase      )
													lowerCamelCase_ 		=     next(UpperCAmelCase      )
													lowerCamelCase_ 		=     {
													    '''total_loss''': '''0.1''',
													    '''iteration''': '''1''',
													    '''my_text''': '''some_value''',
													    '''learning_rate''': '''''',
													    '''num_iterations''': '''''',
													    '''some_boolean''': '''''',
													    '''some_string''': '''''',
													}
													self.assertDictEqual(UpperCAmelCase       ,	UpperCAmelCase      )





"""simple docstring"""


from typing import TYPE_CHECKING

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


A_						       =				{
    """configuration_flava""": [
        """FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """FlavaConfig""",
        """FlavaImageCodebookConfig""",
        """FlavaImageConfig""",
        """FlavaMultimodalConfig""",
        """FlavaTextConfig""",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				["""FlavaFeatureExtractor"""]
						A_						       =				["""FlavaImageProcessor"""]
						A_						       =				["""FlavaProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				[
						    """FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlavaForPreTraining""",
						    """FlavaImageCodebook""",
						    """FlavaImageModel""",
						    """FlavaModel""",
						    """FlavaMultimodalModel""",
						    """FlavaPreTrainedModel""",
						    """FlavaTextModel""",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

						A_						       =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



