

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   					=  logging.getLogger(__name__)





@require_tensorboard
class      snake_case_      (					unittest.TestCase				):


							"""simple docstring"""





							def 							A__	(			self				):
												__lowerCAmelCase       =				'test_project_with_config'
												with tempfile.TemporaryDirectory() as dirpath:
																	__lowerCAmelCase       =				Accelerator(log_with='tensorboard'       ,    project_dir=_A				)
																	__lowerCAmelCase       =				{'num_iterations': 1_2, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
																	accelerator.init_trackers(_A       ,    _A				)
																	accelerator.end_training()
																	for child in Path(F"""{dirpath}/{project_name}"""				).glob('*/**'				):
																						__lowerCAmelCase       =				list(filter(lambda _A				: x.is_file()       ,    child.iterdir()				)				)[0]
																	self.assertNotEqual(str(_A				)       ,    ''				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				'test_project_with_log'
												with tempfile.TemporaryDirectory() as dirpath:
																	__lowerCAmelCase       =				Accelerator(log_with='tensorboard'       ,    project_dir=_A				)
																	accelerator.init_trackers(_A				)
																	__lowerCAmelCase       =				{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																	accelerator.log(_A       ,    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 _A				: x.is_file()       ,    Path(F"""{dirpath}/{project_name}"""				).iterdir()				)				)[0]
																	self.assertNotEqual(str(_A				)       ,    ''				)




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

																						__lowerCAmelCase       =				data[1_2 : 1_2 + int(header[0]				)]  # 8+4
																						__lowerCAmelCase       =				data[1_2 + int(header[0]				) + 4 :]
																						__lowerCAmelCase       =				event_pba.Event()

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




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




							def 							A__	(			self				):
												__lowerCAmelCase       =				ProjectConfiguration(total_limit=3_0				)
												with tempfile.TemporaryDirectory() as dirpath:
																	__lowerCAmelCase       =				Accelerator(log_with='tensorboard'       ,    project_dir=_A       ,    project_config=_A				)





@require_wandb
@mock.patch.dict(os.environ      ,			{"""WANDB_MODE""": """offline"""}				)
class      snake_case_      (					_a      ,			_a				):


							"""simple docstring"""





							def 							A__	(			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 							A__	(			_A       ,    _A       ,    _A = True				):
												__lowerCAmelCase       =				RF"""{section} ([\S\s]*?)\n\n"""
												if record:
																	__lowerCAmelCase       =				RF"""Record: {pattern}"""
												__lowerCAmelCase       =				re.findall(_A       ,    _A				)[0]
												# A config
												if section == "config" or section == "history":
																	__lowerCAmelCase       =				re.findall(R'"([a-zA-Z0-9_.,]+)'       ,    _A				)
																	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]+)"'       ,    _A				)				)




							@skip
							def 							A__	(			self				):
												__lowerCAmelCase       =				'test_project_with_config'
												__lowerCAmelCase       =				Accelerator(log_with='wandb'				)
												__lowerCAmelCase       =				{'num_iterations': 1_2, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
												__lowerCAmelCase       =				{'wandb': {'tags': ['my_tag']}}
												accelerator.init_trackers(_A       ,    _A       ,    _A				)
												__lowerCAmelCase       =				{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
												accelerator.log(_A       ,    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(_A				)]       ,    env=os.environ.copy()				).decode('utf8'       ,    'ignore'				)
																						break

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

												# Actual logging
												__lowerCAmelCase       =				self.parse_log(_A       ,    '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		,       UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				run_name
					__lowerCAmelCase       =				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(_a      ,			"""__init__"""      ,			_a				)
class      snake_case_      (					unittest.TestCase				):


							"""simple docstring"""





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




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





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      =[
							    """total_loss""",
							    """iteration""",
							    """my_text""",
							    """learning_rate""",
							    """num_iterations""",
							    """some_boolean""",
							    """some_string""",
							]

							__UpperCAmelCase      ="""my_custom_tracker"""
							__UpperCAmelCase      =False




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




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




							def 							A__	(			self       ,    _A				):
												logger.info('Call init'				)
												self.writer.writerow(_A				)




							def 							A__	(			self       ,    _A       ,    _A				):
												logger.info('Call log'				)
												self.writer.writerow(_A				)




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





class      snake_case_      (					unittest.TestCase				):


							"""simple docstring"""





							def 							A__	(			self				):
												with tempfile.TemporaryDirectory() as d:
																	__lowerCAmelCase       =				MyCustomTracker(_A				)
																	__lowerCAmelCase       =				Accelerator(log_with=_A				)
																	__lowerCAmelCase       =				{'num_iterations': 1_2, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
																	accelerator.init_trackers('Some name'       ,    _A				)
																	accelerator.end_training()
																	with open(F"""{d}/log.csv"""       ,    'r'				) as f:
																						__lowerCAmelCase       =				csv.DictReader(_A				)
																						__lowerCAmelCase       =				next(_A				)
																						__lowerCAmelCase       =				{
																						    'total_loss': '',
																						    'iteration': '',
																						    'my_text': '',
																						    'learning_rate': '0.01',
																						    'num_iterations': '12',
																						    'some_boolean': 'False',
																						    'some_string': 'some_value',
																						}
																						self.assertDictEqual(_A       ,    _A				)




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









import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCamelCase   					=  2 << 2_0  # 2mb
lowerCamelCase   					=  get_logger(__name__)





class      snake_case_      (					_a				):


							"""simple docstring"""





							def 							A__	(			self				):
												__lowerCAmelCase       =				self._options.get_all_options().get('runner'				)
												return runner in [None, "DirectRunner", "PortableRunner"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if fs.exists(UpperCAmelCase__   ):
										if force_upload:
															logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""   )
										else:
															logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""   )
															return
					with fs.create(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'rb'   ) as local_file:
															__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )
															while chunk:
																				remote_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if os.path.exists(UpperCAmelCase__   ):
										if force_download:
															logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""   )
										else:
															logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""   )
															return
					with fs.open(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'wb'   ) as local_file:
															__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )
															while chunk:
																				local_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )






