
"""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

UpperCAmelCase__				:   Optional[int]   							=			logging.getLogger(__name__)



@require_tensorboard
class      lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




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



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       """test_project_with_log"""
											with tempfile.TemporaryDirectory() as dirpath:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       Accelerator(log_with="""tensorboard"""       ,	project_dir=lowercase__   )
																		accelerator.init_trackers(lowercase__   )
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
																		accelerator.log(lowercase__       ,	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__						:	List[str]									=       list(filter(lambda SCREAMING_SNAKE_CASE__   : x.is_file()       ,	Path(F'''{dirpath}/{project_name}'''   ).iterdir()   )   )[0]
																		self.assertNotEqual(str(lowercase__   )       ,	""""""   )



				def      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




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

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

																									event.ParseFromString(lowercase__   )
																									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(lowercase__       ,	"""Converted tensor was not found in the log file!"""   )



				def      __magic_name__   (self   ) ->		Any:



											"""simple docstring"""




											with self.assertRaisesRegex(lowercase__       ,	"""Logging with `tensorboard` requires a `logging_dir`"""   ):
																		SCREAMING_SNAKE_CASE__						:	Any									=       Accelerator(log_with="""tensorboard"""   )
											with tempfile.TemporaryDirectory() as dirpath:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       Accelerator(log_with="""tensorboard"""       ,	project_dir=lowercase__   )



				def      __magic_name__   (self   ) ->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       ProjectConfiguration(total_limit=30   )
											with tempfile.TemporaryDirectory() as dirpath:
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       Accelerator(log_with="""tensorboard"""       ,	project_dir=lowercase__       ,	project_config=lowercase__   )






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


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		Dict:



											"""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      __magic_name__   (SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = True   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       rF'''{section} ([\S\s]*?)\n\n'''
											if record:
																		SCREAMING_SNAKE_CASE__						:	Any									=       rF'''Record: {pattern}'''
											SCREAMING_SNAKE_CASE__						:	Tuple									=       re.findall(lowercase__       ,	lowercase__   )[0]
											# A config
											if section == "config" or section == "history":
																		SCREAMING_SNAKE_CASE__						:	str									=       re.findall(r"""\"([a-zA-Z0-9_.,]+)"""       ,	lowercase__   )
																		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]+)\""""       ,	lowercase__   )   )



				@skip
				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       """test_project_with_config"""
											SCREAMING_SNAKE_CASE__						:	Tuple									=       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__						:	Union[str, Any]									=       {"""wandb""": {"""tags""": ["""my_tag"""]}}
											accelerator.init_trackers(lowercase__       ,	lowercase__       ,	lowercase__   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
											accelerator.log(lowercase__       ,	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__						:	List[Any]									=       subprocess.check_output(
																									    ["""wandb""", """sync""", """--view""", """--verbose""", str(lowercase__   )]       ,	env=os.environ.copy()   ).decode("""utf8"""       ,	"""ignore"""   )
																									break

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

											# Actual logging
											SCREAMING_SNAKE_CASE__						:	Tuple									=       self.parse_log(lowercase__       ,	"""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       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Tuple									=       run_name
							SCREAMING_SNAKE_CASE__						:	int									=       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      lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				@staticmethod
				def      __magic_name__   (SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = False   ) ->		Optional[int]:



											"""simple docstring"""




											for log in log_list:
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       json.loads(lowercase__   )["""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      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




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



				def      __magic_name__   (self   ) ->		Optional[Any]:



											"""simple docstring"""




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






class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						str 	=       [
				    "total_loss",
				    "iteration",
				    "my_text",
				    "learning_rate",
				    "num_iterations",
				    "some_boolean",
				    "some_string",
				]

				__UpperCamelCase				:						Optional[int] 	=       "my_custom_tracker"
				__UpperCamelCase				:						Union[str, Any] 	=       False



				def __init__(self       ,	SCREAMING_SNAKE_CASE__   ) ->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       open(F'''{dir}/log.csv'''       ,	"""w+"""   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       csv.DictWriter(self.f       ,	fieldnames=self._col_names   )
											self.writer.writeheader()



				@property
				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											return self.writer



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		List[str]:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		int:



											"""simple docstring"""




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



				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											self.f.close()






class      lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




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



				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											with tempfile.TemporaryDirectory() as d:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       MyCustomTracker(lowercase__   )
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       Accelerator(log_with=lowercase__   )
																		accelerator.init_trackers("""Some name"""   )
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
																		accelerator.log(lowercase__       ,	step=0   )
																		accelerator.end_training()
																		with open(F'''{d}/log.csv'''       ,	"""r"""   ) as f:
																									SCREAMING_SNAKE_CASE__						:	Tuple									=       csv.DictReader(lowercase__   )
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       next(lowercase__   )
																									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(lowercase__       ,	lowercase__   )




"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def lowercase_  (       *_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.cuda.device_count()
							print(f'''Device count: {num_processes}'''						)
							with patch_environment(
							    world_size=_snake_case       ,master_addr="""127.0.01"""       ,master_port="""29500"""       ,mixed_precision=args[1].mixed_precision						):
														SCREAMING_SNAKE_CASE__						:	int									=       PrepareForLaunch(_snake_case       ,distributed_type="""MULTI_GPU"""						)
														torch.multiprocessing.start_processes(_snake_case       ,args=_snake_case       ,nprocs=_snake_case       ,start_method="""spawn"""						)


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					UpperCAmelCase__				:   str   							=			rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					UpperCAmelCase__				:   int   							=			[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					UpperCAmelCase__				:   List[str]   							=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					UpperCAmelCase__				:   Dict   							=			argparse.Namespace(cpu=False, mixed_precision='fp16')
					UpperCAmelCase__				:   Any   							=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])




