


'''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

lowerCamelCase__     						=  logging.getLogger(__name__)






@require_tensorboard
class 						lowerCAmelCase__      (      unittest.TestCase						):




			def 				lowerCAmelCase__     (		self	:  Any			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   "test_project_with_config"
					with tempfile.TemporaryDirectory() as dirpath:
							_UpperCAmelCase		:							List[Any]             =   Accelerator(log_with="tensorboard"						,	project_dir=lowerCamelCase__			)
							_UpperCAmelCase		:							Union[str, Any]             =   {"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("*/**"			):
									_UpperCAmelCase		:							str             =   list(filter(lambda lowerCamelCase__			: x.is_file()						,	child.iterdir()			)			)[0]
							self.assertNotEqual(str(lowerCamelCase__			)						,	""			)




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   "test_project_with_log"
					with tempfile.TemporaryDirectory() as dirpath:
							_UpperCAmelCase		:							List[str]             =   Accelerator(log_with="tensorboard"						,	project_dir=lowerCamelCase__			)
							accelerator.init_trackers(lowerCamelCase__			)
							_UpperCAmelCase		:							Optional[Any]             =   {"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
							_UpperCAmelCase		:							int             =   list(filter(lambda lowerCamelCase__			: x.is_file()						,	Path(F"""{dirpath}/{project_name}"""			).iterdir()			)			)[0]
							self.assertNotEqual(str(lowerCamelCase__			)						,	""			)




			def 				lowerCAmelCase__     (		self	:  Dict			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   "test_project_with_log"
					with tempfile.TemporaryDirectory() as dirpath:
							_UpperCAmelCase		:							Tuple             =   Accelerator(log_with="tensorboard"						,	project_dir=lowerCamelCase__			)
							accelerator.init_trackers(lowerCamelCase__			)
							_UpperCAmelCase		:							List[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
							_UpperCAmelCase		:							List[str]             =   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:
									_UpperCAmelCase		:							Union[str, Any]             =   f.read()
							_UpperCAmelCase		:							List[str]             =   False
							while data:
									_UpperCAmelCase		:							str             =   struct.unpack("Q"						,	data[:8]			)

									_UpperCAmelCase		:							List[str]             =   data[12 : 12 + int(header[0]			)]  # 8+4
									_UpperCAmelCase		:							Union[str, Any]             =   data[12 + int(header[0]			) + 4 :]
									_UpperCAmelCase		:							List[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":
															_UpperCAmelCase		:							Dict             =   True
							self.assertTrue(lowerCamelCase__						,	"Converted tensor was not found in the log file!"			)




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Union[str, Any]:



					'''simple docstring'''







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






			def 				lowerCAmelCase__     (		self	:  str			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   ProjectConfiguration(total_limit=30			)
					with tempfile.TemporaryDirectory() as dirpath:
							_UpperCAmelCase		:							Any             =   Accelerator(log_with="tensorboard"						,	project_dir=lowerCamelCase__						,	project_config=lowerCamelCase__			)






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




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''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 				lowerCAmelCase__     (		lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  bool = True			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   RF"""{section} ([\S\s]*?)\n\n"""
					if record:
							_UpperCAmelCase		:							Union[str, Any]             =   RF"""Record: {pattern}"""
					_UpperCAmelCase		:							Optional[int]             =   re.findall(lowerCamelCase__						,	lowerCamelCase__			)[0]
					# A config
					if section == "config" or section == "history":
							_UpperCAmelCase		:							Optional[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	:  Tuple			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   "test_project_with_config"
					_UpperCAmelCase		:							Optional[int]             =   Accelerator(log_with="wandb"			)
					_UpperCAmelCase		:							Optional[int]             =   {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
					_UpperCAmelCase		:							str             =   {"wandb": {"tags": ["my_tag"]}}
					accelerator.init_trackers(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)
					_UpperCAmelCase		:							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":
									_UpperCAmelCase		:							Optional[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
					_UpperCAmelCase		:							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
					_UpperCAmelCase		:							List[Any]             =   self.parse_log(lowerCamelCase__						,	"run"						,	lowerCamelCase__			)
					self.assertEqual(logged_items["tags"]						,	"my_tag"			)

					# Actual logging
					_UpperCAmelCase		:							Optional[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 			__lowerCAmelCase			(self   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							int             =   run_name
		_UpperCAmelCase		:							Any             =   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(UpperCAmelCase__     ,							"__init__"     ,							UpperCAmelCase__						)
class 						lowerCAmelCase__      (      unittest.TestCase						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  bool = False			) ->str:



					'''simple docstring'''







					for log in log_list:
							_UpperCAmelCase		:							List[Any]             =   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	:  Any			) ->List[Any]:



					'''simple docstring'''







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



					'''simple docstring'''







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

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




			def __init__(		self	:  int						,	lowerCamelCase__	:  str			) ->Any:



					'''simple docstring'''







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




			@property
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->List[str]:



					'''simple docstring'''







					return self.writer




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  dict			) ->Dict:



					'''simple docstring'''







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




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  dict						,	lowerCamelCase__	:  Optional[int]			) ->Optional[int]:



					'''simple docstring'''







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






			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->str:



					'''simple docstring'''







					self.f.close()






class 						lowerCAmelCase__      (      unittest.TestCase						):




			def 				lowerCAmelCase__     (		self	:  Dict			) ->Optional[int]:



					'''simple docstring'''







					with tempfile.TemporaryDirectory() as d:
							_UpperCAmelCase		:							Optional[int]             =   MyCustomTracker(lowerCamelCase__			)
							_UpperCAmelCase		:							Dict             =   Accelerator(log_with=lowerCamelCase__			)
							_UpperCAmelCase		:							int             =   {"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:
									_UpperCAmelCase		:							List[str]             =   csv.DictReader(lowerCamelCase__			)
									_UpperCAmelCase		:							str             =   next(lowerCamelCase__			)
									_UpperCAmelCase		:							List[str]             =   {
									    "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	:  Union[str, Any]			) ->Any:



					'''simple docstring'''







					with tempfile.TemporaryDirectory() as d:
							_UpperCAmelCase		:							Optional[Any]             =   MyCustomTracker(lowerCamelCase__			)
							_UpperCAmelCase		:							Optional[int]             =   Accelerator(log_with=lowerCamelCase__			)
							accelerator.init_trackers("Some name"			)
							_UpperCAmelCase		:							str             =   {"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:
									_UpperCAmelCase		:							Union[str, Any]             =   csv.DictReader(lowerCamelCase__			)
									_UpperCAmelCase		:							Optional[Any]             =   next(lowerCamelCase__			)
									_UpperCAmelCase		:							Optional[int]             =   {
									    "total_loss": "0.1",
									    "iteration": "1",
									    "my_text": "some_value",
									    "learning_rate": "",
									    "num_iterations": "",
									    "some_boolean": "",
									    "some_string": "",
									}
									self.assertDictEqual(lowerCamelCase__						,	lowerCamelCase__			)







'''simple docstring'''


import io
import json
import subprocess


lowerCamelCase__     						=  [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

lowerCamelCase__     						=  8






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							List[str]             =   {}
		for src, tgt in pairs:
				_UpperCAmelCase		:							Tuple             =   F"""{src}-{tgt}"""
				_UpperCAmelCase		:							Tuple             =   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
				_UpperCAmelCase		:							List[Any]             =   subprocess.run(__lowerCAmelCase   ,			stdout=subprocess.PIPE ).stdout.decode("utf-8" ).splitlines()
				_UpperCAmelCase		:							Tuple             =   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
				_UpperCAmelCase		:							List[str]             =   subprocess.run(__lowerCAmelCase   ,			stdout=subprocess.PIPE ).stdout.decode("utf-8" ).splitlines()
				_UpperCAmelCase		:							Tuple             =   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
		return text


lowerCamelCase__     						=  get_all_data(pairs, n_objs)
lowerCamelCase__     						=  './fsmt_val_data.json'
with io.open(filename, 'w', encoding='utf-8') as f:
						lowerCamelCase__     						=  json.dump(text, f, indent=2, ensure_ascii=False)



