






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										=		logging.getLogger(__name__)



@require_tensorboard
class 						lowerCamelCase   (unittest.TestCase    ):







		'''simple docstring'''



		def       __UpperCAmelCase    (							self     )			->							Any:
							UpperCAmelCase_					:   Any   =		'test_project_with_config'
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase_					:   int   =		Accelerator(log_with='tensorboard'       ,	project_dir=_UpperCamelCase     )
												UpperCAmelCase_					:   List[Any]   =		{'num_iterations': 1_2, '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('*/**'     ):
																	UpperCAmelCase_					:   Union[str, Any]   =		list(filter(lambda _UpperCamelCase     : x.is_file()       ,	child.iterdir()     )     )[0]
												self.assertNotEqual(str(_UpperCamelCase     )       ,	''     )



		def       __UpperCAmelCase    (							self     )			->							Union[str, Any]:
							UpperCAmelCase_					:   Optional[Any]   =		'test_project_with_log'
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase_					:   Union[str, Any]   =		Accelerator(log_with='tensorboard'       ,	project_dir=_UpperCamelCase     )
												accelerator.init_trackers(_UpperCamelCase     )
												UpperCAmelCase_					:   Tuple   =		{'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
												UpperCAmelCase_					:   Any   =		list(filter(lambda _UpperCamelCase     : x.is_file()       ,	Path(f"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
												self.assertNotEqual(str(_UpperCamelCase     )       ,	''     )



		def       __UpperCAmelCase    (							self     )			->							str:
							UpperCAmelCase_					:   int   =		'test_project_with_log'
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase_					:   Dict   =		Accelerator(log_with='tensorboard'       ,	project_dir=_UpperCamelCase     )
												accelerator.init_trackers(_UpperCamelCase     )
												UpperCAmelCase_					:   Optional[Any]   =		{'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
												UpperCAmelCase_					:   Any   =		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:
																	UpperCAmelCase_					:   Dict   =		f.read()
												UpperCAmelCase_					:   Union[str, Any]   =		False
												while data:
																	UpperCAmelCase_					:   List[Any]   =		struct.unpack('Q'       ,	data[:8]     )

																	UpperCAmelCase_					:   Optional[Any]   =		data[1_2 : 1_2 + int(header[0]     )]  # 8+4
																	UpperCAmelCase_					:   Any   =		data[1_2 + int(header[0]     ) + 4 :]
																	UpperCAmelCase_					:   Optional[Any]   =		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":
																																UpperCAmelCase_					:   List[str]   =		True
												self.assertTrue(_UpperCamelCase       ,	'Converted tensor was not found in the log file!'     )



		def       __UpperCAmelCase    (							self     )			->							Any:
							with self.assertRaisesRegex(_UpperCamelCase       ,	'Logging with `tensorboard` requires a `logging_dir`'     ):
												UpperCAmelCase_					:   Optional[int]   =		Accelerator(log_with='tensorboard'     )
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase_					:   Optional[int]   =		Accelerator(log_with='tensorboard'       ,	project_dir=_UpperCamelCase     )



		def       __UpperCAmelCase    (							self     )			->							Union[str, Any]:
							UpperCAmelCase_					:   Any   =		ProjectConfiguration(total_limit=3_0     )
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase_					:   str   =		Accelerator(log_with='tensorboard'       ,	project_dir=_UpperCamelCase       ,	project_config=_UpperCamelCase     )





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







		'''simple docstring'''



		def       __UpperCAmelCase    (							self     )			->							Any:
							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     )			->							List[Any]:
							UpperCAmelCase_					:   Union[str, Any]   =		rf"{section} ([\S\s]*?)\n\n"
							if record:
												UpperCAmelCase_					:   Tuple   =		rf"Record: {pattern}"
							UpperCAmelCase_					:   Any   =		re.findall(_UpperCamelCase       ,	_UpperCamelCase     )[0]
							# A config
							if section == "config" or section == "history":
												UpperCAmelCase_					:   List[str]   =		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     )			->							Optional[int]:
							UpperCAmelCase_					:   Dict   =		'test_project_with_config'
							UpperCAmelCase_					:   Union[str, Any]   =		Accelerator(log_with='wandb'     )
							UpperCAmelCase_					:   List[str]   =		{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
							UpperCAmelCase_					:   Optional[Any]   =		{'wandb': {'tags': ['my_tag']}}
							accelerator.init_trackers(_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )
							UpperCAmelCase_					:   List[str]   =		{'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":
																	UpperCAmelCase_					:   List[str]   =		subprocess.check_output(
																	    ['wandb', 'sync', '--view', '--verbose', str(_UpperCamelCase     )]       ,	env=os.environ.copy()     ).decode('utf8'       ,	'ignore'     )
																	break

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

							# Actual logging
							UpperCAmelCase_					:   Dict   =		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					:							List[Any]  , __snake_case					:							str  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		run_name
					UpperCAmelCase_					:   Tuple   =		OfflineExperiment(project_name=__snake_case  , offline_directory=__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(_snake_case				,						'''__init__'''				,						_snake_case    )
class 						lowerCamelCase   (unittest.TestCase    ):







		'''simple docstring'''



		@staticmethod
		def       __UpperCAmelCase    (							_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase = False     )			->							Optional[int]:
							for log in log_list:
												UpperCAmelCase_					:   int   =		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     )			->							List[Any]:
							with tempfile.TemporaryDirectory() as d:
												UpperCAmelCase_					:   int   =		CometMLTracker('test_project_with_config'       ,	_UpperCamelCase     )
												UpperCAmelCase_					:   Optional[int]   =		Accelerator(log_with=_UpperCamelCase     )
												UpperCAmelCase_					:   Optional[int]   =		{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
												accelerator.init_trackers(_UpperCamelCase       ,	_UpperCamelCase     )
												accelerator.end_training()
												UpperCAmelCase_					:   Optional[int]   =		os.listdir(_UpperCamelCase     )[0]  # Comet is nice, it's just a zip file here
												# We parse the raw logs
												UpperCAmelCase_					:   str   =		os.path.join(_UpperCamelCase       ,	_UpperCamelCase     )
												UpperCAmelCase_					:   Optional[int]   =		zipfile.ZipFile(_UpperCamelCase       ,	'r'     )
												UpperCAmelCase_					:   str   =		archive.open('messages.json'     ).read().decode('utf-8'     )
							UpperCAmelCase_					:   Tuple   =		log.split('\n'     )[:-1]
							self.assertEqual(self.get_value_from_key(_UpperCamelCase       ,	'num_iterations'       ,	_UpperCamelCase     )       ,	1_2     )
							self.assertEqual(self.get_value_from_key(_UpperCamelCase       ,	'learning_rate'       ,	_UpperCamelCase     )       ,	0.01     )
							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     )			->							List[Any]:
							with tempfile.TemporaryDirectory() as d:
												UpperCAmelCase_					:   int   =		CometMLTracker('test_project_with_config'       ,	_UpperCamelCase     )
												UpperCAmelCase_					:   Tuple   =		Accelerator(log_with=_UpperCamelCase     )
												accelerator.init_trackers(_UpperCamelCase     )
												UpperCAmelCase_					:   Dict   =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
												accelerator.log(_UpperCamelCase       ,	step=0     )
												accelerator.end_training()
												UpperCAmelCase_					:   int   =		os.listdir(_UpperCamelCase     )[0]  # Comet is nice, it's just a zip file here
												# We parse the raw logs
												UpperCAmelCase_					:   Dict   =		os.path.join(_UpperCamelCase       ,	_UpperCamelCase     )
												UpperCAmelCase_					:   Dict   =		zipfile.ZipFile(_UpperCamelCase       ,	'r'     )
												UpperCAmelCase_					:   Optional[int]   =		archive.open('messages.json'     ).read().decode('utf-8'     )
							UpperCAmelCase_					:   Any   =		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   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					Optional[Any] 		=						[
		    '''total_loss''',
		    '''iteration''',
		    '''my_text''',
		    '''learning_rate''',
		    '''num_iterations''',
		    '''some_boolean''',
		    '''some_string''',
		]

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



		def __init__(							self       ,	_UpperCamelCase     )			->							Optional[Any]:
							UpperCAmelCase_					:   Optional[Any]   =		open(f"{dir}/log.csv"       ,	'w+'     )
							UpperCAmelCase_					:   str   =		csv.DictWriter(self.f       ,	fieldnames=self._col_names     )
							self.writer.writeheader()



		@property
		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							return self.writer



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							logger.info('Call init'     )
							self.writer.writerow(_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							Any:
							logger.info('Call log'     )
							self.writer.writerow(_UpperCamelCase     )



		def       __UpperCAmelCase    (							self     )			->							List[str]:
							self.f.close()





class 						lowerCamelCase   (unittest.TestCase    ):







		'''simple docstring'''



		def       __UpperCAmelCase    (							self     )			->							str:
							with tempfile.TemporaryDirectory() as d:
												UpperCAmelCase_					:   List[Any]   =		MyCustomTracker(_UpperCamelCase     )
												UpperCAmelCase_					:   List[Any]   =		Accelerator(log_with=_UpperCamelCase     )
												UpperCAmelCase_					:   Tuple   =		{'num_iterations': 1_2, '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:
																	UpperCAmelCase_					:   Optional[int]   =		csv.DictReader(_UpperCamelCase     )
																	UpperCAmelCase_					:   Union[str, Any]   =		next(_UpperCamelCase     )
																	UpperCAmelCase_					:   Any   =		{
																	    '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     )			->							Optional[int]:
							with tempfile.TemporaryDirectory() as d:
												UpperCAmelCase_					:   str   =		MyCustomTracker(_UpperCamelCase     )
												UpperCAmelCase_					:   List[Any]   =		Accelerator(log_with=_UpperCamelCase     )
												accelerator.init_trackers('Some name'     )
												UpperCAmelCase_					:   Optional[Any]   =		{'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:
																	UpperCAmelCase_					:   str   =		csv.DictReader(_UpperCamelCase     )
																	UpperCAmelCase_					:   Any   =		next(_UpperCamelCase     )
																	UpperCAmelCase_					:   Any   =		{
																	    'total_loss': '0.1',
																	    'iteration': '1',
																	    'my_text': 'some_value',
																	    'learning_rate': '',
																	    'num_iterations': '',
																	    'some_boolean': '',
																	    'some_string': '',
																	}
																	self.assertDictEqual(_UpperCamelCase       ,	_UpperCamelCase     )














def   lowercase__   (  __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					return len_board * len_board_column * row + column




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							set[int]  , ):


					'''simple docstring'''


					if board[row][column] != word[word_index]:
										return False

					if word_index == len(__snake_case						) - 1:
										return True

					UpperCAmelCase_					:   Dict   =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
					UpperCAmelCase_					:   Union[str, Any]   =		len(__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		len(board[0]						)
					for direction in traverts_directions:
										UpperCAmelCase_					:   Optional[int]   =		row + direction[0]
										UpperCAmelCase_					:   Optional[int]   =		column + direction[1]
										if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
															continue

										UpperCAmelCase_					:   Tuple   =		get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)
										if key in visited_points_set:
															continue

										visited_points_set.add(__snake_case						)
										if exits_word(__snake_case  , __snake_case  , __snake_case  , __snake_case  , word_index + 1  , __snake_case						):
															return True

										visited_points_set.remove(__snake_case						)

					return False




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		(
					    'The board should be a non empty matrix of single chars strings.'
					)

					UpperCAmelCase_					:   Optional[Any]   =		len(__snake_case						)
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(__snake_case						)

					for row in board:
										if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
															raise ValueError(__snake_case						)

										for item in row:
															if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) != 1:
																				raise ValueError(__snake_case						)

    # Validate word
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(
										    'The word parameter should be a string of length greater than 0.'						)

					UpperCAmelCase_					:   Any   =		len(board[0]						)
					for i in range(__snake_case						):
										for j in range(__snake_case						):
															if exits_word(
															    __snake_case  , __snake_case  , __snake_case  , __snake_case  , 0  , {get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)}						):
																				return True

					return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()






