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__					:       List[Any]		=       logging.getLogger(__name__)





@require_tensorboard
class 			__snake_case  (      unittest.TestCase     ):




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



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				'test_project_with_config'
									with tempfile.TemporaryDirectory() as dirpath:
													snake_case__ :     Tuple 							=				Accelerator(log_with='tensorboard'   ,	project_dir=__UpperCamelCase		)
													snake_case__ :     int 							=				{'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('*/**'		):
																	snake_case__ :     Dict 							=				list(filter(lambda __UpperCamelCase		: x.is_file()   ,	child.iterdir()		)		)[0]
													self.assertNotEqual(str(__UpperCamelCase		)   ,	''		)




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







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




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



									'''simple docstring'''







									snake_case__ :     int 							=				'test_project_with_log'
									with tempfile.TemporaryDirectory() as dirpath:
													snake_case__ :     Dict 							=				Accelerator(log_with='tensorboard'   ,	project_dir=__UpperCamelCase		)
													accelerator.init_trackers(__UpperCamelCase		)
													snake_case__ :     List[str] 							=				{'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
													snake_case__ :     Tuple 							=				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:
																	snake_case__ :     Union[str, Any] 							=				f.read()
													snake_case__ :     Dict 							=				False
													while data:
																	snake_case__ :     Optional[Any] 							=				struct.unpack('Q'   ,	data[:8]		)

																	snake_case__ :     List[str] 							=				data[12 : 12 + int(header[0]		)]  # 8+4
																	snake_case__ :     List[str] 							=				data[12 + int(header[0]		) + 4 :]
																	snake_case__ :     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":
																													snake_case__ :     Optional[int] 							=				True
													self.assertTrue(__UpperCamelCase   ,	'Converted tensor was not found in the log file!'		)




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



									'''simple docstring'''







									with self.assertRaisesRegex(__UpperCamelCase   ,	'Logging with `tensorboard` requires a `logging_dir`'		):
													snake_case__ :     Any 							=				Accelerator(log_with='tensorboard'		)
									with tempfile.TemporaryDirectory() as dirpath:
													snake_case__ :     Any 							=				Accelerator(log_with='tensorboard'   ,	project_dir=__UpperCamelCase		)




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



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				ProjectConfiguration(total_limit=30		)
									with tempfile.TemporaryDirectory() as dirpath:
													snake_case__ :     Optional[int] 							=				Accelerator(log_with='tensorboard'   ,	project_dir=__UpperCamelCase   ,	project_config=__UpperCamelCase		)





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




					def       __a      (       self		)					->  int:



									'''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       __a      (       __UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase = True		)					->  int:



									'''simple docstring'''







									snake_case__ :     int 							=				rF"""{section} ([\S\s]*?)\n\n"""
									if record:
													snake_case__ :     Optional[int] 							=				rF"""Record: {pattern}"""
									snake_case__ :     int 							=				re.findall(__UpperCamelCase   ,	__UpperCamelCase		)[0]
									# A config
									if section == "config" or section == "history":
													snake_case__ :     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       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				'test_project_with_config'
									snake_case__ :     Union[str, Any] 							=				Accelerator(log_with='wandb'		)
									snake_case__ :     int 							=				{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
									snake_case__ :     int 							=				{'wandb': {'tags': ['my_tag']}}
									accelerator.init_trackers(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)
									snake_case__ :     List[Any] 							=				{'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":
																	snake_case__ :     Any 							=				subprocess.check_output(
																	    ['wandb', 'sync', '--view', '--verbose', str(__UpperCamelCase		)]   ,	env=os.environ.copy()		).decode('utf8'   ,	'ignore'		)
																	break

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

									# Actual logging
									snake_case__ :     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 			UpperCamelCase__						(    self      ,			A__      ,			A__   )       -> List[Any]:
				snake_case__ :     Union[str, Any] 							=				run_name
				snake_case__ :     List[str] 							=				OfflineExperiment(project_name=A__      ,			offline_directory=A__   )
				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 			__snake_case  (      unittest.TestCase     ):




					@staticmethod
					def       __a      (       __UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase = False		)					->  Optional[Any]:



									'''simple docstring'''







									for log in log_list:
													snake_case__ :     List[Any] 							=				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       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







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



									'''simple docstring'''







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

					__lowerCamelCase	 =       """my_custom_tracker"""
					__lowerCamelCase	 =       False




					def __init__(       self   ,	__UpperCamelCase		)					->  List[Any]:



									'''simple docstring'''







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




					@property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									return self.writer




					def       __a      (       self   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







									logger.info('Call init'		)
									self.writer.writerow(__UpperCamelCase		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  List[str]:



									'''simple docstring'''







									logger.info('Call log'		)
									self.writer.writerow(__UpperCamelCase		)




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



									'''simple docstring'''







									self.f.close()





class 			__snake_case  (      unittest.TestCase     ):




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



									'''simple docstring'''







									with tempfile.TemporaryDirectory() as d:
													snake_case__ :     Optional[int] 							=				MyCustomTracker(__UpperCamelCase		)
													snake_case__ :     str 							=				Accelerator(log_with=__UpperCamelCase		)
													snake_case__ :     Dict 							=				{'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:
																	snake_case__ :     str 							=				csv.DictReader(__UpperCamelCase		)
																	snake_case__ :     str 							=				next(__UpperCamelCase		)
																	snake_case__ :     List[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       __a      (       self		)					->  Any:



									'''simple docstring'''







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



from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowerCAmelCase__					:       Dict		=       {
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 			UpperCamelCase__						(    A__   )       -> set[tuple[str, str]]:
				snake_case__ :     Tuple 							=				{node: {node} for node in graph}

				snake_case__ :     Tuple 							=				{node: graph[node][:] for node in graph}

				while len(A__   ) > 2:
								# Choose a random edge.
								snake_case__ :     Union[str, Any] 							=				random.choice(list(graph_copy.keys()   )   )
								snake_case__ :     Any 							=				random.choice(graph_copy[u]   )

								# Contract edge (u, v) to new node uv
								snake_case__ :     Tuple 							=				u + v
								snake_case__ :     Dict 							=				list(set(graph_copy[u] + graph_copy[v]   )   )
								uv_neighbors.remove(A__   )
								uv_neighbors.remove(A__   )
								snake_case__ :     Optional[Any] 							=				uv_neighbors
								for neighbor in uv_neighbors:
												graph_copy[neighbor].append(A__   )

								snake_case__ :     Tuple 							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

								# Remove nodes u and v.
								del graph_copy[u]
								del graph_copy[v]
								for neighbor in uv_neighbors:
												if u in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )
												if v in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )

    # Find cutset.
				snake_case__ :     Tuple 							=				[contracted_nodes[node] for node in graph_copy]
				return {
				    (node, neighbor)
				    for node in groups[0]
				    for neighbor in graph[node]
				    if neighbor in groups[1]
				}


if __name__ == "__main__":
				print(partition_graph(TEST_GRAPH))


