
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_				:							str       =    logging.getLogger(__name__)







@require_tensorboard
class  _lowerCamelCase			(unittest.TestCase					):

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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   'test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                   __snake_case						       =   Accelerator(log_with='tensorboard'							,       project_dir=SCREAMING_SNAKE_CASE_    )
                   accelerator.init_trackers(SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                   accelerator.log(SCREAMING_SNAKE_CASE_							,       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						       =   list(filter(lambda SCREAMING_SNAKE_CASE_    : x.is_file()							,       Path(f'''{dirpath}/{project_name}'''    ).iterdir()    )    )[0]
                   self.assertNotEqual(str(SCREAMING_SNAKE_CASE_    )							,       ''    )

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

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

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

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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   ProjectConfiguration(total_limit=30    )
            with tempfile.TemporaryDirectory() as dirpath:
                   __snake_case						       =   Accelerator(log_with='tensorboard'							,       project_dir=SCREAMING_SNAKE_CASE_							,       project_config=SCREAMING_SNAKE_CASE_    )







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

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

     @skip
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   'test_project_with_config'
            __snake_case						       =   Accelerator(log_with='wandb'    )
            __snake_case						       =   {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
            __snake_case						       =   {'wandb': {'tags': ['my_tag']}}
            accelerator.init_trackers(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
            accelerator.log(SCREAMING_SNAKE_CASE_							,       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						       =   subprocess.check_output(
                              ['wandb', 'sync', '--view', '--verbose', str(SCREAMING_SNAKE_CASE_    )]							,       env=os.environ.copy()    ).decode('utf8'							,       'ignore'    )
                          break

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

            # Actual logging
            __snake_case						       =   self.parse_log(SCREAMING_SNAKE_CASE_							,       '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				:		str    ,__snake_case				:		str    ,__snake_case				:		str					)       ->   List[Any]:
       __snake_case						       =   run_name
       __snake_case						       =   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(lowerCamelCase							,					"""__init__"""							,					lowerCamelCase					)
class  _lowerCamelCase			(unittest.TestCase					):

     @staticmethod
     def 						__lowerCamelCase				(  SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = False    ):
            for log in log_list:
                   __snake_case						       =   json.loads(SCREAMING_SNAKE_CASE_    )['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    ):
            with tempfile.TemporaryDirectory() as d:
                   __snake_case						       =   CometMLTracker('test_project_with_config'							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   Accelerator(log_with=SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   {'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                   accelerator.init_trackers(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   accelerator.end_training()
                   __snake_case						       =   os.listdir(SCREAMING_SNAKE_CASE_    )[0]  # Comet is nice, it's just a zip file here
                   # We parse the raw logs
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   zipfile.ZipFile(SCREAMING_SNAKE_CASE_							,       'r'    )
                   __snake_case						       =   archive.open('messages.json'    ).read().decode('utf-8'    )
            __snake_case						       =   log.split('\n'    )[:-1]
            self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_							,       'num_iterations'							,       SCREAMING_SNAKE_CASE_    )							,       12    )
            self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_							,       'learning_rate'							,       SCREAMING_SNAKE_CASE_    )							,       0.0_1    )
            self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_							,       'some_boolean'							,       SCREAMING_SNAKE_CASE_    )							,       SCREAMING_SNAKE_CASE_    )
            self.assertEqual(self.get_value_from_key(SCREAMING_SNAKE_CASE_							,       'some_string'							,       SCREAMING_SNAKE_CASE_    )							,       'some_value'    )

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







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			[
         """total_loss""",
         """iteration""",
         """my_text""",
         """learning_rate""",
         """num_iterations""",
         """some_boolean""",
         """some_string""",
     ]

     lowercase__				  =			"""my_custom_tracker"""
     lowercase__				  =			False

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

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            logger.info('Call init'    )
            self.writer.writerow(SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            logger.info('Call log'    )
            self.writer.writerow(SCREAMING_SNAKE_CASE_    )

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







class  _lowerCamelCase			(unittest.TestCase					):

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

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


from __future__ import annotations







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = 0    ):
            __snake_case						       =   value
            __snake_case						       =   None
            __snake_case						       =   None





def        __lowercase(							__snake_case				:		Node | None    ,__snake_case				:		Node | None					)       ->   Node | None:
       if treea is None:
              return treea
       if treea is None:
              return treea

       __snake_case						       =   treea.value + treea.value
       __snake_case						       =   merge_two_binary_trees(treea.left    ,treea.left					)
       __snake_case						       =   merge_two_binary_trees(treea.right    ,treea.right					)
       return treea
def        __lowercase(							__snake_case				:		Node | None					)       ->   None:
       if root:
              print(root.value					)
              print_preorder(root.left					)
              print_preorder(root.right					)


if __name__ == "__main__":
 lowerCamelCase_				:							int       =    Node(1)
 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							Tuple       =    Node(3)
 lowerCamelCase_				:							int       =    Node(4)

 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							List[Any]       =    Node(4)
 lowerCamelCase_				:							List[Any]       =    Node(6)
 lowerCamelCase_				:							Optional[Any]       =    Node(9)
 lowerCamelCase_				:							List[str]       =    Node(5)

 print("Tree1 is: ")
 print_preorder(treea)
 print("Tree2 is: ")
 print_preorder(treea)
 lowerCamelCase_				:							int       =    merge_two_binary_trees(treea, treea)
 print("Merged Tree is: ")
 print_preorder(merged_tree)
