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     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

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

        """simple docstring"""

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

        """simple docstring"""

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

                      A_							:       int	      =				data[12 : 12 + int(header[0]      )]  # 8+4
                      A_							:       Dict	      =				data[12 + int(header[0]      ) + 4 :]
                      A_							:       List[str]	      =				event_pba.Event()

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

        """simple docstring"""

        with self.assertRaisesRegex(__magic_name__ ,       '''Logging with `tensorboard` requires a `logging_dir`'''      ):
               A_							:       str	      =				Accelerator(log_with='''tensorboard'''      )
        with tempfile.TemporaryDirectory() as dirpath:
               A_							:       List[Any]	      =				Accelerator(log_with='''tensorboard''' ,       project_dir=__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				ProjectConfiguration(total_limit=30      )
        with tempfile.TemporaryDirectory() as dirpath:
               A_							:       str	      =				Accelerator(log_with='''tensorboard''' ,       project_dir=__magic_name__ ,       project_config=__magic_name__      )






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






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """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  UpperCAmelCase   (    __magic_name__ ,       __magic_name__ ,       __magic_name__ = True      ):

        """simple docstring"""

        A_							:       List[Any]	      =				rf"""{section} ([\S\s]*?)\n\n"""
        if record:
               A_							:       Optional[int]	      =				rf"""Record: {pattern}"""
        A_							:       Optional[Any]	      =				re.findall(__magic_name__ ,       __magic_name__      )[0]
        # A config
        if section == "config" or section == "history":
               A_							:       int	      =				re.findall(r'''"([a-zA-Z0-9_.,]+)''' ,       __magic_name__      )
               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]+)"''' ,       __magic_name__      )      )
 @skip
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				'''test_project_with_config'''
        A_							:       str	      =				Accelerator(log_with='''wandb'''      )
        A_							:       int	      =				{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
        A_							:       Any	      =				{'''wandb''': {'''tags''': ['''my_tag''']}}
        accelerator.init_trackers(__magic_name__ ,       __magic_name__ ,       __magic_name__      )
        A_							:       int	      =				{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
        accelerator.log(__magic_name__ ,       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":
                      A_							:       Optional[int]	      =				subprocess.check_output(
                          ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(__magic_name__      )] ,       env=os.environ.copy()      ).decode('''utf8''' ,       '''ignore'''      )
                      break

        # Check HPS through careful parsing and cleaning
        A_							:       Tuple	      =				self.parse_log(__magic_name__ ,       '''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
        A_							:       Optional[int]	      =				self.parse_log(__magic_name__ ,       '''run''' ,       __magic_name__      )
        self.assertEqual(logged_items['''tags'''] ,       '''my_tag'''      )

        # Actual logging
        A_							:       Optional[Any]	      =				self.parse_log(__magic_name__ ,       '''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       a__      (					self  ,   a  ,   a							)		->	Union[str, Any]:
       A_							:       int	      =				run_name
       A_							:       Union[str, Any]	      =				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(A__      ,	"""__init__"""      ,	A__				)
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 @staticmethod
 def  UpperCAmelCase   (    __magic_name__ ,       __magic_name__ ,       __magic_name__ = False      ):

        """simple docstring"""

        for log in log_list:
               A_							:       List[Any]	      =				json.loads(__magic_name__      )['''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      ):

        """simple docstring"""

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

        """simple docstring"""

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






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   [
     """total_loss""",
     """iteration""",
     """my_text""",
     """learning_rate""",
     """num_iterations""",
     """some_boolean""",
     """some_string""",
 ]

 __magic_name__          =   """my_custom_tracker"""
 __magic_name__          =   False
 def __init__(    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       int	      =				open(f"""{dir}/log.csv""" ,       '''w+'''      )
        A_							:       List[Any]	      =				csv.DictWriter(self.f ,       fieldnames=self._col_names      )
        self.writer.writeheader()
 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return self.writer
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        logger.info('''Call init'''      )
        self.writer.writerow(__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__      ):

        """simple docstring"""

        logger.info('''Call log'''      )
        self.writer.writerow(__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        self.f.close()






class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

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

        """simple docstring"""

        with tempfile.TemporaryDirectory() as d:
               A_							:       Optional[Any]	      =				MyCustomTracker(__magic_name__      )
               A_							:       Union[str, Any]	      =				Accelerator(log_with=__magic_name__      )
               accelerator.init_trackers('''Some name'''      )
               A_							:       Dict	      =				{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
               accelerator.log(__magic_name__ ,       step=0      )
               accelerator.end_training()
               with open(f"""{d}/log.csv""" ,       '''r'''      ) as f:
                      A_							:       int	      =				csv.DictReader(__magic_name__      )
                      A_							:       Union[str, Any]	      =				next(__magic_name__      )
                      A_							:       int	      =				{
                          '''total_loss''': '''0.1''',
                          '''iteration''': '''1''',
                          '''my_text''': '''some_value''',
                          '''learning_rate''': '''''',
                          '''num_iterations''': '''''',
                          '''some_boolean''': '''''',
                          '''some_string''': '''''',
                      }
                      self.assertDictEqual(__magic_name__ ,       __magic_name__      )



def       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a  ,   )		->	tuple[list[list[int]], list[list[str]]]:
       A_							:       Optional[Any]	      =				list(a							)
       A_							:       int	      =				list(a							)
       A_							:       Union[str, Any]	      =				len(a							)
       A_							:       Any	      =				len(a							)

       A_							:       Union[str, Any]	      =				[
           [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]
       A_							:       Optional[int]	      =				[
           ['''0''' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]

       for i in range(1  ,   len_source_seq + 1							):
              A_							:       List[Any]	      =				i * delete_cost
              A_							:       List[Any]	      =				f"""D{source_seq[i - 1]:c}"""

       for i in range(1  ,   len_destination_seq + 1							):
              A_							:       Optional[Any]	      =				i * insert_cost
              A_							:       str	      =				f"""I{destination_seq[i - 1]:c}"""

       for i in range(1  ,   len_source_seq + 1							):
              for j in range(1  ,   len_destination_seq + 1							):
                     if source_seq[i - 1] == destination_seq[j - 1]:
                            A_							:       List[Any]	      =				costs[i - 1][j - 1] + copy_cost
                            A_							:       Tuple	      =				f"""C{source_seq[i - 1]:c}"""
                     else:
                            A_							:       Union[str, Any]	      =				costs[i - 1][j - 1] + replace_cost
                            A_							:       Dict	      =				f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]							)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            A_							:       Dict	      =				costs[i - 1][j] + delete_cost
                            A_							:       Optional[int]	      =				f"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            A_							:       List[Any]	      =				costs[i][j - 1] + insert_cost
                            A_							:       Tuple	      =				f"""I{destination_seq[j - 1]:c}"""

       return costs, ops


def       a__      (					a  ,   a  ,   a							)		->	list[str]:
       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     A_							:       Tuple	      =				assemble_transformation(a  ,   i - 1  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq
              elif ops[i][j][0] == "D":
                     A_							:       Any	      =				assemble_transformation(a  ,   i - 1  ,   a							)
                     seq.append(ops[i][j]							)
                     return seq
              else:
                     A_							:       Tuple	      =				assemble_transformation(a  ,   a  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq


if __name__ == "__main__":
    _lowerCAmelCase  ,			_lowerCAmelCase        =       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

    _lowerCAmelCase        =       len(operations)
    _lowerCAmelCase        =       len(operations[0])
    _lowerCAmelCase        =       assemble_transformation(operations, m - 1, n - 1)

    _lowerCAmelCase        =       list('Python')
    _lowerCAmelCase        =       0
    _lowerCAmelCase        =       0

    with open('min_cost.txt', 'w') as file:
        for op in sequence:
            print(''.join(string))

            if op[0] == "C":
                file.write('%-16s' % 'Copy %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost -= 1
            elif op[0] == "R":
                _lowerCAmelCase        =       op[2]

                file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
                file.write('\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 1
            elif op[0] == "D":
                string.pop(i)

                file.write('%-16s' % 'Delete %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2
            else:
                string.insert(i, op[1])

                file.write('%-16s' % 'Insert %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2

            i += 1

        print(''.join(string))
        print('Cost: ', cost)

        file.write('\r\nMinimum cost: ' + str(cost))


