





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							):


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    _snake_case      : List[str]														=		'test_project_with_config'
    with tempfile.TemporaryDirectory() as dirpath:
      _snake_case      : Any														=		Accelerator(log_with='tensorboard'					, project_dir=UpperCamelCase							)
      _snake_case      : Dict														=		{'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      : Tuple														=		list(filter(lambda UpperCamelCase							: x.is_file()					, child.iterdir()							)							)[0]
      self.assertNotEqual(str(UpperCamelCase							)					, ''							)







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		'test_project_with_log'
    with tempfile.TemporaryDirectory() as dirpath:
      _snake_case      : Tuple														=		Accelerator(log_with='tensorboard'					, project_dir=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()
      # 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 UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		'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      : 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
      _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      : List[Any]														=		f.read()
      _snake_case      : int														=		False
      while data:
        _snake_case      : Union[str, Any]														=		struct.unpack('Q'					, data[:8]							)

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







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    with self.assertRaisesRegex(UpperCamelCase					, 'Logging with `tensorboard` requires a `logging_dir`'							):
      _snake_case      : Union[str, Any]														=		Accelerator(log_with='tensorboard'							)
    with tempfile.TemporaryDirectory() as dirpath:
      _snake_case      : str														=		Accelerator(log_with='tensorboard'					, project_dir=UpperCamelCase							)







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		ProjectConfiguration(total_limit=30							)
    with tempfile.TemporaryDirectory() as dirpath:
      _snake_case      : List[str]														=		Accelerator(log_with='tensorboard'					, project_dir=UpperCamelCase					, project_config=UpperCamelCase							)




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


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''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_       (					UpperCamelCase					:    str					, UpperCamelCase					:    str					, UpperCamelCase					:    bool = True							):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		Rf"""{section} ([\S\s]*?)\n\n"""
    if record:
      _snake_case      : List[Any]														=		Rf"""Record: {pattern}"""
    _snake_case      : List[str]														=		re.findall(UpperCamelCase					, UpperCamelCase							)[0]
    # A config
    if section == "config" or section == "history":
      _snake_case      : Dict														=		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					:    str							):


    '''simple docstring'''





    _snake_case      : List[Any]														=		'test_project_with_config'
    _snake_case      : List[Any]														=		Accelerator(log_with='wandb'							)
    _snake_case      : Optional[Any]														=		{'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
    _snake_case      : str														=		{'wandb': {'tags': ['my_tag']}}
    accelerator.init_trackers(UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Tuple														=		{'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      : Optional[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      : 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
    _snake_case      : str														=		self.parse_log(UpperCamelCase					, 'run'					, UpperCamelCase							)
    self.assertEqual(logged_items['tags']					, 'my_tag'							)

    # Actual logging
    _snake_case      : Tuple														=		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 			lowerCamelCase_		(					self:		List[Any]			,							lowerCAmelCase:		str			,							lowerCAmelCase:		str )->						Optional[int]:
  _snake_case      : int														=		run_name
  _snake_case      : List[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							):


  '''simple docstring'''







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    int					, UpperCamelCase					:    str					, UpperCamelCase					:    bool = False							):


    '''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 UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





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


    '''simple docstring'''





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


  '''simple docstring'''
  a_							:				int					    =[
      """total_loss""",
      """iteration""",
      """my_text""",
      """learning_rate""",
      """num_iterations""",
      """some_boolean""",
      """some_string""",
  ]

  a_							:				List[str]					    ="""my_custom_tracker"""
  a_							:				int					    =False







  def __init__(					self					:    int					, UpperCamelCase					:    str							):


    '''simple docstring'''





    _snake_case      : Tuple														=		open(f"""{dir}/log.csv"""					, 'w+'							)
    _snake_case      : Optional[Any]														=		csv.DictWriter(self.f					, fieldnames=self._col_names							)
    self.writer.writeheader()







  @property
  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    return self.writer







  def UpperCamelCase_       (					self					:    Any					, UpperCamelCase					:    dict							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Any					, UpperCamelCase					:    dict					, UpperCamelCase					:    Optional[int]							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    self.f.close()




class 				_lowerCAmelCase			(	unittest.TestCase							):


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    with tempfile.TemporaryDirectory() as d:
      _snake_case      : Tuple														=		MyCustomTracker(UpperCamelCase							)
      _snake_case      : Tuple														=		Accelerator(log_with=UpperCamelCase							)
      _snake_case      : List[str]														=		{'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      : Union[str, Any]														=		csv.DictReader(UpperCamelCase							)
        _snake_case      : Dict														=		next(UpperCamelCase							)
        _snake_case      : int														=		{
            '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]							):


    '''simple docstring'''





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











def 			lowerCamelCase_		(					lowerCAmelCase:		list			,							lowerCAmelCase:		list )->						list:
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		(
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  _snake_case      : list														=		[]
  for char_count in range(lowerCAmelCase ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count] )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count] )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)




