
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_a   :      Optional[Any]       =				logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			torch.FloatTensor
    __lowercase				:			Optional[torch.FloatTensor]	=       None


def 					a__ ( a :					List[Any]							,  a :					int=0.999							,  a :					List[Any]="cosine"							,  ):

  """simple docstring"""







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(a :					Tuple		):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(a :					List[str]		):
      return math.exp(t * -12.0		)

  else:
    raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'		)

  _snake_case  :    Optional[int]            = []
  for i in range(a		):
    _snake_case  :    Union[str, Any]            = i / num_diffusion_timesteps
    _snake_case  :    int            = (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(a		) / alpha_bar_fn(a		)							,  a		)		)
  return torch.tensor(a							,  dtype=torch.floataa		)






class   _UpperCAmelCase					(       _snake_case		,       _snake_case):
    __lowercase				:			List[str]	=       [e.name for e in KarrasDiffusionSchedulers]
    __lowercase				:			Optional[int]	=       1
    @register_to_config
    def __init__(     self    ,     snake_case_ = 10_00    ,     snake_case_ = 0.0001    ,     snake_case_ = 0.02    ,     snake_case_ = "linear"    ,     snake_case_ = None    ,     snake_case_ = "epsilon"    ,     snake_case_ = "linspace"    ,     snake_case_ = 0    ,     ):
      if trained_betas is not None:
        _snake_case  :    List[Any]            = torch.tensor(snake_case_    ,     dtype=torch.floataa  )
      elif beta_schedule == "linear":
        _snake_case  :    str            = torch.linspace(snake_case_    ,     snake_case_    ,     snake_case_    ,     dtype=torch.floataa  )
      elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        _snake_case  :    Any            = (
            torch.linspace(beta_start**0.5    ,     beta_end**0.5    ,     snake_case_    ,     dtype=torch.floataa  ) ** 2
        )
      elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        _snake_case  :    Any            = betas_for_alpha_bar(snake_case_  )
      else:
        raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'  )

      _snake_case  :    Optional[int]            = 1.0 - self.betas
      _snake_case  :    Union[str, Any]            = torch.cumprod(self.alphas    ,     dim=0  )

      _snake_case  :    int            = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
      _snake_case  :    Dict            = np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
      _snake_case  :    int            = torch.from_numpy(snake_case_  )

      # setable values
      _snake_case  :    int            = None
      _snake_case  :    int            = np.linspace(0    ,     num_train_timesteps - 1    ,     snake_case_    ,     dtype=snake_case_  )[::-1].copy()
      _snake_case  :    Any            = torch.from_numpy(snake_case_  )
      _snake_case  :    Dict            = False
    @property
    def        lowerCamelCase__						(     self  ):
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
        return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      if isinstance(snake_case_    ,     torch.Tensor  ):
        _snake_case  :    Optional[int]            = timestep.to(self.timesteps.device  )
      _snake_case  :    Optional[int]            = (self.timesteps == timestep).nonzero().item()
      _snake_case  :    Any            = self.sigmas[step_index]
      _snake_case  :    Tuple            = sample / ((sigma**2 + 1) ** 0.5)
      _snake_case  :    int            = True
      return sample
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      _snake_case  :    int            = num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
        _snake_case  :    List[Any]            = np.linspace(0    ,     self.config.num_train_timesteps - 1    ,     snake_case_    ,     dtype=snake_case_  )[
            ::-1
        ].copy()
      elif self.config.timestep_spacing == "leading":
        _snake_case  :    Dict            = self.config.num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        _snake_case  :    Any            = (np.arange(0    ,     snake_case_  ) * step_ratio).round()[::-1].copy().astype(snake_case_  )
        timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
        _snake_case  :    Union[str, Any]            = self.config.num_train_timesteps / self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        _snake_case  :    Optional[Any]            = (np.arange(self.config.num_train_timesteps    ,     0    ,     -step_ratio  )).round().copy().astype(snake_case_  )
        timesteps -= 1
      else:
        raise ValueError(
            F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'  )

      _snake_case  :    List[Any]            = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
      _snake_case  :    Tuple            = np.interp(snake_case_    ,     np.arange(0    ,     len(snake_case_  )  )    ,     snake_case_  )
      _snake_case  :    Any            = np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
      _snake_case  :    Union[str, Any]            = torch.from_numpy(snake_case_  ).to(device=snake_case_  )
      if str(snake_case_  ).startswith("mps"  ):
        # mps does not support float64
        _snake_case  :    Dict            = torch.from_numpy(snake_case_  ).to(snake_case_    ,     dtype=torch.floataa  )
      else:
        _snake_case  :    Union[str, Any]            = torch.from_numpy(snake_case_  ).to(device=snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = True    ,     ):

      if (
          isinstance(snake_case_    ,     snake_case_  )
          or isinstance(snake_case_    ,     torch.IntTensor  )
          or isinstance(snake_case_    ,     torch.LongTensor  )
      ):
        raise ValueError(
            (
                "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                " one of the `scheduler.timesteps` as a timestep."
            )    ,     )

      if not self.is_scale_input_called:
        logger.warning(
            "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
            "See `StableDiffusionPipeline` for a usage example."  )

      if isinstance(snake_case_    ,     torch.Tensor  ):
        _snake_case  :    Any            = timestep.to(self.timesteps.device  )

      _snake_case  :    List[str]            = (self.timesteps == timestep).nonzero().item()
      _snake_case  :    List[Any]            = self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        _snake_case  :    Dict            = sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        _snake_case  :    Dict            = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
        raise NotImplementedError("prediction_type not implemented yet: sample"  )
      else:
        raise ValueError(
            F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )

      _snake_case  :    List[str]            = self.sigmas[step_index]
      _snake_case  :    Any            = self.sigmas[step_index + 1]
      _snake_case  :    Union[str, Any]            = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      _snake_case  :    Optional[Any]            = (sigma_to**2 - sigma_up**2) ** 0.5

      # 2. Convert to an ODE derivative
      _snake_case  :    str            = (sample - pred_original_sample) / sigma

      _snake_case  :    Optional[int]            = sigma_down - sigma

      _snake_case  :    Dict            = sample + derivative * dt

      _snake_case  :    Any            = model_output.device
      _snake_case  :    List[Any]            = randn_tensor(model_output.shape    ,     dtype=model_output.dtype    ,     device=snake_case_    ,     generator=snake_case_  )

      _snake_case  :    Union[str, Any]            = prev_sample + noise * sigma_up

      if not return_dict:
        return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=snake_case_    ,     pred_original_sample=snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     ):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      _snake_case  :    str            = self.sigmas.to(device=original_samples.device    ,     dtype=original_samples.dtype  )
      if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_  ):
        # mps does not support float64
        _snake_case  :    int            = self.timesteps.to(original_samples.device    ,     dtype=torch.floataa  )
        _snake_case  :    Dict            = timesteps.to(original_samples.device    ,     dtype=torch.floataa  )
      else:
        _snake_case  :    Any            = self.timesteps.to(original_samples.device  )
        _snake_case  :    List[str]            = timesteps.to(original_samples.device  )

      _snake_case  :    Dict            = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

      _snake_case  :    str            = sigmas[step_indices].flatten()
      while len(sigma.shape  ) < len(original_samples.shape  ):
        _snake_case  :    List[str]            = sigma.unsqueeze(-1  )

      _snake_case  :    List[Any]            = original_samples + noise * sigma
      return noisy_samples


    def __len__(     self  ):
      return self.config.num_train_timesteps



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__						:	int	     = logging.getLogger(__name__)






@require_tensorboard
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      _A:    int						     =			'''test_project_with_config'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         _A:    Any						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
         accelerator.init_trackers(lowerCAmelCase_     ,     lowerCAmelCase_	)
         accelerator.end_training()
         for child in Path(F"""{dirpath}/{project_name}"""	).glob('''*/**'''	):
            _A:    int						     =			list(filter(lambda lowerCAmelCase_	: x.is_file()     ,     child.iterdir()	)	)[0]
         self.assertNotEqual(str(lowerCAmelCase_	)     ,     ''''''	)


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         accelerator.init_trackers(lowerCAmelCase_	)
         _A:    str						     =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
         accelerator.log(lowerCAmelCase_     ,     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 lowerCAmelCase_	: x.is_file()     ,     Path(F"""{dirpath}/{project_name}"""	).iterdir()	)	)[0]
         self.assertNotEqual(str(lowerCAmelCase_	)     ,     ''''''	)


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

      """simple docstring"""
      _A:    Tuple						     =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    int						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         accelerator.init_trackers(lowerCAmelCase_	)
         _A:    Optional[int]						     =			{'''tensor''': torch.tensor(1	)}
         accelerator.log(lowerCAmelCase_     ,     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 lowerCAmelCase_	: 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(lowerCAmelCase_     ,     '''rb'''	) as f:
            _A:    Tuple						     =			f.read()
         _A:    Optional[Any]						     =			False
         while data:
            _A:    str						     =			struct.unpack('''Q'''     ,     data[:8]	)

            _A:    List[Any]						     =			data[1_2 : 1_2 + int(header[0]	)]  # 8+4
            _A:    Any						     =			data[1_2 + int(header[0]	) + 4 :]
            _A:    Any						     =			event_pba.Event()

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


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

      """simple docstring"""
      with self.assertRaisesRegex(lowerCAmelCase_     ,     '''Logging with `tensorboard` requires a `logging_dir`'''	):
         _A:    Optional[int]						     =			Accelerator(log_with='''tensorboard'''	)
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				str	):

      """simple docstring"""
      _A:    Dict						     =			ProjectConfiguration(total_limit=3_0	)
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    str						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_     ,     project_config=lowerCAmelCase_	)






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





   '''simple docstring'''


   def 					__magic_name__							(   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 					__magic_name__							(   lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = True	):

      """simple docstring"""
      _A:    List[Any]						     =			RF"""{section} ([\S\s]*?)\n\n"""
      if record:
         _A:    Tuple						     =			RF"""Record: {pattern}"""
      _A:    int						     =			re.findall(lowerCAmelCase_     ,     lowerCAmelCase_	)[0]
      # A config
      if section == "config" or section == "history":
         _A:    Dict						     =			re.findall(R'''"([a-zA-Z0-9_.,]+)'''     ,     lowerCAmelCase_	)
         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]+)"'''     ,     lowerCAmelCase_	)	)


   @skip
   def 					__magic_name__							(   self	:				Dict	):

      """simple docstring"""
      _A:    int						     =			'''test_project_with_config'''
      _A:    List[str]						     =			Accelerator(log_with='''wandb'''	)
      _A:    List[str]						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
      _A:    Optional[Any]						     =			{'''wandb''': {'''tags''': ['''my_tag''']}}
      accelerator.init_trackers(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Union[str, Any]						     =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
      accelerator.log(lowerCAmelCase_     ,     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[Any]						     =			subprocess.check_output(
                ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(lowerCAmelCase_	)]     ,     env=os.environ.copy()	).decode('''utf8'''     ,     '''ignore'''	)
            break

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

      # Actual logging
      _A:    Tuple						     =			self.parse_log(lowerCAmelCase_     ,     '''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						,						a						,						a ) ->  int:
   _A:    List[Any]						     =			run_name
   _A:    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(SCREAMING_SNAKE_CASE__				,				'''__init__'''				,				SCREAMING_SNAKE_CASE__  )
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   @staticmethod
   def 					__magic_name__							(   lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      for log in log_list:
         _A:    Optional[Any]						     =			json.loads(lowerCAmelCase_	)['''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 					__magic_name__							(   self	:				Optional[int]	):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
         _A:    int						     =			CometMLTracker('''test_project_with_config'''     ,     lowerCAmelCase_	)
         _A:    List[Any]						     =			Accelerator(log_with=lowerCAmelCase_	)
         _A:    Any						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
         accelerator.init_trackers(lowerCAmelCase_     ,     lowerCAmelCase_	)
         accelerator.end_training()
         _A:    List[Any]						     =			os.listdir(lowerCAmelCase_	)[0]  # Comet is nice, it's just a zip file here
         # We parse the raw logs
         _A:    int						     =			os.path.join(lowerCAmelCase_     ,     lowerCAmelCase_	)
         _A:    Tuple						     =			zipfile.ZipFile(lowerCAmelCase_     ,     '''r'''	)
         _A:    Tuple						     =			archive.open('''messages.json'''	).read().decode('''utf-8'''	)
      _A:    Tuple						     =			log.split('''\n'''	)[:-1]
      self.assertEqual(self.get_value_from_key(lowerCAmelCase_     ,     '''num_iterations'''     ,     lowerCAmelCase_	)     ,     1_2	)
      self.assertEqual(self.get_value_from_key(lowerCAmelCase_     ,     '''learning_rate'''     ,     lowerCAmelCase_	)     ,     0.01	)
      self.assertEqual(self.get_value_from_key(lowerCAmelCase_     ,     '''some_boolean'''     ,     lowerCAmelCase_	)     ,     lowerCAmelCase_	)
      self.assertEqual(self.get_value_from_key(lowerCAmelCase_     ,     '''some_string'''     ,     lowerCAmelCase_	)     ,     '''some_value'''	)


   def 					__magic_name__							(   self	:				int	):

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






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       str												=       [
       '''total_loss''',
       '''iteration''',
       '''my_text''',
       '''learning_rate''',
       '''num_iterations''',
       '''some_boolean''',
       '''some_string''',
   ]

   __UpperCamelCase						:       Optional[int]												=       '''my_custom_tracker'''
   __UpperCamelCase						:       Optional[int]												=       False


   def __init__(   self	:				Dict     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Optional[int]						     =			open(F"""{dir}/log.csv"""     ,     '''w+'''	)
      _A:    str						     =			csv.DictWriter(self.f     ,     fieldnames=self._col_names	)
      self.writer.writeheader()


   @property
   def 					__magic_name__							(   self	:				int	):

      """simple docstring"""
      return self.writer


   def 					__magic_name__							(   self	:				List[Any]     ,     lowerCAmelCase_	:				dict	):

      """simple docstring"""
      logger.info('''Call init'''	)
      self.writer.writerow(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				dict     ,     lowerCAmelCase_	:				Optional[int]	):

      """simple docstring"""
      logger.info('''Call log'''	)
      self.writer.writerow(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      self.f.close()






class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
         _A:    str						     =			MyCustomTracker(lowerCAmelCase_	)
         _A:    Optional[int]						     =			Accelerator(log_with=lowerCAmelCase_	)
         _A:    List[str]						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
         accelerator.init_trackers('''Some name'''     ,     lowerCAmelCase_	)
         accelerator.end_training()
         with open(F"""{d}/log.csv"""     ,     '''r'''	) as f:
            _A:    str						     =			csv.DictReader(lowerCAmelCase_	)
            _A:    int						     =			next(lowerCAmelCase_	)
            _A:    int						     =			{
                '''total_loss''': '''''',
                '''iteration''': '''''',
                '''my_text''': '''''',
                '''learning_rate''': '''0.01''',
                '''num_iterations''': '''12''',
                '''some_boolean''': '''False''',
                '''some_string''': '''some_value''',
            }
            self.assertDictEqual(lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Any	):

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



