"""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


__UpperCamelCase       :    Optional[int]         =		logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class   UpperCAmelCase_	(				lowercase__   ):
    snake_case_											=      42
    snake_case_											=      None


def   __UpperCAmelCase     (	_snake_case		:       Optional[int],				_snake_case		:       List[str]=0.9_9_9,				_snake_case		:       Optional[int]="cosine",				):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_snake_case		:       Dict       ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(_snake_case		:       List[str]       ):
         return math.exp(t * -1_2.0       )

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

   _lowercase							     =       []
   for i in range(_snake_case       ):
      _lowercase							     =       i / num_diffusion_timesteps
      _lowercase							     =       (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(_snake_case       ) / alpha_bar_fn(_snake_case       ),				_snake_case       )       )
   return torch.tensor(_snake_case,				dtype=torch.floataa       )
class   UpperCAmelCase_	(				lowercase__,    lowercase__   ):
    snake_case_											=      [e.name for e in KarrasDiffusionSchedulers]
    snake_case_											=      1
    @register_to_config
    def __init__(	self			:       Union[str, Any]   ,						_lowercase			:       int = 1_0_0_0   ,						_lowercase			:       float = 0.00_01   ,						_lowercase			:       float = 0.02   ,						_lowercase			:       str = "linear"   ,						_lowercase			:       Optional[Union[np.ndarray, List[float]]] = None   ,						_lowercase			:       str = "epsilon"   ,						_lowercase			:       str = "linear"   ,						_lowercase			:       Optional[bool] = False   ,						_lowercase			:       str = "linspace"   ,						_lowercase			:       int = 0   ,						)						->    List[Any]:
       if trained_betas is not None:
          _lowercase							     =       torch.tensor(_lowercase   ,						dtype=torch.floataa		)
       elif beta_schedule == "linear":
          _lowercase							     =       torch.linspace(_lowercase   ,						_lowercase   ,						_lowercase   ,						dtype=torch.floataa		)
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          _lowercase							     =       (
              torch.linspace(beta_start**0.5   ,						beta_end**0.5   ,						_lowercase   ,						dtype=torch.floataa		) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          _lowercase							     =       betas_for_alpha_bar(_lowercase		)
       else:
          raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""		)

       _lowercase							     =       1.0 - self.betas
       _lowercase							     =       torch.cumprod(self.alphas   ,						dim=0		)

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

       # setable values
       _lowercase							     =       None
       _lowercase							     =       np.linspace(0   ,						num_train_timesteps - 1   ,						_lowercase   ,						dtype=_lowercase		)[::-1].copy()
       _lowercase							     =       torch.from_numpy(_lowercase		)
       _lowercase							     =       False
       _lowercase							     =       use_karras_sigmas
    @property
    def 		_lowerCamelCase      (	self			:       str		)						->    Any:
       # 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			:       Union[str, Any]   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       Union[float, torch.FloatTensor]		)						->    torch.FloatTensor:
       if isinstance(_lowercase   ,						torch.Tensor		):
          _lowercase							     =       timestep.to(self.timesteps.device		)
       _lowercase							     =       (self.timesteps == timestep).nonzero().item()
       _lowercase							     =       self.sigmas[step_index]

       _lowercase							     =       sample / ((sigma**2 + 1) ** 0.5)

       _lowercase							     =       True
       return sample
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       Union[str, torch.device] = None		)						->    str:
       _lowercase							     =       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":
          _lowercase							     =       np.linspace(0   ,						self.config.num_train_timesteps - 1   ,						_lowercase   ,						dtype=_lowercase		)[
              ::-1
          ].copy()
       elif self.config.timestep_spacing == "leading":
          _lowercase							     =       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
          _lowercase							     =       (np.arange(0   ,						_lowercase		) * step_ratio).round()[::-1].copy().astype(_lowercase		)
          timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
          _lowercase							     =       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
          _lowercase							     =       (np.arange(self.config.num_train_timesteps   ,						0   ,						-step_ratio		)).round().copy().astype(_lowercase		)
          timesteps -= 1
       else:
          raise ValueError(
              f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""		)

       _lowercase							     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
       _lowercase							     =       np.log(_lowercase		)

       if self.config.interpolation_type == "linear":
          _lowercase							     =       np.interp(_lowercase   ,						np.arange(0   ,						len(_lowercase		)		)   ,						_lowercase		)
       elif self.config.interpolation_type == "log_linear":
          _lowercase							     =       torch.linspace(np.log(sigmas[-1]		)   ,						np.log(sigmas[0]		)   ,						num_inference_steps + 1		).exp()
       else:
          raise ValueError(
              f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
              " 'linear' or 'log_linear'"		)

       if self.use_karras_sigmas:
          _lowercase							     =       self._convert_to_karras(in_sigmas=_lowercase   ,						num_inference_steps=self.num_inference_steps		)
          _lowercase							     =       np.array([self._sigma_to_t(_lowercase   ,						_lowercase		) for sigma in sigmas]		)

       _lowercase							     =       np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
       _lowercase							     =       torch.from_numpy(_lowercase		).to(device=_lowercase		)
       if str(_lowercase		).startswith("mps"		):
          # mps does not support float64
          _lowercase							     =       torch.from_numpy(_lowercase		).to(_lowercase   ,						dtype=torch.floataa		)
       else:
          _lowercase							     =       torch.from_numpy(_lowercase		).to(device=_lowercase		)
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Dict		)						->    Optional[int]:
       # get log sigma
       _lowercase							     =       np.log(_lowercase		)

       # get distribution
       _lowercase							     =       log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       _lowercase							     =       np.cumsum((dists >= 0)   ,						axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
       _lowercase							     =       low_idx + 1

       _lowercase							     =       log_sigmas[low_idx]
       _lowercase							     =       log_sigmas[high_idx]

       # interpolate sigmas
       _lowercase							     =       (low - log_sigma) / (low - high)
       _lowercase							     =       np.clip(_lowercase   ,						0   ,						1		)

       # transform interpolation to time range
       _lowercase							     =       (1 - w) * low_idx + w * high_idx
       _lowercase							     =       t.reshape(sigma.shape		)
       return t
    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       List[str]		)						->    torch.FloatTensor:
       _lowercase							     =       in_sigmas[-1].item()
       _lowercase							     =       in_sigmas[0].item()

       _lowercase							     =       7.0  # 7.0 is the value used in the paper
       _lowercase							     =       np.linspace(0   ,						1   ,						_lowercase		)
       _lowercase							     =       sigma_min ** (1 / rho)
       _lowercase							     =       sigma_max ** (1 / rho)
       _lowercase							     =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       Union[float, torch.FloatTensor]   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       float = 0.0   ,						_lowercase			:       float = 0.0   ,						_lowercase			:       float = float("inf"		)   ,						_lowercase			:       float = 1.0   ,						_lowercase			:       Optional[torch.Generator] = None   ,						_lowercase			:       bool = True   ,						)						->    Union[EulerDiscreteSchedulerOutput, Tuple]:

       if (
           isinstance(_lowercase   ,						_lowercase		)
           or isinstance(_lowercase   ,						torch.IntTensor		)
           or isinstance(_lowercase   ,						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(_lowercase   ,						torch.Tensor		):
          _lowercase							     =       timestep.to(self.timesteps.device		)

       _lowercase							     =       (self.timesteps == timestep).nonzero().item()
       _lowercase							     =       self.sigmas[step_index]

       _lowercase							     =       min(s_churn / (len(self.sigmas		) - 1)   ,						2**0.5 - 1		) if s_tmin <= sigma <= s_tmax else 0.0

       _lowercase							     =       randn_tensor(
           model_output.shape   ,						dtype=model_output.dtype   ,						device=model_output.device   ,						generator=_lowercase		)

       _lowercase							     =       noise * s_noise
       _lowercase							     =       sigma * (gamma + 1)

       if gamma > 0:
          _lowercase							     =       sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       # NOTE: "original_sample" should not be an expected prediction_type but is left in for
       # backwards compatibility
       if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
          _lowercase							     =       model_output
       elif self.config.prediction_type == "epsilon":
          _lowercase							     =       sample - sigma_hat * model_output
       elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          _lowercase							     =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
          raise ValueError(
              f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""		)

       # 2. Convert to an ODE derivative
       _lowercase							     =       (sample - pred_original_sample) / sigma_hat

       _lowercase							     =       self.sigmas[step_index + 1] - sigma_hat

       _lowercase							     =       sample + derivative * dt

       if not return_dict:
          return (prev_sample,)

       return EulerDiscreteSchedulerOutput(prev_sample=_lowercase   ,						pred_original_sample=_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       torch.FloatTensor   ,						_lowercase			:       torch.FloatTensor   ,						)						->    torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       _lowercase							     =       self.sigmas.to(device=original_samples.device   ,						dtype=original_samples.dtype		)
       if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase		):
          # mps does not support float64
          _lowercase							     =       self.timesteps.to(original_samples.device   ,						dtype=torch.floataa		)
          _lowercase							     =       timesteps.to(original_samples.device   ,						dtype=torch.floataa		)
       else:
          _lowercase							     =       self.timesteps.to(original_samples.device		)
          _lowercase							     =       timesteps.to(original_samples.device		)

       _lowercase							     =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       _lowercase							     =       original_samples + noise * sigma
       return noisy_samples




    def __len__(	self			:       Any		)						->    Optional[int]:
       return self.config.num_train_timesteps
"""simple docstring"""


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       :    Any         =		logging.getLogger(__name__)
@require_tensorboard
class   UpperCAmelCase_	(				unittest.TestCase   ):
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Tuple:
       _lowercase							     =       "test_project_with_config"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers(_lowercase   ,						_lowercase		)
          accelerator.end_training()
          for child in Path(f"""{dirpath}/{project_name}"""		).glob("*/**"		):
             _lowercase							     =       list(filter(lambda _lowercase		: x.is_file()   ,						child.iterdir()		)		)[0]
          self.assertNotEqual(str(_lowercase		)   ,						""		)
    def 		_lowerCamelCase      (	self			:       str		)						->    str:
       _lowercase							     =       "test_project_with_log"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          accelerator.init_trackers(_lowercase		)
          _lowercase							     =       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
          accelerator.log(_lowercase   ,						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
          _lowercase							     =       list(filter(lambda _lowercase		: x.is_file()   ,						Path(f"""{dirpath}/{project_name}"""		).iterdir()		)		)[0]
          self.assertNotEqual(str(_lowercase		)   ,						""		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    int:
       _lowercase							     =       "test_project_with_log"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          accelerator.init_trackers(_lowercase		)
          _lowercase							     =       {"tensor": torch.tensor(1		)}
          accelerator.log(_lowercase   ,						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
          _lowercase							     =       list(filter(lambda _lowercase		: 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(_lowercase   ,						"rb"		) as f:
             _lowercase							     =       f.read()
          _lowercase							     =       False
          while data:
             _lowercase							     =       struct.unpack("Q"   ,						data[:8]		)

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

             event.ParseFromString(_lowercase		)
             if event.HasField("summary"		):
                for value in event.summary.value:
                   if value.simple_value == 1.0 and value.tag == "tensor":
                      _lowercase							     =       True
          self.assertTrue(_lowercase   ,						"Converted tensor was not found in the log file!"		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Dict:
       with self.assertRaisesRegex(_lowercase   ,						"Logging with `tensorboard` requires a `logging_dir`"		):
          _lowercase							     =       Accelerator(log_with="tensorboard"		)
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)




    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Dict:
       _lowercase							     =       ProjectConfiguration(total_limit=3_0		)
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase   ,						project_config=_lowercase		)
@require_wandb
@mock.patch.dict(os.environ,    {"""WANDB_MODE""": """offline"""}   )
class   UpperCAmelCase_	(				lowercase__,    lowercase__   ):
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    int:
       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      (	_lowercase			:       str   ,						_lowercase			:       str   ,						_lowercase			:       bool = True		)						->    Dict:
       _lowercase							     =       Rf"""{section} ([\S\s]*?)\n\n"""
       if record:
          _lowercase							     =       Rf"""Record: {pattern}"""
       _lowercase							     =       re.findall(_lowercase   ,						_lowercase		)[0]
       # A config
       if section == "config" or section == "history":
          _lowercase							     =       re.findall(R"\"([a-zA-Z0-9_.,]+)"   ,						_lowercase		)
          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]+)\""   ,						_lowercase		)		)




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

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

       # Actual logging
       _lowercase							     =       self.parse_log(_lowercase   ,						"history"		)
       self.assertEqual(logged_items["total_loss"]   ,						"0.1"		)
       self.assertEqual(logged_items["iteration"]   ,						"1"		)
       self.assertEqual(logged_items["my_text"]   ,						"some_value"		)
       self.assertEqual(logged_items["_step"]   ,						"0"		)


def   __UpperCAmelCase     (	self		:       Optional[int],				_snake_case		:       str,				_snake_case		:       str       ):
   _lowercase							     =       run_name
   _lowercase							     =       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(lowercase__,    """__init__""",    lowercase__   )
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @staticmethod
    def 		_lowerCamelCase      (	_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       bool = False		)						->    List[str]:
       for log in log_list:
          _lowercase							     =       json.loads(_lowercase		)["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			:       int		)						->    List[str]:
       with tempfile.TemporaryDirectory() as d:
          _lowercase							     =       CometMLTracker("test_project_with_config"   ,						_lowercase		)
          _lowercase							     =       Accelerator(log_with=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers(_lowercase   ,						_lowercase		)
          accelerator.end_training()
          _lowercase							     =       os.listdir(_lowercase		)[0]  # Comet is nice, it's just a zip file here
          # We parse the raw logs
          _lowercase							     =       os.path.join(_lowercase   ,						_lowercase		)
          _lowercase							     =       zipfile.ZipFile(_lowercase   ,						"r"		)
          _lowercase							     =       archive.open("messages.json"		).read().decode("utf-8"		)
       _lowercase							     =       log.split("\n"		)[:-1]
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"num_iterations"   ,						_lowercase		)   ,						1_2		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"learning_rate"   ,						_lowercase		)   ,						0.01		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"some_boolean"   ,						_lowercase		)   ,						_lowercase		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"some_string"   ,						_lowercase		)   ,						"some_value"		)




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

    snake_case_											=      """my_custom_tracker"""
    snake_case_											=      False
    def __init__(	self			:       List[Any]   ,						_lowercase			:       str		)						->    Dict:
       _lowercase							     =       open(f"""{dir}/log.csv"""   ,						"w+"		)
       _lowercase							     =       csv.DictWriter(self.f   ,						fieldnames=self._col_names		)
       self.writer.writeheader()
    @property
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Dict:
       return self.writer
    def 		_lowerCamelCase      (	self			:       Optional[Any]   ,						_lowercase			:       dict		)						->    Optional[int]:
       logger.info("Call init"		)
       self.writer.writerow(_lowercase		)
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       dict   ,						_lowercase			:       Optional[int]		)						->    List[str]:
       logger.info("Call log"		)
       self.writer.writerow(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       self.f.close()
class   UpperCAmelCase_	(				unittest.TestCase   ):
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    str:
       with tempfile.TemporaryDirectory() as d:
          _lowercase							     =       MyCustomTracker(_lowercase		)
          _lowercase							     =       Accelerator(log_with=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers("Some name"   ,						_lowercase		)
          accelerator.end_training()
          with open(f"""{d}/log.csv"""   ,						"r"		) as f:
             _lowercase							     =       csv.DictReader(_lowercase		)
             _lowercase							     =       next(_lowercase		)
             _lowercase							     =       {
                 "total_loss": "",
                 "iteration": "",
                 "my_text": "",
                 "learning_rate": "0.01",
                 "num_iterations": "12",
                 "some_boolean": "False",
                 "some_string": "some_value",
             }
             self.assertDictEqual(_lowercase   ,						_lowercase		)




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