



'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__,	unittest.TestCase      ):
			A__								=  StableDiffusionXLPipeline
			A__								=  TEXT_TO_IMAGE_PARAMS
			A__								=  TEXT_TO_IMAGE_BATCH_PARAMS
			A__								=  TEXT_TO_IMAGE_IMAGE_PARAMS
			A__								=  TEXT_TO_IMAGE_IMAGE_PARAMS



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      Tuple:
							"""simple docstring"""





							torch.manual_seed(0			)
							snake_case__      :   Any      =    UNetaDConditionModel(
							    block_out_channels=(32, 64)    ,			layers_per_block=2    ,			sample_size=32    ,			in_channels=4    ,			out_channels=4    ,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,			attention_head_dim=(2, 4)    ,			use_linear_projection=__UpperCamelCase    ,			addition_embed_type='''text_time'''    ,			addition_time_embed_dim=8    ,			transformer_layers_per_block=(1, 2)    ,			projection_class_embeddings_input_dim=80    ,			cross_attention_dim=64    ,			)
							snake_case__      :   Tuple      =    EulerDiscreteScheduler(
							    beta_start=0.00085    ,			beta_end=0.012    ,			steps_offset=1    ,			beta_schedule='''scaled_linear'''    ,			timestep_spacing='''leading'''    ,			)
							torch.manual_seed(0			)
							snake_case__      :   Union[str, Any]      =    AutoencoderKL(
							    block_out_channels=[32, 64]    ,			in_channels=3    ,			out_channels=3    ,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,			latent_channels=4    ,			sample_size=128    ,			)
							torch.manual_seed(0			)
							snake_case__      :   List[Any]      =    CLIPTextConfig(
							    bos_token_id=0    ,			eos_token_id=2    ,			hidden_size=32    ,			intermediate_size=37    ,			layer_norm_eps=1e-05    ,			num_attention_heads=4    ,			num_hidden_layers=5    ,			pad_token_id=1    ,			vocab_size=1000    ,			hidden_act='''gelu'''    ,			projection_dim=32    ,			)
							snake_case__      :   List[str]      =    CLIPTextModel(__UpperCamelCase			)
							snake_case__      :   Dict      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    ,			local_files_only=__UpperCamelCase			)

							snake_case__      :   Optional[int]      =    CLIPTextModelWithProjection(__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    ,			local_files_only=__UpperCamelCase			)

							snake_case__      :   List[str]      =    {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''text_encoder_2''': text_encoder_a,
							    '''tokenizer_2''': tokenizer_a,
							    # "safety_checker": None,
							    # "feature_extractor": None,
							}
							return components



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Union[str, Any]=0			)      ->      Optional[Any]:
							"""simple docstring"""





							if str(__UpperCamelCase			).startswith('''mps'''			):
											snake_case__      :   List[Any]      =    torch.manual_seed(__UpperCamelCase			)
							else:
											snake_case__      :   List[Any]      =    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)
							snake_case__      :   Dict      =    {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 5.0,
							    '''output_type''': '''numpy''',
							}
							return inputs



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   Any      =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							snake_case__      :   Union[str, Any]      =    self.get_dummy_components()
							snake_case__      :   List[Any]      =    StableDiffusionXLPipeline(**__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    sd_pipe.to(__UpperCamelCase			)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							snake_case__      :   Dict      =    self.get_dummy_inputs(__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    sd_pipe(**__UpperCamelCase			).images
							snake_case__      :   List[Any]      =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							snake_case__      :   Union[str, Any]      =    np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2



			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   List[str]      =    self.get_dummy_components()
							snake_case__      :   Optional[Any]      =    StableDiffusionXLPipeline(**__UpperCamelCase			)
							snake_case__      :   str      =    sd_pipe.to(__UpperCamelCase			)
							snake_case__      :   Tuple      =    sd_pipe.to(__UpperCamelCase			)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							# forward without prompt embeds
							snake_case__      :   str      =    self.get_dummy_inputs(__UpperCamelCase			)
							snake_case__      :   Any      =    3 * ['''this is a negative prompt''']
							snake_case__      :   Optional[int]      =    negative_prompt
							snake_case__      :   Tuple      =    3 * [inputs['''prompt''']]

							snake_case__      :   Tuple      =    sd_pipe(**__UpperCamelCase			)
							snake_case__      :   str      =    output.images[0, -3:, -3:, -1]

							# forward with prompt embeds
							snake_case__      :   Tuple      =    self.get_dummy_inputs(__UpperCamelCase			)
							snake_case__      :   List[Any]      =    3 * ['''this is a negative prompt''']
							snake_case__      :   List[Any]      =    3 * [inputs.pop('''prompt'''			)]

							(
							    (
							    snake_case__
							)  ,      (
							    snake_case__
							)  ,      (
							    snake_case__
							)  ,      (
							    snake_case__
							)  ,      
							)      :   List[Any]      =    sd_pipe.encode_prompt(__UpperCamelCase    ,			negative_prompt=__UpperCamelCase			)

							snake_case__      :   Dict      =    sd_pipe(
							    **__UpperCamelCase    ,			prompt_embeds=__UpperCamelCase    ,			negative_prompt_embeds=__UpperCamelCase    ,			pooled_prompt_embeds=__UpperCamelCase    ,			negative_pooled_prompt_embeds=__UpperCamelCase    ,			)
							snake_case__      :   Optional[Any]      =    output.images[0, -3:, -3:, -1]

							# make sure that it's equal
							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4



			def 				lowerCAmelCase (     self		:						Dict			)      ->      Dict:
							"""simple docstring"""





							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3			)







			def 				lowerCAmelCase (     self		:						int			)      ->      Dict:
							"""simple docstring"""





							super().test_inference_batch_single_identical(expected_max_diff=3e-3			)






@slow
@require_torch_gpu
class 			_SCREAMING_SNAKE_CASE				(unittest.TestCase      ):



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[str]="cpu"    ,			__UpperCamelCase		:						Tuple=torch.floataa    ,			__UpperCamelCase		:						List[str]=0			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Dict      =    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)
							snake_case__      :   int      =    np.random.RandomState(__UpperCamelCase			).standard_normal((1, 4, 64, 64)			)
							snake_case__      :   List[Any]      =    torch.from_numpy(__UpperCamelCase			).to(device=__UpperCamelCase    ,			dtype=__UpperCamelCase			)
							snake_case__      :   int      =    {
							    '''prompt''': '''a photograph of an astronaut riding a horse''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs







			def 				lowerCAmelCase (     self		:						int			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Dict      =    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''			)
							pipe.to(__UpperCamelCase			)
							pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							snake_case__      :   Optional[Any]      =    self.get_inputs(__UpperCamelCase			)
							snake_case__      :   Any      =    pipe(**__UpperCamelCase			).images
							snake_case__      :   Optional[int]      =    image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							snake_case__      :   Tuple      =    np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]			)
							assert np.abs(image_slice - expected_slice			).max() < 7e-3





"""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		)