




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import OwlViTImageProcessor

class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def __init__(  self       :			str			,							snake_case       :			Any			,							snake_case       :			Union[str, Any]=7			,							snake_case       :			Optional[Any]=3			,							snake_case       :			str=18			,							snake_case       :			Union[str, Any]=30			,							snake_case       :			List[Any]=400			,							snake_case       :			List[Any]=True			,							snake_case       :			int=None			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			str=None			,							snake_case       :			Any=True			,							snake_case       :			Optional[Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,							snake_case       :			Optional[int]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,							snake_case       :			List[Any]=True			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    num_channels
          __UpperCamelCase			=    image_size
          __UpperCamelCase			=    min_resolution
          __UpperCamelCase			=    max_resolution
          __UpperCamelCase			=    do_resize
          __UpperCamelCase			=    size if size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_center_crop
          __UpperCamelCase			=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_normalize
          __UpperCamelCase			=    image_mean
          __UpperCamelCase			=    image_std
          __UpperCamelCase			=    do_convert_rgb







      def    snake_case						(  self       :			Union[str, Any]					):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }







@require_torch
@require_vision
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			List[str]     =       OwlViTImageProcessor if is_vision_available() else None







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    OwlViTImageProcessingTester(self					)







      @property
      def    snake_case						(  self       :			Optional[int]					):
          return self.image_processor_tester.prepare_image_processor_dict()







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          self.assertTrue(hasattr(snake_case			,							'''do_resize'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''size'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_center_crop'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''center_crop'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_normalize'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''image_mean'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''image_std'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_convert_rgb'''					)					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict					)
          self.assertEqual(image_processor.size			,							{'''height''': 18, '''width''': 18}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 18, '''width''': 18}					)

          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict			,							size=42			,							crop_size=84					)
          self.assertEqual(image_processor.size			,							{'''height''': 42, '''width''': 42}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 84, '''width''': 84}					)







      def    snake_case						(  self       :			Tuple					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case					)

          for image in image_inputs:
              self.assertIsInstance(snake_case			,							Image.Image					)

          # Test not batched input
          __UpperCamelCase			=    image_processing(image_inputs[0]			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)







      def    snake_case						(  self       :			List[str]					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random numpy tensors
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case			,							numpify=snake_case					)
          for image in image_inputs:
              self.assertIsInstance(snake_case			,							np.ndarray					)

          # Test not batched input
          __UpperCamelCase			=    image_processing(image_inputs[0]			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)







      def    snake_case						(  self       :			int					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case			,							torchify=snake_case					)
          for image in image_inputs:
              self.assertIsInstance(snake_case			,							torch.Tensor					)

          # Test not batched input
          __UpperCamelCase			=    image_processing(image_inputs[0]			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)














import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
					from comet_ml import OfflineExperiment

if is_tensorboard_available():
					import struct

					import tensorboard.compat.proto.event_pba as event_pba

lowerCAmelCase_     =			logging.getLogger(__name__)
@require_tensorboard
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def    A(	self		:							Dict		)     -> int:


						'''simple docstring'''
						A									=   'test_project_with_config'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										A									=   {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										accelerator.end_training()
										for child in Path(f'{dirpath}/{project_name}'		).glob('*/**'		):
														A									=   list(filter(lambda _SCREAMING_SNAKE_CASE		: x.is_file()    ,child.iterdir()		)		)[0]
										self.assertNotEqual(str(_SCREAMING_SNAKE_CASE		)    ,''		)
		def    A(	self		:							Any		)     -> Dict:


						'''simple docstring'''
						A									=   'test_project_with_log'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE		)
										A									=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
										accelerator.log(_SCREAMING_SNAKE_CASE    ,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									=   list(filter(lambda _SCREAMING_SNAKE_CASE		: x.is_file()    ,Path(f'{dirpath}/{project_name}'		).iterdir()		)		)[0]
										self.assertNotEqual(str(_SCREAMING_SNAKE_CASE		)    ,''		)
		def    A(	self		:							int		)     -> Any:


						'''simple docstring'''
						A									=   'test_project_with_log'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE		)
										A									=   {'tensor': torch.tensor(1		)}
										accelerator.log(_SCREAMING_SNAKE_CASE    ,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									=   list(filter(lambda _SCREAMING_SNAKE_CASE		: 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(_SCREAMING_SNAKE_CASE    ,'rb'		) as f:
														A									=   f.read()
										A									=   False
										while data:
														A									=   struct.unpack('Q'    ,data[:8]		)

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

														event.ParseFromString(_SCREAMING_SNAKE_CASE		)
														if event.HasField('summary'		):
																		for value in event.summary.value:
																						if value.simple_value == 1.0 and value.tag == "tensor":
																										A									=   True
										self.assertTrue(_SCREAMING_SNAKE_CASE    ,'Converted tensor was not found in the log file!'		)
		def    A(	self		:							List[str]		)     -> Union[str, Any]:


						'''simple docstring'''
						with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE    ,'Logging with `tensorboard` requires a `logging_dir`'		):
										A									=   Accelerator(log_with='tensorboard'		)
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							Optional[Any]		)     -> Tuple:


						'''simple docstring'''
						A									=   ProjectConfiguration(total_limit=3_0		)
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE    ,project_config=_SCREAMING_SNAKE_CASE		)


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







		"""simple docstring"""



		def    A(	self		:							Dict		)     -> Optional[int]:


						'''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    A(	_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							bool = True		)     -> str:


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







		@skip
		def    A(	self		:							List[Any]		)     -> Optional[Any]:


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

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

						# Actual logging
						A									=   self.parse_log(_SCREAMING_SNAKE_CASE    ,'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   snake_case		(				self						:							Union[str, Any],    UpperCAmelCase						:							str,    UpperCAmelCase						:							str ):
				A									=   run_name
				A									=   OfflineExperiment(project_name=UpperCAmelCase,    offline_directory=UpperCAmelCase )
				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(snake_case__      ,							"__init__"      ,							snake_case__			)
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		@staticmethod
		def    A(	_SCREAMING_SNAKE_CASE		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							bool = False		)     -> List[Any]:


						'''simple docstring'''
						for log in log_list:
										A									=   json.loads(_SCREAMING_SNAKE_CASE		)['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    A(	self		:							int		)     -> int:


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







		def    A(	self		:							Optional[int]		)     -> Optional[int]:


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


class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		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]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> List[str]:


						'''simple docstring'''
						A									=   open(f'{dir}/log.csv'    ,'w+'		)
						A									=   csv.DictWriter(self.f    ,fieldnames=self._col_names		)
						self.writer.writeheader()
		@property
		def    A(	self		:							int		)     -> Union[str, Any]:


						'''simple docstring'''
						return self.writer
		def    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							dict		)     -> Any:


						'''simple docstring'''
						logger.info('Call init'		)
						self.writer.writerow(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							dict    ,_SCREAMING_SNAKE_CASE		:							Optional[int]		)     -> List[Any]:


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







		def    A(	self		:							Union[str, Any]		)     -> Tuple:


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


class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def    A(	self		:							Union[str, Any]		)     -> int:


						'''simple docstring'''
						with tempfile.TemporaryDirectory() as d:
										A									=   MyCustomTracker(_SCREAMING_SNAKE_CASE		)
										A									=   Accelerator(log_with=_SCREAMING_SNAKE_CASE		)
										A									=   {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
										accelerator.init_trackers('Some name'    ,_SCREAMING_SNAKE_CASE		)
										accelerator.end_training()
										with open(f'{d}/log.csv'    ,'r'		) as f:
														A									=   csv.DictReader(_SCREAMING_SNAKE_CASE		)
														A									=   next(_SCREAMING_SNAKE_CASE		)
														A									=   {
														    'total_loss': '',
														    'iteration': '',
														    'my_text': '',
														    'learning_rate': '0.01',
														    'num_iterations': '12',
														    'some_boolean': 'False',
														    'some_string': 'some_value',
														}
														self.assertDictEqual(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							List[Any]		)     -> Tuple:


						'''simple docstring'''
						with tempfile.TemporaryDirectory() as d:
										A									=   MyCustomTracker(_SCREAMING_SNAKE_CASE		)
										A									=   Accelerator(log_with=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers('Some name'		)
										A									=   {'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
										accelerator.log(_SCREAMING_SNAKE_CASE    ,step=0		)
										accelerator.end_training()
										with open(f'{d}/log.csv'    ,'r'		) as f:
														A									=   csv.DictReader(_SCREAMING_SNAKE_CASE		)
														A									=   next(_SCREAMING_SNAKE_CASE		)
														A									=   {
														    'total_loss': '0.1',
														    'iteration': '1',
														    'my_text': 'some_value',
														    'learning_rate': '',
														    'num_iterations': '',
														    'some_boolean': '',
														    'some_string': '',
														}
														self.assertDictEqual(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)





