






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


lowerCAmelCase_     =			logging.get_logger(__name__)
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = ["pixel_values"]
		def __init__(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,_SCREAMING_SNAKE_CASE		:							Dict[str, int] = None    ,_SCREAMING_SNAKE_CASE		:							PILImageResampling = PIL.Image.NEAREST    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,_SCREAMING_SNAKE_CASE		:							Dict[str, int] = None    ,_SCREAMING_SNAKE_CASE		:							Union[int, float] = 1 / 2_5_5    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[float, List[float]]] = None    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[float, List[float]]] = None    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,**_SCREAMING_SNAKE_CASE		:							Union[str, Any]    ,)     -> None:


						'''simple docstring'''
						super().__init__(**_SCREAMING_SNAKE_CASE		)
						A									=   size if size is not None else {'height': 3_4_6, 'width': 3_4_6}
						A									=   get_size_dict(_SCREAMING_SNAKE_CASE		)
						A									=   crop_size if crop_size is not None else {'height': 2_8_9, 'width': 2_8_9}
						A									=   get_size_dict(_SCREAMING_SNAKE_CASE    ,param_name='crop_size'		)

						A									=   do_resize
						A									=   size
						A									=   resample
						A									=   do_center_crop
						A									=   crop_size
						A									=   do_rescale
						A									=   rescale_factor
						A									=   rescale_offset
						A									=   do_normalize
						A									=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A									=   image_std if image_std is not None else IMAGENET_STANDARD_STD
						A									=   include_top
		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							np.ndarray    ,_SCREAMING_SNAKE_CASE		:							Dict[str, int]    ,_SCREAMING_SNAKE_CASE		:							PILImageResampling = PIL.Image.NEAREST    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[str, ChannelDimension]] = None    ,**_SCREAMING_SNAKE_CASE		:							int    ,)     -> np.ndarray:


						'''simple docstring'''
						A									=   get_size_dict(_SCREAMING_SNAKE_CASE		)
						if "height" not in size or "width" not in size:
										raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'		)
						return resize(
						    _SCREAMING_SNAKE_CASE    ,size=(size['height'], size['width'])    ,resample=_SCREAMING_SNAKE_CASE    ,data_format=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							str    ,_SCREAMING_SNAKE_CASE		:							np.ndarray    ,_SCREAMING_SNAKE_CASE		:							Dict[str, int]    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[str, ChannelDimension]] = None    ,**_SCREAMING_SNAKE_CASE		:							Any    ,)     -> np.ndarray:


						'''simple docstring'''
						A									=   get_size_dict(_SCREAMING_SNAKE_CASE		)
						if "height" not in size or "width" not in size:
										raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'		)
						return center_crop(_SCREAMING_SNAKE_CASE    ,size=(size['height'], size['width'])    ,data_format=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							np.ndarray    ,_SCREAMING_SNAKE_CASE		:							Union[int, float]    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[str, ChannelDimension]] = None    ,**_SCREAMING_SNAKE_CASE		:							List[Any]    ,)     -> List[Any]:


						'''simple docstring'''
						if offset:
										A									=   (image - 127.5) * scale
										if data_format is not None:
														A									=   to_channel_dimension_format(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										A									=   rescaled_image.astype(np.floataa		)
						else:
										A									=   rescale(_SCREAMING_SNAKE_CASE    ,scale=_SCREAMING_SNAKE_CASE    ,data_format=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
						return rescaled_image
		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							np.ndarray    ,_SCREAMING_SNAKE_CASE		:							Union[float, List[float]]    ,_SCREAMING_SNAKE_CASE		:							Union[float, List[float]]    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[str, ChannelDimension]] = None    ,**_SCREAMING_SNAKE_CASE		:							str    ,)     -> np.ndarray:


						'''simple docstring'''
						return normalize(_SCREAMING_SNAKE_CASE    ,mean=_SCREAMING_SNAKE_CASE    ,std=_SCREAMING_SNAKE_CASE    ,data_format=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							ImageInput    ,_SCREAMING_SNAKE_CASE		:							bool = None    ,_SCREAMING_SNAKE_CASE		:							Dict[str, int] = None    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							bool = None    ,_SCREAMING_SNAKE_CASE		:							Dict[str, int] = None    ,_SCREAMING_SNAKE_CASE		:							bool = None    ,_SCREAMING_SNAKE_CASE		:							float = None    ,_SCREAMING_SNAKE_CASE		:							bool = None    ,_SCREAMING_SNAKE_CASE		:							bool = None    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[float, List[float]]] = None    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[float, List[float]]] = None    ,_SCREAMING_SNAKE_CASE		:							bool = None    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[str, TensorType]] = None    ,_SCREAMING_SNAKE_CASE		:							ChannelDimension = ChannelDimension.FIRST    ,**_SCREAMING_SNAKE_CASE		:							List[Any]    ,)     -> PIL.Image.Image:


						'''simple docstring'''
						A									=   do_resize if do_resize is not None else self.do_resize
						A									=   resample if resample is not None else self.resample
						A									=   do_center_crop if do_center_crop is not None else self.do_center_crop
						A									=   do_rescale if do_rescale is not None else self.do_rescale
						A									=   rescale_factor if rescale_factor is not None else self.rescale_factor
						A									=   rescale_offset if rescale_offset is not None else self.rescale_offset
						A									=   do_normalize if do_normalize is not None else self.do_normalize
						A									=   image_mean if image_mean is not None else self.image_mean
						A									=   image_std if image_std is not None else self.image_std
						A									=   include_top if include_top is not None else self.include_top

						A									=   size if size is not None else self.size
						A									=   get_size_dict(_SCREAMING_SNAKE_CASE		)
						A									=   crop_size if crop_size is not None else self.crop_size
						A									=   get_size_dict(_SCREAMING_SNAKE_CASE    ,param_name='crop_size'		)

						A									=   make_list_of_images(_SCREAMING_SNAKE_CASE		)

						if not valid_images(_SCREAMING_SNAKE_CASE		):
										raise ValueError(
										    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
										    'torch.Tensor, tf.Tensor or jax.ndarray.'		)

						if do_resize and size is None or resample is None:
										raise ValueError('Size and resample must be specified if do_resize is True.'		)

						if do_center_crop and crop_size is None:
										raise ValueError('Crop size must be specified if do_center_crop is True.'		)

						if do_rescale and rescale_factor is None:
										raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

						if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('Image mean and std must be specified if do_normalize is True.'		)

						# All transformations expect numpy arrays.
						A									=   [to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

						if do_resize:
										A									=   [self.resize(image=_SCREAMING_SNAKE_CASE    ,size=_SCREAMING_SNAKE_CASE    ,resample=_SCREAMING_SNAKE_CASE		) for image in images]

						if do_center_crop:
										A									=   [self.center_crop(image=_SCREAMING_SNAKE_CASE    ,size=_SCREAMING_SNAKE_CASE		) for image in images]

						if do_rescale:
										A									=   [self.rescale(image=_SCREAMING_SNAKE_CASE    ,scale=_SCREAMING_SNAKE_CASE    ,offset=_SCREAMING_SNAKE_CASE		) for image in images]

						if do_normalize:
										A									=   [self.normalize(image=_SCREAMING_SNAKE_CASE    ,mean=_SCREAMING_SNAKE_CASE    ,std=_SCREAMING_SNAKE_CASE		) for image in images]

						if include_top:
										A									=   [self.normalize(image=_SCREAMING_SNAKE_CASE    ,mean=[0, 0, 0]    ,std=_SCREAMING_SNAKE_CASE		) for image in images]

						A									=   [to_channel_dimension_format(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) for image in images]

						A									=   {'pixel_values': images}
						return BatchFeature(data=_SCREAMING_SNAKE_CASE    ,tensor_type=_SCREAMING_SNAKE_CASE		)













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		)





