





'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase__      :   str      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   List[Any]      	=			{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	'''m2m_100'''
					SCREAMING_SNAKE_CASE							=	['''past_key_values''']
					SCREAMING_SNAKE_CASE							=	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
					def __init__(   self       :      str		,	UpperCAmelCase_       :      Tuple=128_112		,	UpperCAmelCase_       :      Optional[Any]=1_024		,	UpperCAmelCase_       :      Union[str, Any]=12		,	UpperCAmelCase_       :      Optional[int]=4_096		,	UpperCAmelCase_       :      List[str]=16		,	UpperCAmelCase_       :      List[Any]=12		,	UpperCAmelCase_       :      str=4_096		,	UpperCAmelCase_       :      Tuple=16		,	UpperCAmelCase_       :      List[str]=0.05		,	UpperCAmelCase_       :      Any=0.05		,	UpperCAmelCase_       :      Tuple=True		,	UpperCAmelCase_       :      Any=True		,	UpperCAmelCase_       :      List[str]="relu"		,	UpperCAmelCase_       :      Any=1_024		,	UpperCAmelCase_       :      Any=0.1		,	UpperCAmelCase_       :      Dict=0.1		,	UpperCAmelCase_       :      int=0.0		,	UpperCAmelCase_       :      int=0.02		,	UpperCAmelCase_       :      Tuple=2		,	UpperCAmelCase_       :      int=True		,	UpperCAmelCase_       :      int=1		,	UpperCAmelCase_       :      Union[str, Any]=0		,	UpperCAmelCase_       :      int=2		,	**UpperCAmelCase_       :      str		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     vocab_size
							__UpperCAmelCase	:							int     =     max_position_embeddings
							__UpperCAmelCase	:							Optional[int]     =     d_model
							__UpperCAmelCase	:							Any     =     encoder_ffn_dim
							__UpperCAmelCase	:							List[str]     =     encoder_layers
							__UpperCAmelCase	:							List[Any]     =     encoder_attention_heads
							__UpperCAmelCase	:							Optional[int]     =     decoder_ffn_dim
							__UpperCAmelCase	:							int     =     decoder_layers
							__UpperCAmelCase	:							Any     =     decoder_attention_heads
							__UpperCAmelCase	:							Any     =     dropout
							__UpperCAmelCase	:							Tuple     =     attention_dropout
							__UpperCAmelCase	:							Any     =     activation_dropout
							__UpperCAmelCase	:							Optional[int]     =     activation_function
							__UpperCAmelCase	:							List[Any]     =     init_std
							__UpperCAmelCase	:							List[Any]     =     encoder_layerdrop
							__UpperCAmelCase	:							Any     =     decoder_layerdrop
							__UpperCAmelCase	:							List[Any]     =     use_cache
							__UpperCAmelCase	:							List[Any]     =     encoder_layers
							__UpperCAmelCase	:							Union[str, Any]     =     scale_embedding  # scale factor will be sqrt(d_model) if True

							super().__init__(
							    pad_token_id=UpperCAmelCase_		,	bos_token_id=UpperCAmelCase_		,	eos_token_id=UpperCAmelCase_		,	is_encoder_decoder=UpperCAmelCase_		,	decoder_start_token_id=UpperCAmelCase_		,	**UpperCAmelCase_		,	)






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					@property
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
							        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
							    ]		)

							if self.use_past:
									__UpperCAmelCase	:							Tuple     =     {0: "batch"}
									__UpperCAmelCase	:							Optional[Any]     =     {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
									__UpperCAmelCase	:							int     =     {0: "batch", 1: "decoder_sequence"}
									__UpperCAmelCase	:							Optional[int]     =     {0: "batch", 1: "decoder_sequence"}

							if self.use_past:
									self.fill_with_past_key_values_(UpperCAmelCase_		,	direction="inputs"		)
							return common_inputs
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      PreTrainedTokenizer		,	UpperCAmelCase_       :      int = -1		,	UpperCAmelCase_       :      int = -1		,	UpperCAmelCase_       :      bool = False		,	UpperCAmelCase_       :      Optional[TensorType] = None		,	):







							"""simple docstring"""
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							__UpperCAmelCase	:							Union[str, Any]     =     compute_effective_axis_dimension(
							    UpperCAmelCase_		,	fixed_dimension=OnnxConfig.default_fixed_batch		,	num_token_to_add=0		)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							__UpperCAmelCase	:							Optional[int]     =     tokenizer.num_special_tokens_to_add(UpperCAmelCase_		)
							__UpperCAmelCase	:							int     =     compute_effective_axis_dimension(
							    UpperCAmelCase_		,	fixed_dimension=OnnxConfig.default_fixed_sequence		,	num_token_to_add=UpperCAmelCase_		)

							# Generate dummy inputs according to compute batch and sequence
							__UpperCAmelCase	:							Union[str, Any]     =     [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
							__UpperCAmelCase	:							Any     =     dict(tokenizer(UpperCAmelCase_		,	return_tensors=UpperCAmelCase_		)		)
							return common_inputs
					def 		lowerCamelCase_   (   self       :      str		,	UpperCAmelCase_       :      PreTrainedTokenizer		,	UpperCAmelCase_       :      int = -1		,	UpperCAmelCase_       :      int = -1		,	UpperCAmelCase_       :      bool = False		,	UpperCAmelCase_       :      Optional[TensorType] = None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		)

							# Generate decoder inputs
							__UpperCAmelCase	:							Dict     =     seq_length if not self.use_past else 1
							__UpperCAmelCase	:							List[str]     =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							Optional[int]     =     {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
							__UpperCAmelCase	:							List[Any]     =     dict(**UpperCAmelCase_		,	**UpperCAmelCase_		)

							if self.use_past:
									if not is_torch_available():
											raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
									else:
											import torch
									__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[Any]     =     common_inputs["input_ids"].shape
									__UpperCAmelCase	:							List[Any]     =     common_inputs["decoder_input_ids"].shape[1]
									__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     self.num_attention_heads
									__UpperCAmelCase	:							Union[str, Any]     =     (
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									__UpperCAmelCase	:							Tuple     =     decoder_seq_length + 3
									__UpperCAmelCase	:							Optional[int]     =     (
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									__UpperCAmelCase	:							Optional[int]     =     torch.cat(
									    [common_inputs["decoder_attention_mask"], torch.ones(UpperCAmelCase_		,	UpperCAmelCase_		)]		,	dim=1		)

									__UpperCAmelCase	:							List[Any]     =     []
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									__UpperCAmelCase  ,			__UpperCAmelCase	:							List[Any]     =     self.num_layers
									__UpperCAmelCase	:							List[str]     =     min(UpperCAmelCase_		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     max(UpperCAmelCase_		,	UpperCAmelCase_		) - min_num_layers
									__UpperCAmelCase	:							Tuple     =     "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

									for _ in range(UpperCAmelCase_		):
											common_inputs["past_key_values"].append(
											    (
											        torch.zeros(UpperCAmelCase_		),
											        torch.zeros(UpperCAmelCase_		),
											        torch.zeros(UpperCAmelCase_		),
											        torch.zeros(UpperCAmelCase_		),
											    )		)
									# TODO: test this.
									__UpperCAmelCase	:							Any     =     encoder_shape if remaining_side_name == "encoder" else decoder_shape
									for _ in range(UpperCAmelCase_		,	UpperCAmelCase_		):
											common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase_		), torch.zeros(UpperCAmelCase_		))		)
							return common_inputs






					SCREAMING_SNAKE_CASE							=	_generate_dummy_inputs_for_default_and_seqaseq_lm











'''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

lowerCAmelCase__      :   Tuple      	=			logging.getLogger(__name__)


@require_tensorboard
class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Tuple     =     "test_project_with_config"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							str     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									__UpperCAmelCase	:							Union[str, Any]     =     {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
									accelerator.init_trackers(UpperCAmelCase_		,	UpperCAmelCase_		)
									accelerator.end_training()
									for child in Path(f"{dirpath}/{project_name}"		).glob("*/**"		):
											__UpperCAmelCase	:							Optional[Any]     =     list(filter(lambda UpperCAmelCase_		: x.is_file()		,	child.iterdir()		)		)[0]
									self.assertNotEqual(str(UpperCAmelCase_		)		,	""		)
					def 		lowerCamelCase_   (   self       :      List[Any]		):







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







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							List[Any]     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									accelerator.init_trackers(UpperCAmelCase_		)
									__UpperCAmelCase	:							int     =     {"tensor": torch.tensor(1		)}
									accelerator.log(UpperCAmelCase_		,	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
									__UpperCAmelCase	:							Dict     =     list(filter(lambda UpperCAmelCase_		: 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(UpperCAmelCase_		,	"rb"		) as f:
											__UpperCAmelCase	:							Dict     =     f.read()
									__UpperCAmelCase	:							Dict     =     False
									while data:
											__UpperCAmelCase	:							str     =     struct.unpack("Q"		,	data[:8]		)

											__UpperCAmelCase	:							List[str]     =     data[12 : 12 + int(header[0]		)]  # 8+4
											__UpperCAmelCase	:							Tuple     =     data[12 + int(header[0]		) + 4 :]
											__UpperCAmelCase	:							Optional[int]     =     event_pba.Event()

											event.ParseFromString(UpperCAmelCase_		)
											if event.HasField("summary"		):
													for value in event.summary.value:
															if value.simple_value == 1.0 and value.tag == "tensor":
																	__UpperCAmelCase	:							List[str]     =     True
									self.assertTrue(UpperCAmelCase_		,	"Converted tensor was not found in the log file!"		)
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							with self.assertRaisesRegex(UpperCAmelCase_		,	"Logging with `tensorboard` requires a `logging_dir`"		):
									__UpperCAmelCase	:							Union[str, Any]     =     Accelerator(log_with="tensorboard"		)
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							str     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)






					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     ProjectConfiguration(total_limit=30		)
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							Optional[Any]     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		,	project_config=UpperCAmelCase_		)






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







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""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 		lowerCamelCase_   (   UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool = True		):







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






					@skip
					def 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     "test_project_with_config"
							__UpperCAmelCase	:							List[Any]     =     Accelerator(log_with="wandb"		)
							__UpperCAmelCase	:							Any     =     {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
							__UpperCAmelCase	:							Any     =     {"wandb": {"tags": ["my_tag"]}}
							accelerator.init_trackers(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							int     =     {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
							accelerator.log(UpperCAmelCase_		,	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":
											__UpperCAmelCase	:							List[str]     =     subprocess.check_output(
											    ["wandb", "sync", "--view", "--verbose", str(UpperCAmelCase_		)]		,	env=os.environ.copy()		).decode("utf8"		,	"ignore"		)
											break

        # Check HPS through careful parsing and cleaning
							__UpperCAmelCase	:							Dict     =     self.parse_log(UpperCAmelCase_		,	"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
							__UpperCAmelCase	:							Union[str, Any]     =     self.parse_log(UpperCAmelCase_		,	"run"		,	UpperCAmelCase_		)
							self.assertEqual(logged_items["tags"]		,	"my_tag"		)

							# Actual logging
							__UpperCAmelCase	:							Optional[Any]     =     self.parse_log(UpperCAmelCase_		,	"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,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							str     =     run_name
		__UpperCAmelCase	:							Optional[int]     =     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      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					@staticmethod
					def 		lowerCamelCase_   (   UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool = False		):







							"""simple docstring"""
							for log in log_list:
									__UpperCAmelCase	:							List[Any]     =     json.loads(UpperCAmelCase_		)["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		):







							"""simple docstring"""
							with tempfile.TemporaryDirectory() as d:
									__UpperCAmelCase	:							List[Any]     =     CometMLTracker("test_project_with_config"		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[int]     =     Accelerator(log_with=UpperCAmelCase_		)
									__UpperCAmelCase	:							Tuple     =     {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
									accelerator.init_trackers(UpperCAmelCase_		,	UpperCAmelCase_		)
									accelerator.end_training()
									__UpperCAmelCase	:							List[Any]     =     os.listdir(UpperCAmelCase_		)[0]  # Comet is nice, it's just a zip file here
									# We parse the raw logs
									__UpperCAmelCase	:							List[Any]     =     os.path.join(UpperCAmelCase_		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							List[str]     =     zipfile.ZipFile(UpperCAmelCase_		,	"r"		)
									__UpperCAmelCase	:							Optional[int]     =     archive.open("messages.json"		).read().decode("utf-8"		)
							__UpperCAmelCase	:							Optional[Any]     =     log.split("\n"		)[:-1]
							self.assertEqual(self.get_value_from_key(UpperCAmelCase_		,	"num_iterations"		,	UpperCAmelCase_		)		,	12		)
							self.assertEqual(self.get_value_from_key(UpperCAmelCase_		,	"learning_rate"		,	UpperCAmelCase_		)		,	0.01		)
							self.assertEqual(self.get_value_from_key(UpperCAmelCase_		,	"some_boolean"		,	UpperCAmelCase_		)		,	UpperCAmelCase_		)
							self.assertEqual(self.get_value_from_key(UpperCAmelCase_		,	"some_string"		,	UpperCAmelCase_		)		,	"some_value"		)






					def 		lowerCamelCase_   (   self       :      Dict		):







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






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	[
					    '''total_loss''',
					    '''iteration''',
					    '''my_text''',
					    '''learning_rate''',
					    '''num_iterations''',
					    '''some_boolean''',
					    '''some_string''',
					]

					SCREAMING_SNAKE_CASE							=	'''my_custom_tracker'''
					SCREAMING_SNAKE_CASE							=	False
					def __init__(   self       :      List[Any]		,	UpperCAmelCase_       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     open(f"{dir}/log.csv"		,	"w+"		)
							__UpperCAmelCase	:							Optional[int]     =     csv.DictWriter(self.f		,	fieldnames=self._col_names		)
							self.writer.writeheader()
					@property
					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							return self.writer
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      dict		):







							"""simple docstring"""
							logger.info("Call init"		)
							self.writer.writerow(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      dict		,	UpperCAmelCase_       :      Optional[int]		):







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






					def 		lowerCamelCase_   (   self       :      str		):







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






class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      Tuple		):







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






					def 		lowerCamelCase_   (   self       :      Dict		):







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




