



"""simple docstring"""


import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			[
    """yjernite/retribert-base-uncased""",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				RetriBertConfig
						__UpperCamelCase													=				None
						__UpperCamelCase													=				"""retribert"""






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:int   )							->     Union[str, Any]:
												if isinstance(lowercase_  ,    nn.Linear   ):
																		module.weight.data.normal_(mean=0.0  ,    std=self.config.initializer_range   )
																		if module.bias is not None:
																								module.bias.data.zero_()
												elif isinstance(lowercase_  ,    nn.Embedding   ):
																		module.weight.data.normal_(mean=0.0  ,    std=self.config.initializer_range   )
																		if module.padding_idx is not None:
																								module.weight.data[module.padding_idx].zero_()
												elif isinstance(lowercase_  ,    nn.LayerNorm   ):
																		module.bias.data.zero_()
																		module.weight.data.fill_(1.0   )


snake_case_								=			R"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""







@add_start_docstrings(
    """Bert Based model to embed queries or document for document retrieval."""      ,  SCREAMING_SNAKE_CASE_      ,  )
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:Tuple  ,    lowercase_					:RetriBertConfig   )							->     None:
												super().__init__(lowercase_   )
												UpperCAmelCase			 =					config.projection_dim

												UpperCAmelCase			 =					BertModel(lowercase_   )
												UpperCAmelCase			 =					None if config.share_encoders else BertModel(lowercase_   )
												UpperCAmelCase			 =					nn.Dropout(config.hidden_dropout_prob   )
												UpperCAmelCase			 =					nn.Linear(config.hidden_size  ,    config.projection_dim  ,    bias=lowercase_   )
												UpperCAmelCase			 =					nn.Linear(config.hidden_size  ,    config.projection_dim  ,    bias=lowercase_   )

												UpperCAmelCase			 =					nn.CrossEntropyLoss(reduction='mean'   )

												# Initialize weights and apply final processing
												self.post_init()






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:Any  ,    lowercase_					:Dict  ,    lowercase_					:List[Any]  ,    lowercase_					:Dict=-1  ,    )							->     Optional[Any]:
												# reproduces BERT forward pass with checkpointing
												if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
																		return sent_encoder(lowercase_  ,    attention_mask=lowercase_   )[1]
												else:
																		# prepare implicit variables
																		UpperCAmelCase			 =					input_ids.device
																		UpperCAmelCase			 =					input_ids.size()
																		UpperCAmelCase			 =					torch.zeros(lowercase_  ,    dtype=torch.long  ,    device=lowercase_   )
																		UpperCAmelCase			 =					[None] * sent_encoder.config.num_hidden_layers
																		UpperCAmelCase			 =					sent_encoder.get_extended_attention_mask(
																		    lowercase_  ,    lowercase_   )

																		# define function for checkpointing
																		def partial_encode(*lowercase_					:Optional[Any]   ):
																								UpperCAmelCase			 =					sent_encoder.encoder(
																								    inputs[0]  ,    attention_mask=inputs[1]  ,    head_mask=lowercase_  ,    )
																								UpperCAmelCase			 =					encoder_outputs[0]
																								UpperCAmelCase			 =					sent_encoder.pooler(lowercase_   )
																								return pooled_output

																		# run embedding layer on everything at once
																		UpperCAmelCase			 =					sent_encoder.embeddings(
																		    input_ids=lowercase_  ,    position_ids=lowercase_  ,    token_type_ids=lowercase_  ,    inputs_embeds=lowercase_   )
																		# run encoding and pooling on one mini-batch at a time
																		UpperCAmelCase			 =					[]
																		for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
																								UpperCAmelCase			 =					embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																								UpperCAmelCase			 =					extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																								UpperCAmelCase			 =					checkpoint.checkpoint(lowercase_  ,    lowercase_  ,    lowercase_   )
																								pooled_output_list.append(lowercase_   )
																		return torch.cat(lowercase_  ,    dim=0   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:Any  ,    lowercase_					:Tuple=None  ,    lowercase_					:Union[str, Any]=-1  ,    )							->     int:
												UpperCAmelCase			 =					self.embed_sentences_checkpointed(
												    lowercase_  ,    lowercase_  ,    self.bert_query  ,    lowercase_  ,    )
												return self.project_query(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Any  ,    lowercase_					:int=None  ,    lowercase_					:Any=-1  ,    )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.embed_sentences_checkpointed(
												    lowercase_  ,    lowercase_  ,    self.bert_query if self.bert_doc is None else self.bert_doc  ,    lowercase_  ,    )
												return self.project_doc(lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.LongTensor  ,    lowercase_					:Optional[torch.FloatTensor]  ,    lowercase_					:torch.LongTensor  ,    lowercase_					:Optional[torch.FloatTensor]  ,    lowercase_					:int = -1  ,    )							->     torch.FloatTensor:
												UpperCAmelCase			 =					input_ids_query.device
												UpperCAmelCase			 =					self.embed_questions(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					self.embed_answers(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					torch.mm(lowercase_  ,    a_reps.t()   )
												UpperCAmelCase			 =					self.ce_loss(lowercase_  ,    torch.arange(compare_scores.shape[1]   ).to(lowercase_   )   )
												UpperCAmelCase			 =					self.ce_loss(compare_scores.t()  ,    torch.arange(compare_scores.shape[0]   ).to(lowercase_   )   )
												UpperCAmelCase			 =					(loss_qa + loss_aq) / 2
												return loss












'''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_		)




