





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

import torch

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


if is_comet_ml_available():
			from comet_ml import OfflineExperiment

if is_tensorboard_available():
			import struct

			import tensorboard.compat.proto.event_pba as event_pba

__UpperCamelCase:  Optional[Any]   =   logging.getLogger(__name__)


@require_tensorboard
class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





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

	def       snake_case__(	self:	Union[str, Any] ):
								lowercase__				:			Union[str, Any]            =		'test_project_with_log'
								with tempfile.TemporaryDirectory() as dirpath:
															lowercase__				:			Optional[Any]            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_ )
															accelerator.init_trackers(lowerCamelCase_ )
															lowercase__				:			Tuple            =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
															accelerator.log(lowerCamelCase_,				step=0 )
															accelerator.end_training()
															# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
															# Names are randomly generated each time
															lowercase__				:			Union[str, Any]            =		list(filter(lambda lowerCamelCase_ : x.is_file(),				Path(F"""{dirpath}/{project_name}""" ).iterdir() ) )[0]
															self.assertNotEqual(str(lowerCamelCase_ ),				'' )

	def       snake_case__(	self:	Any ):
								lowercase__				:			str            =		'test_project_with_log'
								with tempfile.TemporaryDirectory() as dirpath:
															lowercase__				:			Tuple            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_ )
															accelerator.init_trackers(lowerCamelCase_ )
															lowercase__				:			List[str]            =		{'tensor': torch.tensor(1 )}
															accelerator.log(lowerCamelCase_,				step=0 )
															accelerator.end_training()
															# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
															# Names are randomly generated each time
															lowercase__				:			Optional[Any]            =		list(filter(lambda lowerCamelCase_ : 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(lowerCamelCase_,				'rb' ) as f:
																						lowercase__				:			Union[str, Any]            =		f.read()
															lowercase__				:			str            =		False
															while data:
																						lowercase__				:			List[str]            =		struct.unpack('Q',				data[:8] )

																						lowercase__				:			Union[str, Any]            =		data[12 : 12 + int(header[0] )]  # 8+4
																						lowercase__				:			Tuple            =		data[12 + int(header[0] ) + 4 :]
																						lowercase__				:			List[str]            =		event_pba.Event()

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

	def       snake_case__(	self:	List[Any] ):
								with self.assertRaisesRegex(lowerCamelCase_,				'Logging with `tensorboard` requires a `logging_dir`' ):
															lowercase__				:			Optional[int]            =		Accelerator(log_with='tensorboard' )
								with tempfile.TemporaryDirectory() as dirpath:
															lowercase__				:			Dict            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_ )





	def       snake_case__(	self:	Union[str, Any] ):
								lowercase__				:			Optional[Any]            =		ProjectConfiguration(total_limit=30 )
								with tempfile.TemporaryDirectory() as dirpath:
															lowercase__				:			List[str]            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_,				project_config=lowerCamelCase_ )


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


	'''simple docstring'''





	def       snake_case__(	self:	Union[str, Any] ):
								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       snake_case__(	lowerCamelCase_:	str,				lowerCamelCase_:	str,				lowerCamelCase_:	bool = True ):
								lowercase__				:			List[str]            =		rF"""{section} ([\S\s]*?)\n\n"""
								if record:
															lowercase__				:			List[Any]            =		rF"""Record: {pattern}"""
								lowercase__				:			int            =		re.findall(lowerCamelCase_,				lowerCamelCase_ )[0]
								# A config
								if section == "config" or section == "history":
															lowercase__				:			Union[str, Any]            =		re.findall(r'"([a-zA-Z0-9_.,]+)',				lowerCamelCase_ )
															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]+)"',				lowerCamelCase_ ) )





	@skip
	def       snake_case__(	self:	List[Any] ):
								lowercase__				:			str            =		'test_project_with_config'
								lowercase__				:			str            =		Accelerator(log_with='wandb' )
								lowercase__				:			Optional[int]            =		{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
								lowercase__				:			Optional[int]            =		{'wandb': {'tags': ['my_tag']}}
								accelerator.init_trackers(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )
								lowercase__				:			List[Any]            =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
								accelerator.log(lowerCamelCase_,				step=0 )
								accelerator.end_training()
								# The latest offline log is stored at wandb/latest-run/*.wandb
								for child in Path(F"""{self.tmpdir}/wandb/latest-run""" ).glob('*' ):
															if child.is_file() and child.suffix == ".wandb":
																						lowercase__				:			List[str]            =		subprocess.check_output(
																						    ['wandb', 'sync', '--view', '--verbose', str(lowerCamelCase_ )],				env=os.environ.copy() ).decode('utf8',				'ignore' )
																						break

        # Check HPS through careful parsing and cleaning
								lowercase__				:			List[Any]            =		self.parse_log(lowerCamelCase_,				'config' )
								self.assertEqual(logged_items['num_iterations'],				'12' )
								self.assertEqual(logged_items['learning_rate'],				'0.01' )
								self.assertEqual(logged_items['some_boolean'],				'false' )
								self.assertEqual(logged_items['some_string'],				'some_value' )
								self.assertEqual(logged_items['some_string'],				'some_value' )

								# Run tags
								lowercase__				:			Tuple            =		self.parse_log(lowerCamelCase_,				'run',				lowerCamelCase_ )
								self.assertEqual(logged_items['tags'],				'my_tag' )

								# Actual logging
								lowercase__				:			List[Any]            =		self.parse_log(lowerCamelCase_,				'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 			SCREAMING_SNAKE_CASE__     (					self    :   str      ,						_lowercase    :   str      ,						_lowercase    :   str )      ->       Any:




							'''simple docstring'''
							lowercase__				:			str            =		run_name
							lowercase__				:			str            =		OfflineExperiment(project_name=_lowercase      ,						offline_directory=_lowercase )
							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(_UpperCamelCase      ,						"__init__"      ,						_UpperCamelCase			)
class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	@staticmethod
	def       snake_case__(	lowerCamelCase_:	int,				lowerCamelCase_:	str,				lowerCamelCase_:	bool = False ):
								for log in log_list:
															lowercase__				:			Tuple            =		json.loads(lowerCamelCase_ )['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       snake_case__(	self:	Optional[Any] ):
								with tempfile.TemporaryDirectory() as d:
															lowercase__				:			Any            =		CometMLTracker('test_project_with_config',				lowerCamelCase_ )
															lowercase__				:			str            =		Accelerator(log_with=lowerCamelCase_ )
															lowercase__				:			str            =		{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
															accelerator.init_trackers(lowerCamelCase_,				lowerCamelCase_ )
															accelerator.end_training()
															lowercase__				:			List[Any]            =		os.listdir(lowerCamelCase_ )[0]  # Comet is nice, it's just a zip file here
															# We parse the raw logs
															lowercase__				:			int            =		os.path.join(lowerCamelCase_,				lowerCamelCase_ )
															lowercase__				:			List[str]            =		zipfile.ZipFile(lowerCamelCase_,				'r' )
															lowercase__				:			List[Any]            =		archive.open('messages.json' ).read().decode('utf-8' )
								lowercase__				:			Optional[int]            =		log.split('\n' )[:-1]
								self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'num_iterations',				lowerCamelCase_ ),				12 )
								self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'learning_rate',				lowerCamelCase_ ),				0.0_1 )
								self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'some_boolean',				lowerCamelCase_ ),				lowerCamelCase_ )
								self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'some_string',				lowerCamelCase_ ),				'some_value' )





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


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							[
	    "total_loss",
	    "iteration",
	    "my_text",
	    "learning_rate",
	    "num_iterations",
	    "some_boolean",
	    "some_string",
	]

	_A       	=							"my_custom_tracker"
	_A       	=							False

	def __init__(	self:	str,				lowerCamelCase_:	str ):
								lowercase__				:			int            =		open(F"""{dir}/log.csv""",				'w+' )
								lowercase__				:			Optional[Any]            =		csv.DictWriter(self.f,				fieldnames=self._col_names )
								self.writer.writeheader()

	@property
	def       snake_case__(	self:	Tuple ):
								return self.writer

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	dict ):
								logger.info('Call init' )
								self.writer.writerow(lowerCamelCase_ )

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	dict,				lowerCamelCase_:	Optional[int] ):
								logger.info('Call log' )
								self.writer.writerow(lowerCamelCase_ )





	def       snake_case__(	self:	Optional[int] ):
								self.f.close()


class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	def       snake_case__(	self:	Tuple ):
								with tempfile.TemporaryDirectory() as d:
															lowercase__				:			Optional[int]            =		MyCustomTracker(lowerCamelCase_ )
															lowercase__				:			Any            =		Accelerator(log_with=lowerCamelCase_ )
															lowercase__				:			List[Any]            =		{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
															accelerator.init_trackers('Some name',				lowerCamelCase_ )
															accelerator.end_training()
															with open(F"""{d}/log.csv""",				'r' ) as f:
																						lowercase__				:			Dict            =		csv.DictReader(lowerCamelCase_ )
																						lowercase__				:			List[Any]            =		next(lowerCamelCase_ )
																						lowercase__				:			List[Any]            =		{
																						    'total_loss': '',
																						    'iteration': '',
																						    'my_text': '',
																						    'learning_rate': '0.01',
																						    'num_iterations': '12',
																						    'some_boolean': 'False',
																						    'some_string': 'some_value',
																						}
																						self.assertDictEqual(lowerCamelCase_,				lowerCamelCase_ )





	def       snake_case__(	self:	Tuple ):
								with tempfile.TemporaryDirectory() as d:
															lowercase__				:			int            =		MyCustomTracker(lowerCamelCase_ )
															lowercase__				:			Union[str, Any]            =		Accelerator(log_with=lowerCamelCase_ )
															accelerator.init_trackers('Some name' )
															lowercase__				:			str            =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
															accelerator.log(lowerCamelCase_,				step=0 )
															accelerator.end_training()
															with open(F"""{d}/log.csv""",				'r' ) as f:
																						lowercase__				:			List[Any]            =		csv.DictReader(lowerCamelCase_ )
																						lowercase__				:			Optional[int]            =		next(lowerCamelCase_ )
																						lowercase__				:			Dict            =		{
																						    'total_loss': '0.1',
																						    'iteration': '1',
																						    'my_text': 'some_value',
																						    'learning_rate': '',
																						    'num_iterations': '',
																						    'some_boolean': '',
																						    'some_string': '',
																						}
																						self.assertDictEqual(lowerCamelCase_,				lowerCamelCase_ )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:  Any   =   {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   ["""DetaImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[str]   =   [
			    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DetaForObjectDetection""",
			    """DetaModel""",
			    """DetaPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_deta import DetaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deta import (
						    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DetaForObjectDetection,
						    DetaModel,
						    DetaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  int   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


