



'''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						:		int						      = logging.getLogger(__name__)






@require_tensorboard
class        UpperCAmelCase__						(		unittest.TestCase			):



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	"test_project_with_config"
						with tempfile.TemporaryDirectory() as dirpath:
							__lowerCAmelCase       				=	Accelerator(log_with="tensorboard"      ,					project_dir=UpperCamelCase  )
							__lowerCAmelCase       				=	{"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("*/**"  ):
								__lowerCAmelCase       				=	list(filter(lambda UpperCamelCase  : x.is_file()      ,					child.iterdir()  )  )[0]
							self.assertNotEqual(str(UpperCamelCase  )      ,					""  )



					def 							UpperCAmelCase_			(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	"test_project_with_log"
						with tempfile.TemporaryDirectory() as dirpath:
							__lowerCAmelCase       				=	Accelerator(log_with="tensorboard"      ,					project_dir=UpperCamelCase  )
							accelerator.init_trackers(UpperCamelCase  )
							__lowerCAmelCase       				=	{"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
							__lowerCAmelCase       				=	list(filter(lambda UpperCamelCase  : x.is_file()      ,					Path(F'''{dirpath}/{project_name}'''  ).iterdir()  )  )[0]
							self.assertNotEqual(str(UpperCamelCase  )      ,					""  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	"test_project_with_log"
						with tempfile.TemporaryDirectory() as dirpath:
							__lowerCAmelCase       				=	Accelerator(log_with="tensorboard"      ,					project_dir=UpperCamelCase  )
							accelerator.init_trackers(UpperCamelCase  )
							__lowerCAmelCase       				=	{"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
							__lowerCAmelCase       				=	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:
								__lowerCAmelCase       				=	f.read()
							__lowerCAmelCase       				=	False
							while data:
								__lowerCAmelCase       				=	struct.unpack("Q"      ,					data[:8]  )

								__lowerCAmelCase       				=	data[12 : 12 + int(header[0]  )]  # 8+4
								__lowerCAmelCase       				=	data[12 + int(header[0]  ) + 4 :]
								__lowerCAmelCase       				=	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":
											__lowerCAmelCase       				=	True
							self.assertTrue(UpperCamelCase      ,					"Converted tensor was not found in the log file!"  )



					def 							UpperCAmelCase_			(     self  )  ->      Union[str, Any]:
						with self.assertRaisesRegex(UpperCamelCase      ,					"Logging with `tensorboard` requires a `logging_dir`"  ):
							__lowerCAmelCase       				=	Accelerator(log_with="tensorboard"  )
						with tempfile.TemporaryDirectory() as dirpath:
							__lowerCAmelCase       				=	Accelerator(log_with="tensorboard"      ,					project_dir=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	ProjectConfiguration(total_limit=30  )
						with tempfile.TemporaryDirectory() as dirpath:
							__lowerCAmelCase       				=	Accelerator(log_with="tensorboard"      ,					project_dir=UpperCamelCase      ,					project_config=UpperCamelCase  )






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



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						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 							UpperCAmelCase_			(     UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = True  )  ->      Any:
						__lowerCAmelCase       				=	rF'''{section} ([\S\s]*?)\n\n'''
						if record:
							__lowerCAmelCase       				=	rF'''Record: {pattern}'''
						__lowerCAmelCase       				=	re.findall(UpperCamelCase      ,					UpperCamelCase  )[0]
						# A config
						if section == "config" or section == "history":
							__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      str:
						__lowerCAmelCase       				=	"test_project_with_config"
						__lowerCAmelCase       				=	Accelerator(log_with="wandb"  )
						__lowerCAmelCase       				=	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
						__lowerCAmelCase       				=	{"wandb": {"tags": ["my_tag"]}}
						accelerator.init_trackers(UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )
						__lowerCAmelCase       				=	{"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":
								__lowerCAmelCase       				=	subprocess.check_output(
								    ["wandb", "sync", "--view", "--verbose", str(UpperCamelCase  )]      ,					env=os.environ.copy()  ).decode("utf8"      ,					"ignore"  )
								break

        # Check HPS through careful parsing and cleaning
						__lowerCAmelCase       				=	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
						__lowerCAmelCase       				=	self.parse_log(UpperCamelCase      ,					"run"      ,					UpperCamelCase  )
						self.assertEqual(logged_items["tags"]      ,					"my_tag"  )

						# Actual logging
						__lowerCAmelCase       				=	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 	__lowerCAmelCase       (				self :    Optional[Any]					,		lowerCamelCase :    str					,		lowerCamelCase :    str       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	run_name
	__lowerCAmelCase       				=	OfflineExperiment(project_name=lowerCamelCase					,		offline_directory=lowerCamelCase       )
	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        UpperCAmelCase__						(		unittest.TestCase			):



					@staticmethod
					def 							UpperCAmelCase_			(     UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = False  )  ->      Any:
						for log in log_list:
							__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      List[str]:
						with tempfile.TemporaryDirectory() as d:
							__lowerCAmelCase       				=	CometMLTracker("test_project_with_config"      ,					UpperCamelCase  )
							__lowerCAmelCase       				=	Accelerator(log_with=UpperCamelCase  )
							__lowerCAmelCase       				=	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
							accelerator.init_trackers(UpperCamelCase      ,					UpperCamelCase  )
							accelerator.end_training()
							__lowerCAmelCase       				=	os.listdir(UpperCamelCase  )[0]  # Comet is nice, it's just a zip file here
							# We parse the raw logs
							__lowerCAmelCase       				=	os.path.join(UpperCamelCase      ,					UpperCamelCase  )
							__lowerCAmelCase       				=	zipfile.ZipFile(UpperCamelCase      ,					"r"  )
							__lowerCAmelCase       				=	archive.open("messages.json"  ).read().decode("utf-8"  )
						__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						with tempfile.TemporaryDirectory() as d:
							__lowerCAmelCase       				=	CometMLTracker("test_project_with_config"      ,					UpperCamelCase  )
							__lowerCAmelCase       				=	Accelerator(log_with=UpperCamelCase  )
							accelerator.init_trackers(UpperCamelCase  )
							__lowerCAmelCase       				=	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
							accelerator.log(UpperCamelCase      ,					step=0  )
							accelerator.end_training()
							__lowerCAmelCase       				=	os.listdir(UpperCamelCase  )[0]  # Comet is nice, it's just a zip file here
							# We parse the raw logs
							__lowerCAmelCase       				=	os.path.join(UpperCamelCase      ,					UpperCamelCase  )
							__lowerCAmelCase       				=	zipfile.ZipFile(UpperCamelCase      ,					"r"  )
							__lowerCAmelCase       				=	archive.open("messages.json"  ).read().decode("utf-8"  )
						__lowerCAmelCase       				=	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        UpperCAmelCase__						(		UpperCamelCase__			):
					a   :  Optional[Any]   =				[
					    """total_loss""",
					    """iteration""",
					    """my_text""",
					    """learning_rate""",
					    """num_iterations""",
					    """some_boolean""",
					    """some_string""",
					]

					a   :  Any   =				"""my_custom_tracker"""
					a   :  Union[str, Any]   =				False



					def __init__(     self      ,					UpperCamelCase  )  ->      Any:
						__lowerCAmelCase       				=	open(F'''{dir}/log.csv'''      ,					"w+"  )
						__lowerCAmelCase       				=	csv.DictWriter(self.f      ,					fieldnames=self._col_names  )
						self.writer.writeheader()



					@property
					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						return self.writer



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      Dict:
						logger.info("Call init"  )
						self.writer.writerow(UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Tuple:
						logger.info("Call log"  )
						self.writer.writerow(UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						self.f.close()






class        UpperCAmelCase__						(		unittest.TestCase			):



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						with tempfile.TemporaryDirectory() as d:
							__lowerCAmelCase       				=	MyCustomTracker(UpperCamelCase  )
							__lowerCAmelCase       				=	Accelerator(log_with=UpperCamelCase  )
							__lowerCAmelCase       				=	{"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:
								__lowerCAmelCase       				=	csv.DictReader(UpperCamelCase  )
								__lowerCAmelCase       				=	next(UpperCamelCase  )
								__lowerCAmelCase       				=	{
								    "total_loss": "",
								    "iteration": "",
								    "my_text": "",
								    "learning_rate": "0.01",
								    "num_iterations": "12",
								    "some_boolean": "False",
								    "some_string": "some_value",
								}
								self.assertDictEqual(UpperCamelCase      ,					UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						with tempfile.TemporaryDirectory() as d:
							__lowerCAmelCase       				=	MyCustomTracker(UpperCamelCase  )
							__lowerCAmelCase       				=	Accelerator(log_with=UpperCamelCase  )
							accelerator.init_trackers("Some name"  )
							__lowerCAmelCase       				=	{"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:
								__lowerCAmelCase       				=	csv.DictReader(UpperCamelCase  )
								__lowerCAmelCase       				=	next(UpperCamelCase  )
								__lowerCAmelCase       				=	{
								    "total_loss": "0.1",
								    "iteration": "1",
								    "my_text": "some_value",
								    "learning_rate": "",
								    "num_iterations": "",
								    "some_boolean": "",
								    "some_string": "",
								}
								self.assertDictEqual(UpperCamelCase      ,					UpperCamelCase  )




'''simple docstring'''



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image






class        UpperCAmelCase__						(		unittest.TestCase				,							UpperCamelCase__			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	load_tool("image-question-answering"  )
						self.tool.setup()
						__lowerCAmelCase       				=	load_tool("image-question-answering"      ,					remote=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )