


'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey






def __UpperCamelCase (      __lowerCamelCase		:			Optional[int]   ,	__lowerCamelCase		:			int					)   ->  Optional[int]:







     '''simple docstring'''






     _a      ,      _a            =		img.shape[0], img.shape[1]

     def to_grayscale(__lowerCamelCase		:			Tuple   ,	__lowerCamelCase		:			List[Any]   ,	__lowerCamelCase		:			List[str]					):
          return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue

     def normalize(__lowerCamelCase		:			Dict					):
          return min(__lowerCamelCase   ,	255					)

     for i in range(__lowerCamelCase					):
          for j in range(__lowerCamelCase					):
               _a            =		int(to_grayscale(*img[i][j]					)					)
               _a            =		[
                   normalize(__lowerCamelCase					),
                   normalize(greyscale + factor					),
                   normalize(greyscale + 2 * factor					),
               ]

     return img


if __name__ == "__main__":
   # read original image
   lowercase__					  =		{
       percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()





'''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

lowercase__					  =		logging.getLogger(__name__)





@require_tensorboard
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






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






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






     def 			a_       (      self		)							->     str:
          _a            =		"test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase		)
               accelerator.init_trackers(__UpperCamelCase		)
               _a            =		{"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
               _a            =		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:
                    _a            =		f.read()
               _a            =		False
               while data:
                    _a            =		struct.unpack("Q"  ,				data[:8]		)

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






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






     def 			a_       (      self		)							->     Any:
          _a            =		ProjectConfiguration(total_limit=30		)
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase  ,				project_config=__UpperCamelCase		)







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






     def 			a_       (      self		)							->     List[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 			a_       (      __UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase = True		)							->     Optional[int]:
          _a            =		Rf"{section} ([\S\s]*?)\n\n"
          if record:
               _a            =		Rf"Record: {pattern}"
          _a            =		re.findall(__UpperCamelCase  ,				__UpperCamelCase		)[0]
          # A config
          if section == "config" or section == "history":
               _a            =		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 			a_       (      self		)							->     Dict:
          _a            =		"test_project_with_config"
          _a            =		Accelerator(log_with="wandb"		)
          _a            =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          _a            =		{"wandb": {"tags": ["my_tag"]}}
          accelerator.init_trackers(__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)
          _a            =		{"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":
                    _a            =		subprocess.check_output(
                        ["wandb", "sync", "--view", "--verbose", str(__UpperCamelCase		)]  ,				env=os.environ.copy()		).decode("utf8"  ,				"ignore"		)
                    break

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

          # Actual logging
          _a            =		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		:			List[Any]   ,	__lowerCamelCase		:			str   ,	__lowerCamelCase		:			str					)   ->  List[Any]:







     '''simple docstring'''






     _a            =		run_name
     _a            =		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(lowerCamelCase__			,      '''__init__'''			,      lowerCamelCase__			)
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






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

     UpperCAmelCase			       =					'''my_custom_tracker'''
     UpperCAmelCase			       =					False






     def __init__(      self  ,				__UpperCamelCase		)							->     int:
          _a            =		open(f"{dir}/log.csv"  ,				"w+"		)
          _a            =		csv.DictWriter(self.f  ,				fieldnames=self._col_names		)
          self.writer.writeheader()






     @property
     def 			a_       (      self		)							->     Optional[int]:
          return self.writer






     def 			a_       (      self  ,				__UpperCamelCase		)							->     List[str]:
          logger.info("Call init"		)
          self.writer.writerow(__UpperCamelCase		)






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






     def 			a_       (      self		)							->     Union[str, Any]:
          self.f.close()







class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Optional[int]:
          with tempfile.TemporaryDirectory() as d:
               _a            =		MyCustomTracker(__UpperCamelCase		)
               _a            =		Accelerator(log_with=__UpperCamelCase		)
               _a            =		{"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:
                    _a            =		csv.DictReader(__UpperCamelCase		)
                    _a            =		next(__UpperCamelCase		)
                    _a            =		{
                        "total_loss": "",
                        "iteration": "",
                        "my_text": "",
                        "learning_rate": "0.01",
                        "num_iterations": "12",
                        "some_boolean": "False",
                        "some_string": "some_value",
                    }
                    self.assertDictEqual(__UpperCamelCase  ,				__UpperCamelCase		)






     def 			a_       (      self		)							->     List[Any]:
          with tempfile.TemporaryDirectory() as d:
               _a            =		MyCustomTracker(__UpperCamelCase		)
               _a            =		Accelerator(log_with=__UpperCamelCase		)
               accelerator.init_trackers("Some name"		)
               _a            =		{"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:
                    _a            =		csv.DictReader(__UpperCamelCase		)
                    _a            =		next(__UpperCamelCase		)
                    _a            =		{
                        "total_loss": "0.1",
                        "iteration": "1",
                        "my_text": "some_value",
                        "learning_rate": "",
                        "num_iterations": "",
                        "some_boolean": "",
                        "some_string": "",
                    }
                    self.assertDictEqual(__UpperCamelCase  ,				__UpperCamelCase		)

