




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							=      logging.getLogger(__name__)


@require_tensorboard
class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   ="""test_project_with_config"""
            with tempfile.TemporaryDirectory() as dirpath:
                   a			   =Accelerator(log_with="""tensorboard""" ,    project_dir=_lowerCAmelCase	)
                   a			   ={"""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("""*/**"""	):
                          a			   =list(filter(lambda _lowerCAmelCase	: x.is_file() ,    child.iterdir()	)	)[0]
                   self.assertNotEqual(str(_lowerCAmelCase	) ,    """"""	)
     def        lowerCAmelCase__  (				self	):
            a			   ="""test_project_with_log"""
            with tempfile.TemporaryDirectory() as dirpath:
                   a			   =Accelerator(log_with="""tensorboard""" ,    project_dir=_lowerCAmelCase	)
                   accelerator.init_trackers(_lowerCAmelCase	)
                   a			   ={"""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
                   a			   =list(filter(lambda _lowerCAmelCase	: x.is_file() ,    Path(F'''{dirpath}/{project_name}'''	).iterdir()	)	)[0]
                   self.assertNotEqual(str(_lowerCAmelCase	) ,    """"""	)
     def        lowerCAmelCase__  (				self	):
            a			   ="""test_project_with_log"""
            with tempfile.TemporaryDirectory() as dirpath:
                   a			   =Accelerator(log_with="""tensorboard""" ,    project_dir=_lowerCAmelCase	)
                   accelerator.init_trackers(_lowerCAmelCase	)
                   a			   ={"""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
                   a			   =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:
                          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(_lowerCAmelCase	)
                          if event.HasField("""summary"""	):
                                 for value in event.summary.value:
                                        if value.simple_value == 1.0 and value.tag == "tensor":
                                               a			   =True
                   self.assertTrue(_lowerCAmelCase ,    """Converted tensor was not found in the log file!"""	)
     def        lowerCAmelCase__  (				self	):
            with self.assertRaisesRegex(_lowerCAmelCase ,    """Logging with `tensorboard` requires a `logging_dir`"""	):
                   a			   =Accelerator(log_with="""tensorboard"""	)
            with tempfile.TemporaryDirectory() as dirpath:
                   a			   =Accelerator(log_with="""tensorboard""" ,    project_dir=_lowerCAmelCase	)






     def        lowerCAmelCase__  (				self	):
            a			   =ProjectConfiguration(total_limit=30	)
            with tempfile.TemporaryDirectory() as dirpath:
                   a			   =Accelerator(log_with="""tensorboard""" ,    project_dir=_lowerCAmelCase ,    project_config=_lowerCAmelCase	)



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



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            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__  (				_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True	):
            a			   =rF'''{section} ([\S\s]*?)\n\n'''
            if record:
                   a			   =rF'''Record: {pattern}'''
            a			   =re.findall(_lowerCAmelCase ,    _lowerCAmelCase	)[0]
            # A config
            if section == "config" or section == "history":
                   a			   =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        lowerCAmelCase__  (				self	):
            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(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
            a			   ={"""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":
                          a			   =subprocess.check_output(
                              ["""wandb""", """sync""", """--view""", """--verbose""", str(_lowerCAmelCase	)] ,    env=os.environ.copy()	).decode("""utf8""" ,    """ignore"""	)
                          break

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

            # Actual logging
            a			   =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 							lowerCamelCase    (							self	:      List[Any]   ,       UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str      )->			List[str]:

       """simple docstring"""




       a			   =run_name
       a			   =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(UpperCAmelCase__      ,		"__init__"      ,		UpperCAmelCase__				)
class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = False	):
            for log in log_list:
                   a			   =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        lowerCAmelCase__  (				self	):
            with tempfile.TemporaryDirectory() as d:
                   a			   =CometMLTracker("""test_project_with_config""" ,    _lowerCAmelCase	)
                   a			   =Accelerator(log_with=_lowerCAmelCase	)
                   a			   ={"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
                   accelerator.init_trackers(_lowerCAmelCase ,    _lowerCAmelCase	)
                   accelerator.end_training()
                   a			   =os.listdir(_lowerCAmelCase	)[0]  # Comet is nice, it's just a zip file here
                   # We parse the raw logs
                   a			   =os.path.join(_lowerCAmelCase ,    _lowerCAmelCase	)
                   a			   =zipfile.ZipFile(_lowerCAmelCase ,    """r"""	)
                   a			   =archive.open("""messages.json"""	).read().decode("""utf-8"""	)
            a			   =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	) ,    0.01	)
            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        lowerCAmelCase__  (				self	):
            with tempfile.TemporaryDirectory() as d:
                   a			   =CometMLTracker("""test_project_with_config""" ,    _lowerCAmelCase	)
                   a			   =Accelerator(log_with=_lowerCAmelCase	)
                   accelerator.init_trackers(_lowerCAmelCase	)
                   a			   ={"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
                   accelerator.log(_lowerCAmelCase ,    step=0	)
                   accelerator.end_training()
                   a			   =os.listdir(_lowerCAmelCase	)[0]  # Comet is nice, it's just a zip file here
                   # We parse the raw logs
                   a			   =os.path.join(_lowerCAmelCase ,    _lowerCAmelCase	)
                   a			   =zipfile.ZipFile(_lowerCAmelCase ,    """r"""	)
                   a			   =archive.open("""messages.json"""	).read().decode("""utf-8"""	)
            a			   =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        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Union[str, Any]     					=    [
         "total_loss",
         "iteration",
         "my_text",
         "learning_rate",
         "num_iterations",
         "some_boolean",
         "some_string",
     ]

     _SCREAMING_SNAKE_CASE      :					List[str]     					=    "my_custom_tracker"
     _SCREAMING_SNAKE_CASE      :					Dict     					=    False
     def __init__(				self ,    _lowerCAmelCase	):
            a			   =open(F'''{dir}/log.csv''' ,    """w+"""	)
            a			   =csv.DictWriter(self.f ,    fieldnames=self._col_names	)
            self.writer.writeheader()
     @property
     def        lowerCAmelCase__  (				self	):
            return self.writer
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            logger.info("""Call init"""	)
            self.writer.writerow(_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            logger.info("""Call log"""	)
            self.writer.writerow(_lowerCAmelCase	)






     def        lowerCAmelCase__  (				self	):
            self.f.close()



class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            with tempfile.TemporaryDirectory() as d:
                   a			   =MyCustomTracker(_lowerCAmelCase	)
                   a			   =Accelerator(log_with=_lowerCAmelCase	)
                   a			   ={"""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:
                          a			   =csv.DictReader(_lowerCAmelCase	)
                          a			   =next(_lowerCAmelCase	)
                          a			   ={
                              """total_loss""": """""",
                              """iteration""": """""",
                              """my_text""": """""",
                              """learning_rate""": """0.01""",
                              """num_iterations""": """12""",
                              """some_boolean""": """False""",
                              """some_string""": """some_value""",
                          }
                          self.assertDictEqual(_lowerCAmelCase ,    _lowerCAmelCase	)






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






import math
import os

import cva
import numpy as np

_lowerCamelCase							=      255.0





def 							lowerCamelCase    (							UpperCAmelCase_	:      float   ,       UpperCAmelCase_	:      float      )->			float:

       """simple docstring"""




       a			   =np.mean((original - contrast) ** 2      )
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       a			   =os.path.dirname(os.path.realpath(UpperCAmelCase_      )      )
       # Loading images (original image and compressed image)
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/original_image.png"""      )      )
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/compressed_image.png"""      )   ,       1      )

       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-base.png"""      )      )
       a			   =cva.imread(
           os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-comp-10.jpg"""      )   ,       1      )

       # Value expected: 29.73dB
       print("""-- First Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )

       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )


if __name__ == "__main__":
    main()
