

"""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

SCREAMING_SNAKE_CASE_       :						str					=    logging.getLogger(__name__)



@require_tensorboard
class        a    (							unittest.TestCase					):



    """simple docstring"""



    def 					UpperCamelCase						(  self:			List[Any]							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			Optional[int]							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			Dict							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			Dict							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			str							):






         """simple docstring"""


         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        a    (							_lowerCamelCase,						_lowerCamelCase					):



    """simple docstring"""



    def 					UpperCamelCase						(  self:			Dict							):






         """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 					UpperCamelCase						(  UpperCamelCase:			str     ,    UpperCamelCase:			str     ,    UpperCamelCase:			bool = True							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			Union[str, Any]							):






         """simple docstring"""


         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 			_snake_case  (     self	:       Any       , UpperCAmelCase_	:       str       , UpperCAmelCase_	:       str      ):
     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(_lowerCamelCase,						"__init__",						_lowerCamelCase					)
class        a    (							unittest.TestCase					):



    """simple docstring"""



    @staticmethod
    def 					UpperCamelCase						(  UpperCamelCase:			Optional[Any]     ,    UpperCamelCase:			str     ,    UpperCamelCase:			bool = False							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			Optional[Any]							):






         """simple docstring"""


         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        a    (							_lowerCamelCase					):



    """simple docstring"""



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

    UpperCAmelCase    			=       "my_custom_tracker"
    UpperCAmelCase    			=       False

    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	open(f"""{dir}/log.csv"""     ,    """w+"""							)
         A__				      =	csv.DictWriter(self.f     ,    fieldnames=self._col_names							)
         self.writer.writeheader()

    @property
    def 					UpperCamelCase						(  self:			List[Any]							):






         """simple docstring"""


         return self.writer

    def 					UpperCamelCase						(  self:			Union[str, Any]     ,    UpperCamelCase:			dict							):






         """simple docstring"""


         logger.info("""Call init"""							)
         self.writer.writerow(UpperCamelCase							)

    def 					UpperCamelCase						(  self:			List[str]     ,    UpperCamelCase:			dict     ,    UpperCamelCase:			Optional[int]							):






         """simple docstring"""


         logger.info("""Call log"""							)
         self.writer.writerow(UpperCamelCase							)





    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         self.f.close()



class        a    (							unittest.TestCase					):



    """simple docstring"""



    def 					UpperCamelCase						(  self:			List[str]							):






         """simple docstring"""


         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 					UpperCamelCase						(  self:			List[str]							):






         """simple docstring"""


         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							)









"""simple docstring"""



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]=3      ):
     A__				      =	int(mask / 2      )
     # copy image size
     A__				      =	zeros_like(UpperCAmelCase_      )
     for i in range(UpperCAmelCase_       , gray_img.shape[0] - bd      ):
          for j in range(UpperCAmelCase_       , gray_img.shape[1] - bd      ):
               # get mask according with mask
               A__				      =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
               # calculate mask median
               A__				      =	sort(UpperCAmelCase_      )[inta(divide((multiply(UpperCAmelCase_       , UpperCAmelCase_      ))       , 2      ) + 1      )]
               A__				      =	median
     return median_img


if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE_       :						int					=    imread('../image_data/lena.jpg')
 # turn image in gray scale value
 SCREAMING_SNAKE_CASE_       :						str					=    cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 SCREAMING_SNAKE_CASE_       :						List[Any]					=    median_filter(gray, 3)
 SCREAMING_SNAKE_CASE_       :						str					=    median_filter(gray, 5)

 # show result images
 imshow('median filter with 3x3 mask', medianaxa)
 imshow('median filter with 5x5 mask', medianaxa)
 waitKey(0)






