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_    		=				logging.getLogger(__name__)





@require_tensorboard
class   lowerCAmelCase    (       unittest.TestCase      ):



     def       a__	(       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       a__	(       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       a__	(       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       a__	(       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       a__	(       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   lowerCAmelCase    (       _a      ,		_a      ):



     def       a__	(       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       a__	(       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       a__	(       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 				UpperCamelCase					(     self							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    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(_a      ,		"""__init__"""      ,		_a      )
class   lowerCAmelCase    (       unittest.TestCase      ):



     @staticmethod
     def       a__	(       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       a__	(       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       a__	(       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   lowerCAmelCase    (       _a      ):
     _SCREAMING_SNAKE_CASE  :  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  :  Optional[Any]		=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       a__	(       self							):
            return self.writer



     def       a__	(       self					,   lowerCAmelCase__							):
            logger.info('Call init'							)
            self.writer.writerow(lowerCAmelCase__							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            logger.info('Call log'							)
            self.writer.writerow(lowerCAmelCase__							)





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





class   lowerCAmelCase    (       unittest.TestCase      ):



     def       a__	(       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       a__	(       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 numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')