




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__      :			Tuple           =					logging.getLogger(__name__)




@require_tensorboard
class 			a__       (					unittest.TestCase      ):







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          a      =      "test_project_with_config"
          with tempfile.TemporaryDirectory() as dirpath:
               a      =      Accelerator(log_with="tensorboard"  ,		project_dir=A )
               a      =      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
               accelerator.init_trackers(A  ,		A )
               accelerator.end_training()
               for child in Path(F'''{dirpath}/{project_name}''' ).glob("*/**" ):
                    a      =      list(filter(lambda A : x.is_file()  ,		child.iterdir() ) )[0]
               self.assertNotEqual(str(A )  ,		"" )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          a      =      "test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
               a      =      Accelerator(log_with="tensorboard"  ,		project_dir=A )
               accelerator.init_trackers(A )
               a      =      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
               accelerator.log(A  ,		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 A : x.is_file()  ,		Path(F'''{dirpath}/{project_name}''' ).iterdir() ) )[0]
               self.assertNotEqual(str(A )  ,		"" )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      "test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
               a      =      Accelerator(log_with="tensorboard"  ,		project_dir=A )
               accelerator.init_trackers(A )
               a      =      {"tensor": torch.tensor(1 )}
               accelerator.log(A  ,		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 A : 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(A  ,		"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(A )
                    if event.HasField("summary" ):
                         for value in event.summary.value:
                              if value.simple_value == 1.0 and value.tag == "tensor":
                                   a      =      True
               self.assertTrue(A  ,		"Converted tensor was not found in the log file!" )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          with self.assertRaisesRegex(A  ,		"Logging with `tensorboard` requires a `logging_dir`" ):
               a      =      Accelerator(log_with="tensorboard" )
          with tempfile.TemporaryDirectory() as dirpath:
               a      =      Accelerator(log_with="tensorboard"  ,		project_dir=A )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



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







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







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''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 			lowerCAmelCase_				(	A  ,		A  ,		A = True )  ->    Dict:



          '''simple docstring'''



          a      =      rF'''{section} ([\S\s]*?)\n\n'''
          if record:
               a      =      rF'''Record: {pattern}'''
          a      =      re.findall(A  ,		A )[0]
          # A config
          if section == "config" or section == "history":
               a      =      re.findall(r"\"([a-zA-Z0-9_.,]+)"  ,		A )
               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]+)\""  ,		A ) )







     @skip
     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''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(A  ,		A  ,		A )
          a      =      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
          accelerator.log(A  ,		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(A )]  ,		env=os.environ.copy() ).decode("utf8"  ,		"ignore" )
                    break

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

          # Actual logging
          a      =      self.parse_log(A  ,		"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 					SCREAMING_SNAKE_CASE							(   self		,					__UpperCamelCase		,					__UpperCamelCase)			->     Optional[Any]:
     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 			a__       (					unittest.TestCase      ):







     @staticmethod
     def 			lowerCAmelCase_				(	A  ,		A  ,		A = False )  ->    Tuple:



          '''simple docstring'''



          for log in log_list:
               a      =      json.loads(A )["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 )  ->    Dict:



          '''simple docstring'''



          with tempfile.TemporaryDirectory() as d:
               a      =      CometMLTracker("test_project_with_config"  ,		A )
               a      =      Accelerator(log_with=A )
               a      =      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
               accelerator.init_trackers(A  ,		A )
               accelerator.end_training()
               a      =      os.listdir(A )[0]  # Comet is nice, it's just a zip file here
               # We parse the raw logs
               a      =      os.path.join(A  ,		A )
               a      =      zipfile.ZipFile(A  ,		"r" )
               a      =      archive.open("messages.json" ).read().decode("utf-8" )
          a      =      log.split("\n" )[:-1]
          self.assertEqual(self.get_value_from_key(A  ,		"num_iterations"  ,		A )  ,		12 )
          self.assertEqual(self.get_value_from_key(A  ,		"learning_rate"  ,		A )  ,		0.0_1 )
          self.assertEqual(self.get_value_from_key(A  ,		"some_boolean"  ,		A )  ,		A )
          self.assertEqual(self.get_value_from_key(A  ,		"some_string"  ,		A )  ,		"some_value" )







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          with tempfile.TemporaryDirectory() as d:
               a      =      CometMLTracker("test_project_with_config"  ,		A )
               a      =      Accelerator(log_with=A )
               accelerator.init_trackers(A )
               a      =      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
               accelerator.log(A  ,		step=0 )
               accelerator.end_training()
               a      =      os.listdir(A )[0]  # Comet is nice, it's just a zip file here
               # We parse the raw logs
               a      =      os.path.join(A  ,		A )
               a      =      zipfile.ZipFile(A  ,		"r" )
               a      =      archive.open("messages.json" ).read().decode("utf-8" )
          a      =      log.split("\n" )[:-1]
          self.assertEqual(self.get_value_from_key(A  ,		"curr_step"  ,		A )  ,		0 )
          self.assertEqual(self.get_value_from_key(A  ,		"total_loss" )  ,		0.1 )
          self.assertEqual(self.get_value_from_key(A  ,		"iteration" )  ,		1 )
          self.assertEqual(self.get_value_from_key(A  ,		"my_text" )  ,		"some_value" )







class 			a__       (					UpperCamelCase__      ):
     a		:			Tuple							=	[
         """total_loss""",
         """iteration""",
         """my_text""",
         """learning_rate""",
         """num_iterations""",
         """some_boolean""",
         """some_string""",
     ]

     a		:			str							=	"""my_custom_tracker"""
     a		:			Any							=	False







     def __init__(	self  ,		A )  ->    Dict:



          '''simple docstring'''



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







     @property
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          return self.writer







     def 			lowerCAmelCase_				(	self  ,		A )  ->    str:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Union[str, Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



          self.f.close()







class 			a__       (					unittest.TestCase      ):







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



          with tempfile.TemporaryDirectory() as d:
               a      =      MyCustomTracker(A )
               a      =      Accelerator(log_with=A )
               a      =      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
               accelerator.init_trackers("Some name"  ,		A )
               accelerator.end_training()
               with open(F'''{d}/log.csv'''  ,		"r" ) as f:
                    a      =      csv.DictReader(A )
                    a      =      next(A )
                    a      =      {
                        "total_loss": "",
                        "iteration": "",
                        "my_text": "",
                        "learning_rate": "0.01",
                        "num_iterations": "12",
                        "some_boolean": "False",
                        "some_string": "some_value",
                    }
                    self.assertDictEqual(A  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          with tempfile.TemporaryDirectory() as d:
               a      =      MyCustomTracker(A )
               a      =      Accelerator(log_with=A )
               accelerator.init_trackers("Some name" )
               a      =      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
               accelerator.log(A  ,		step=0 )
               accelerator.end_training()
               with open(F'''{d}/log.csv'''  ,		"r" ) as f:
                    a      =      csv.DictReader(A )
                    a      =      next(A )
                    a      =      {
                        "total_loss": "0.1",
                        "iteration": "1",
                        "my_text": "some_value",
                        "learning_rate": "",
                        "num_iterations": "",
                        "some_boolean": "",
                        "some_string": "",
                    }
                    self.assertDictEqual(A  ,		A )







import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

   # Generate a list of 10 random numbers
   lowercase__      :			List[str]           =					[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print("Random numbers", random_numbers_list)

