





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   :							Optional[Any]     =	logging.getLogger(__name__)







@require_tensorboard
class 						_lowerCAmelCase(						unittest.TestCase):

		"""simple docstring"""







		def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
					__A				 =       '''test_project_with_config'''
					with tempfile.TemporaryDirectory() as dirpath:
								__A				 =       Accelerator(log_with='''tensorboard'''  ,    project_dir=__snake_case     )
								__A				 =       {'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
								accelerator.init_trackers(__snake_case  ,    __snake_case     )
								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(__snake_case     )  ,    ''''''     )

		def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
					__A				 =       '''test_project_with_log'''
					with tempfile.TemporaryDirectory() as dirpath:
								__A				 =       Accelerator(log_with='''tensorboard'''  ,    project_dir=__snake_case     )
								accelerator.init_trackers(__snake_case     )
								__A				 =       {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
								accelerator.log(__snake_case  ,    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(__snake_case     )  ,    ''''''     )

		def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
					__A				 =       '''test_project_with_log'''
					with tempfile.TemporaryDirectory() as dirpath:
								__A				 =       Accelerator(log_with='''tensorboard'''  ,    project_dir=__snake_case     )
								accelerator.init_trackers(__snake_case     )
								__A				 =       {'''tensor''': torch.tensor(1     )}
								accelerator.log(__snake_case  ,    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(__snake_case  ,    '''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(__snake_case     )
											if event.HasField('''summary'''     ):
														for value in event.summary.value:
																	if value.simple_value == 1.0 and value.tag == "tensor":
																				__A				 =       True
								self.assertTrue(__snake_case  ,    '''Converted tensor was not found in the log file!'''     )

		def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
					with self.assertRaisesRegex(__snake_case  ,    '''Logging with `tensorboard` requires a `logging_dir`'''     ):
								__A				 =       Accelerator(log_with='''tensorboard'''     )
					with tempfile.TemporaryDirectory() as dirpath:
								__A				 =       Accelerator(log_with='''tensorboard'''  ,    project_dir=__snake_case     )





		def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
					__A				 =       ProjectConfiguration(total_limit=30     )
					with tempfile.TemporaryDirectory() as dirpath:
								__A				 =       Accelerator(log_with='''tensorboard'''  ,    project_dir=__snake_case  ,    project_config=__snake_case     )







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

		"""simple docstring"""







		def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
					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 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = True     )-> Optional[int]:
					__A				 =       rf"{section} ([\S\s]*?)\n\n"
					if record:
								__A				 =       rf"Record: {pattern}"
					__A				 =       re.findall(__snake_case  ,    __snake_case     )[0]
					# A config
					if section == "config" or section == "history":
								__A				 =       re.findall(r'''"([a-zA-Z0-9_.,]+)'''  ,    __snake_case     )
								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]+)"'''  ,    __snake_case     )     )





		@skip
		def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
					__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(__snake_case  ,    __snake_case  ,    __snake_case     )
					__A				 =       {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
					accelerator.log(__snake_case  ,    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(__snake_case     )]  ,    env=os.environ.copy()     ).decode('''utf8'''  ,    '''ignore'''     )
											break

        # Check HPS through careful parsing and cleaning
					__A				 =       self.parse_log(__snake_case  ,    '''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(__snake_case  ,    '''run'''  ,    __snake_case     )
					self.assertEqual(logged_items['''tags''']  ,    '''my_tag'''     )

					# Actual logging
					__A				 =       self.parse_log(__snake_case  ,    '''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    ,	snake_case    ,	snake_case     )  ->						Optional[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):

		"""simple docstring"""







		@staticmethod
		def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = False     )-> List[Any]:
					for log in log_list:
								__A				 =       json.loads(__snake_case     )['''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 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
					with tempfile.TemporaryDirectory() as d:
								__A				 =       CometMLTracker('''test_project_with_config'''  ,    __snake_case     )
								__A				 =       Accelerator(log_with=__snake_case     )
								__A				 =       {'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
								accelerator.init_trackers(__snake_case  ,    __snake_case     )
								accelerator.end_training()
								__A				 =       os.listdir(__snake_case     )[0]  # Comet is nice, it's just a zip file here
								# We parse the raw logs
								__A				 =       os.path.join(__snake_case  ,    __snake_case     )
								__A				 =       zipfile.ZipFile(__snake_case  ,    '''r'''     )
								__A				 =       archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
					__A				 =       log.split('''\n'''     )[:-1]
					self.assertEqual(self.get_value_from_key(__snake_case  ,    '''num_iterations'''  ,    __snake_case     )  ,    12     )
					self.assertEqual(self.get_value_from_key(__snake_case  ,    '''learning_rate'''  ,    __snake_case     )  ,    0.01     )
					self.assertEqual(self.get_value_from_key(__snake_case  ,    '''some_boolean'''  ,    __snake_case     )  ,    __snake_case     )
					self.assertEqual(self.get_value_from_key(__snake_case  ,    '''some_string'''  ,    __snake_case     )  ,    '''some_value'''     )





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







class 						_lowerCAmelCase(						A__):

		"""simple docstring"""







		lowerCamelCase__      					=   [
		    '''total_loss''',
		    '''iteration''',
		    '''my_text''',
		    '''learning_rate''',
		    '''num_iterations''',
		    '''some_boolean''',
		    '''some_string''',
		]

		lowerCamelCase__      					=   '''my_custom_tracker'''
		lowerCamelCase__      					=   False

		def __init__( self  ,    UpperCAmelCase     )-> Dict:
					__A				 =       open(f"{dir}/log.csv"  ,    '''w+'''     )
					__A				 =       csv.DictWriter(self.f  ,    fieldnames=self._col_names     )
					self.writer.writeheader()

		@property
		def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
					return self.writer

		def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> List[Any]:
					logger.info('''Call init'''     )
					self.writer.writerow(__snake_case     )

		def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> List[Any]:
					logger.info('''Call log'''     )
					self.writer.writerow(__snake_case     )





		def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
					self.f.close()







class 						_lowerCAmelCase(						unittest.TestCase):

		"""simple docstring"""







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





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








import numpy as np
def 				__UpperCamelCase  (		snake_case     )  ->						Any:


   '''simple docstring'''







   __A ,						__A				 =       a.shape
   __A				 =       min(snake_case    ,	snake_case     )
   __A				 =       np.eye(snake_case     )
   __A				 =       a.copy()

   for k in range(t - 1     ):
      # select a column of modified matrix A':
      __A				 =       r[k:, [k]]
      # construct first basis vector
      __A				 =       np.zeros_like(snake_case     )
      __A				 =       1.0
      # determine scaling factor
      __A				 =       np.linalg.norm(snake_case     )
      # construct vector v for Householder reflection
      __A				 =       x + np.sign(x[0]     ) * alpha * ea
      v /= np.linalg.norm(snake_case     )

      # construct the Householder matrix
      __A				 =       np.eye(m - k     ) - 2.0 * v @ v.T
      # pad with ones and zeros as necessary
      __A				 =       np.block([[np.eye(snake_case     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

      __A				 =       q @ q_k.T
      __A				 =       q_k @ r

   return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()

