





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				    =      logging.getLogger(__name__)

@require_tensorboard
class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					'''test_project_with_config'''
      with tempfile.TemporaryDirectory() as dirpath:
        __lowerCAmelCase								=					Accelerator(log_with='''tensorboard'''      ,    project_dir=snake_case				)
        __lowerCAmelCase								=					{'''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('''*/**'''				):
          __lowerCAmelCase								=					list(filter(lambda snake_case				: x.is_file()      ,    child.iterdir()				)				)[0]
        self.assertNotEqual(str(snake_case				)      ,    ''''''				)





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
        __lowerCAmelCase								=					Accelerator(log_with='''tensorboard'''      ,    project_dir=snake_case				)
        accelerator.init_trackers(snake_case				)
        __lowerCAmelCase								=					{'''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
        __lowerCAmelCase								=					list(filter(lambda snake_case				: x.is_file()      ,    Path(f'''{dirpath}/{project_name}'''				).iterdir()				)				)[0]
        self.assertNotEqual(str(snake_case				)      ,    ''''''				)





    def 						A		( self    :		Tuple				):

      """simple docstring"""





      __lowerCAmelCase								=					'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
        __lowerCAmelCase								=					Accelerator(log_with='''tensorboard'''      ,    project_dir=snake_case				)
        accelerator.init_trackers(snake_case				)
        __lowerCAmelCase								=					{'''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
        __lowerCAmelCase								=					list(filter(lambda snake_case				: 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:
          __lowerCAmelCase								=					f.read()
        __lowerCAmelCase								=					False
        while data:
          __lowerCAmelCase								=					struct.unpack('''Q'''      ,    data[:8]				)

          __lowerCAmelCase								=					data[12 : 12 + int(header[0]				)]  # 8+4
          __lowerCAmelCase								=					data[12 + int(header[0]				) + 4 :]
          __lowerCAmelCase								=					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":
                __lowerCAmelCase								=					True
        self.assertTrue(snake_case      ,    '''Converted tensor was not found in the log file!'''				)





    def 						A		( self    :		Tuple				):

      """simple docstring"""





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





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

      """simple docstring"""





      __lowerCAmelCase								=					ProjectConfiguration(total_limit=30				)
      with tempfile.TemporaryDirectory() as dirpath:
        __lowerCAmelCase								=					Accelerator(log_with='''tensorboard'''      ,    project_dir=snake_case      ,    project_config=snake_case				)


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






    '''simple docstring'''





    def 						A		( self    :		List[str]				):

      """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 						A		( snake_case    :		str      ,    snake_case    :		str      ,    snake_case    :		bool = True				):

      """simple docstring"""





      __lowerCAmelCase								=					rf'''{section} ([\S\s]*?)\n\n'''
      if record:
        __lowerCAmelCase								=					rf'''Record: {pattern}'''
      __lowerCAmelCase								=					re.findall(snake_case      ,    snake_case				)[0]
      # A config
      if section == "config" or section == "history":
        __lowerCAmelCase								=					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 						A		( self    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					'''test_project_with_config'''
      __lowerCAmelCase								=					Accelerator(log_with='''wandb'''				)
      __lowerCAmelCase								=					{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
      __lowerCAmelCase								=					{'''wandb''': {'''tags''': ['''my_tag''']}}
      accelerator.init_trackers(snake_case      ,    snake_case      ,    snake_case				)
      __lowerCAmelCase								=					{'''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":
          __lowerCAmelCase								=					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
      __lowerCAmelCase								=					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
      __lowerCAmelCase								=					self.parse_log(snake_case      ,    '''run'''      ,    snake_case				)
      self.assertEqual(logged_items['''tags''']      ,    '''my_tag'''				)

      # Actual logging
      __lowerCAmelCase								=					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        __SCREAMING_SNAKE_CASE	(				self:		Optional[Any]  ,				__lowercase:		str  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					run_name
  __lowerCAmelCase								=					OfflineExperiment(project_name=__lowercase  ,				offline_directory=__lowercase)
  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 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





    @staticmethod
    def 						A		( snake_case    :		Optional[int]      ,    snake_case    :		str      ,    snake_case    :		bool = False				):

      """simple docstring"""





      for log in log_list:
        __lowerCAmelCase								=					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 						A		( self    :		Optional[int]				):

      """simple docstring"""





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

      """simple docstring"""





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

      """simple docstring"""





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





    @property
    def 						A		( self    :		int				):

      """simple docstring"""





      return self.writer





    def 						A		( self    :		List[str]      ,    snake_case    :		dict				):

      """simple docstring"""





      logger.info('''Call init'''				)
      self.writer.writerow(snake_case				)





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		dict      ,    snake_case    :		Optional[int]				):

      """simple docstring"""





      logger.info('''Call log'''				)
      self.writer.writerow(snake_case				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      self.f.close()


class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





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

      """simple docstring"""





      with tempfile.TemporaryDirectory() as d:
        __lowerCAmelCase								=					MyCustomTracker(snake_case				)
        __lowerCAmelCase								=					Accelerator(log_with=snake_case				)
        __lowerCAmelCase								=					{'''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:
          __lowerCAmelCase								=					csv.DictReader(snake_case				)
          __lowerCAmelCase								=					next(snake_case				)
          __lowerCAmelCase								=					{
              '''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 						A		( self    :		Dict				):

      """simple docstring"""





      with tempfile.TemporaryDirectory() as d:
        __lowerCAmelCase								=					MyCustomTracker(snake_case				)
        __lowerCAmelCase								=					Accelerator(log_with=snake_case				)
        accelerator.init_trackers('''Some name'''				)
        __lowerCAmelCase								=					{'''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:
          __lowerCAmelCase								=					csv.DictReader(snake_case				)
          __lowerCAmelCase								=					next(snake_case				)
          __lowerCAmelCase								=					{
              '''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 json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase				    =      (30, 487)
__lowercase				    =      (36, 1_024)
__lowercase				    =      (100, 100)
__lowercase				    =      100

__lowercase				    =      datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

__lowercase    ,							__lowercase				    =      os.path.split(__file__)
__lowercase				    =      os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any  ,				__lowercase:		Optional[Any]  ,				__lowercase:		List[Any]):



  '''simple docstring'''






  with ArrowWriter(features=__lowercase  ,				path=os.path.join(__lowercase  ,				'''beta.arrow''')) as writer:
    for key, record in dummy_data:
      __lowerCAmelCase								=					my_features.encode_example(__lowercase)
      writer.write(__lowercase)
    __lowerCAmelCase						,    __lowerCAmelCase								=					writer.finalize()


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Union[str, Any]  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[int]  ,				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					{}
  __lowerCAmelCase								=					(
      read_unformated,
      read_formatted_as_numpy,
      read_batch_unformated,
      read_batch_formatted_as_numpy,
      read_col_unformated,
      read_col_formatted_as_numpy,
  )
  with tempfile.TemporaryDirectory() as tmp_dir:
    __lowerCAmelCase								=					datasets.Features({'''image''': ArrayaD(__lowercase  ,				dtype='''float32''')})
    __lowerCAmelCase								=					generate_examples(features=__lowercase  ,				num_examples=__lowercase)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''')))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': SPEED_TEST_SHAPE})
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,				)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with open(__lowercase  ,				'''wb''') as f:
    f.write(json.dumps(__lowercase).encode('''utf-8'''))


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()
