
"""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

__lowerCAmelCase      :					str   	=       logging.getLogger(__name__)




@require_tensorboard
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_config"""
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__   )
      __magic_name__    					=	{"""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("""*/**"""   ):
        __magic_name__    					=	list(filter(lambda UpperCamelCase__   : x.is_file()      ,							child.iterdir()   )   )[0]
      self.assertNotEqual(str(UpperCamelCase__   )      ,							""""""   )
  def 							_lowercase							(			self      :			int   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_log"""
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__   )
      accelerator.init_trackers(UpperCamelCase__   )
      __magic_name__    					=	{"""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
      __magic_name__    					=	list(filter(lambda UpperCamelCase__   : x.is_file()      ,							Path(F'''{dirpath}/{project_name}'''   ).iterdir()   )   )[0]
      self.assertNotEqual(str(UpperCamelCase__   )      ,							""""""   )
  def 							_lowercase							(			self      :			List[str]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_log"""
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__   )
      accelerator.init_trackers(UpperCamelCase__   )
      __magic_name__    					=	{"""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
      __magic_name__    					=	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:
        __magic_name__    					=	f.read()
      __magic_name__    					=	False
      while data:
        __magic_name__    					=	struct.unpack("""Q"""      ,							data[:8]   )

        __magic_name__    					=	data[12 : 12 + int(header[0]   )]  # 8+4
        __magic_name__    					=	data[12 + int(header[0]   ) + 4 :]
        __magic_name__    					=	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":
              __magic_name__    					=	True
      self.assertTrue(UpperCamelCase__      ,							"""Converted tensor was not found in the log file!"""   )
  def 							_lowercase							(			self      :			str   )      ->     List[str]:







    """simple docstring"""






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





  def 							_lowercase							(			self      :			Dict   )      ->     Tuple:







    """simple docstring"""






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







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


  '''simple docstring'''







  def 							_lowercase							(			self      :			List[str]   )      ->     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 							_lowercase							(			UpperCamelCase__      :			str      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			bool = True   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	RF'''{section} ([\S\s]*?)\n\n'''
    if record:
      __magic_name__    					=	RF'''Record: {pattern}'''
    __magic_name__    					=	re.findall(UpperCamelCase__      ,							UpperCamelCase__   )[0]
    # A config
    if section == "config" or section == "history":
      __magic_name__    					=	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 							_lowercase							(			self      :			Any   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_config"""
    __magic_name__    					=	Accelerator(log_with="""wandb"""   )
    __magic_name__    					=	{"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
    __magic_name__    					=	{"""wandb""": {"""tags""": ["""my_tag"""]}}
    accelerator.init_trackers(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )
    __magic_name__    					=	{"""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":
        __magic_name__    					=	subprocess.check_output(
            ["""wandb""", """sync""", """--view""", """--verbose""", str(UpperCamelCase__   )]      ,							env=os.environ.copy()   ).decode("""utf8"""      ,							"""ignore"""   )
        break

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

    # Actual logging
    __magic_name__    					=	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 a__   (	self,					A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	run_name
  __magic_name__    					=	OfflineExperiment(project_name=A_,					offline_directory=A_ )
  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 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  @staticmethod
  def 							_lowercase							(			UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			bool = False   )      ->     Optional[Any]:







    """simple docstring"""






    for log in log_list:
      __magic_name__    					=	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 							_lowercase							(			self      :			Any   )      ->     Union[str, Any]:







    """simple docstring"""






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







    """simple docstring"""






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


  '''simple docstring'''







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

  a__   	=		"""my_custom_tracker"""
  a__   	=		False
  def __init__(			self      :			Any      ,							UpperCamelCase__      :			str   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	open(F'''{dir}/log.csv'''      ,							"""w+"""   )
    __magic_name__    					=	csv.DictWriter(self.f      ,							fieldnames=self._col_names   )
    self.writer.writeheader()
  @property
  def 							_lowercase							(			self      :			Optional[int]   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.writer
  def 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			dict   )      ->     Any:







    """simple docstring"""






    logger.info("""Call init"""   )
    self.writer.writerow(UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			dict      ,							UpperCamelCase__      :			Optional[int]   )      ->     str:







    """simple docstring"""






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





  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






    self.f.close()







class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  def 							_lowercase							(			self      :			str   )      ->     Any:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as d:
      __magic_name__    					=	MyCustomTracker(UpperCamelCase__   )
      __magic_name__    					=	Accelerator(log_with=UpperCamelCase__   )
      __magic_name__    					=	{"""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:
        __magic_name__    					=	csv.DictReader(UpperCamelCase__   )
        __magic_name__    					=	next(UpperCamelCase__   )
        __magic_name__    					=	{
            """total_loss""": """""",
            """iteration""": """""",
            """my_text""": """""",
            """learning_rate""": """0.01""",
            """num_iterations""": """12""",
            """some_boolean""": """False""",
            """some_string""": """some_value""",
        }
        self.assertDictEqual(UpperCamelCase__      ,							UpperCamelCase__   )





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







    """simple docstring"""






    with tempfile.TemporaryDirectory() as d:
      __magic_name__    					=	MyCustomTracker(UpperCamelCase__   )
      __magic_name__    					=	Accelerator(log_with=UpperCamelCase__   )
      accelerator.init_trackers("""Some name"""   )
      __magic_name__    					=	{"""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:
        __magic_name__    					=	csv.DictReader(UpperCamelCase__   )
        __magic_name__    					=	next(UpperCamelCase__   )
        __magic_name__    					=	{
            """total_loss""": """0.1""",
            """iteration""": """1""",
            """my_text""": """some_value""",
            """learning_rate""": """""",
            """num_iterations""": """""",
            """some_boolean""": """""",
            """some_string""": """""",
        }
        self.assertDictEqual(UpperCamelCase__      ,							UpperCamelCase__   )



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





  __magic_name__    					=	np.arange(start=3,					stop=A_,					step=4 ).tolist() if k > 0 else [10]
  __magic_name__    					=	_quantize(A_,					A_ )
  # count number of elements per group
  __magic_name__    					=	np.unique(A_,					return_counts=A_ )[1]
  __magic_name__    					=	[0] + bins + [np.inf]
  logger.info("""Using {} as bins for aspect lengths quantization""".format(A_ ) )
  logger.info("""Count of instances per bin: {}""".format(A_ ) )
  return groups





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

    __magic_name__    					=	0
    for idx in self.sampler:
      __magic_name__    					=	self.group_ids[idx]
      buffer_per_group[group_id].append(UpperCamelCase__   )
      samples_per_group[group_id].append(UpperCamelCase__   )
      if len(buffer_per_group[group_id]   ) == self.batch_size:
        yield buffer_per_group[group_id]  # TODO
        num_batches += 1
        del buffer_per_group[group_id]
      assert len(buffer_per_group[group_id]   ) < self.batch_size

    # now we have run out of elements that satisfy
    # the group criteria, let's return the remaining
    # elements so that the size of the sampler is
    # deterministic
    __magic_name__    					=	len(self   )
    __magic_name__    					=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      __magic_name__    					=	[]
      for group_id, idxs in sorted(buffer_per_group.items()      ,							key=lambda UpperCamelCase__   : x[0]   ):
        batch_idx.extend(UpperCamelCase__   )
        if len(UpperCamelCase__   ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          __magic_name__    					=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(UpperCamelCase__   ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





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







    """simple docstring"""






    return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size

