"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__UpperCamelCase       :    Optional[int]         =		logging.get_logger(__name__)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       str   ,						*_lowercase			:       Optional[Any]   ,						**_lowercase			:       Optional[Any]		)						->    None:
       warnings.warn(
           "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use MobileNetV2ImageProcessor instead."   ,						_lowercase   ,						)
       super().__init__(*_lowercase   ,						**_lowercase		)
"""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

__UpperCamelCase       :    Any         =		logging.getLogger(__name__)
@require_tensorboard
class   UpperCAmelCase_	(				unittest.TestCase   ):
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Tuple:
       _lowercase							     =       "test_project_with_config"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers(_lowercase   ,						_lowercase		)
          accelerator.end_training()
          for child in Path(f"""{dirpath}/{project_name}"""		).glob("*/**"		):
             _lowercase							     =       list(filter(lambda _lowercase		: x.is_file()   ,						child.iterdir()		)		)[0]
          self.assertNotEqual(str(_lowercase		)   ,						""		)
    def 		_lowerCamelCase      (	self			:       str		)						->    str:
       _lowercase							     =       "test_project_with_log"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          accelerator.init_trackers(_lowercase		)
          _lowercase							     =       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
          accelerator.log(_lowercase   ,						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
          _lowercase							     =       list(filter(lambda _lowercase		: x.is_file()   ,						Path(f"""{dirpath}/{project_name}"""		).iterdir()		)		)[0]
          self.assertNotEqual(str(_lowercase		)   ,						""		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    int:
       _lowercase							     =       "test_project_with_log"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          accelerator.init_trackers(_lowercase		)
          _lowercase							     =       {"tensor": torch.tensor(1		)}
          accelerator.log(_lowercase   ,						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
          _lowercase							     =       list(filter(lambda _lowercase		: 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(_lowercase   ,						"rb"		) as f:
             _lowercase							     =       f.read()
          _lowercase							     =       False
          while data:
             _lowercase							     =       struct.unpack("Q"   ,						data[:8]		)

             _lowercase							     =       data[1_2 : 1_2 + int(header[0]		)]  # 8+4
             _lowercase							     =       data[1_2 + int(header[0]		) + 4 :]
             _lowercase							     =       event_pba.Event()

             event.ParseFromString(_lowercase		)
             if event.HasField("summary"		):
                for value in event.summary.value:
                   if value.simple_value == 1.0 and value.tag == "tensor":
                      _lowercase							     =       True
          self.assertTrue(_lowercase   ,						"Converted tensor was not found in the log file!"		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Dict:
       with self.assertRaisesRegex(_lowercase   ,						"Logging with `tensorboard` requires a `logging_dir`"		):
          _lowercase							     =       Accelerator(log_with="tensorboard"		)
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)




    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Dict:
       _lowercase							     =       ProjectConfiguration(total_limit=3_0		)
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase   ,						project_config=_lowercase		)
@require_wandb
@mock.patch.dict(os.environ,    {"""WANDB_MODE""": """offline"""}   )
class   UpperCAmelCase_	(				lowercase__,    lowercase__   ):
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    int:
       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      (	_lowercase			:       str   ,						_lowercase			:       str   ,						_lowercase			:       bool = True		)						->    Dict:
       _lowercase							     =       Rf"""{section} ([\S\s]*?)\n\n"""
       if record:
          _lowercase							     =       Rf"""Record: {pattern}"""
       _lowercase							     =       re.findall(_lowercase   ,						_lowercase		)[0]
       # A config
       if section == "config" or section == "history":
          _lowercase							     =       re.findall(R"\"([a-zA-Z0-9_.,]+)"   ,						_lowercase		)
          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]+)\""   ,						_lowercase		)		)




    @skip
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Union[str, Any]:
       _lowercase							     =       "test_project_with_config"
       _lowercase							     =       Accelerator(log_with="wandb"		)
       _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
       _lowercase							     =       {"wandb": {"tags": ["my_tag"]}}
       accelerator.init_trackers(_lowercase   ,						_lowercase   ,						_lowercase		)
       _lowercase							     =       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
       accelerator.log(_lowercase   ,						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":
             _lowercase							     =       subprocess.check_output(
                 ["wandb", "sync", "--view", "--verbose", str(_lowercase		)]   ,						env=os.environ.copy()		).decode("utf8"   ,						"ignore"		)
             break

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

       # Actual logging
       _lowercase							     =       self.parse_log(_lowercase   ,						"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		:       Optional[int],				_snake_case		:       str,				_snake_case		:       str       ):
   _lowercase							     =       run_name
   _lowercase							     =       OfflineExperiment(project_name=_snake_case,				offline_directory=_snake_case       )
   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(lowercase__,    """__init__""",    lowercase__   )
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @staticmethod
    def 		_lowerCamelCase      (	_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       bool = False		)						->    List[str]:
       for log in log_list:
          _lowercase							     =       json.loads(_lowercase		)["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			:       int		)						->    List[str]:
       with tempfile.TemporaryDirectory() as d:
          _lowercase							     =       CometMLTracker("test_project_with_config"   ,						_lowercase		)
          _lowercase							     =       Accelerator(log_with=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers(_lowercase   ,						_lowercase		)
          accelerator.end_training()
          _lowercase							     =       os.listdir(_lowercase		)[0]  # Comet is nice, it's just a zip file here
          # We parse the raw logs
          _lowercase							     =       os.path.join(_lowercase   ,						_lowercase		)
          _lowercase							     =       zipfile.ZipFile(_lowercase   ,						"r"		)
          _lowercase							     =       archive.open("messages.json"		).read().decode("utf-8"		)
       _lowercase							     =       log.split("\n"		)[:-1]
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"num_iterations"   ,						_lowercase		)   ,						1_2		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"learning_rate"   ,						_lowercase		)   ,						0.01		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"some_boolean"   ,						_lowercase		)   ,						_lowercase		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"some_string"   ,						_lowercase		)   ,						"some_value"		)




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

    snake_case_											=      """my_custom_tracker"""
    snake_case_											=      False
    def __init__(	self			:       List[Any]   ,						_lowercase			:       str		)						->    Dict:
       _lowercase							     =       open(f"""{dir}/log.csv"""   ,						"w+"		)
       _lowercase							     =       csv.DictWriter(self.f   ,						fieldnames=self._col_names		)
       self.writer.writeheader()
    @property
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Dict:
       return self.writer
    def 		_lowerCamelCase      (	self			:       Optional[Any]   ,						_lowercase			:       dict		)						->    Optional[int]:
       logger.info("Call init"		)
       self.writer.writerow(_lowercase		)
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       dict   ,						_lowercase			:       Optional[int]		)						->    List[str]:
       logger.info("Call log"		)
       self.writer.writerow(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       self.f.close()
class   UpperCAmelCase_	(				unittest.TestCase   ):
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    str:
       with tempfile.TemporaryDirectory() as d:
          _lowercase							     =       MyCustomTracker(_lowercase		)
          _lowercase							     =       Accelerator(log_with=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers("Some name"   ,						_lowercase		)
          accelerator.end_training()
          with open(f"""{d}/log.csv"""   ,						"r"		) as f:
             _lowercase							     =       csv.DictReader(_lowercase		)
             _lowercase							     =       next(_lowercase		)
             _lowercase							     =       {
                 "total_loss": "",
                 "iteration": "",
                 "my_text": "",
                 "learning_rate": "0.01",
                 "num_iterations": "12",
                 "some_boolean": "False",
                 "some_string": "some_value",
             }
             self.assertDictEqual(_lowercase   ,						_lowercase		)




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