'''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

__snake_case       :   Any  		=    logging.getLogger(__name__)
@require_tensorboard
class 							lowercase_    (     unittest.TestCase						):







    def   lowerCamelCase_    (	self				)				->       str:





     """simple docstring"""

     UpperCAmelCase_				       =					"test_project_with_config"
     with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase_				       =					Accelerator(log_with="tensorboard"       ,     project_dir=UpperCamelCase__				)
      UpperCAmelCase_				       =					{"num_iterations": 1_2, "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("*/**"				):
       UpperCAmelCase_				       =					list(filter(lambda UpperCamelCase__				: x.is_file()       ,     child.iterdir()				)				)[0]
      self.assertNotEqual(str(UpperCamelCase__				)       ,     ""				)







    def   lowerCamelCase_    (	self				)				->       int:





     """simple docstring"""

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







    def   lowerCamelCase_    (	self				)				->       Dict:





     """simple docstring"""

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

       UpperCAmelCase_				       =					data[1_2 : 1_2 + int(header[0]				)]  # 8+4
       UpperCAmelCase_				       =					data[1_2 + int(header[0]				) + 4 :]
       UpperCAmelCase_				       =					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":
          UpperCAmelCase_				       =					True
      self.assertTrue(UpperCamelCase__       ,     "Converted tensor was not found in the log file!"				)







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





     """simple docstring"""

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







    def   lowerCamelCase_    (	self				)				->       Tuple:





     """simple docstring"""

     UpperCAmelCase_				       =					ProjectConfiguration(total_limit=3_0				)
     with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase_				       =					Accelerator(log_with="tensorboard"       ,     project_dir=UpperCamelCase__       ,     project_config=UpperCamelCase__				)


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







    def   lowerCamelCase_    (	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   lowerCamelCase_    (	UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = True				)				->       Dict:





     """simple docstring"""

     UpperCAmelCase_				       =					RF"""{section} ([\S\s]*?)\n\n"""
     if record:
      UpperCAmelCase_				       =					RF"""Record: {pattern}"""
     UpperCAmelCase_				       =					re.findall(UpperCamelCase__       ,     UpperCamelCase__				)[0]
     # A config
     if section == "config" or section == "history":
      UpperCAmelCase_				       =					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   lowerCamelCase_    (	self				)				->       Optional[int]:





     """simple docstring"""

     UpperCAmelCase_				       =					"test_project_with_config"
     UpperCAmelCase_				       =					Accelerator(log_with="wandb"				)
     UpperCAmelCase_				       =					{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
     UpperCAmelCase_				       =					{"wandb": {"tags": ["my_tag"]}}
     accelerator.init_trackers(UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__				)
     UpperCAmelCase_				       =					{"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":
       UpperCAmelCase_				       =					subprocess.check_output(
           ["wandb", "sync", "--view", "--verbose", str(UpperCamelCase__				)]       ,     env=os.environ.copy()				).decode("utf8"       ,     "ignore"				)
       break

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

     # Actual logging
     UpperCAmelCase_				       =					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 lowerCamelCase__						(       self       ,			A_       ,			A_       ):
 UpperCAmelCase_				       =					run_name
 UpperCAmelCase_				       =					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 							lowercase_    (     unittest.TestCase						):







    @staticmethod
    def   lowerCamelCase_    (	UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = False				)				->       List[str]:





     """simple docstring"""

     for log in log_list:
      UpperCAmelCase_				       =					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   lowerCamelCase_    (	self				)				->       int:





     """simple docstring"""

     with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase_				       =					CometMLTracker("test_project_with_config"       ,     UpperCamelCase__				)
      UpperCAmelCase_				       =					Accelerator(log_with=UpperCamelCase__				)
      UpperCAmelCase_				       =					{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
      accelerator.init_trackers(UpperCamelCase__       ,     UpperCamelCase__				)
      accelerator.end_training()
      UpperCAmelCase_				       =					os.listdir(UpperCamelCase__				)[0]  # Comet is nice, it's just a zip file here
      # We parse the raw logs
      UpperCAmelCase_				       =					os.path.join(UpperCamelCase__       ,     UpperCamelCase__				)
      UpperCAmelCase_				       =					zipfile.ZipFile(UpperCamelCase__       ,     "r"				)
      UpperCAmelCase_				       =					archive.open("messages.json"				).read().decode("utf-8"				)
     UpperCAmelCase_				       =					log.split("\n"				)[:-1]
     self.assertEqual(self.get_value_from_key(UpperCamelCase__       ,     "num_iterations"       ,     UpperCamelCase__				)       ,     1_2				)
     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   lowerCamelCase_    (	self				)				->       Any:





     """simple docstring"""

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

    a_             =   """my_custom_tracker"""
    a_             =   False







    def __init__(	self       ,     UpperCamelCase__				)				->       str:





     """simple docstring"""

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







    @property
    def   lowerCamelCase_    (	self				)				->       Any:





     """simple docstring"""

     return self.writer







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Tuple:





     """simple docstring"""

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







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__				)				->       str:





     """simple docstring"""

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







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





     """simple docstring"""

     self.f.close()


class 							lowercase_    (     unittest.TestCase						):







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





     """simple docstring"""

     with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase_				       =					MyCustomTracker(UpperCamelCase__				)
      UpperCAmelCase_				       =					Accelerator(log_with=UpperCamelCase__				)
      UpperCAmelCase_				       =					{"num_iterations": 1_2, "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:
       UpperCAmelCase_				       =					csv.DictReader(UpperCamelCase__				)
       UpperCAmelCase_				       =					next(UpperCamelCase__				)
       UpperCAmelCase_				       =					{
           "total_loss": "",
           "iteration": "",
           "my_text": "",
           "learning_rate": "0.01",
           "num_iterations": "12",
           "some_boolean": "False",
           "some_string": "some_value",
       }
       self.assertDictEqual(UpperCamelCase__       ,     UpperCamelCase__				)







    def   lowerCamelCase_    (	self				)				->       Optional[int]:





     """simple docstring"""

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

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case       :   int  		=    {
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   Optional[Any]  		=    [
          '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''VideoMAEForPreTraining''',
          '''VideoMAEModel''',
          '''VideoMAEPreTrainedModel''',
          '''VideoMAEForVideoClassification''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   str  		=    ['''VideoMAEFeatureExtractor''']
      __snake_case       :   Any  		=    ['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
      from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_videomae import (
                VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                VideoMAEForPreTraining,
                VideoMAEForVideoClassification,
                VideoMAEModel,
                VideoMAEPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_videomae import VideoMAEFeatureExtractor
            from .image_processing_videomae import VideoMAEImageProcessor

else:
      import sys

      __snake_case       :   List[str]  		=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
