




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

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




@require_tensorboard
class    _a  (  unittest.TestCase):

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      "test_project_with_config"
          with tempfile.TemporaryDirectory() as dirpath:
              UpperCamelCase__:					Optional[Any]												=      Accelerator(log_with="tensorboard"					,  project_dir=lowercase__			)
              UpperCamelCase__:					List[Any]												=      {"num_iterations": 12, "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("*/**"			):
                  UpperCamelCase__:					Optional[int]												=      list(filter(lambda __lowerCamelCase			: x.is_file()					,  child.iterdir()			)			)[0]
              self.assertNotEqual(str(lowercase__			)					,  ""			)






      def 	UpperCAmelCase_   (						self:	int			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      "test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
              UpperCamelCase__:					List[str]												=      Accelerator(log_with="tensorboard"					,  project_dir=lowercase__			)
              accelerator.init_trackers(lowercase__			)
              UpperCamelCase__:					Union[str, Any]												=      {"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
              UpperCamelCase__:					List[str]												=      list(filter(lambda __lowerCamelCase			: 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:
                  UpperCamelCase__:					Optional[Any]												=      f.read()
              UpperCamelCase__:					Any												=      False
              while data:
                  UpperCamelCase__:					Tuple												=      struct.unpack("Q"					,  data[:8]			)

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






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          with self.assertRaisesRegex(lowercase__					,  "Logging with `tensorboard` requires a `logging_dir`"			):
              UpperCamelCase__:					int												=      Accelerator(log_with="tensorboard"			)
          with tempfile.TemporaryDirectory() as dirpath:
              UpperCamelCase__:					Union[str, Any]												=      Accelerator(log_with="tensorboard"					,  project_dir=lowercase__			)






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      ProjectConfiguration(total_limit=30			)
          with tempfile.TemporaryDirectory() as dirpath:
              UpperCamelCase__:					Union[str, Any]												=      Accelerator(log_with="tensorboard"					,  project_dir=lowercase__					,  project_config=lowercase__			)




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

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	int			):
          '''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 	UpperCAmelCase_   (						__lowerCamelCase:	Optional[Any]					,  __lowerCamelCase:	Dict					,  __lowerCamelCase:	Union[str, Any] = True			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      RF"{section} ([\S\s]*?)\n\n"
          if record:
              UpperCamelCase__:					Dict												=      RF"Record: {pattern}"
          UpperCamelCase__:					Tuple												=      re.findall(lowercase__					,  lowercase__			)[0]
          # A config
          if section == "config" or section == "history":
              UpperCamelCase__:					List[str]												=      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 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


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

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

          # Actual logging
          UpperCamelCase__:					Optional[int]												=      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 							lowerCAmelCase_				(    self  ,A_  ,A_):
    UpperCamelCase__:					str												=      run_name
    UpperCamelCase__:					Any												=      OfflineExperiment(project_name=SCREAMING_SNAKE_CASE_  ,offline_directory=SCREAMING_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(UpperCamelCase__		,	"""__init__"""		,	UpperCamelCase__)
class    _a  (  unittest.TestCase):

      """simple docstring"""


      @staticmethod
      def 	UpperCAmelCase_   (						__lowerCamelCase:	int					,  __lowerCamelCase:	Any					,  __lowerCamelCase:	Any = False			):
          '''simple docstring'''


          for log in log_list:
              UpperCamelCase__:					Any												=      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 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          with tempfile.TemporaryDirectory() as d:
              UpperCamelCase__:					Dict												=      CometMLTracker("test_project_with_config"					,  lowercase__			)
              UpperCamelCase__:					Tuple												=      Accelerator(log_with=lowercase__			)
              UpperCamelCase__:					Optional[Any]												=      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
              accelerator.init_trackers(lowercase__					,  lowercase__			)
              accelerator.end_training()
              UpperCamelCase__:					List[Any]												=      os.listdir(lowercase__			)[0]  # Comet is nice, it's just a zip file here
              # We parse the raw logs
              UpperCamelCase__:					Any												=      os.path.join(lowercase__					,  lowercase__			)
              UpperCamelCase__:					Optional[Any]												=      zipfile.ZipFile(lowercase__					,  "r"			)
              UpperCamelCase__:					Optional[int]												=      archive.open("messages.json"			).read().decode("utf-8"			)
          UpperCamelCase__:					str												=      log.split("\n"			)[:-1]
          self.assertEqual(self.get_value_from_key(lowercase__					,  "num_iterations"					,  lowercase__			)					,  12			)
          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 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


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

      """simple docstring"""


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

      UpperCamelCase__       						=   "my_custom_tracker"
      UpperCamelCase__       						=   False






      def __init__(						self:	Optional[int]					,  __lowerCamelCase:	Union[str, Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Tuple												=      open(F"{dir}/log.csv"					,  "w+"			)
          UpperCamelCase__:					Union[str, Any]												=      csv.DictWriter(self.f					,  fieldnames=self._col_names			)
          self.writer.writeheader()






      @property
      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          return self.writer






      def 	UpperCAmelCase_   (						self:	Optional[int]					,  __lowerCamelCase:	int			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	List[Any]					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	List[Any]			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          self.f.close()




class    _a  (  unittest.TestCase):

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          with tempfile.TemporaryDirectory() as d:
              UpperCamelCase__:					Tuple												=      MyCustomTracker(lowercase__			)
              UpperCamelCase__:					Dict												=      Accelerator(log_with=lowercase__			)
              UpperCamelCase__:					List[Any]												=      {"num_iterations": 12, "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:
                  UpperCamelCase__:					Dict												=      csv.DictReader(lowercase__			)
                  UpperCamelCase__:					List[str]												=      next(lowercase__			)
                  UpperCamelCase__:					List[Any]												=      {
                      "total_loss": "",
                      "iteration": "",
                      "my_text": "",
                      "learning_rate": "0.01",
                      "num_iterations": "12",
                      "some_boolean": "False",
                      "some_string": "some_value",
                  }
                  self.assertDictEqual(lowercase__					,  lowercase__			)






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          with tempfile.TemporaryDirectory() as d:
              UpperCamelCase__:					Union[str, Any]												=      MyCustomTracker(lowercase__			)
              UpperCamelCase__:					List[Any]												=      Accelerator(log_with=lowercase__			)
              accelerator.init_trackers("Some name"			)
              UpperCamelCase__:					Optional[Any]												=      {"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:
                  UpperCamelCase__:					List[Any]												=      csv.DictReader(lowercase__			)
                  UpperCamelCase__:					Tuple												=      next(lowercase__			)
                  UpperCamelCase__:					List[Any]												=      {
                      "total_loss": "0.1",
                      "iteration": "1",
                      "my_text": "some_value",
                      "learning_rate": "",
                      "num_iterations": "",
                      "some_boolean": "",
                      "some_string": "",
                  }
                  self.assertDictEqual(lowercase__					,  lowercase__			)








import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax




@require_flax
class    _a  :

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	int			):
          '''simple docstring'''


          UpperCamelCase__							,				UpperCamelCase__:					Optional[int]												=      self.prepare_init_args_and_inputs_for_common()

          UpperCamelCase__:					Tuple												=      self.model_class(**__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      model.init(inputs_dict["prng_key"]					,  inputs_dict["sample"]			)
          jax.lax.stop_gradient(__lowerCamelCase			)

          UpperCamelCase__:					int												=      model.apply(__lowerCamelCase					,  inputs_dict["sample"]			)

          if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
              UpperCamelCase__:					str												=      output.sample

          self.assertIsNotNone(__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      inputs_dict["sample"].shape
          self.assertEqual(output.shape					,  __lowerCamelCase					,  "Input and output shapes do not match"			)






      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          UpperCamelCase__							,				UpperCamelCase__:					List[str]												=      self.prepare_init_args_and_inputs_for_common()

          UpperCamelCase__:					List[Any]												=      16
          UpperCamelCase__:					int												=      (16, 32)

          UpperCamelCase__:					Tuple												=      self.model_class(**__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      model.init(inputs_dict["prng_key"]					,  inputs_dict["sample"]			)
          jax.lax.stop_gradient(__lowerCamelCase			)

          UpperCamelCase__:					Union[str, Any]												=      model.apply(__lowerCamelCase					,  inputs_dict["sample"]			)

          if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
              UpperCamelCase__:					Dict												=      output.sample

          self.assertIsNotNone(__lowerCamelCase			)
          UpperCamelCase__:					Optional[Any]												=      inputs_dict["sample"].shape
          self.assertEqual(output.shape					,  __lowerCamelCase					,  "Input and output shapes do not match"			)






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      "kwargs" in inspect.signature(self.model_class.__init__			).parameters
          UpperCamelCase__:					Any												=      len(self.model_class._deprecated_kwargs			) > 0

          if has_kwarg_in_model_class and not has_deprecated_kwarg:
              raise ValueError(
                  F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
                  " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
                  " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
                  " [<deprecated_argument>]`"			)

          if not has_kwarg_in_model_class and has_deprecated_kwarg:
              raise ValueError(
                  F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
                  " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
                  F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
                  " from `_deprecated_kwargs = [<deprecated_argument>]`"			)

