






"""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

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


@require_tensorboard
class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       "test_project_with_config"
												with tempfile.TemporaryDirectory() as dirpath:
																		lowercase__				:      int=       Accelerator(log_with="tensorboard"       ,  project_dir=snake_case__			)
																		lowercase__				:      List[Any]=       {"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("*/**"			):
																								lowercase__				:      List[str]=       list(filter(lambda snake_case__			: x.is_file()       ,  child.iterdir()			)			)[0]
																		self.assertNotEqual(str(snake_case__			)       ,  ""			)




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




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       "test_project_with_log"
												with tempfile.TemporaryDirectory() as dirpath:
																		lowercase__				:      List[Any]=       Accelerator(log_with="tensorboard"       ,  project_dir=snake_case__			)
																		accelerator.init_trackers(snake_case__			)
																		lowercase__				:      str=       {"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
																		lowercase__				:      Dict=       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:
																								lowercase__				:      Union[str, Any]=       f.read()
																		lowercase__				:      List[str]=       False
																		while data:
																								lowercase__				:      List[Any]=       struct.unpack("Q"       ,  data[:8]			)

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




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with self.assertRaisesRegex(snake_case__       ,  "Logging with `tensorboard` requires a `logging_dir`"			):
																		lowercase__				:      Union[str, Any]=       Accelerator(log_with="tensorboard"			)
												with tempfile.TemporaryDirectory() as dirpath:
																		lowercase__				:      Optional[int]=       Accelerator(log_with="tensorboard"       ,  project_dir=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       ProjectConfiguration(total_limit=30			)
												with tempfile.TemporaryDirectory() as dirpath:
																		lowercase__				:      Any=       Accelerator(log_with="tensorboard"       ,  project_dir=snake_case__       ,  project_config=snake_case__			)


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




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''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_       (   snake_case__       ,  snake_case__       ,  snake_case__ = True			):
												'''simple docstring'''
												lowercase__				:      str=       rF'''{section} ([\S\s]*?)\n\n'''
												if record:
																		lowercase__				:      List[str]=       rF'''Record: {pattern}'''
												lowercase__				:      Tuple=       re.findall(snake_case__       ,  snake_case__			)[0]
												# A config
												if section == "config" or section == "history":
																		lowercase__				:      Dict=       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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       "test_project_with_config"
												lowercase__				:      List[str]=       Accelerator(log_with="wandb"			)
												lowercase__				:      Optional[int]=       {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
												lowercase__				:      List[Any]=       {"wandb": {"tags": ["my_tag"]}}
												accelerator.init_trackers(snake_case__       ,  snake_case__       ,  snake_case__			)
												lowercase__				:      int=       {"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":
																								lowercase__				:      str=       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
												lowercase__				:      Tuple=       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
												lowercase__				:      str=       self.parse_log(snake_case__       ,  "run"       ,  snake_case__			)
												self.assertEqual(logged_items["tags"]       ,  "my_tag"			)

												# Actual logging
												lowercase__				:      List[str]=       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 						lowercase__(self					,		A					,		A						)				->Optional[int]:




						"""simple docstring"""



						lowercase__				:      List[Any]=       run_name
						lowercase__				:      Any=       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(SCREAMING_SNAKE_CASE__  ,  "__init__"  ,  SCREAMING_SNAKE_CASE__							)
class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						@staticmethod
						def 		UpperCAmelCase_       (   snake_case__       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												for log in log_list:
																		lowercase__				:      List[Any]=       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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with tempfile.TemporaryDirectory() as d:
																		lowercase__				:      Optional[Any]=       CometMLTracker("test_project_with_config"       ,  snake_case__			)
																		lowercase__				:      Any=       Accelerator(log_with=snake_case__			)
																		lowercase__				:      Union[str, Any]=       {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
																		accelerator.init_trackers(snake_case__       ,  snake_case__			)
																		accelerator.end_training()
																		lowercase__				:      int=       os.listdir(snake_case__			)[0]  # Comet is nice, it's just a zip file here
																		# We parse the raw logs
																		lowercase__				:      Optional[Any]=       os.path.join(snake_case__       ,  snake_case__			)
																		lowercase__				:      Optional[int]=       zipfile.ZipFile(snake_case__       ,  "r"			)
																		lowercase__				:      Dict=       archive.open("messages.json"			).read().decode("utf-8"			)
												lowercase__				:      Any=       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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with tempfile.TemporaryDirectory() as d:
																		lowercase__				:      Tuple=       CometMLTracker("test_project_with_config"       ,  snake_case__			)
																		lowercase__				:      Any=       Accelerator(log_with=snake_case__			)
																		accelerator.init_trackers(snake_case__			)
																		lowercase__				:      int=       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
																		accelerator.log(snake_case__       ,  step=0			)
																		accelerator.end_training()
																		lowercase__				:      Union[str, Any]=       os.listdir(snake_case__			)[0]  # Comet is nice, it's just a zip file here
																		# We parse the raw logs
																		lowercase__				:      Optional[int]=       os.path.join(snake_case__       ,  snake_case__			)
																		lowercase__				:      int=       zipfile.ZipFile(snake_case__       ,  "r"			)
																		lowercase__				:      Union[str, Any]=       archive.open("messages.json"			).read().decode("utf-8"			)
												lowercase__				:      List[str]=       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     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""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       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      str=       open(F'''{dir}/log.csv'''       ,  "w+"			)
												lowercase__				:      str=       csv.DictWriter(self.f       ,  fieldnames=self._col_names			)
												self.writer.writeheader()




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.writer




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												logger.info("Call init"			)
												self.writer.writerow(snake_case__			)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												logger.info("Call log"			)
												self.writer.writerow(snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												self.f.close()


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




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












from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
else:
     from .consistency_models import ConsistencyModelPipeline
     from .dance_diffusion import DanceDiffusionPipeline
     from .ddim import DDIMPipeline
     from .ddpm import DDPMPipeline
     from .dit import DiTPipeline
     from .latent_diffusion import LDMSuperResolutionPipeline
     from .latent_diffusion_uncond import LDMPipeline
     from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
     from .pndm import PNDMPipeline
     from .repaint import RePaintPipeline
     from .score_sde_ve import ScoreSdeVePipeline
     from .stochastic_karras_ve import KarrasVePipeline

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
     from .audioldm import AudioLDMPipeline
     from .controlnet import (
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
     )
     from .deepfloyd_if import (
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
     )
     from .kandinsky import (
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
     )
     from .kandinskya_a import (
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
     )
     from .latent_diffusion import LDMTextToImagePipeline
     from .paint_by_example import PaintByExamplePipeline
     from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
     from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
     from .stable_diffusion import (
         CycleDiffusionPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
     )
     from .stable_diffusion_safe import StableDiffusionPipelineSafe
     from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
     from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
     from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
     from .versatile_diffusion import (
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
     )
     from .vq_diffusion import VQDiffusionPipeline


try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_onnx_objects import *  # noqa F403
else:
     from .onnx_utils import OnnxRuntimeModel

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .stable_diffusion import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_objects import *  # noqa F403
else:
     from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .controlnet import FlaxStableDiffusionControlNetPipeline
     from .stable_diffusion import (
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )
try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline
