
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       =					logging.getLogger(__name__)





@require_tensorboard
class      _lowerCAmelCase (     unittest.TestCase	):




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




				def 					__a						( self					)    ->				Optional[int]:
							lowerCAmelCase_      =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase_      =     Accelerator(log_with="tensorboard"    ,    project_dir=_UpperCamelCase					)
										accelerator.init_trackers(_UpperCamelCase					)
										lowerCAmelCase_      =     {"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
										lowerCAmelCase_      =     list(filter(lambda _UpperCamelCase					: x.is_file()    ,    Path(f"""{dirpath}/{project_name}"""					).iterdir()					)					)[0]
										self.assertNotEqual(str(_UpperCamelCase					)    ,    ""					)




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase_      =     Accelerator(log_with="tensorboard"    ,    project_dir=_UpperCamelCase					)
										accelerator.init_trackers(_UpperCamelCase					)
										lowerCAmelCase_      =     {"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
										lowerCAmelCase_      =     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:
													lowerCAmelCase_      =     f.read()
										lowerCAmelCase_      =     False
										while data:
													lowerCAmelCase_      =     struct.unpack("Q"    ,    data[:8]					)

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




				def 					__a						( self					)    ->				str:
							with self.assertRaisesRegex(_UpperCamelCase    ,    "Logging with `tensorboard` requires a `logging_dir`"					):
										lowerCAmelCase_      =     Accelerator(log_with="tensorboard"					)
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase_      =     Accelerator(log_with="tensorboard"    ,    project_dir=_UpperCamelCase					)




				def 					__a						( self					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     ProjectConfiguration(total_limit=30					)
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase_      =     Accelerator(log_with="tensorboard"    ,    project_dir=_UpperCamelCase    ,    project_config=_UpperCamelCase					)





@require_wandb
@mock.patch.dict(os.environ ,				{'''WANDB_MODE''': '''offline'''}	)
class      _lowerCAmelCase (     __a ,				__a	):




				def 					__a						( self					)    ->				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 					__a						( _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = True					)    ->				int:
							lowerCAmelCase_      =     rf"""{section} ([\S\s]*?)\n\n"""
							if record:
										lowerCAmelCase_      =     rf"""Record: {pattern}"""
							lowerCAmelCase_      =     re.findall(_UpperCamelCase    ,    _UpperCamelCase					)[0]
							# A config
							if section == "config" or section == "history":
										lowerCAmelCase_      =     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 					__a						( self					)    ->				str:
							lowerCAmelCase_      =     "test_project_with_config"
							lowerCAmelCase_      =     Accelerator(log_with="wandb"					)
							lowerCAmelCase_      =     {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
							lowerCAmelCase_      =     {"wandb": {"tags": ["my_tag"]}}
							accelerator.init_trackers(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     {"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":
													lowerCAmelCase_      =     subprocess.check_output(
													    ["wandb", "sync", "--view", "--verbose", str(_UpperCamelCase					)]    ,    env=os.environ.copy()					).decode("utf8"    ,    "ignore"					)
													break

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

							# Actual logging
							lowerCAmelCase_      =     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  :  Any   ,							__lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  str    ):




			"""simple docstring"""


			lowerCAmelCase_      =     run_name
			lowerCAmelCase_      =     OfflineExperiment(project_name=__lowerCAmelCase   ,							offline_directory=__lowerCAmelCase    )
			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      _lowerCAmelCase (     unittest.TestCase	):




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

				_lowercase      		='''my_custom_tracker'''
				_lowercase      		=False




				def __init__( self    ,    _UpperCamelCase					)    ->				Dict:
							lowerCAmelCase_      =     open(f"""{dir}/log.csv"""    ,    "w+"					)
							lowerCAmelCase_      =     csv.DictWriter(self.f    ,    fieldnames=self._col_names					)
							self.writer.writeheader()




				@property
				def 					__a						( self					)    ->				int:
							return self.writer




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							logger.info("Call init"					)
							self.writer.writerow(_UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				List[str]:
							logger.info("Call log"					)
							self.writer.writerow(_UpperCamelCase					)




				def 					__a						( self					)    ->				str:
							self.f.close()





class      _lowerCAmelCase (     unittest.TestCase	):




				def 					__a						( self					)    ->				Dict:
							with tempfile.TemporaryDirectory() as d:
										lowerCAmelCase_      =     MyCustomTracker(_UpperCamelCase					)
										lowerCAmelCase_      =     Accelerator(log_with=_UpperCamelCase					)
										lowerCAmelCase_      =     {"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:
													lowerCAmelCase_      =     csv.DictReader(_UpperCamelCase					)
													lowerCAmelCase_      =     next(_UpperCamelCase					)
													lowerCAmelCase_      =     {
													    "total_loss": "",
													    "iteration": "",
													    "my_text": "",
													    "learning_rate": "0.01",
													    "num_iterations": "12",
													    "some_boolean": "False",
													    "some_string": "some_value",
													}
													self.assertDictEqual(_UpperCamelCase    ,    _UpperCamelCase					)




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





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							pass



