"""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_    :    Dict											=						logging.getLogger(__name__)


@require_tensorboard
class      _lowercase						(				unittest.TestCase					):

				def     A	(       self    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""test_project_with_config"""
										with tempfile.TemporaryDirectory() as dirpath:
																UpperCAmelCase__   		=						Accelerator(log_with="""tensorboard"""    ,      project_dir=_lowercase       )
																UpperCAmelCase__   		=						{"""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("""*/**"""       ):
																						UpperCAmelCase__   		=						list(filter(lambda _lowercase       : x.is_file()    ,      child.iterdir()       )       )[0]
																self.assertNotEqual(str(_lowercase       )    ,      """"""       )

				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""test_project_with_log"""
										with tempfile.TemporaryDirectory() as dirpath:
																UpperCAmelCase__   		=						Accelerator(log_with="""tensorboard"""    ,      project_dir=_lowercase       )
																accelerator.init_trackers(_lowercase       )
																UpperCAmelCase__   		=						{"""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__   		=						list(filter(lambda _lowercase       : x.is_file()    ,      Path(f'{dirpath}/{project_name}'       ).iterdir()       )       )[0]
																self.assertNotEqual(str(_lowercase       )    ,      """"""       )

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""test_project_with_log"""
										with tempfile.TemporaryDirectory() as dirpath:
																UpperCAmelCase__   		=						Accelerator(log_with="""tensorboard"""    ,      project_dir=_lowercase       )
																accelerator.init_trackers(_lowercase       )
																UpperCAmelCase__   		=						{"""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(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:
																						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(_lowercase       )
																						if event.HasField("""summary"""       ):
																												for value in event.summary.value:
																																		if value.simple_value == 1.0 and value.tag == "tensor":
																																								UpperCAmelCase__   		=						True
																self.assertTrue(_lowercase    ,      """Converted tensor was not found in the log file!"""       )

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										with self.assertRaisesRegex(_lowercase    ,      """Logging with `tensorboard` requires a `logging_dir`"""       ):
																UpperCAmelCase__   		=						Accelerator(log_with="""tensorboard"""       )
										with tempfile.TemporaryDirectory() as dirpath:
																UpperCAmelCase__   		=						Accelerator(log_with="""tensorboard"""    ,      project_dir=_lowercase       )

				def     A	(       self    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						ProjectConfiguration(total_limit=3_0       )
										with tempfile.TemporaryDirectory() as dirpath:
																UpperCAmelCase__   		=						Accelerator(log_with="""tensorboard"""    ,      project_dir=_lowercase    ,      project_config=_lowercase       )


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

				def     A	(       self    :      Any       ):


										'''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     A	(       _lowercase    :      str    ,      _lowercase    :      str    ,      _lowercase    :      bool = True       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						rf'{section} ([\S\s]*?)\n\n'
										if record:
																UpperCAmelCase__   		=						rf'Record: {pattern}'
										UpperCAmelCase__   		=						re.findall(_lowercase    ,      _lowercase       )[0]
										# A config
										if section == "config" or section == "history":
																UpperCAmelCase__   		=						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     A	(       self    :      Tuple       ):


										'''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(_lowercase    ,      _lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						{"""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__   		=						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__   		=						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__   		=						self.parse_log(_lowercase    ,      """run"""    ,      _lowercase       )
										self.assertEqual(logged_items["""tags"""]    ,      """my_tag"""       )

										# Actual logging
										UpperCAmelCase__   		=						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 					a__ (						self    :     Any						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     str     )->		Dict:
						UpperCAmelCase__   		=						run_name
						UpperCAmelCase__   		=						OfflineExperiment(project_name=UpperCAmelCase__						,     offline_directory=UpperCAmelCase__     )
						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(__lowerCamelCase , '__init__' , __lowerCamelCase					)
class      _lowercase						(				unittest.TestCase					):

				@staticmethod
				def     A	(       _lowercase    :      Optional[Any]    ,      _lowercase    :      str    ,      _lowercase    :      bool = False       ):


										'''simple docstring'''
										for log in log_list:
																UpperCAmelCase__   		=						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     A	(       self    :      int       ):


										'''simple docstring'''
										with tempfile.TemporaryDirectory() as d:
																UpperCAmelCase__   		=						CometMLTracker("""test_project_with_config"""    ,      _lowercase       )
																UpperCAmelCase__   		=						Accelerator(log_with=_lowercase       )
																UpperCAmelCase__   		=						{"""num_iterations""": 1_2, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
																accelerator.init_trackers(_lowercase    ,      _lowercase       )
																accelerator.end_training()
																UpperCAmelCase__   		=						os.listdir(_lowercase       )[0]  # Comet is nice, it's just a zip file here
																# We parse the raw logs
																UpperCAmelCase__   		=						os.path.join(_lowercase    ,      _lowercase       )
																UpperCAmelCase__   		=						zipfile.ZipFile(_lowercase    ,      """r"""       )
																UpperCAmelCase__   		=						archive.open("""messages.json"""       ).read().decode("""utf-8"""       )
										UpperCAmelCase__   		=						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     A	(       self    :      List[str]       ):


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

				lowerCamelCase__									=    'my_custom_tracker'
				lowerCamelCase__									=    False

				def __init__(       self    :      int    ,      _lowercase    :      str       ):


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

				@property
				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										return self.writer

				def     A	(       self    :      Tuple    ,      _lowercase    :      dict       ):


										'''simple docstring'''
										logger.info("""Call init"""       )
										self.writer.writerow(_lowercase       )

				def     A	(       self    :      str    ,      _lowercase    :      dict    ,      _lowercase    :      Optional[int]       ):


										'''simple docstring'''
										logger.info("""Call log"""       )
										self.writer.writerow(_lowercase       )

				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										self.f.close()


class      _lowercase						(				unittest.TestCase					):

				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										with tempfile.TemporaryDirectory() as d:
																UpperCAmelCase__   		=						MyCustomTracker(_lowercase       )
																UpperCAmelCase__   		=						Accelerator(log_with=_lowercase       )
																UpperCAmelCase__   		=						{"""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:
																						UpperCAmelCase__   		=						csv.DictReader(_lowercase       )
																						UpperCAmelCase__   		=						next(_lowercase       )
																						UpperCAmelCase__   		=						{
																						    """total_loss""": """""",
																						    """iteration""": """""",
																						    """my_text""": """""",
																						    """learning_rate""": """0.01""",
																						    """num_iterations""": """12""",
																						    """some_boolean""": """False""",
																						    """some_string""": """some_value""",
																						}
																						self.assertDictEqual(_lowercase    ,      _lowercase       )

				def     A	(       self    :      Tuple       ):


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


"""simple docstring"""







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						UpperCamelCase_    :    Union[str, Any]											=						True
						UpperCamelCase_    :    int											=						""""""
except Exception as e:
						UpperCamelCase_    :    Any											=						False
						UpperCamelCase_    :    Any											=						(
						    F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
						)


from PIL import Image  # noqa: E402


class      _lowercase						(				__lowerCamelCase , __lowerCamelCase					):
				lowerCamelCase__									=    'mel_config.json'

				@register_to_config
				def __init__(       self    :      int    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_2_0_5_0    ,      _lowercase    :      int = 2_0_4_8    ,      _lowercase    :      int = 5_1_2    ,      _lowercase    :      int = 8_0    ,      _lowercase    :      int = 3_2    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						hop_length
										UpperCAmelCase__   		=						sample_rate
										UpperCAmelCase__   		=						n_fft
										UpperCAmelCase__   		=						top_db
										UpperCAmelCase__   		=						n_iter
										self.set_resolution(_lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error       )

				def     A	(       self    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						x_res
										UpperCAmelCase__   		=						y_res
										UpperCAmelCase__   		=						self.y_res
										UpperCAmelCase__   		=						self.x_res * self.hop_length - 1

				def     A	(       self    :      Dict    ,      _lowercase    :      str = None    ,      _lowercase    :      np.ndarray = None       ):


										'''simple docstring'''
										if audio_file is not None:
																UpperCAmelCase__		, UpperCAmelCase__   		=						librosa.load(_lowercase    ,      mono=_lowercase    ,      sr=self.sr       )
										else:
																UpperCAmelCase__   		=						raw_audio

										# Pad with silence if necessary.
										if len(self.audio       ) < self.x_res * self.hop_length:
																UpperCAmelCase__   		=						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio       ),)       )]       )

				def     A	(       self    :      int       ):


										'''simple docstring'''
										return len(self.audio       ) // self.slice_size

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      int = 0       ):


										'''simple docstring'''
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										return self.sr

				def     A	(       self    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowercase       )    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_mels=self.n_mels       )
										UpperCAmelCase__   		=						librosa.power_to_db(_lowercase    ,      ref=np.max    ,      top_db=self.top_db       )
										UpperCAmelCase__   		=						(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,      2_5_5       ) + 0.5).astype(np.uinta       )
										UpperCAmelCase__   		=						Image.fromarray(_lowercase       )
										return image

				def     A	(       self    :      Dict    ,      _lowercase    :      Image.Image       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.frombuffer(image.tobytes()    ,      dtype="""uint8"""       ).reshape((image.height, image.width)       )
										UpperCAmelCase__   		=						bytedata.astype("""float"""       ) * self.top_db / 2_5_5 - self.top_db
										UpperCAmelCase__   		=						librosa.db_to_power(_lowercase       )
										UpperCAmelCase__   		=						librosa.feature.inverse.mel_to_audio(
										    _lowercase    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_iter=self.n_iter       )
										return audio

