






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

__lowerCamelCase						= logging.getLogger(__name__)

@require_tensorboard
class      UpperCamelCase_							(				unittest.TestCase       ):




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




						def      snake_case__(    self       )			->						Union[str, Any]:
													_a  :    int			=						'''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																				_a  :    Dict			=						Accelerator(log_with='''tensorboard'''		,	project_dir=lowercase       )
																				accelerator.init_trackers(lowercase       )
																				_a  :    str			=						{'''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
																				_a  :    int			=						list(filter(lambda lowercase       : x.is_file()		,	Path(F'{dirpath}/{project_name}'       ).iterdir()       )       )[0]
																				self.assertNotEqual(str(lowercase       )		,	''''''       )




						def      snake_case__(    self       )			->						str:
													_a  :    int			=						'''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																				_a  :    int			=						Accelerator(log_with='''tensorboard'''		,	project_dir=lowercase       )
																				accelerator.init_trackers(lowercase       )
																				_a  :    int			=						{'''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
																				_a  :    Optional[int]			=						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:
																											_a  :    List[str]			=						f.read()
																				_a  :    int			=						False
																				while data:
																											_a  :    Union[str, Any]			=						struct.unpack('''Q'''		,	data[:8]       )

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




						def      snake_case__(    self       )			->						str:
													with self.assertRaisesRegex(lowercase		,	'''Logging with `tensorboard` requires a `logging_dir`'''       ):
																				_a  :    str			=						Accelerator(log_with='''tensorboard'''       )
													with tempfile.TemporaryDirectory() as dirpath:
																				_a  :    Union[str, Any]			=						Accelerator(log_with='''tensorboard'''		,	project_dir=lowercase       )







						def      snake_case__(    self       )			->						List[Any]:
													_a  :    Dict			=						ProjectConfiguration(total_limit=30       )
													with tempfile.TemporaryDirectory() as dirpath:
																				_a  :    Union[str, Any]			=						Accelerator(log_with='''tensorboard'''		,	project_dir=lowercase		,	project_config=lowercase       )

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




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

													# Actual logging
													_a  :    Optional[Any]			=						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    UpperCamelCase__  (   self    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		Dict:







							"""simple docstring"""





							_a  :    List[Any]			=						run_name
							_a  :    Optional[Any]			=						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(UpperCamelCase ,       '''__init__''' ,       UpperCamelCase       )
class      UpperCamelCase_							(				unittest.TestCase       ):




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

						lowercase									=  '''my_custom_tracker'''
						lowercase									=  False




						def __init__(    self		,	lowercase       )			->						Tuple:
													_a  :    Union[str, Any]			=						open(F'{dir}/log.csv'		,	'''w+'''       )
													_a  :    Union[str, Any]			=						csv.DictWriter(self.f		,	fieldnames=self._col_names       )
													self.writer.writeheader()




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




						def      snake_case__(    self		,	lowercase       )			->						Optional[Any]:
													logger.info('''Call init'''       )
													self.writer.writerow(lowercase       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Tuple:
													logger.info('''Call log'''       )
													self.writer.writerow(lowercase       )







						def      snake_case__(    self       )			->						Dict:
													self.f.close()

class      UpperCamelCase_							(				unittest.TestCase       ):




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







import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__lowerCamelCase						= logging.get_logger(__name__)

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''semantic'''




						def __init__(    self		,	lowercase=10_000		,	lowercase=True		,	lowercase=768		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=10_048		,	lowercase=129_595		,	lowercase=129_599		,	lowercase=10_000		,	lowercase=256		,	lowercase=49.9		,	**lowercase		,	)			->						int:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	eos_token_id=lowercase		,	renormalize_logits=lowercase		,	max_new_tokens=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    Optional[int]			=						text_encoding_offset
													_a  :    str			=						text_pad_token
													_a  :    List[str]			=						eos_token_id
													_a  :    Tuple			=						semantic_infer_token
													_a  :    int			=						semantic_vocab_size
													_a  :    List[str]			=						max_input_semantic_length
													_a  :    Union[str, Any]			=						semantic_rate_hz

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''coarse_acoustics'''




						def __init__(    self		,	lowercase=True		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=12_048		,	lowercase=75		,	lowercase=2		,	lowercase=12_050		,	lowercase=256		,	lowercase = 630		,	lowercase = 60		,	**lowercase		,	)			->						List[Any]:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	renormalize_logits=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    str			=						coarse_semantic_pad_token
													_a  :    Any			=						coarse_rate_hz
													_a  :    Dict			=						n_coarse_codebooks
													_a  :    str			=						coarse_infer_token
													_a  :    List[Any]			=						max_coarse_input_length
													_a  :    Union[str, Any]			=						max_coarse_history
													_a  :    Any			=						sliding_window_len

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''fine_acoustics'''




						def __init__(    self		,	lowercase=0.5		,	lowercase=512		,	lowercase=1_024		,	lowercase=8		,	**lowercase		,	)			->						str:
													super().__init__(temperature=lowercase       )

													_a  :    Tuple			=						max_fine_history_length
													_a  :    Tuple			=						max_fine_input_length
													_a  :    Any			=						n_fine_codebooks

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''bark'''
						lowercase									=  True




						def __init__(    self		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase=24_000		,	lowercase=1_024		,	**lowercase		,	)			->						List[str]:
													if semantic_config is None:
																				_a  :    str			=						{}
																				logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

													if coarse_acoustics_config is None:
																				_a  :    Any			=						{}
																				logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''       )

													if fine_acoustics_config is None:
																				_a  :    int			=						{}
																				logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''       )

													_a  :    List[Any]			=						BarkSemanticGenerationConfig(**lowercase       )
													_a  :    Union[str, Any]			=						BarkCoarseGenerationConfig(**lowercase       )
													_a  :    str			=						BarkFineGenerationConfig(**lowercase       )

													_a  :    List[str]			=						sample_rate
													_a  :    str			=						codebook_size




						@classmethod
						def      snake_case__(    cls		,	lowercase		,	lowercase		,	lowercase		,	**lowercase		,	)			->						List[Any]:
													return cls(
													    semantic_config=semantic_config.to_dict()		,	coarse_acoustics_config=coarse_acoustics_config.to_dict()		,	fine_acoustics_config=fine_acoustics_config.to_dict()		,	**lowercase		,	)







						def      snake_case__(    self       )			->						List[str]:
													_a  :    Any			=						copy.deepcopy(self.__dict__       )

													_a  :    str			=						self.semantic_config.to_dict()
													_a  :    Any			=						self.coarse_acoustics_config.to_dict()
													_a  :    Union[str, Any]			=						self.fine_acoustics_config.to_dict()

													_a  :    List[str]			=						self.__class__.model_type
													return output