


'''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    				=							logging.getLogger(__name__)




@require_tensorboard
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														lowerCAmelCase__          =	'''test_project_with_config'''
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCAmelCase__          =	Accelerator(log_with='''tensorboard'''					,    project_dir=lowerCamelCase_   )
																					lowerCAmelCase__          =	{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																					accelerator.init_trackers(lowerCamelCase_					,    lowerCamelCase_   )
																					accelerator.end_training()
																					for child in Path(F"""{dirpath}/{project_name}"""   ).glob('''*/**'''   ):
																												lowerCAmelCase__          =	list(filter(lambda lowerCamelCase_   : x.is_file()					,    child.iterdir()   )   )[0]
																					self.assertNotEqual(str(lowerCamelCase_   )					,    ''''''   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	'''test_project_with_log'''
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCAmelCase__          =	Accelerator(log_with='''tensorboard'''					,    project_dir=lowerCamelCase_   )
																					accelerator.init_trackers(lowerCamelCase_   )
																					lowerCAmelCase__          =	{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																					accelerator.log(lowerCamelCase_					,    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 lowerCamelCase_   : x.is_file()					,    Path(F"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
																					self.assertNotEqual(str(lowerCamelCase_   )					,    ''''''   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	'''test_project_with_log'''
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCAmelCase__          =	Accelerator(log_with='''tensorboard'''					,    project_dir=lowerCamelCase_   )
																					accelerator.init_trackers(lowerCamelCase_   )
																					lowerCAmelCase__          =	{'''tensor''': torch.tensor(1   )}
																					accelerator.log(lowerCamelCase_					,    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 lowerCamelCase_   : x.is_file()					,    Path(F"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
																					# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
																					with open(lowerCamelCase_					,    '''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(lowerCamelCase_   )
																												if event.HasField('''summary'''   ):
																																			for value in event.summary.value:
																																										if value.simple_value == 1.0 and value.tag == "tensor":
																																																	lowerCAmelCase__          =	True
																					self.assertTrue(lowerCamelCase_					,    '''Converted tensor was not found in the log file!'''   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														with self.assertRaisesRegex(lowerCamelCase_					,    '''Logging with `tensorboard` requires a `logging_dir`'''   ):
																					lowerCAmelCase__          =	Accelerator(log_with='''tensorboard'''   )
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCAmelCase__          =	Accelerator(log_with='''tensorboard'''					,    project_dir=lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	ProjectConfiguration(total_limit=30   )
														with tempfile.TemporaryDirectory() as dirpath:
																					lowerCAmelCase__          =	Accelerator(log_with='''tensorboard'''					,    project_dir=lowerCamelCase_					,    project_config=lowerCamelCase_   )




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





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, 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   __SCREAMING_SNAKE_CASE							(		lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = True   )       ->							List[Any]:
														lowerCAmelCase__          =	rF"""{section} ([\S\s]*?)\n\n"""
														if record:
																					lowerCAmelCase__          =	rF"""Record: {pattern}"""
														lowerCAmelCase__          =	re.findall(lowerCamelCase_					,    lowerCamelCase_   )[0]
														# A config
														if section == "config" or section == "history":
																					lowerCAmelCase__          =	re.findall(r'''"([a-zA-Z0-9_.,]+)'''					,    lowerCamelCase_   )
																					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]+)"'''					,    lowerCamelCase_   )   )







							@skip
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														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(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__          =	{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
														accelerator.log(lowerCamelCase_					,    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(lowerCamelCase_   )]					,    env=os.environ.copy()   ).decode('''utf8'''					,    '''ignore'''   )
																												break

        # Check HPS through careful parsing and cleaning
														lowerCAmelCase__          =	self.parse_log(lowerCamelCase_					,    '''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(lowerCamelCase_					,    '''run'''					,    lowerCamelCase_   )
														self.assertEqual(logged_items['''tags''']					,    '''my_tag'''   )

														# Actual logging
														lowerCAmelCase__          =	self.parse_log(lowerCamelCase_					,    '''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 					_snake_case   (     self ,    A ,    A       )						->    Any:
							lowerCAmelCase__          =	run_name
							lowerCAmelCase__          =	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(a__				,				"__init__"				,				a__  )
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							@staticmethod
							def   __SCREAMING_SNAKE_CASE							(		lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = False   )       ->							Optional[Any]:
														for log in log_list:
																					lowerCAmelCase__          =	json.loads(lowerCamelCase_   )['''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   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														with tempfile.TemporaryDirectory() as d:
																					lowerCAmelCase__          =	CometMLTracker('''test_project_with_config'''					,    lowerCamelCase_   )
																					lowerCAmelCase__          =	Accelerator(log_with=lowerCamelCase_   )
																					lowerCAmelCase__          =	{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																					accelerator.init_trackers(lowerCamelCase_					,    lowerCamelCase_   )
																					accelerator.end_training()
																					lowerCAmelCase__          =	os.listdir(lowerCamelCase_   )[0]  # Comet is nice, it's just a zip file here
																					# We parse the raw logs
																					lowerCAmelCase__          =	os.path.join(lowerCamelCase_					,    lowerCamelCase_   )
																					lowerCAmelCase__          =	zipfile.ZipFile(lowerCamelCase_					,    '''r'''   )
																					lowerCAmelCase__          =	archive.open('''messages.json'''   ).read().decode('''utf-8'''   )
														lowerCAmelCase__          =	log.split('''\n'''   )[:-1]
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''num_iterations'''					,    lowerCamelCase_   )					,    12   )
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''learning_rate'''					,    lowerCamelCase_   )					,    0.01   )
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''some_boolean'''					,    lowerCamelCase_   )					,    lowerCamelCase_   )
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''some_string'''					,    lowerCamelCase_   )					,    '''some_value'''   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														with tempfile.TemporaryDirectory() as d:
																					lowerCAmelCase__          =	CometMLTracker('''test_project_with_config'''					,    lowerCamelCase_   )
																					lowerCAmelCase__          =	Accelerator(log_with=lowerCamelCase_   )
																					accelerator.init_trackers(lowerCamelCase_   )
																					lowerCAmelCase__          =	{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																					accelerator.log(lowerCamelCase_					,    step=0   )
																					accelerator.end_training()
																					lowerCAmelCase__          =	os.listdir(lowerCamelCase_   )[0]  # Comet is nice, it's just a zip file here
																					# We parse the raw logs
																					lowerCAmelCase__          =	os.path.join(lowerCamelCase_					,    lowerCamelCase_   )
																					lowerCAmelCase__          =	zipfile.ZipFile(lowerCamelCase_					,    '''r'''   )
																					lowerCAmelCase__          =	archive.open('''messages.json'''   ).read().decode('''utf-8'''   )
														lowerCAmelCase__          =	log.split('''\n'''   )[:-1]
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''curr_step'''					,    lowerCamelCase_   )					,    0   )
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''total_loss'''   )					,    0.1   )
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''iteration'''   )					,    1   )
														self.assertEqual(self.get_value_from_key(lowerCamelCase_					,    '''my_text'''   )					,    '''some_value'''   )




class 		a__				(							a__  ):





							'''simple docstring'''






							lowercase__			:   List[Any]							=      [
							    "total_loss",
							    "iteration",
							    "my_text",
							    "learning_rate",
							    "num_iterations",
							    "some_boolean",
							    "some_string",
							]

							lowercase__			:   Dict							=      "my_custom_tracker"
							lowercase__			:   List[Any]							=      False







							def __init__(		self					,    lowerCamelCase_   )       ->							List[Any]:
														lowerCAmelCase__          =	open(F"""{dir}/log.csv"""					,    '''w+'''   )
														lowerCAmelCase__          =	csv.DictWriter(self.f					,    fieldnames=self._col_names   )
														self.writer.writeheader()







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														return self.writer







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							List[Any]:
														logger.info('''Call init'''   )
														self.writer.writerow(lowerCamelCase_   )







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







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




class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														with tempfile.TemporaryDirectory() as d:
																					lowerCAmelCase__          =	MyCustomTracker(lowerCamelCase_   )
																					lowerCAmelCase__          =	Accelerator(log_with=lowerCamelCase_   )
																					lowerCAmelCase__          =	{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																					accelerator.init_trackers('''Some name'''					,    lowerCamelCase_   )
																					accelerator.end_training()
																					with open(F"""{d}/log.csv"""					,    '''r'''   ) as f:
																												lowerCAmelCase__          =	csv.DictReader(lowerCamelCase_   )
																												lowerCAmelCase__          =	next(lowerCamelCase_   )
																												lowerCAmelCase__          =	{
																												    '''total_loss''': '''''',
																												    '''iteration''': '''''',
																												    '''my_text''': '''''',
																												    '''learning_rate''': '''0.01''',
																												    '''num_iterations''': '''12''',
																												    '''some_boolean''': '''False''',
																												    '''some_string''': '''some_value''',
																												}
																												self.assertDictEqual(lowerCamelCase_					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														with tempfile.TemporaryDirectory() as d:
																					lowerCAmelCase__          =	MyCustomTracker(lowerCamelCase_   )
																					lowerCAmelCase__          =	Accelerator(log_with=lowerCamelCase_   )
																					accelerator.init_trackers('''Some name'''   )
																					lowerCAmelCase__          =	{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																					accelerator.log(lowerCamelCase_					,    step=0   )
																					accelerator.end_training()
																					with open(F"""{d}/log.csv"""					,    '''r'''   ) as f:
																												lowerCAmelCase__          =	csv.DictReader(lowerCamelCase_   )
																												lowerCAmelCase__          =	next(lowerCamelCase_   )
																												lowerCAmelCase__          =	{
																												    '''total_loss''': '''0.1''',
																												    '''iteration''': '''1''',
																												    '''my_text''': '''some_value''',
																												    '''learning_rate''': '''''',
																												    '''num_iterations''': '''''',
																												    '''some_boolean''': '''''',
																												    '''some_string''': '''''',
																												}
																												self.assertDictEqual(lowerCamelCase_					,    lowerCamelCase_   )



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=							{
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SwitchTransformersEncoderModel''',
		    '''SwitchTransformersForConditionalGeneration''',
		    '''SwitchTransformersModel''',
		    '''SwitchTransformersPreTrainedModel''',
		    '''SwitchTransformersTop1Router''',
		    '''SwitchTransformersSparseMLP''',
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

		__UpperCAmelCase    				=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)