


"""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			 =   logging.getLogger(__name__)
@require_tensorboard
class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






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






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






							def 					lowerCAmelCase     (						self :     List[Any]				):
													UpperCAmelCase__		=    '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''		, project_dir=lowerCamelCase_				)
																			accelerator.init_trackers(lowerCamelCase_				)
																			UpperCAmelCase__		=    {'''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
																			UpperCAmelCase__		=    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:
																									UpperCAmelCase__		=    f.read()
																			UpperCAmelCase__		=    False
																			while data:
																									UpperCAmelCase__		=    struct.unpack('''Q'''		, data[:8]				)

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






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






							def 					lowerCAmelCase     (						self :     Optional[int]				):
													UpperCAmelCase__		=    ProjectConfiguration(total_limit=30				)
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase__		=    Accelerator(log_with='''tensorboard'''		, project_dir=lowerCamelCase_		, project_config=lowerCamelCase_				)




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


							"""simple docstring"""






							def 					lowerCAmelCase     (						self :     Dict				):
													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 					lowerCAmelCase     (						lowerCamelCase_ :     str		, lowerCamelCase_ :     str		, lowerCamelCase_ :     bool = True				):
													UpperCAmelCase__		=    Rf"""{section} ([\S\s]*?)\n\n"""
													if record:
																			UpperCAmelCase__		=    Rf"""Record: {pattern}"""
													UpperCAmelCase__		=    re.findall(lowerCamelCase_		, lowerCamelCase_				)[0]
													# A config
													if section == "config" or section == "history":
																			UpperCAmelCase__		=    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 					lowerCAmelCase     (						self :     List[Any]				):
													UpperCAmelCase__		=    '''test_project_with_config'''
													UpperCAmelCase__		=    Accelerator(log_with='''wandb'''				)
													UpperCAmelCase__		=    {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
													UpperCAmelCase__		=    {'''wandb''': {'''tags''': ['''my_tag''']}}
													accelerator.init_trackers(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)
													UpperCAmelCase__		=    {'''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":
																									UpperCAmelCase__		=    subprocess.check_output(
																									    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(lowerCamelCase_				)]		, env=os.environ.copy()				).decode('''utf8'''		, '''ignore'''				)
																									break

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

													# Actual logging
													UpperCAmelCase__		=    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 					a__ (       self	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						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(__SCREAMING_SNAKE_CASE				,    """__init__"""				,    __SCREAMING_SNAKE_CASE )
class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






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


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      [
							    """total_loss""",
							    """iteration""",
							    """my_text""",
							    """learning_rate""",
							    """num_iterations""",
							    """some_boolean""",
							    """some_string""",
							]

							SCREAMING_SNAKE_CASE										=      """my_custom_tracker"""
							SCREAMING_SNAKE_CASE										=      False






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






							@property
							def 					lowerCAmelCase     (						self :     Any				):
													return self.writer






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     dict				):
													logger.info('''Call init'''				)
													self.writer.writerow(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     dict		, lowerCamelCase_ :     Optional[int]				):
													logger.info('''Call log'''				)
													self.writer.writerow(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Optional[Any]				):
													self.f.close()




class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""






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






							def 					lowerCAmelCase     (						self :     Union[str, Any]				):
													with tempfile.TemporaryDirectory() as d:
																			UpperCAmelCase__		=    MyCustomTracker(lowerCamelCase_				)
																			UpperCAmelCase__		=    Accelerator(log_with=lowerCamelCase_				)
																			accelerator.init_trackers('''Some name'''				)
																			UpperCAmelCase__		=    {'''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:
																									UpperCAmelCase__		=    csv.DictReader(lowerCamelCase_				)
																									UpperCAmelCase__		=    next(lowerCamelCase_				)
																									UpperCAmelCase__		=    {
																									    '''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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						__a			 =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)