






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

__snake_case			:			Tuple			   =   logging.getLogger(__name__)


@require_tensorboard
class 			__SCREAMING_SNAKE_CASE    (      unittest.TestCase):






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""



												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												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 			__SCREAMING_SNAKE_CASE    (      UpperCamelCase__						,    UpperCamelCase__):






						def 					UpperCamelCase__					(		self      ):






												"""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 					UpperCamelCase__					(		_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = True      ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												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 		_UpperCamelCase      (			self				:    Optional[Any]			,				UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    int						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  run_name
						lowerCAmelCase__			  =  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 			__SCREAMING_SNAKE_CASE    (      unittest.TestCase):






						@staticmethod
						def 					UpperCamelCase__					(		_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = False      ):






												"""simple docstring"""



												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""



												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""



												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 			__SCREAMING_SNAKE_CASE    (      UpperCamelCase__):
						_SCREAMING_SNAKE_CASE		:				Tuple            =    [
						    """total_loss""",
						    """iteration""",
						    """my_text""",
						    """learning_rate""",
						    """num_iterations""",
						    """some_boolean""",
						    """some_string""",
						]

						_SCREAMING_SNAKE_CASE		:				str            =    """my_custom_tracker"""
						_SCREAMING_SNAKE_CASE		:				Any            =    False






						def __init__(		self   ,  _UpperCamelCase      ):






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






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return self.writer






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






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






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






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







						def 					UpperCamelCase__					(		self      ):






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




class 			__SCREAMING_SNAKE_CASE    (      unittest.TestCase):






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""



												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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""



												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 os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	MPNetTokenizer
     a		:			Dict							=	MPNetTokenizerFast
     a		:			Optional[int]							=	True
     a		:			Any							=	True







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          super().setUp()

          a      =      [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          a      =      os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8" ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          a      =      "UNwant\u00E9d,running"
          a      =      "unwanted, running"
          return input_text, output_text







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.tokenizer_class(self.vocab_file )

          a      =      tokenizer.tokenize("UNwant\u00E9d,running" )
          self.assertListEqual(A  ,		["un", "##want", "##ed", ",", "runn", "##ing"] )
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A )  ,		[9, 6, 7, 12, 10, 11] )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

