code: 
  
  
  '''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__	:      Any				=						logging.getLogger(__name__)
  
  
  
  
  
  @require_tensorboard
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  Union[str, Any]:
  									__lowerCamelCase     :			str              =	"test_project_with_config"
  									with tempfile.TemporaryDirectory() as dirpath:
  														__lowerCamelCase     :			Optional[int]              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)
  														__lowerCamelCase     :			Optional[Any]              =	{"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[Any]              =	list(filter(lambda SCREAMING_SNAKE_CASE_			: x.is_file()    ,  child.iterdir()			)			)[0]
  														self.assertNotEqual(str(lowerCamelCase__			)    ,  ''			)
  
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  Tuple:
  									__lowerCamelCase     :			Union[str, Any]              =	"test_project_with_log"
  									with tempfile.TemporaryDirectory() as dirpath:
  														__lowerCamelCase     :			Any              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)
  														accelerator.init_trackers(lowerCamelCase__			)
  														__lowerCamelCase     :			Tuple              =	{"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     :			int              =	list(filter(lambda SCREAMING_SNAKE_CASE_			: x.is_file()    ,  Path(f'{dirpath}/{project_name}'			).iterdir()			)			)[0]
  														self.assertNotEqual(str(lowerCamelCase__			)    ,  ''			)
  
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  Union[str, Any]:
  									__lowerCamelCase     :			Optional[Any]              =	"test_project_with_log"
  									with tempfile.TemporaryDirectory() as dirpath:
  														__lowerCamelCase     :			List[str]              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)
  														accelerator.init_trackers(lowerCamelCase__			)
  														__lowerCamelCase     :			Dict              =	{"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     :			Tuple              =	list(filter(lambda SCREAMING_SNAKE_CASE_			: 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     :			Optional[Any]              =	f.read()
  														__lowerCamelCase     :			List[str]              =	False
  														while data:
  																			__lowerCamelCase     :			List[Any]              =	struct.unpack('Q'    ,  data[:8]			)
  
  																			__lowerCamelCase     :			Any              =	data[12 : 12 + int(header[0]			)]  # 8+4
  																			__lowerCamelCase     :			Optional[Any]              =	data[12 + int(header[0]			) + 4 :]
  																			__lowerCamelCase     :			Dict              =	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     :			List[Any]              =	True
  														self.assertTrue(lowerCamelCase__    ,  'Converted tensor was not found in the log file!'			)
  
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  Any:
  									with self.assertRaisesRegex(lowerCamelCase__    ,  'Logging with `tensorboard` requires a `logging_dir`'			):
  														__lowerCamelCase     :			str              =	Accelerator(log_with='tensorboard'			)
  									with tempfile.TemporaryDirectory() as dirpath:
  														__lowerCamelCase     :			int              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)
  
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  Tuple:
  									__lowerCamelCase     :			str              =	ProjectConfiguration(total_limit=30			)
  									with tempfile.TemporaryDirectory() as dirpath:
  														__lowerCamelCase     :			Dict              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__    ,  project_config=lowerCamelCase__			)
  
  
  
  
  
  
  
  @require_wandb
  @mock.patch.dict(os.environ      ,	{'WANDB_MODE': 'offline'}						)
  class      UpperCAmelCase_						(_a      ,	_a						):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				def    lowercase_       (							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    lowercase_       (							SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = True			)       ->  int:
  									__lowerCamelCase     :			Any              =	rf'{section} ([\S\s]*?)\n\n'
  									if record:
  														__lowerCamelCase     :			Any              =	rf'Record: {pattern}'
  									__lowerCamelCase     :			Dict              =	re.findall(lowerCamelCase__    ,  lowerCamelCase__			)[0]
  									# A config
  									if section == "config" or section == "history":
  														__lowerCamelCase     :			Optional[Any]              =	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    lowercase_       (							self			)       ->  Any:
  									__lowerCamelCase     :			Any              =	"test_project_with_config"
  									__lowerCamelCase     :			Optional[int]              =	Accelerator(log_with='wandb'			)
  									__lowerCamelCase     :			int              =	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
  									__lowerCamelCase     :			Optional[int]              =	{"wandb": {"tags": ["my_tag"]}}
  									accelerator.init_trackers(lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__			)
  									__lowerCamelCase     :			Dict              =	{"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     :			Any              =	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     :			int              =	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     :			Optional[Any]              =	self.parse_log(lowerCamelCase__    ,  'run'    ,  lowerCamelCase__			)
  									self.assertEqual(logged_items['tags']    ,  'my_tag'			)
  
  									# Actual logging
  									__lowerCamelCase     :			Union[str, Any]              =	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     UpperCAmelCase__      (			self					:       str   ,					UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       int							)  ->	Optional[int]:
  					__lowerCamelCase     :			Dict              =	run_name
  					__lowerCamelCase     :			Union[str, 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(_a      ,	'__init__'      ,	_a						)
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				@staticmethod
  				def    lowercase_       (							SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = False			)       ->  int:
  									for log in log_list:
  														__lowerCamelCase     :			Optional[int]              =	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    lowercase_       (							self			)       ->  str:
  									with tempfile.TemporaryDirectory() as d:
  														__lowerCamelCase     :			Optional[Any]              =	CometMLTracker('test_project_with_config'    ,  lowerCamelCase__			)
  														__lowerCamelCase     :			List[str]              =	Accelerator(log_with=lowerCamelCase__			)
  														__lowerCamelCase     :			List[Any]              =	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
  														accelerator.init_trackers(lowerCamelCase__    ,  lowerCamelCase__			)
  														accelerator.end_training()
  														__lowerCamelCase     :			Optional[Any]              =	os.listdir(lowerCamelCase__			)[0]  # Comet is nice, it's just a zip file here
  														# We parse the raw logs
  														__lowerCamelCase     :			List[Any]              =	os.path.join(lowerCamelCase__    ,  lowerCamelCase__			)
  														__lowerCamelCase     :			Tuple              =	zipfile.ZipFile(lowerCamelCase__    ,  'r'			)
  														__lowerCamelCase     :			Tuple              =	archive.open('messages.json'			).read().decode('utf-8'			)
  									__lowerCamelCase     :			List[Any]              =	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__			)    ,  0.0_1			)
  									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    lowercase_       (							self			)       ->  str:
  									with tempfile.TemporaryDirectory() as d:
  														__lowerCamelCase     :			int              =	CometMLTracker('test_project_with_config'    ,  lowerCamelCase__			)
  														__lowerCamelCase     :			Union[str, Any]              =	Accelerator(log_with=lowerCamelCase__			)
  														accelerator.init_trackers(lowerCamelCase__			)
  														__lowerCamelCase     :			List[str]              =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
  														accelerator.log(lowerCamelCase__    ,  step=0			)
  														accelerator.end_training()
  														__lowerCamelCase     :			Optional[Any]              =	os.listdir(lowerCamelCase__			)[0]  # Comet is nice, it's just a zip file here
  														# We parse the raw logs
  														__lowerCamelCase     :			Dict              =	os.path.join(lowerCamelCase__    ,  lowerCamelCase__			)
  														__lowerCamelCase     :			List[Any]              =	zipfile.ZipFile(lowerCamelCase__    ,  'r'			)
  														__lowerCamelCase     :			List[str]              =	archive.open('messages.json'			).read().decode('utf-8'			)
  									__lowerCamelCase     :			Dict              =	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      UpperCAmelCase_						(_a						):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				lowerCamelCase						:      str							  =    [
  				    'total_loss',
  				    'iteration',
  				    'my_text',
  				    'learning_rate',
  				    'num_iterations',
  				    'some_boolean',
  				    'some_string',
  				]
  
  				lowerCamelCase						:      Optional[Any]							  =    'my_custom_tracker'
  				lowerCamelCase						:      Union[str, Any]							  =    False
  
  
  
  
  
  
  
  				def __init__(							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Dict:
  									__lowerCamelCase     :			Tuple              =	open(f'{dir}/log.csv'    ,  'w+'			)
  									__lowerCamelCase     :			Union[str, Any]              =	csv.DictWriter(self.f    ,  fieldnames=self._col_names			)
  									self.writer.writeheader()
  
  
  
  
  
  
  
  				@property
  				def    lowercase_       (							self			)       ->  Union[str, Any]:
  									return self.writer
  
  
  
  
  
  
  
  				def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
  									logger.info('Call init'			)
  									self.writer.writerow(lowerCamelCase__			)
  
  
  
  
  
  
  
  				def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
  									logger.info('Call log'			)
  									self.writer.writerow(lowerCamelCase__			)
  
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  Optional[int]:
  									self.f.close()
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  Dict:
  									with tempfile.TemporaryDirectory() as d:
  														__lowerCamelCase     :			Optional[int]              =	MyCustomTracker(lowerCamelCase__			)
  														__lowerCamelCase     :			Optional[int]              =	Accelerator(log_with=lowerCamelCase__			)
  														__lowerCamelCase     :			List[str]              =	{"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     :			Dict              =	csv.DictReader(lowerCamelCase__			)
  																			__lowerCamelCase     :			Tuple              =	next(lowerCamelCase__			)
  																			__lowerCamelCase     :			Union[str, Any]              =	{
  																			    "total_loss": "",
  																			    "iteration": "",
  																			    "my_text": "",
  																			    "learning_rate": "0.01",
  																			    "num_iterations": "12",
  																			    "some_boolean": "False",
  																			    "some_string": "some_value",
  																			}
  																			self.assertDictEqual(lowerCamelCase__    ,  lowerCamelCase__			)
  
  
  
  
  
  
  
  				def    lowercase_       (							self			)       ->  int:
  									with tempfile.TemporaryDirectory() as d:
  														__lowerCamelCase     :			str              =	MyCustomTracker(lowerCamelCase__			)
  														__lowerCamelCase     :			List[str]              =	Accelerator(log_with=lowerCamelCase__			)
  														accelerator.init_trackers('Some name'			)
  														__lowerCamelCase     :			Optional[Any]              =	{"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     :			Dict              =	csv.DictReader(lowerCamelCase__			)
  																			__lowerCamelCase     :			Tuple              =	next(lowerCamelCase__			)
  																			__lowerCamelCase     :			List[Any]              =	{
  																			    "total_loss": "0.1",
  																			    "iteration": "1",
  																			    "my_text": "some_value",
  																			    "learning_rate": "",
  																			    "num_iterations": "",
  																			    "some_boolean": "",
  																			    "some_string": "",
  																			}
  																			self.assertDictEqual(lowerCamelCase__    ,  lowerCamelCase__			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  @require_tokenizers
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Any							  =    BartTokenizer
      lowerCamelCase						:      Optional[Any]							  =    BartTokenizerFast
      lowerCamelCase						:      Tuple							  =    True
      lowerCamelCase						:      Optional[Any]							  =    filter_roberta_detectors
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           super().setUp()
           __lowerCamelCase     :			List[Any]              =	[
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           __lowerCamelCase     :			Optional[Any]              =	dict(zip(SCREAMING_SNAKE_CASE_    ,  range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
           __lowerCamelCase     :			Union[str, Any]              =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           __lowerCamelCase     :			int              =	{'unk_token': '<unk>'}
  
           __lowerCamelCase     :			int              =	os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES['vocab_file']			)
           __lowerCamelCase     :			str              =	os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES['merges_file']			)
           with open(self.vocab_file    ,  'w'    ,  encoding='utf-8'			) as fp:
                fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '\n'			)
           with open(self.merges_file    ,  'w'    ,  encoding='utf-8'			) as fp:
                fp.write('\n'.join(SCREAMING_SNAKE_CASE_			)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           kwargs.update(self.special_tokens_map			)
           return self.tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  int:
           kwargs.update(self.special_tokens_map			)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           return "lower newer", "lower newer"
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  str:
           return BartTokenizer.from_pretrained('facebook/bart-large'			)
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  Any:
           return BartTokenizerFast.from_pretrained('facebook/bart-large'			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			str              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
           __lowerCamelCase     :			Union[str, Any]              =	[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
  
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  max_length=len(SCREAMING_SNAKE_CASE_			)    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                self.assertEqual((2, 9)    ,  batch.input_ids.shape			)
                self.assertEqual((2, 9)    ,  batch.attention_mask.shape			)
                __lowerCamelCase     :			str              =	batch.input_ids.tolist()[0]
                self.assertListEqual(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                # Test that special tokens are reset
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Optional[int]              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Tuple              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                # check if input_ids are returned and no labels
                self.assertIn('input_ids'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertIn('attention_mask'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertNotIn('labels'    ,  SCREAMING_SNAKE_CASE_			)
                self.assertNotIn('decoder_attention_mask'    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Any              =	[
               'Summary of the text.',
               'Another summary.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  max_length=32    ,  padding='max_length'    ,  return_tensors='pt'			)
                self.assertEqual(32    ,  targets['input_ids'].shape[1]			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Tuple:
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			Optional[Any]              =	tokenizer(
                    ['I am a small frog' * 10_24, 'I am a small frog']    ,  padding=SCREAMING_SNAKE_CASE_    ,  truncation=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                self.assertEqual(batch.input_ids.shape    ,  (2, 10_24)			)
  
  
  
  
  
  
  
      @require_torch
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			Dict              =	['A long paragraph for summarization.']
           __lowerCamelCase     :			str              =	[
               'Summary of the text.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
                __lowerCamelCase     :			Optional[Any]              =	inputs['input_ids']
                __lowerCamelCase     :			Dict              =	targets['input_ids']
                self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
                self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
                self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
                self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[Any]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                     __lowerCamelCase     :			List[str]              =	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Optional[int]              =	self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Any              =	'A, <mask> AllenNLP sentence.'
                     __lowerCamelCase     :			Tuple              =	tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Union[str, Any]              =	tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_SNAKE_CASE_			)
  
                     # token_type_ids should put 0 everywhere
                     self.assertEqual(sum(tokens_r['token_type_ids']			)    ,  sum(tokens_p['token_type_ids']			)			)
  
                     # attention_mask should put 1 everywhere, so sum over length should be 1
                     self.assertEqual(
                         sum(tokens_r['attention_mask']			) / len(tokens_r['attention_mask']			)    ,  sum(tokens_p['attention_mask']			) / len(tokens_p['attention_mask']			)    ,  )
  
                     __lowerCamelCase     :			int              =	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
                     __lowerCamelCase     :			Optional[Any]              =	tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']			)
  
                     # Rust correctly handles the space before the mask while python doesnt
                     self.assertSequenceEqual(tokens_p['input_ids']    ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
                     self.assertSequenceEqual(tokens_r['input_ids']    ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
  
                     self.assertSequenceEqual(
                         SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
                     self.assertSequenceEqual(
                         SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
  
  
  
  
  
  
  