code: 
  '''simple docstring'''
  
  
  
  import concurrent.futures
  import json
  import os
  import shutil
  import tempfile
  import unittest
  
  from transformers import AutoTokenizer, PreTrainedTokenizerFast
  from transformers.testing_utils import require_tokenizers
  
  from ..test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class 			A							(       __UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[str]       		=  PreTrainedTokenizerFast
  			lowerCamelCase							:							Tuple       		=  False
  			lowerCamelCase							:							Dict       		=  True
  			lowerCamelCase							:							Any       		=  """tokenizer_file"""
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       False  # because we don't have pretrained_vocab_files_map
  									super().setUp()
  									lowercase__							 =       True
  
  									lowercase__							 =       ["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
  									lowercase__							 =       """SaulLu/dummy-tokenizer-bytelevel-bpe"""
  
  									# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
  									lowercase__							 =       [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
  
  									lowercase__							 =       PreTrainedTokenizerFast.from_pretrained(model_paths[0]       )
  									tokenizer.save_pretrained(self.tmpdirname       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.tmpdirname
  									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
  									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
  																					try:
  																											lowercase__							 =       tempfile.mkdtemp()
  																											lowercase__							 =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase__       ,     **lowerCamelCase__       )
  
  																											tokenizer.save_pretrained(self.tmpdirname       )
  																											super().test_training_new_tokenizer()
  																					finally:
  																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
  																											# is restored
  																											shutil.rmtree(self.tmpdirname       )
  																											lowercase__							 =       tmpdirname_orig
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.tmpdirname
  									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
  									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
  																					try:
  																											lowercase__							 =       tempfile.mkdtemp()
  																											lowercase__							 =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase__       ,     **lowerCamelCase__       )
  
  																											tokenizer.save_pretrained(self.tmpdirname       )
  																											super().test_training_new_tokenizer_with_special_tokens_change()
  																					finally:
  																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
  																											# is restored
  																											shutil.rmtree(self.tmpdirname       )
  																											lowercase__							 =       tmpdirname_orig
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name       )
  
  									lowercase__							 =       ("""a""" for _ in range(1_000       ))
  									lowercase__							 =       tokenizer.train_new_from_iterator(text_iterator=lowerCamelCase__       ,     length=1_000       ,     vocab_size=50       )
  
  									lowercase__							 =       new_tokenizer.encode("""a🤗"""       )
  									self.assertEqual(lowerCamelCase__       ,     [64, 172, 253, 97, 245]       )
  
  
  
  
  
  @require_tokenizers
  class 			A							(       unittest.TestCase				):
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       AutoTokenizer.from_pretrained("""bert-base-cased"""       )
  									lowercase__							 =       json.loads(tokenizer._tokenizer.to_str()       )
  									lowercase__							 =       len(lowerCamelCase__       )
  
  									with tempfile.TemporaryDirectory() as tmp_dir:
  															# Hack to save this in the tokenizer_config.json
  															lowercase__							 =       ["""tokenizer.4.0.0.json"""]
  															tokenizer.save_pretrained(lowerCamelCase__       )
  															json.dump(lowerCamelCase__       ,     open(os.path.join(lowerCamelCase__       ,     """tokenizer.4.0.0.json"""       )       ,     """w"""       )       )
  
  															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
  															lowercase__							 =       AutoTokenizer.from_pretrained(lowerCamelCase__       )
  															self.assertEqual(len(lowerCamelCase__       )       ,     len(lowerCamelCase__       ) + 1       )
  															lowercase__							 =       json.loads(new_tokenizer._tokenizer.to_str()       )
  															self.assertIn("""huggingface"""       ,     json_tokenizer["""model"""]["""vocab"""]       )
  
  															# Will need to be adjusted if we reach v42 and this test is still here.
  															# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
  															shutil.move(os.path.join(lowerCamelCase__       ,     """tokenizer.4.0.0.json"""       )       ,     os.path.join(lowerCamelCase__       ,     """tokenizer.42.0.0.json"""       )       )
  															lowercase__							 =       ["""tokenizer.42.0.0.json"""]
  															tokenizer.save_pretrained(lowerCamelCase__       )
  															lowercase__							 =       AutoTokenizer.from_pretrained(lowerCamelCase__       )
  															self.assertEqual(len(lowerCamelCase__       )       ,     len(lowerCamelCase__       )       )
  															lowercase__							 =       json.loads(new_tokenizer._tokenizer.to_str()       )
  															self.assertNotIn("""huggingface"""       ,     json_tokenizer["""model"""]["""vocab"""]       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       """hf-internal-testing/test-two-tokenizers"""
  
  									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
  									lowercase__							 =       AutoTokenizer.from_pretrained(lowerCamelCase__       )
  									self.assertEqual(len(lowerCamelCase__       )       ,     28_997       )
  									lowercase__							 =       json.loads(tokenizer._tokenizer.to_str()       )
  									self.assertIn("""huggingface"""       ,     json_tokenizer["""model"""]["""vocab"""]       )
  
  									# Testing an older version by monkey-patching the version in the module it's used.
  									import transformers as old_transformers
  
  									lowercase__							 =       """3.0.0"""
  									lowercase__							 =       old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCamelCase__       )
  									self.assertEqual(len(lowerCamelCase__       )       ,     28_996       )
  									lowercase__							 =       json.loads(old_tokenizer._tokenizer.to_str()       )
  									self.assertNotIn("""huggingface"""       ,     json_tokenizer["""model"""]["""vocab"""]       )
  
  
  
  
  
  @require_tokenizers
  class 			A							(       unittest.TestCase				):
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""       )
  									lowercase__							 =       """The Matrix is a 1999 science fiction action film."""
  
  									with concurrent.futures.ThreadPoolExecutor() as executor:
  															lowercase__							 =       [executor.submit(self.fetch       ,     lowerCamelCase__       ,     lowerCamelCase__       ) for i in range(10       )]
  															lowercase__							 =       [future.result() for future in futures]
  															self.assertEqual(lowerCamelCase__       ,     [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10       )]       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return tokenizer.encode(lowerCamelCase__       ,     truncation="""longest_first"""       ,     padding="""longest"""       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  __A          =    torch.cuda.is_available()
  __A          =    [" Sam ate lunch today.", "Sams lunch ingredients."]
  __A          =    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  __A          =    {
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-0_8,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       """\n""".join(lowercase__	)
  						Path(lowercase__	).open("""w"""	).writelines(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						for split in ["train", "val", "test"]:
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.source'''	)		,		lowercase__	)
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.target'''	)		,		lowercase__	)
  						return tmp_dir
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls
  
  
  
  			@require_torch_multi_gpu
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """no_teacher""": True,
  									    """freeze_encoder""": True,
  									    """gpus""": 2,
  									    """overwrite_output_dir""": True,
  									    """sortish_sampler""": True,
  									}
  									self._test_distiller_cli_fork(lowerCamelCase__       ,     check_contents=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=True       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """label_smoothing""": 0.0,
  									    """early_stopping_patience""": -1,
  									    """train_batch_size""": 1,
  									    """eval_batch_size""": 2,
  									    """max_epochs""": 2,
  									    """alpha_mlm""": 0.2,
  									    """alpha_ce""": 0.8,
  									    """do_predict""": True,
  									    """model_name_or_path""": """sshleifer/tinier_bart""",
  									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
  									    """val_check_interval""": 0.5,
  									}
  									default_updates.update(lowerCamelCase__       )
  									lowercase__							 =       CHEAP_ARGS.copy()
  									lowercase__							 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  									lowercase__							 =       self.get_auto_remove_tmp_dir()
  									args_d.update(data_dir=lowerCamelCase__       ,     output_dir=lowerCamelCase__       ,     **lowerCamelCase__       )
  
  									def convert(lowerCamelCase__       ,     lowerCamelCase__       ):
  															if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
  																					return ""
  															if v is False or v is None:
  																					return ""
  															if v is True:  # or len(str(v))==0:
  																					return F'''--{k}'''
  															return F'''--{k}={v}'''
  
  									lowercase__							 =       [x for x in (convert(lowerCamelCase__       ,     lowerCamelCase__       ) for k, v in args_d.items()) if len(lowerCamelCase__       )]
  									lowercase__							 =       [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
  									execute_subprocess_async(lowerCamelCase__       ,     env=self.get_env()       )
  
  									lowercase__							 =       os.listdir(lowerCamelCase__       )
  									lowercase__							 =       {os.path.basename(lowerCamelCase__       ) for p in contents}
  									lowercase__							 =       [p for p in contents if p.endswith("""ckpt"""       )]
  									assert len(lowerCamelCase__       ) > 0
  
  									self.assertIn("""test_generations.txt"""       ,     lowerCamelCase__       )
  									self.assertIn("""test_results.txt"""       ,     lowerCamelCase__       )
  
  									# get the following from the module, (we don't have access to `model` here)
  									lowercase__							 =       os.path.join(lowerCamelCase__       ,     """metrics.json"""       )
  									lowercase__							 =       """rouge2"""
  
  									lowercase__							 =       load_json(lowerCamelCase__       )
  									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
  									print(lowerCamelCase__       )
  									lowercase__							 =       metrics["""val"""][-1]
  									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]       ,     0.01       )
  									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']       ,     lowerCamelCase__       )
  									self.assertEqual(len(metrics["""test"""]       )       ,     1       )
  									lowercase__							 =       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1       )
  									self.assertEqual(len(metrics["""val"""]       )       ,     lowerCamelCase__       )
  
  
  
  
  
  
  