code: 
  '''simple docstring'''
  
  
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  __A          =    """\
  @inproceedings{post-2018-call,
      title = \"A Call for Clarity in Reporting {BLEU} Scores\",
      author = \"Post, Matt\",
      booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
      month = oct,
      year = \"2018\",
      address = \"Belgium, Brussels\",
      publisher = \"Association for Computational Linguistics\",
      url = \"https://www.aclweb.org/anthology/W18-6319\",
      pages = \"186--191\",
  }
  """
  
  __A          =    """\
  SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
  Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
  It also knows all the standard test sets and handles downloading, processing, and tokenization for you.
  
  See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
  """
  
  __A          =    """
  Produces BLEU scores along with its sufficient statistics
  from a source against one or more references.
  
  Args:
      predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
      references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
      smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
          - `'none'`: no smoothing
          - `'floor'`: increment zero counts
          - `'add-k'`: increment num/denom by k for n>1
          - `'exp'`: exponential decay
      smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
      tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
          - `'none'`: No tokenization.
          - `'zh'`: Chinese tokenization.
          - `'13a'`: mimics the `mteval-v13a` script from Moses.
          - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
          - `'char'`: Language-agnostic character-level tokenization.
          - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
      lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
      force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
      use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.
  
  Returns:
      'score': BLEU score,
      'counts': Counts,
      'totals': Totals,
      'precisions': Precisions,
      'bp': Brevity penalty,
      'sys_len': predictions length,
      'ref_len': reference length,
  
  Examples:
  
      Example 1:
          >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions, references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          100.0
  
      Example 2:
          >>> predictions = [\"hello there general kenobi\",
          ...                 \"on our way to ankh morpork\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"],
          ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions,
          ...                             references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          39.8
  """
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION				)
  class 			A							(       datasets.Metric				):
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if version.parse(scb.__version__       ) < version.parse("""1.4.12"""       ):
  															raise ImportWarning(
  															    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n"""
  															    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""       )
  									return datasets.MetricInfo(
  									    description=_DESCRIPTION       ,     citation=_CITATION       ,     homepage="""https://github.com/mjpost/sacreBLEU"""       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(
  									        {
  									            """predictions""": datasets.Value("""string"""       ,     id="""sequence"""       ),
  									            """references""": datasets.Sequence(datasets.Value("""string"""       ,     id="""sequence"""       )       ,     id="""references"""       ),
  									        }       )       ,     codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]       ,     reference_urls=[
  									        """https://github.com/mjpost/sacreBLEU""",
  									        """https://en.wikipedia.org/wiki/BLEU""",
  									        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
  									    ]       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__="exp"       ,     lowerCamelCase__=None       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       ,     lowerCamelCase__=None       ,     lowerCamelCase__=False       ,     )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       len(references[0]       )
  									if any(len(lowerCamelCase__       ) != references_per_prediction for refs in references       ):
  															raise ValueError("""Sacrebleu requires the same number of references for each prediction"""       )
  									lowercase__							 =       [[refs[i] for refs in references] for i in range(lowerCamelCase__       )]
  									lowercase__							 =       scb.corpus_bleu(
  									    lowerCamelCase__       ,     lowerCamelCase__       ,     smooth_method=lowerCamelCase__       ,     smooth_value=lowerCamelCase__       ,     force=lowerCamelCase__       ,     lowercase=lowerCamelCase__       ,     use_effective_order=lowerCamelCase__       ,     **({"""tokenize""": tokenize} if tokenize else {})       ,     )
  									lowercase__							 =       {
  									    'score': output.score,
  									    'counts': output.counts,
  									    'totals': output.totals,
  									    'precisions': output.precisions,
  									    'bp': output.bp,
  									    'sys_len': output.sys_len,
  									    'ref_len': output.ref_len,
  									}
  									return output_dict
  
  
  
  
  
  
  
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__       )
  
  
  
  
  
  
  