code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  SCREAMING_SNAKE_CASE       = "\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"
  
  SCREAMING_SNAKE_CASE       = "\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"
  
  SCREAMING_SNAKE_CASE       = "\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    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).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    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`).\n    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:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    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`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION							)
  class 			UpperCAmelCase_						(		datasets.Metric							):
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							Any:
  
  
  
  
       '''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 							__magic_name__							(					self		: Any			,				snake_case_		: str			,				snake_case_		: Dict			,				snake_case_		: Dict="exp"			,				snake_case_		: str=None			,				snake_case_		: Any=False			,				snake_case_		: Union[str, Any]=False			,				snake_case_		: Optional[int]=None			,				snake_case_		: Union[str, Any]=False			,				)   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       len(references[0]   )
       if any(len(snake_case_   ) != references_per_prediction for refs in references   ):
        raise ValueError("Sacrebleu requires the same number of references for each prediction"   )
       A__ =       [[refs[i] for refs in references] for i in range(snake_case_   )]
       A__ =       scb.corpus_bleu(
           snake_case_			,				snake_case_			,				smooth_method=snake_case_			,				smooth_value=snake_case_			,				force=snake_case_			,				lowercase=snake_case_			,				use_effective_order=snake_case_			,				**({"tokenize": tokenize} if tokenize else {})			,				)
       A__ =       {
           "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 logging
  from pathlib import Path
  
  import numpy as np
  import pytorch_lightning as pl
  import torch
  from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
  from pytorch_lightning.utilities import rank_zero_only
  
  from utils import save_json
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[Any]:
   A__ =       filter(lambda lowercase_		: p.requires_grad    ,				model.parameters()		)
   A__ =       sum([np.prod(p.size()		) for p in model_parameters]		)
   return params
  
  
  SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)
  
  
  
  
  class 			UpperCAmelCase_						(		pl.Callback							):
  
  
  
  
      def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Tuple   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
       pl_module.logger.log_metrics(snake_case_   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule			,				snake_case_		: str			,				snake_case_		: List[str]=True   )   ->							None:
  
  
  
  
       '''simple docstring'''
  
       logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
       A__ =       trainer.callback_metrics
       trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}   )
       # Log results
       A__ =       Path(pl_module.hparams.output_dir   )
       if type_path == "test":
        A__ =       od / "test_results.txt"
        A__ =       od / "test_generations.txt"
       else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        A__ =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
        A__ =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=snake_case_   )
        generations_file.parent.mkdir(exist_ok=snake_case_   )
       with open(snake_case_			,				"a+"   ) as writer:
        for key in sorted(snake_case_   ):
         if key in ["log", "progress_bar", "preds"]:
          continue
         A__ =       metrics[key]
         if isinstance(snake_case_			,				torch.Tensor   ):
          A__ =       val.item()
         A__ =       F"""{key}: {val:.6f}\n"""
         writer.write(snake_case_   )
  
       if not save_generations:
        return
  
       if "preds" in metrics:
        A__ =       "\n".join(metrics["preds"]   )
        generations_file.open("w+"   ).write(snake_case_   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: str			,				snake_case_		: Tuple			,				snake_case_		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       try:
        A__ =       pl_module.model.model.num_parameters()
       except AttributeError:
        A__ =       pl_module.model.num_parameters()
  
       A__ =       count_trainable_parameters(snake_case_   )
       # mp stands for million parameters
       trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
       return self._write_logs(snake_case_			,				snake_case_			,				"test"   )
  
  
  
  
      @rank_zero_only
      def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: pl.Trainer			,				snake_case_		: int   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
       # Uncommenting this will save val generations
       # return self._write_logs(trainer, pl_module, "valid")
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=1    ,				lowercase_=False		)		->	Dict:
   if metric == "rouge2":
    A__ =       "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
    A__ =       "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "loss":
    A__ =       "{val_avg_loss:.4f}-{step_count}"
   else:
    raise NotImplementedError(
        f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
        " this function."		)
  
   A__ =       ModelCheckpoint(
       dirpath=lowercase_    ,				filename=lowercase_    ,				monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				save_top_k=lowercase_    ,				)
   return checkpoint_callback
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   return EarlyStopping(
       monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				patience=lowercase_    ,				verbose=lowercase_    ,				)
  
  
  
  
  
  