
import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__lowerCAmelCase			:     Tuple           =   '\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\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'

__lowerCAmelCase			:     Union[str, Any]           =   '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

__lowerCAmelCase			:     Union[str, Any]           =   '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,       _KWARGS_DESCRIPTION     )
class        UpperCAmelCase_			( datasets.Metric     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Optional[int]    )			->	Dict:







    """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="""http://www.cs.umd.edu/~snover/tercom/"""			,							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#ter"""]			,							reference_urls=[
            """https://github.com/jhclark/tercom""",
        ]			,							)





  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = False			,							)			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       len(references[0]    )
    if any(len(UpperCamelCase__    ) != references_per_prediction for refs in references    ):
      raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
    __magic_name__				     =       [[refs[i] for refs in references] for i in range(UpperCamelCase__    )]

    __magic_name__				     =       TER(
        normalized=UpperCamelCase__			,							no_punct=UpperCamelCase__			,							asian_support=UpperCamelCase__			,							case_sensitive=UpperCamelCase__			,							)
    __magic_name__				     =       sb_ter.corpus_score(UpperCamelCase__			,							UpperCamelCase__    )

    return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     List[Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     int           =   {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""biogpt"""
  def __init__( self			: List[str]			,							UpperCamelCase__			: Optional[Any]=4_2384			,							UpperCamelCase__			: Union[str, Any]=1024			,							UpperCamelCase__			: Any=24			,							UpperCamelCase__			: Union[str, Any]=16			,							UpperCamelCase__			: Tuple=4096			,							UpperCamelCase__			: Tuple="gelu"			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: str=1024			,							UpperCamelCase__			: Any=0.02			,							UpperCamelCase__			: List[str]=1E-12			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: Optional[int]=True			,							UpperCamelCase__			: Union[str, Any]=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Dict=0			,							UpperCamelCase__			: List[str]=2			,							**UpperCamelCase__			: Optional[int]			,							)			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       initializer_range
    __magic_name__				     =       layer_norm_eps
    __magic_name__				     =       scale_embedding
    __magic_name__				     =       use_cache
    __magic_name__				     =       layerdrop
    __magic_name__				     =       activation_dropout
    super().__init__(pad_token_id=UpperCamelCase__			,							bos_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							**UpperCamelCase__    )

