


import sacrebleu as scb
from packaging import version

import datasets


UpperCAmelCase			=    """\
@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\",
}
"""

UpperCAmelCase			=    """\
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.
"""

UpperCAmelCase			=    """
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 lowerCAmelCase_						( datasets.Metric       ):

 '''simple docstring'''

 def 						UpperCamelCase__     (	self					):
       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 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase="exp"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=False		,      ):
       snake_case_       =      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'''					)
       snake_case_       =      [[refs[i] for refs in references] for i in range(_UpperCAmelCase					)]
       snake_case_       =      scb.corpus_bleu(
           _UpperCAmelCase		,      _UpperCAmelCase		,      smooth_method=_UpperCAmelCase		,      smooth_value=_UpperCAmelCase		,      force=_UpperCAmelCase		,      lowercase=_UpperCAmelCase		,      use_effective_order=_UpperCAmelCase		,      **({'''tokenize''': tokenize} if tokenize else {})		,      )
       snake_case_       =      {
           '''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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase			=    {
    """configuration_big_bird""": ["""BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BigBirdConfig""", """BigBirdOnnxConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    ["""BigBirdTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BigBirdForCausalLM""",
       """BigBirdForMaskedLM""",
       """BigBirdForMultipleChoice""",
       """BigBirdForPreTraining""",
       """BigBirdForQuestionAnswering""",
       """BigBirdForSequenceClassification""",
       """BigBirdForTokenClassification""",
       """BigBirdLayer""",
       """BigBirdModel""",
       """BigBirdPreTrainedModel""",
       """load_tf_weights_in_big_bird""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase			=    [
       """FlaxBigBirdForCausalLM""",
       """FlaxBigBirdForMaskedLM""",
       """FlaxBigBirdForMultipleChoice""",
       """FlaxBigBirdForPreTraining""",
       """FlaxBigBirdForQuestionAnswering""",
       """FlaxBigBirdForSequenceClassification""",
       """FlaxBigBirdForTokenClassification""",
       """FlaxBigBirdModel""",
       """FlaxBigBirdPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird import BigBirdTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_big_bird_fast import BigBirdTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_big_bird import (
          BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
          BigBirdForCausalLM,
          BigBirdForMaskedLM,
          BigBirdForMultipleChoice,
          BigBirdForPreTraining,
          BigBirdForQuestionAnswering,
          BigBirdForSequenceClassification,
          BigBirdForTokenClassification,
          BigBirdLayer,
          BigBirdModel,
          BigBirdPreTrainedModel,
          load_tf_weights_in_big_bird,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
          FlaxBigBirdPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)