




'''simple docstring'''



import sacrebleu as scb
from packaging import version

import datasets


lowerCamelCase_		       =						'\\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_		       =						'\\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'

lowerCamelCase_		       =						'\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 		lowercase_    ( datasets.Metric   ):


   """simple docstring"""

   def 							lowerCAmelCase_			(    self					:	List[str]    ):

      """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 							lowerCAmelCase_			(    self					:	Dict  ,      __lowerCamelCase					:	Dict  ,      __lowerCamelCase					:	Any  ,      __lowerCamelCase					:	List[Any]="exp"  ,      __lowerCamelCase					:	Optional[int]=None  ,      __lowerCamelCase					:	int=False  ,      __lowerCamelCase					:	List[str]=False  ,      __lowerCamelCase					:	Union[str, Any]=None  ,      __lowerCamelCase					:	Any=False  ,      ):

      """simple docstring"""
      _SCREAMING_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"    )
      _SCREAMING_SNAKE_CASE       =      [[refs[i] for refs in references] for i in range(__UpperCamelCase    )]
      _SCREAMING_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 {})  ,      )
      _SCREAMING_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







'''simple docstring'''







import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_lowerCamelCase							:						int								=  get_logger(__name__)


if TYPE_CHECKING:
						from .builder import DatasetBuilder






def 		_lowercase		(	lowercase_						,							lowercase_ = None  ):



		'''simple docstring'''







		_snake_case	       =       importlib.import_module(lowercase_  )

		# TODO(QL): always update the module to add subsequent new authentication
		if hasattr(lowercase_						,							"_patched_for_streaming"  ) and module._patched_for_streaming:
				return

		def wrap_auth(lowercase_  ):
				@wraps(lowercase_  )
				def wrapper(*lowercase_						,							**lowercase_  ):
						return function(*lowercase_						,							use_auth_token=lowercase_						,							**lowercase_  )

				_snake_case	       =       "wrap_auth"
				return wrapper

		# open files in a streaming fashion
		patch_submodule(lowercase_						,							"open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.listdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.walk"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"glob.glob"						,							wrap_auth(lowercase_  )  ).start()
		# allow to navigate in remote zip files
		patch_submodule(lowercase_						,							"os.path.join"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.dirname"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.basename"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.relpath"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.split"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.splitext"						,							lowercase_  ).start()
		# allow checks on paths
		patch_submodule(lowercase_						,							"os.path.exists"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isfile"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.getsize"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pathlib.Path"						,							lowercase_  ).start()
		# file readers
		patch_submodule(lowercase_						,							"gzip.open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"numpy.load"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pandas.read_csv"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"pandas.read_excel"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"scipy.io.loadmat"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"xml.etree.ElementTree.parse"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"xml.dom.minidom.parse"						,							wrap_auth(lowercase_  )  ).start()
		_snake_case	       =       True






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		extend_module_for_streaming(builder.__module__						,							use_auth_token=builder.use_auth_token  )
		# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
		if not builder.__module__.startswith("datasets."  ):  # check that it's not a packaged builder like csv
				for imports in get_imports(inspect.getfile(builder.__class__  )  ):
						if imports[0] == "internal":
								_snake_case	       =       imports[1]
								_snake_case	       =       ".".join(builder.__module__.split("."  )[:-1] + [internal_import_name]  )
								extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
		from .builder import DatasetBuilder

		_snake_case	       =       [
		    cls.__module__
		    for cls in type(lowercase_  ).__mro__[1:]  # make sure it's not the same module we've already patched
		    if issubclass(cls						,							lowercase_  ) and cls.__module__ != DatasetBuilder.__module__
		]  # check it's not a standard builder from datasets.builder
		for module in parent_builder_modules:
				extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )





