


import sacrebleu as scb
from packaging import version

import datasets


_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"

_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"

_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  A_ (			datasets.Metric  ):







 """simple docstring"""





 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Tuple:
      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       :			List[Any]							,     _a       :			Union[str, Any]							,     _a       :			str							,     _a       :			Optional[Any]="exp"							,     _a       :			Tuple=None							,     _a       :			Dict=False							,     _a       :			List[str]=False							,     _a       :			str=None							,     _a       :			int=False							,     )     ->      Dict:
      UpperCAmelCase    :					str			=      len(references[0]					)
      if any(len(_a					) != references_per_prediction for refs in references					):
           raise ValueError("Sacrebleu requires the same number of references for each prediction"					)
      UpperCAmelCase    :					str			=      [[refs[i] for refs in references] for i in range(_a					)]
      UpperCAmelCase    :					Optional[int]			=      scb.corpus_bleu(
          _a							,     _a							,     smooth_method=_a							,     smooth_value=_a							,     force=_a							,     lowercase=_a							,     use_effective_order=_a							,     **({"tokenize": tokenize} if tokenize else {})							,     )
      UpperCAmelCase    :					Tuple			=      {
          "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







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case				=			[[1_2, 1_0], [3, 9]]
_snake_case				=			[[3, 4], [7, 4]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case				=			[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case				=			[1]
_snake_case				=			[2]

_snake_case				=			logging.getLogger()
_snake_case				=			logging.DEBUG
_snake_case				=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} returned integer'			)
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_addition.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) + np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[Any]			=      matop.add(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} with different matrix dims'			)
               matop.add(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} returned integer'			)
               matop.subtract(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) - np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[int]			=      matop.subtract(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          logger.info(f'\n\t{test_multiplication.__name__} returned integer'			)
          with pytest.raises(A__			):
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'			)
          UpperCAmelCase    :					List[str]			=      (np.matmul(A__		,		A__			)).tolist()
          UpperCAmelCase    :					int			=      matop.multiply(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(
                   f'\n\t{test_multiplication.__name__} does not meet dim requirements'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					int			=      (3.5 * np.array(A__			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.scalar_multiply(A__		,		3.5			)
     assert theo == act







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					List[Any]			=      (np.identity(5			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.identity(5			)
     assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"		,		[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]			)
def 	A (		A__			):
     if (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_transpose.__name__} returned integer'			)
               matop.transpose(A__			)
     else:
          UpperCAmelCase    :					Dict			=      (np.transpose(A__			)).tolist()
          UpperCAmelCase    :					List[str]			=      matop.transpose(A__		,		return_map=A__			)
          assert theo == act



