



import sacrebleu as scb
from packaging import version

import datasets


A__       =			"""\
@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\",
}
"""

A__       =			"""\
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.
"""

A__       =			"""
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			):




				def 				snake_case  (				self						):



									"""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 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case="exp"		,							_snake_case=None		,							_snake_case=False		,							_snake_case=False		,							_snake_case=None		,							_snake_case=False		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       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"""						)
									_lowerCAmelCase         =       [[refs[i] for refs in references] for i in range(_snake_case						)]
									_lowerCAmelCase         =       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 {})		,							)
									_lowerCAmelCase         =       {
									    """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 os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch






def  lowerCamelCase_  (     ):
 lowerCamelCase__	=  Dataset.from_dict({"i": range(17)})
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  3
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(lowercase__) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






@pytest.mark.parametrize("shards_per_node"					,  [1, 2, 3])
def  lowerCamelCase_  (     lowercase__):
 def gen(lowercase__):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  shards_per_node * world_size
 lowerCamelCase__	=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(lowercase__)]}
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__					,  gen_kwargs=lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  2
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))

 lowerCamelCase__	=  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle(seed=42)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle()

 lowerCamelCase__	=  split_dataset_by_node(full_ds.shuffle(seed=42)					,  rank=0					,  world_size=lowercase__)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(full_ds.shuffle()					,  rank=0					,  world_size=lowercase__)






@pytest.mark.parametrize("streaming"					,  [False, True])
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  2
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())






@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,  [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,  )
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  True
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())

