
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__lowerCAmelCase					:			List[str]			      =		[
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]


class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       str				,       _snake_case		:       bool				,       _snake_case		:       str = None				,       _snake_case		:       list = None				):
          __lowercase		:			Optional[Any]	    =       None
          __lowercase		:			Any	    =       os.path.abspath(os.path.join('''examples'''				,       '''by_feature'''				)				)
          __lowercase		:			List[str]	    =       os.path.abspath('''examples'''				)
          for item in os.listdir(_snake_case				):
              if item not in EXCLUDE_EXAMPLES:
                  __lowercase		:			Union[str, Any]	    =       os.path.join(_snake_case				,       _snake_case				)
                  if os.path.isfile(_snake_case				) and ".py" in item_path:
                      with self.subTest(
                          tested_script=_snake_case				,       feature_script=_snake_case				,       tested_section='''main()''' if parser_only else '''training_function()'''				,       ):
                          __lowercase		:			List[Any]	    =       compare_against_test(
                              os.path.join(_snake_case				,       _snake_case				)				,       _snake_case				,       _snake_case				,       _snake_case				)
                          __lowercase		:			List[Any]	    =       '''\n'''.join(_snake_case				)
                          if special_strings is not None:
                              for string in special_strings:
                                  __lowercase		:			Dict	    =       diff.replace(_snake_case				,       ''''''				)
                          self.assertEqual(_snake_case				,       ''''''				)




      def 		snake_case_			(      self		:       List[Any]				):
          self.one_complete_example('''complete_nlp_example.py'''				,       _snake_case				)
          self.one_complete_example('''complete_nlp_example.py'''				,       _snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			List[str]	    =       os.path.abspath(os.path.join('''examples'''				,       '''cv_example.py'''				)				)
          __lowercase		:			Union[str, Any]	    =       [
              ''' ''' * 16 + '''{\n\n''',
              ''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
              ''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
              ''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
              ''' ''' * 20 + '''"epoch": epoch,\n\n''',
              ''' ''' * 16 + '''},\n\n''',
              ''' ''' * 16 + '''step=epoch,\n''',
              ''' ''' * 12,
              ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
          ]
          self.one_complete_example('''complete_cv_example.py'''				,       _snake_case				,       _snake_case				,       _snake_case				)
          self.one_complete_example('''complete_cv_example.py'''				,       _snake_case				,       _snake_case				,       _snake_case				)






@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''}      )
class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			List[str]        =    False




      @classmethod
      def 		snake_case_			(      cls		:       List[Any]				):
          super().setUpClass()
          __lowercase		:			Optional[int]	    =       tempfile.mkdtemp()
          __lowercase		:			Tuple	    =       os.path.join(cls._tmpdir				,       '''default_config.yml'''				)

          write_basic_config(save_location=cls.configPath				)
          __lowercase		:			Union[str, Any]	    =       ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]




      @classmethod
      def 		snake_case_			(      cls		:       Tuple				):
          super().tearDownClass()
          shutil.rmtree(cls._tmpdir				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			List[str]	    =       F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
          run_command(self._launch_args + testargs				)
          self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,       '''epoch_0'''				)				)				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Union[str, Any]	    =       F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
          __lowercase		:			Union[str, Any]	    =       run_command(self._launch_args + testargs				)
          self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,       '''step_2'''				)				)				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			int	    =       F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,       "epoch_0"				)}\n        '.split()
          __lowercase		:			Any	    =       run_command(self._launch_args + testargs				,       return_stdout=_snake_case				)
          self.assertNotIn('''epoch 0:'''				,       _snake_case				)
          self.assertIn('''epoch 1:'''				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,       "step_2"				)}\n        '.split()
          __lowercase		:			List[str]	    =       run_command(self._launch_args + testargs				,       return_stdout=_snake_case				)
          if torch.cuda.is_available():
              __lowercase		:			Optional[int]	    =       torch.cuda.device_count()
          else:
              __lowercase		:			Optional[int]	    =       1
          if num_processes > 1:
              self.assertNotIn('''epoch 0:'''				,       _snake_case				)
              self.assertIn('''epoch 1:'''				,       _snake_case				)
          else:
              self.assertIn('''epoch 0:'''				,       _snake_case				)
              self.assertIn('''epoch 1:'''				,       _snake_case				)




      @slow
      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Union[str, Any]	    =       '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
          with mock.patch.dict(os.environ				,       {'''TESTING_MOCKED_DATALOADERS''': '''0'''}				):
              __lowercase		:			Optional[Any]	    =       run_command(self._launch_args + testargs				,       return_stdout=_snake_case				)
              __lowercase		:			List[Any]	    =       re.findall('''({.+})'''				,       _snake_case				)
              __lowercase		:			Optional[Any]	    =       [r for r in results if '''accuracy''' in r][-1]
              __lowercase		:			List[str]	    =       ast.literal_eval(_snake_case				)
              self.assertGreaterEqual(results['''accuracy''']				,       0.75				)




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			Tuple	    =       ['''examples/by_feature/multi_process_metrics.py''']
          run_command(self._launch_args + testargs				)




      @require_trackers
      @mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}				)
      def 		snake_case_			(      self		:       List[str]				):
          with tempfile.TemporaryDirectory() as tmpdir:
              __lowercase		:			Optional[int]	    =       F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
              run_command(self._launch_args + testargs				)
              self.assertTrue(os.path.exists(os.path.join(_snake_case				,       '''tracking'''				)				)				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			str	    =       ['''examples/by_feature/gradient_accumulation.py''']
          run_command(self._launch_args + testargs				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			str	    =       ['''examples/by_feature/local_sgd.py''']
          run_command(self._launch_args + testargs				)




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase					:			Tuple			      =		"\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\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					:			Dict			      =		"\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__lowerCAmelCase					:			Optional[int]			      =		"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:

    def remove_articles(__lowerCAmelCase	):
        __lowercase		:			int	    =       re.compile(R'''\b(a|an|the)\b'''	,					re.UNICODE	)
        return re.sub(__lowerCAmelCase	,					''' '''	,					__lowerCAmelCase	)

    def white_space_fix(__lowerCAmelCase	):
        return " ".join(text.split()	)

    def remove_punc(__lowerCAmelCase	):
        __lowercase		:			int	    =       set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(__lowerCAmelCase	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase	)	)	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						List[str]:
    return int(normalize_answer(__lowerCAmelCase	) == normalize_answer(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [any(compute_exact(__lowerCAmelCase	,					__lowerCAmelCase	) for ref in refs	) for pred, refs in zip(__lowerCAmelCase	,					__lowerCAmelCase	)]
    return (sum(__lowerCAmelCase	) / len(__lowerCAmelCase	)) * 100


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Union[str, Any]	    =       [rgram for rgrams in rgramslist for rgram in rgrams]
    __lowercase		:			Dict	    =       Counter(__lowerCAmelCase	)

    __lowercase		:			Union[str, Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			str	    =       Counter()
    for sgram, scount in sgramcounter.items():
        __lowercase		:			List[Any]	    =       scount * numref

    __lowercase		:			Optional[Any]	    =       Counter(__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       Counter()
    for cgram, ccount in cgramcounter.items():
        __lowercase		:			Union[str, Any]	    =       ccount * numref

    # KEEP
    __lowercase		:			Optional[Any]	    =       sgramcounter_rep & cgramcounter_rep
    __lowercase		:			Dict	    =       keepgramcounter_rep & rgramcounter
    __lowercase		:			str	    =       sgramcounter_rep & rgramcounter

    __lowercase		:			Tuple	    =       0
    __lowercase		:			int	    =       0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    __lowercase		:			Any	    =       1
    __lowercase		:			Union[str, Any]	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			List[str]	    =       keeptmpscorea / len(__lowerCAmelCase	)
    if len(__lowerCAmelCase	) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        __lowercase		:			Dict	    =       keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    __lowercase		:			Dict	    =       0
    if keepscore_precision > 0 or keepscore_recall > 0:
        __lowercase		:			str	    =       2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    __lowercase		:			Tuple	    =       sgramcounter_rep - cgramcounter_rep
    __lowercase		:			str	    =       delgramcounter_rep - rgramcounter
    __lowercase		:			int	    =       sgramcounter_rep - rgramcounter
    __lowercase		:			Dict	    =       0
    __lowercase		:			List[str]	    =       0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    __lowercase		:			Union[str, Any]	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Union[str, Any]	    =       deltmpscorea / len(__lowerCAmelCase	)

    # ADDITION
    __lowercase		:			int	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)
    __lowercase		:			Tuple	    =       set(__lowerCAmelCase	) & set(__lowerCAmelCase	)
    __lowercase		:			Any	    =       set(__lowerCAmelCase	) - set(__lowerCAmelCase	)

    __lowercase		:			Tuple	    =       0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    __lowercase		:			int	    =       1
    __lowercase		:			Any	    =       1
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			Any	    =       addtmpscore / len(__lowerCAmelCase	)
    if len(__lowerCAmelCase	) > 0:
        __lowercase		:			str	    =       addtmpscore / len(__lowerCAmelCase	)
    __lowercase		:			int	    =       0
    if addscore_precision > 0 or addscore_recall > 0:
        __lowercase		:			Any	    =       2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    __lowercase		:			Tuple	    =       len(__lowerCAmelCase	)

    __lowercase		:			str	    =       ssent.split(''' '''	)
    __lowercase		:			List[str]	    =       csent.split(''' '''	)
    __lowercase		:			Tuple	    =       []
    __lowercase		:			Any	    =       []
    __lowercase		:			Optional[Any]	    =       []
    __lowercase		:			Optional[int]	    =       []
    __lowercase		:			Dict	    =       []
    __lowercase		:			Optional[int]	    =       []

    __lowercase		:			Dict	    =       []
    __lowercase		:			str	    =       []
    __lowercase		:			Dict	    =       []
    __lowercase		:			List[Any]	    =       []
    for rsent in rsents:
        __lowercase		:			Any	    =       rsent.split(''' '''	)
        __lowercase		:			Optional[int]	    =       []
        __lowercase		:			List[Any]	    =       []
        __lowercase		:			Tuple	    =       []
        ragramslist.append(__lowerCAmelCase	)
        for i in range(0	,					len(__lowerCAmelCase	) - 1	):
            if i < len(__lowerCAmelCase	) - 1:
                __lowercase		:			List[str]	    =       ragrams[i] + ''' ''' + ragrams[i + 1]
                ragrams.append(__lowerCAmelCase	)
            if i < len(__lowerCAmelCase	) - 2:
                __lowercase		:			Union[str, Any]	    =       ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                ragrams.append(__lowerCAmelCase	)
            if i < len(__lowerCAmelCase	) - 3:
                __lowercase		:			int	    =       ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                ragrams.append(__lowerCAmelCase	)
        ragramslist.append(__lowerCAmelCase	)
        ragramslist.append(__lowerCAmelCase	)
        ragramslist.append(__lowerCAmelCase	)

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			Dict	    =       sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			int	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			str	    =       sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(__lowerCAmelCase	)

    for i in range(0	,					len(__lowerCAmelCase	) - 1	):
        if i < len(__lowerCAmelCase	) - 1:
            __lowercase		:			int	    =       cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 2:
            __lowercase		:			Any	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(__lowerCAmelCase	)
        if i < len(__lowerCAmelCase	) - 3:
            __lowercase		:			Optional[int]	    =       cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(__lowerCAmelCase	)

    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			List[str]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Optional[int]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Optional[Any]	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    ((__lowercase)    ,      (__lowercase)    ,      (__lowercase))		:			Any	    =       SARIngram(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			List[Any]	    =       sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    __lowercase		:			Any	    =       sum([delascore, delascore, delascore, delascore]	) / 4
    __lowercase		:			int	    =       sum([addascore, addascore, addascore, addascore]	) / 4
    __lowercase		:			str	    =       (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = True	,					__lowerCAmelCase = "13a"	,					__lowerCAmelCase = True	) ->						List[str]:
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        __lowercase		:			List[Any]	    =       sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__	).major >= 2:
            __lowercase		:			Union[str, Any]	    =       sacrebleu.metrics.bleu._get_tokenizer(__lowerCAmelCase	)()(__lowerCAmelCase	)
        else:
            __lowercase		:			str	    =       sacrebleu.TOKENIZERS[tokenizer]()(__lowerCAmelCase	)
    elif tokenizer == "moses":
        __lowercase		:			Tuple	    =       sacremoses.MosesTokenizer().tokenize(__lowerCAmelCase	,					return_str=__lowerCAmelCase	,					escape=__lowerCAmelCase	)
    elif tokenizer == "penn":
        __lowercase		:			str	    =       sacremoses.MosesTokenizer().penn_tokenize(__lowerCAmelCase	,					return_str=__lowerCAmelCase	)
    else:
        __lowercase		:			int	    =       sentence

    if not return_str:
        __lowercase		:			List[Any]	    =       normalized_sent.split()

    return normalized_sent


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    if not (len(__lowerCAmelCase	) == len(__lowerCAmelCase	) == len(__lowerCAmelCase	)):
        raise ValueError('''Sources length must match predictions and references lengths.'''	)
    __lowercase		:			str	    =       0
    for src, pred, refs in zip(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	):
        sari_score += SARIsent(normalize(__lowerCAmelCase	)	,					normalize(__lowerCAmelCase	)	,					[normalize(__lowerCAmelCase	) for sent in refs]	)
    __lowercase		:			int	    =       sari_score / len(__lowerCAmelCase	)
    return 100 * sari_score


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase="exp"	,					__lowerCAmelCase=None	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					) ->						str:
    __lowercase		:			List[Any]	    =       len(references[0]	)
    if any(len(__lowerCAmelCase	) != references_per_prediction for refs in references	):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
    __lowercase		:			int	    =       [[refs[i] for refs in references] for i in range(__lowerCAmelCase	)]
    __lowercase		:			List[str]	    =       sacrebleu.corpus_bleu(
        __lowerCAmelCase	,					__lowerCAmelCase	,					smooth_method=__lowerCAmelCase	,					smooth_value=__lowerCAmelCase	,					force=__lowerCAmelCase	,					lowercase=__lowerCAmelCase	,					use_effective_order=__lowerCAmelCase	,					)
    return output.score





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



      """simple docstring"""







      def 		snake_case_			(      self		:       Dict				):
          return datasets.MetricInfo(
              description=_DESCRIPTION				,       citation=_CITATION				,       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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                  '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                  '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                  '''https://github.com/mjpost/sacreBLEU''',
              ]				,       reference_urls=[
                  '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                  '''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		:       Optional[int]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       List[Any]				):
          __lowercase		:			List[Any]	    =       {}
          result.update({'''sari''': compute_sari(sources=_snake_case				,       predictions=_snake_case				,       references=_snake_case				)}				)
          result.update({'''sacrebleu''': compute_sacrebleu(predictions=_snake_case				,       references=_snake_case				)}				)
          result.update({'''exact''': compute_em(predictions=_snake_case				,       references=_snake_case				)}				)
          return result


