import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowercase											=      '''
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@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",
}
'''

__lowercase											=      '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


__lowercase											=      '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    def remove_articles(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[Any]    						=	re.compile(R'''\b(a|an|the)\b'''						,    re.UNICODE			)
        return re.sub(SCREAMING_SNAKE_CASE						,    ''' '''						,    SCREAMING_SNAKE_CASE			)

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

    def remove_punc(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[Any]    						=	set(string.punctuation			)
        return "".join(ch for ch in text if ch not in exclude			)

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

    return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE			)			)			)			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return int(normalize_answer(SCREAMING_SNAKE_CASE			) == normalize_answer(SCREAMING_SNAKE_CASE			)			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	[any(compute_exact(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			) for ref in refs			) for pred, refs in zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)]
    return (sum(SCREAMING_SNAKE_CASE			) / len(SCREAMING_SNAKE_CASE			)) * 100



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	[rgram for rgrams in rgramslist for rgram in rgrams]
    __UpperCamelCase  :List[Any]    						=	Counter(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Tuple    						=	Counter(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[Any]    						=	Counter()
    for sgram, scount in sgramcounter.items():
        __UpperCamelCase  :Optional[int]    						=	scount * numref

    __UpperCamelCase  :str    						=	Counter(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[str]    						=	Counter()
    for cgram, ccount in cgramcounter.items():
        __UpperCamelCase  :int    						=	ccount * numref

    # KEEP
    __UpperCamelCase  :Any    						=	sgramcounter_rep & cgramcounter_rep
    __UpperCamelCase  :Union[str, Any]    						=	keepgramcounter_rep & rgramcounter
    __UpperCamelCase  :Any    						=	sgramcounter_rep & rgramcounter

    __UpperCamelCase  :Dict    						=	0
    __UpperCamelCase  :Union[str, Any]    						=	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.
    __UpperCamelCase  :Any    						=	1
    __UpperCamelCase  :List[Any]    						=	1
    if len(SCREAMING_SNAKE_CASE			) > 0:
        __UpperCamelCase  :Dict    						=	keeptmpscorea / len(SCREAMING_SNAKE_CASE			)
    if len(SCREAMING_SNAKE_CASE			) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        __UpperCamelCase  :int    						=	keeptmpscorea / sum(keepgramcounterall_rep.values()			)
    __UpperCamelCase  :Optional[int]    						=	0
    if keepscore_precision > 0 or keepscore_recall > 0:
        __UpperCamelCase  :Optional[int]    						=	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    __UpperCamelCase  :Dict    						=	sgramcounter_rep - cgramcounter_rep
    __UpperCamelCase  :List[str]    						=	delgramcounter_rep - rgramcounter
    __UpperCamelCase  :Tuple    						=	sgramcounter_rep - rgramcounter
    __UpperCamelCase  :str    						=	0
    __UpperCamelCase  :Dict    						=	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.
    __UpperCamelCase  :Optional[Any]    						=	1
    if len(SCREAMING_SNAKE_CASE			) > 0:
        __UpperCamelCase  :Optional[Any]    						=	deltmpscorea / len(SCREAMING_SNAKE_CASE			)

    # ADDITION
    __UpperCamelCase  :Optional[Any]    						=	set(SCREAMING_SNAKE_CASE			) - set(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Dict    						=	set(SCREAMING_SNAKE_CASE			) & set(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[str]    						=	set(SCREAMING_SNAKE_CASE			) - set(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :str    						=	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.
    __UpperCamelCase  :Tuple    						=	1
    __UpperCamelCase  :str    						=	1
    if len(SCREAMING_SNAKE_CASE			) > 0:
        __UpperCamelCase  :str    						=	addtmpscore / len(SCREAMING_SNAKE_CASE			)
    if len(SCREAMING_SNAKE_CASE			) > 0:
        __UpperCamelCase  :Dict    						=	addtmpscore / len(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[str]    						=	0
    if addscore_precision > 0 or addscore_recall > 0:
        __UpperCamelCase  :Union[str, Any]    						=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	len(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :str    						=	ssent.split(''' '''			)
    __UpperCamelCase  :Optional[int]    						=	csent.split(''' '''			)
    __UpperCamelCase  :Optional[int]    						=	[]
    __UpperCamelCase  :Optional[int]    						=	[]
    __UpperCamelCase  :str    						=	[]
    __UpperCamelCase  :Dict    						=	[]
    __UpperCamelCase  :int    						=	[]
    __UpperCamelCase  :Union[str, Any]    						=	[]

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

    for i in range(0						,    len(SCREAMING_SNAKE_CASE			) - 1			):
        if i < len(SCREAMING_SNAKE_CASE			) - 1:
            __UpperCamelCase  :List[Any]    						=	sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(SCREAMING_SNAKE_CASE			)
        if i < len(SCREAMING_SNAKE_CASE			) - 2:
            __UpperCamelCase  :Optional[int]    						=	sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(SCREAMING_SNAKE_CASE			)
        if i < len(SCREAMING_SNAKE_CASE			) - 3:
            __UpperCamelCase  :Union[str, Any]    						=	sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(SCREAMING_SNAKE_CASE			)

    for i in range(0						,    len(SCREAMING_SNAKE_CASE			) - 1			):
        if i < len(SCREAMING_SNAKE_CASE			) - 1:
            __UpperCamelCase  :List[Any]    						=	cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(SCREAMING_SNAKE_CASE			)
        if i < len(SCREAMING_SNAKE_CASE			) - 2:
            __UpperCamelCase  :str    						=	cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(SCREAMING_SNAKE_CASE			)
        if i < len(SCREAMING_SNAKE_CASE			) - 3:
            __UpperCamelCase  :List[str]    						=	cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(SCREAMING_SNAKE_CASE			)

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = True						,    SCREAMING_SNAKE_CASE = "13a"						,    SCREAMING_SNAKE_CASE = True			):
    '''simple docstring'''







    if lowercase:
        __UpperCamelCase  :Dict    						=	sentence.lower()

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

    if not return_str:
        __UpperCamelCase  :int    						=	normalized_sent.split()

    return normalized_sent



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if not (len(SCREAMING_SNAKE_CASE			) == len(SCREAMING_SNAKE_CASE			) == len(SCREAMING_SNAKE_CASE			)):
        raise ValueError('''Sources length must match predictions and references lengths.'''			)
    __UpperCamelCase  :str    						=	0
    for src, pred, refs in zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
        sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE			)						,    normalize(SCREAMING_SNAKE_CASE			)						,    [normalize(SCREAMING_SNAKE_CASE			) for sent in refs]			)
    __UpperCamelCase  :Tuple    						=	sari_score / len(SCREAMING_SNAKE_CASE			)
    return 100 * sari_score



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="exp"						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False						,    ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	len(references[0]			)
    if any(len(SCREAMING_SNAKE_CASE			) != references_per_prediction for refs in references			):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''			)
    __UpperCamelCase  :List[str]    						=	[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE			)]
    __UpperCamelCase  :int    						=	sacrebleu.corpus_bleu(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    smooth_method=SCREAMING_SNAKE_CASE						,    smooth_value=SCREAMING_SNAKE_CASE						,    force=SCREAMING_SNAKE_CASE						,    lowercase=SCREAMING_SNAKE_CASE						,    use_effective_order=SCREAMING_SNAKE_CASE						,    )
    return output.score


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






    '''simple docstring'''






    def     UpperCamelCase__     (			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     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			Union[str, Any]:
        __UpperCamelCase  :Optional[Any]    						=	{}
        result.update({'''sari''': compute_sari(sources=__lowercase				,				predictions=__lowercase				,				references=__lowercase)})
        result.update({'''sacrebleu''': compute_sacrebleu(predictions=__lowercase				,				references=__lowercase)})
        result.update({'''exact''': compute_em(predictions=__lowercase				,				references=__lowercase)})
        return result






import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return x.sum()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):  # picklable for multiprocessing
    '''simple docstring'''







    return i + 1


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		int
    a__			:		str


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Dict    						=	{}
        __UpperCamelCase  :int    						=	[]
        __UpperCamelCase  :int    						=	1
        __UpperCamelCase  :int    						=	[1, 2]
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :Dict    						=	{'''a''': [1, 2], '''b''': [3, 4]}
        __UpperCamelCase  :Optional[int]    						=	{'''a''': {'''1''': 1}, '''b''': 2}
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
        __UpperCamelCase  :str    						=	{}
        __UpperCamelCase  :Any    						=	[]
        __UpperCamelCase  :Optional[Any]    						=	2
        __UpperCamelCase  :Optional[int]    						=	[2, 3]
        __UpperCamelCase  :List[Any]    						=	{'''a''': 2, '''b''': 3}
        __UpperCamelCase  :List[str]    						=	{'''a''': [2, 3], '''b''': [4, 5]}
        __UpperCamelCase  :int    						=	{'''a''': {'''1''': 2}, '''b''': 3}
        __UpperCamelCase  :Union[str, Any]    						=	{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase)				,				__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	2
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				num_proc=__lowercase)				,				__lowercase)

        __UpperCamelCase  :Dict    						=	{'''a''': np.eye(2), '''b''': np.zeros(3), '''c''': np.ones(2)}
        __UpperCamelCase  :Optional[Any]    						=	{'''a''': 2, '''b''': 0, '''c''': 2}
        __UpperCamelCase  :Optional[int]    						=	{
            '''a''': np.eye(2).astype(__lowercase),
            '''b''': np.zeros(3).astype(__lowercase),
            '''c''': np.ones(2).astype(__lowercase),
        }
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase)				,				__lowercase)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase).items()}				,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,				)
        self.assertEqual(map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase				,				num_proc=__lowercase)				,				__lowercase)
        self.assertEqual(
            {k: v.tolist() for k, v in map_nested(__lowercase				,				__lowercase				,				map_numpy=__lowercase				,				num_proc=__lowercase).items()}				,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}				,				)
        with self.assertRaises(__lowercase):  # can't pickle a local lambda
            map_nested(lambda __lowercase: x + 1				,				__lowercase				,				num_proc=__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :List[str]    						=	{'''a''': 1, '''b''': 2}
        __UpperCamelCase  :int    						=	{'''a''': 3, '''b''': 4}
        __UpperCamelCase  :List[str]    						=	{'''a''': 5, '''b''': 6}
        __UpperCamelCase  :List[Any]    						=	sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))])
        self.assertEqual(sorted(zip_dict(__lowercase				,				__lowercase				,				__lowercase))				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:


        class 			lowerCamelCase_  :






            '''simple docstring'''






            a__			:		int      			=   """bar"""
        __UpperCamelCase  :List[str]    						=	Foo()
        self.assertEqual(foo.my_attr				,				'''bar''')
        with temporary_assignment(__lowercase				,				'''my_attr'''				,				'''BAR'''):
            self.assertEqual(foo.my_attr				,				'''BAR''')
        self.assertEqual(foo.my_attr				,				'''bar''')



@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc'''						,    [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]						,    )
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    with patch('''datasets.utils.py_utils._single_map_nested'''			) as mock_single_map_nested, patch(
        '''datasets.parallel.parallel.Pool'''			) as mock_multiprocessing_pool:
        __UpperCamelCase  :Union[str, Any]    						=	{f"""{i}""": i for i in range(SCREAMING_SNAKE_CASE			)}
        __UpperCamelCase  :Dict    						=	map_nested(lambda SCREAMING_SNAKE_CASE			: x + 10						,    SCREAMING_SNAKE_CASE						,    num_proc=SCREAMING_SNAKE_CASE						,    parallel_min_length=16			)
        if expected_num_proc == 1:
            assert mock_single_map_nested.called
            assert not mock_multiprocessing_pool.called
        else:
            assert not mock_single_map_nested.called
            assert mock_multiprocessing_pool.called
            assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    @require_tf
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        import tensorflow as tf
        from tensorflow.keras import layers

        __UpperCamelCase  :Optional[Any]    						=	layers.Dense(2)

        def gen_random_output():
            __UpperCamelCase  :Optional[Any]    						=	tf.random.uniform((1, 3))
            return model(__lowercase).numpy()

        with temp_seed(42				,				set_tensorflow=__lowercase):
            __UpperCamelCase  :Tuple    						=	gen_random_output()
        with temp_seed(42				,				set_tensorflow=__lowercase):
            __UpperCamelCase  :int    						=	gen_random_output()
        __UpperCamelCase  :List[str]    						=	gen_random_output()

        np.testing.assert_equal(__lowercase				,				__lowercase)
        self.assertGreater(np.abs(outa - outa).sum()				,				0)







    @require_torch
    def     UpperCamelCase__     (			self) ->			List[str]:
        import torch

        def gen_random_output():
            __UpperCamelCase  :List[Any]    						=	torch.nn.Linear(3				,				2)
            __UpperCamelCase  :Optional[Any]    						=	torch.rand(1				,				3)
            return model(__lowercase).detach().numpy()

        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :Union[str, Any]    						=	gen_random_output()
        with temp_seed(42				,				set_pytorch=__lowercase):
            __UpperCamelCase  :List[str]    						=	gen_random_output()
        __UpperCamelCase  :Dict    						=	gen_random_output()

        np.testing.assert_equal(__lowercase				,				__lowercase)
        self.assertGreater(np.abs(outa - outa).sum()				,				0)







    def     UpperCamelCase__     (			self) ->			str:
        def gen_random_output():
            return np.random.rand(1				,				3)

        with temp_seed(42):
            __UpperCamelCase  :List[Any]    						=	gen_random_output()
        with temp_seed(42):
            __UpperCamelCase  :Optional[Any]    						=	gen_random_output()
        __UpperCamelCase  :Optional[Any]    						=	gen_random_output()

        np.testing.assert_equal(__lowercase				,				__lowercase)
        self.assertGreater(np.abs(outa - outa).sum()				,				0)



@pytest.mark.parametrize('''input_data'''						,    [{}]			)
def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).data
    assert output_data == input_data



@pytest.mark.parametrize(
    '''data, expected_output'''						,    [
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ]						,    )
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	NestedDataStructure(SCREAMING_SNAKE_CASE			).flatten()
    assert output == expected_output



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	A(x=1						,    y='''foobar'''			)
    __UpperCamelCase  :Dict    						=	{'''x''': 1, '''y''': '''foobar'''}
    assert asdict(SCREAMING_SNAKE_CASE			) == expected_output

    __UpperCamelCase  :str    						=	{'''a''': {'''b''': A(x=10						,    y='''foo'''			)}, '''c''': [A(x=20						,    y='''bar'''			)]}
    __UpperCamelCase  :List[Any]    						=	{'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
    assert asdict(SCREAMING_SNAKE_CASE			) == expected_output

    with pytest.raises(SCREAMING_SNAKE_CASE			):
        asdict([1, A(x=10						,    y='''foo'''			)]			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return text.split()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    yield (time.time(), content)
    time.sleep(2			)
    yield (time.time(), content)



def lowerCamelCase						(     ):
    '''simple docstring'''







    with Pool(2			) as pool:
        __UpperCamelCase  :Tuple    						=	list(iflatmap_unordered(SCREAMING_SNAKE_CASE						,    _split_text						,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
        assert out.count('''hello'''			) == 10
        assert out.count('''there'''			) == 10
        assert len(SCREAMING_SNAKE_CASE			) == 20

    # check multiprocess from pathos (uses dill for pickling)
    with multiprocess.Pool(2			) as pool:
        __UpperCamelCase  :Any    						=	list(iflatmap_unordered(SCREAMING_SNAKE_CASE						,    _split_text						,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
        assert out.count('''hello'''			) == 10
        assert out.count('''there'''			) == 10
        assert len(SCREAMING_SNAKE_CASE			) == 20

    # check that we get items as fast as possible
    with Pool(2			) as pool:
        __UpperCamelCase  :Dict    						=	[]
        for yield_time, content in iflatmap_unordered(
            SCREAMING_SNAKE_CASE						,    _aseconds_generator_of_aitems_with_timing						,    kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]			):
            assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
            out.append(SCREAMING_SNAKE_CASE			)
        assert out.count('''a'''			) == 2
        assert out.count('''b'''			) == 2
        assert len(SCREAMING_SNAKE_CASE			) == 4





