






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE 							=						'\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'

SCREAMING_SNAKE_CASE 							=						'\\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'


SCREAMING_SNAKE_CASE 							=						'\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      _lowerCamelCase	(						__A    :							Optional[int] )      ->					int:

    def remove_articles(__A    :							List[str] ):
        _UpperCAmelCase				:      List[str]			  =				re.compile(r'''\b(a|an|the)\b'''		,		re.UNICODE )
        return re.sub(__A		,		''' '''		,		__A )

    def white_space_fix(__A    :							Dict ):
        return " ".join(text.split() )

    def remove_punc(__A    :							Any ):
        _UpperCAmelCase				:      Tuple			  =				set(string.punctuation )
        return "".join(ch for ch in text if ch not in exclude )

    def lower(__A    :							Optional[Any] ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							int )      ->					Optional[Any]:
    return int(normalize_answer(__A ) == normalize_answer(__A ) )

def      _lowerCamelCase	(						__A    :							str		,		__A    :							List[Any] )      ->					str:
    _UpperCAmelCase				:      Dict			  =				[any(compute_exact(__A		,		__A ) for ref in refs ) for pred, refs in zip(__A		,		__A )]
    return (sum(__A ) / len(__A )) * 100

def      _lowerCamelCase	(						__A    :							Dict		,		__A    :							Any		,		__A    :							Tuple		,		__A    :							Dict )      ->					Any:
    _UpperCAmelCase				:      Optional[int]			  =				[rgram for rgrams in rgramslist for rgram in rgrams]
    _UpperCAmelCase				:      List[Any]			  =				Counter(__A )

    _UpperCAmelCase				:      Union[str, Any]			  =				Counter(__A )
    _UpperCAmelCase				:      Tuple			  =				Counter()
    for sgram, scount in sgramcounter.items():
        _UpperCAmelCase				:      Union[str, Any]			  =				scount * numref

    _UpperCAmelCase				:      List[str]			  =				Counter(__A )
    _UpperCAmelCase				:      Optional[int]			  =				Counter()
    for cgram, ccount in cgramcounter.items():
        _UpperCAmelCase				:      Optional[Any]			  =				ccount * numref

    # KEEP
    _UpperCAmelCase				:      List[str]			  =				sgramcounter_rep & cgramcounter_rep
    _UpperCAmelCase				:      Optional[int]			  =				keepgramcounter_rep & rgramcounter
    _UpperCAmelCase				:      Union[str, Any]			  =				sgramcounter_rep & rgramcounter

    _UpperCAmelCase				:      Tuple			  =				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(__A ) > 0:
        _UpperCAmelCase				:      List[str]			  =				keeptmpscorea / len(__A )
    if len(__A ) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        _UpperCAmelCase				:      List[str]			  =				keeptmpscorea / sum(keepgramcounterall_rep.values() )
    _UpperCAmelCase				:      List[str]			  =				0
    if keepscore_precision > 0 or keepscore_recall > 0:
        _UpperCAmelCase				:      str			  =				2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    _UpperCAmelCase				:      List[Any]			  =				sgramcounter_rep - cgramcounter_rep
    _UpperCAmelCase				:      Optional[Any]			  =				delgramcounter_rep - rgramcounter
    _UpperCAmelCase				:      Any			  =				sgramcounter_rep - rgramcounter
    _UpperCAmelCase				:      List[str]			  =				0
    _UpperCAmelCase				:      List[Any]			  =				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				:      Dict			  =				1
    if len(__A ) > 0:
        _UpperCAmelCase				:      Optional[int]			  =				deltmpscorea / len(__A )

    # ADDITION
    _UpperCAmelCase				:      int			  =				set(__A ) - set(__A )
    _UpperCAmelCase				:      str			  =				set(__A ) & set(__A )
    _UpperCAmelCase				:      Optional[int]			  =				set(__A ) - set(__A )

    _UpperCAmelCase				:      List[Any]			  =				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				:      Dict			  =				1
    _UpperCAmelCase				:      Tuple			  =				1
    if len(__A ) > 0:
        _UpperCAmelCase				:      Union[str, Any]			  =				addtmpscore / len(__A )
    if len(__A ) > 0:
        _UpperCAmelCase				:      int			  =				addtmpscore / len(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				0
    if addscore_precision > 0 or addscore_recall > 0:
        _UpperCAmelCase				:      str			  =				2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)

def      _lowerCamelCase	(						__A    :							List[str]		,		__A    :							Optional[Any]		,		__A    :							int )      ->					Union[str, Any]:
    _UpperCAmelCase				:      List[str]			  =				len(__A )

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

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

    for i in range(0		,		len(__A ) - 1 ):
        if i < len(__A ) - 1:
            _UpperCAmelCase				:      Tuple			  =				sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(__A )
        if i < len(__A ) - 2:
            _UpperCAmelCase				:      List[Any]			  =				sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(__A )
        if i < len(__A ) - 3:
            _UpperCAmelCase				:      Any			  =				sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(__A )

    for i in range(0		,		len(__A ) - 1 ):
        if i < len(__A ) - 1:
            _UpperCAmelCase				:      Union[str, Any]			  =				cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(__A )
        if i < len(__A ) - 2:
            _UpperCAmelCase				:      Dict			  =				cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(__A )
        if i < len(__A ) - 3:
            _UpperCAmelCase				:      Tuple			  =				cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(__A )

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

def      _lowerCamelCase	(						__A    :							int		,		__A    :							bool = True		,		__A    :							str = "13a"		,		__A    :							bool = 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:
        _UpperCAmelCase				:      Union[str, Any]			  =				sentence.lower()

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

    if not return_str:
        _UpperCAmelCase				:      Union[str, Any]			  =				normalized_sent.split()

    return normalized_sent

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

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							Optional[Any]		,		__A    :							Any="exp"		,		__A    :							List[str]=None		,		__A    :							List[str]=False		,		__A    :							str=False		,		__A    :							List[str]=False		,		)      ->					Tuple:
    _UpperCAmelCase				:      Dict			  =				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				:      Union[str, Any]			  =				sacrebleu.corpus_bleu(
        __A		,		__A		,		smooth_method=__A		,		smooth_value=__A		,		force=__A		,		lowercase=__A		,		use_effective_order=__A		,		)
    return output.score


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






 '''simple docstring'''




 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     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							,  _A							,  _A							,  _A) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				{}
     result.update({'''sari''': compute_sari(sources=_A							,  predictions=_A							,  references=_A)})
     result.update({'''sacrebleu''': compute_sacrebleu(predictions=_A							,  references=_A)})
     result.update({'''exact''': compute_em(predictions=_A							,  references=_A)})
     return result












import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


SCREAMING_SNAKE_CASE 							=						WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])

def      _lowerCamelCase	(						__A    :							Optional[Any] )      ->					int:
    _UpperCAmelCase				:      Optional[int]			  =				test_results.split(''' ''' )

    _UpperCAmelCase				:      Optional[int]			  =				0
    _UpperCAmelCase				:      Union[str, Any]			  =				0

    # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
    # When it is too long, those signs are not present.
    _UpperCAmelCase				:      Tuple			  =				expressions[-2] if '''=''' in expressions[-1] else expressions[-1]

    for i, expression in enumerate(__A ):
        if "failed" in expression:
            failed += int(expressions[i - 1] )
        if "passed" in expression:
            success += int(expressions[i - 1] )

    return failed, success, time_spent

def      _lowerCamelCase	(						__A    :							List[str] )      ->					int:
    _UpperCAmelCase				:      Optional[Any]			  =				{}
    _UpperCAmelCase				:      int			  =				None
    _UpperCAmelCase				:      Any			  =				False
    for line in failures_short_lines.split('''\n''' ):
        if re.search(r'''_ \[doctest\]'''		,		__A ):
            _UpperCAmelCase				:      Tuple			  =				True
            _UpperCAmelCase				:      Optional[Any]			  =				line.split(''' ''' )[2]
        elif in_error and not line.split(''' ''' )[0].isdigit():
            _UpperCAmelCase				:      int			  =				line
            _UpperCAmelCase				:      Optional[Any]			  =				False

    return failures
class 							A_    :






 '''simple docstring'''




 def __init__(	self							,  _A							,  _A) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				title

     _UpperCAmelCase				:      List[str]			  =				doc_test_results['''time_spent'''].split(''',''')[0]
     _UpperCAmelCase				:      List[Any]			  =				doc_test_results['''success''']
     _UpperCAmelCase				:      Optional[Any]			  =				doc_test_results['''failures''']
     _UpperCAmelCase				:      Tuple			  =				self.n_success + self.n_failures

     # Failures and success of the modeling tests
     _UpperCAmelCase				:      Optional[int]			  =				doc_test_results


 @property
 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				[self._time_spent]
     _UpperCAmelCase				:      Dict			  =				0

     for time in time_spent:
         _UpperCAmelCase				:      Optional[int]			  =				time.split(''':''')

         # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
         if len(_A) == 1:
             _UpperCAmelCase				:      List[Any]			  =				[0, 0, time_parts[0]]

         _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Optional[int]			  =				int(time_parts[0]), int(time_parts[1]), float(time_parts[2])
         total_secs += hours * 3600 + minutes * 60 + seconds

     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
     return f'''{int(_A)}h{int(_A)}m{int(_A)}s'''


 @property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     return {"type": "header", "text": {"type": "plain_text", "text": self.title}}


 @property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     return {
         "type": "section",
         "text": {
             "type": "plain_text",
             "text": f'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
             "emoji": True,
         },
         "accessory": {
             "type": "button",
             "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
             "url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
         },
     }


 @property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     return {
         "type": "section",
         "text": {
             "type": "plain_text",
             "text": (
                 f'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
                 f''' {self.time}.'''
             ),
             "emoji": True,
         },
         "accessory": {
             "type": "button",
             "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
             "url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
         },
     }


 @property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				40
     _UpperCAmelCase				:      List[str]			  =				{k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(_A							,  _A)}

     _UpperCAmelCase				:      List[Any]			  =				''''''
     for category, failures in category_failures.items():
         if len(_A) == 0:
             continue

         if report != "":
             report += "\n\n"

         report += f'''*{category} failures*:'''.ljust(line_length // 2).rjust(line_length // 2) + "\n"
         report += "`"
         report += "`\n`".join(_A)
         report += "`"

     return {
         "type": "section",
         "text": {
             "type": "mrkdwn",
             "text": f'''The following examples had failures:\n\n\n{report}\n''',
         },
     }


 @property
 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				[self.header]

     if self.n_failures > 0:
         blocks.append(self.failures)

     if self.n_failures > 0:
         blocks.extend([self.category_failures])

     if self.n_failures == 0:
         blocks.append(self.no_failures)

     return json.dumps(_A)


 @staticmethod
 def  snake_case__				(	) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				[
         {
             '''type''': '''section''',
             '''text''': {
                 '''type''': '''plain_text''',
                 '''text''': '''There was an issue running the tests.''',
             },
             '''accessory''': {
                 '''type''': '''button''',
                 '''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
                 '''url''': f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
             },
         }
     ]

     print('''Sending the following payload''')
     print(json.dumps({'''blocks''': json.loads(_A)}))

     client.chat_postMessage(
         channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']							,  text='''There was an issue running the tests.'''							,  blocks=_A							,  )


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     print('''Sending the following payload''')
     print(json.dumps({'''blocks''': json.loads(self.payload)}))

     _UpperCAmelCase				:      str			  =				f'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else '''All tests passed.'''

     _UpperCAmelCase				:      Optional[Any]			  =				client.chat_postMessage(
         channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']							,  blocks=self.payload							,  text=_A							,  )


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				''''''
     for key, value in failures.items():
         _UpperCAmelCase				:      str			  =				value[:200] + ''' [Truncated]''' if len(_A) > 250 else value
         failures_text += f'''*{key}*\n_{value}_\n\n'''

     _UpperCAmelCase				:      Optional[Any]			  =				job_name
     _UpperCAmelCase				:      Any			  =				{'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

     if job_link is not None:
         _UpperCAmelCase				:      List[Any]			  =				{
             '''type''': '''button''',
             '''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
             '''url''': job_link,
         }

     return [
         {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
         content,
         {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
     ]


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     if self.thread_ts is None:
         raise ValueError('''Can only post reply if a post has been made.''')

     _UpperCAmelCase				:      Tuple			  =				self.doc_test_results.pop('''job_link''')
     self.doc_test_results.pop('''failures''')
     self.doc_test_results.pop('''success''')
     self.doc_test_results.pop('''time_spent''')

     _UpperCAmelCase				:      Any			  =				sorted(self.doc_test_results.items()							,  key=lambda _A: t[0])
     for job, job_result in sorted_dict:
         if len(job_result['''failures''']):
             _UpperCAmelCase				:      Dict			  =				f'''*Num failures* :{len(job_result['failed'])} \n'''
             _UpperCAmelCase				:      Tuple			  =				job_result['''failures''']
             _UpperCAmelCase				:      Dict			  =				self.get_reply_blocks(_A							,  _A							,  _A							,  text=_A)

             print('''Sending the following reply''')
             print(json.dumps({'''blocks''': blocks}))

             client.chat_postMessage(
                 channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']							,  text=f'''Results for {job}'''							,  blocks=_A							,  thread_ts=self.thread_ts['''ts''']							,  )

             time.sleep(1)



def      _lowerCamelCase	(						)      ->					str:
    _UpperCAmelCase				:      Any			  =				os.environ['''GITHUB_RUN_ID''']
    _UpperCAmelCase				:      Any			  =				f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
    _UpperCAmelCase				:      Optional[int]			  =				requests.get(__A ).json()
    _UpperCAmelCase				:      Optional[int]			  =				{}

    try:
        jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
        _UpperCAmelCase				:      int			  =				math.ceil((result['''total_count'''] - 100) / 100 )

        for i in range(__A ):
            _UpperCAmelCase				:      int			  =				requests.get(url + f'''&page={i + 2}''' ).json()
            jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )

        return jobs
    except Exception as e:
        print('''Unknown error, could not fetch links.'''		,		__A )

    return {}

def      _lowerCamelCase	(						__A    :							str )      ->					Tuple:
    _UpperCAmelCase				:      Optional[Any]			  =				{}

    if os.path.exists(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				os.listdir(__A )
        for file in files:
            try:
                with open(os.path.join(__A		,		__A )		,		encoding='''utf-8''' ) as f:
                    _UpperCAmelCase				:      Tuple			  =				f.read()
            except UnicodeDecodeError as e:
                raise ValueError(f'''Could not open {os.path.join(__A		,		__A )}.''' ) from e

    return _artifact

def      _lowerCamelCase	(						)      ->					List[str]:
    class 							A_    :






     '''simple docstring'''




     def __init__(	self							,  _A) ->		int:

         """simple docstring"""



         _UpperCAmelCase				:      List[Any]			  =				name
         _UpperCAmelCase				:      Union[str, Any]			  =				[]


     def __str__(	self) ->		int:

         """simple docstring"""



         return self.name


     def  snake_case__				(	self							,  _A) ->		int:

         """simple docstring"""



         self.paths.append({'''name''': self.name, '''path''': path})



    _UpperCAmelCase				:      Dict[str, Artifact]			  =				{}

    _UpperCAmelCase				:      Dict			  =				filter(os.path.isdir		,		os.listdir() )
    for directory in directories:
        _UpperCAmelCase				:      List[str]			  =				directory
        if artifact_name not in _available_artifacts:
            _UpperCAmelCase				:      Optional[int]			  =				Artifact(__A )

            _available_artifacts[artifact_name].add_path(__A )

    return _available_artifacts


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						get_job_links()
  SCREAMING_SNAKE_CASE 							=						retrieve_available_artifacts()

  SCREAMING_SNAKE_CASE 							=						collections.OrderedDict(
      [
          ('*.py', 'API Examples'),
          ('*.md', 'MD Examples'),
      ]
  )

  # This dict will contain all the information relative to each doc test category:
  # - failed: list of failed tests
  # - failures: dict in the format 'test': 'error_message'
  SCREAMING_SNAKE_CASE 							=						{
      v: {
          'failed': [],
          'failures': {},
      }
      for v in docs.values()
  }

  # Link to the GitHub Action job
  SCREAMING_SNAKE_CASE 							=						github_actions_job_links.get('run_doctests')

  SCREAMING_SNAKE_CASE 							=						available_artifacts['doc_tests_gpu_test_reports'].paths[0]
  SCREAMING_SNAKE_CASE 							=						retrieve_artifact(artifact_path['name'])
  if "stats" in artifact:
    SCREAMING_SNAKE_CASE							,			SCREAMING_SNAKE_CASE							,			SCREAMING_SNAKE_CASE 							=						handle_test_results(artifact['stats'])
    SCREAMING_SNAKE_CASE 							=						failed
    SCREAMING_SNAKE_CASE 							=						success
    SCREAMING_SNAKE_CASE 							=						time_spent[1:-1] + ', '

    SCREAMING_SNAKE_CASE 							=						extract_first_line_failure(artifact['failures_short'])
    for line in artifact["summary_short"].split('\n'):
      if re.search('FAILED', line):
        SCREAMING_SNAKE_CASE 							=						line.replace('FAILED ', '')
        SCREAMING_SNAKE_CASE 							=						line.split()[0].replace('\n', '')

        if "::" in line:
          SCREAMING_SNAKE_CASE							,			SCREAMING_SNAKE_CASE 							=						line.split('::')
        else:
          SCREAMING_SNAKE_CASE							,			SCREAMING_SNAKE_CASE 							=						line, line

        for file_regex in docs.keys():
          if fnmatch(file_path, file_regex):
            SCREAMING_SNAKE_CASE 							=						docs[file_regex]
            doc_test_results[category]["failed"].append(test)

            SCREAMING_SNAKE_CASE 							=						all_failures[test] if test in all_failures else 'N/A'
            SCREAMING_SNAKE_CASE 							=						failure
            break

  SCREAMING_SNAKE_CASE 							=						Message('🤗 Results of the doc tests.', doc_test_results)
  message.post()
  message.post_reply()




