"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a					:	Union[str, Any]							=		{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 		a_						( unittest.TestCase	):
    a      :				List[Any]     =				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    a      :				str     =				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        a      :				List[Any]     =				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        a      :				List[str]     =				{
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Dict     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   ZeroShotClassificationPipeline(
            model=__UpperCamelCase     ,					tokenizer=__UpperCamelCase     ,					candidate_labels=["""polics""", """health"""]     )
        return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   classifier("""Who are you voting for in 2020?"""     ,					candidate_labels="""politics"""     )
        self.assertEqual(__UpperCamelCase     ,					{"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     )]}     )

        # No kwarg
        _UpperCAmelCase          =   classifier("""Who are you voting for in 2020?"""     ,					["""politics"""]     )
        self.assertEqual(__UpperCamelCase     ,					{"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     )]}     )

        _UpperCAmelCase          =   classifier("""Who are you voting for in 2020?"""     ,					candidate_labels=["""politics"""]     )
        self.assertEqual(__UpperCamelCase     ,					{"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     )]}     )

        _UpperCAmelCase          =   classifier("""Who are you voting for in 2020?"""     ,					candidate_labels="""politics, public health"""     )
        self.assertEqual(
            __UpperCamelCase     ,					{"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )]}     )
        self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]     )     )     ,					1.0     )

        _UpperCAmelCase          =   classifier("""Who are you voting for in 2020?"""     ,					candidate_labels=["""politics""", """public health"""]     )
        self.assertEqual(
            __UpperCamelCase     ,					{"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )]}     )
        self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]     )     )     ,					1.0     )

        _UpperCAmelCase          =   classifier(
            """Who are you voting for in 2020?"""     ,					candidate_labels="""politics"""     ,					hypothesis_template="""This text is about {}"""     )
        self.assertEqual(__UpperCamelCase     ,					{"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     )]}     )

        # https://github.com/huggingface/transformers/issues/13846
        _UpperCAmelCase          =   classifier(["""I am happy"""]     ,					["""positive""", """negative"""]     )
        self.assertEqual(
            __UpperCamelCase     ,					[
                {"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )]}
                for i in range(1     )
            ]     ,					)
        _UpperCAmelCase          =   classifier(["""I am happy""", """I am sad"""]     ,					["""positive""", """negative"""]     )
        self.assertEqual(
            __UpperCamelCase     ,					[
                {"""sequence""": ANY(__UpperCamelCase     ), """labels""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )], """scores""": [ANY(__UpperCamelCase     ), ANY(__UpperCamelCase     )]}
                for i in range(2     )
            ]     ,					)

        with self.assertRaises(__UpperCamelCase     ):
            classifier(""""""     ,					candidate_labels="""politics"""     )

        with self.assertRaises(__UpperCamelCase     ):
            classifier(__UpperCamelCase     ,					candidate_labels="""politics"""     )

        with self.assertRaises(__UpperCamelCase     ):
            classifier("""Who are you voting for in 2020?"""     ,					candidate_labels=""""""     )

        with self.assertRaises(__UpperCamelCase     ):
            classifier("""Who are you voting for in 2020?"""     ,					candidate_labels=__UpperCamelCase     )

        with self.assertRaises(__UpperCamelCase     ):
            classifier(
                """Who are you voting for in 2020?"""     ,					candidate_labels="""politics"""     ,					hypothesis_template="""Not formatting template"""     ,					)

        with self.assertRaises(__UpperCamelCase     ):
            classifier(
                """Who are you voting for in 2020?"""     ,					candidate_labels="""politics"""     ,					hypothesis_template=__UpperCamelCase     ,					)

        self.run_entailment_id(__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Pipeline     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   zero_shot_classifier.model.config
        _UpperCAmelCase          =   config.labelaid
        _UpperCAmelCase          =   zero_shot_classifier.entailment_id

        _UpperCAmelCase          =   {"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
        self.assertEqual(zero_shot_classifier.entailment_id     ,					-1     )

        _UpperCAmelCase          =   {"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
        self.assertEqual(zero_shot_classifier.entailment_id     ,					0     )

        _UpperCAmelCase          =   {"""ENTAIL""": 0, """NON-ENTAIL""": 1}
        self.assertEqual(zero_shot_classifier.entailment_id     ,					0     )

        _UpperCAmelCase          =   {"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
        self.assertEqual(zero_shot_classifier.entailment_id     ,					2     )

        _UpperCAmelCase          =   original_labelaid
        self.assertEqual(__UpperCamelCase     ,					zero_shot_classifier.entailment_id     )







    @require_torch
    def      _snake_case					(			self			:       Dict     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline(
            """zero-shot-classification"""     ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,					framework="""pt"""     ,					)
        # There was a regression in 4.10 for this
        # Adding a test so we don't make the mistake again.
        # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
        zero_shot_classifier(
            """Who are you voting for in 2020?""" * 1_00     ,					candidate_labels=["""politics""", """public health""", """science"""]     )







    @require_torch
    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline(
            """zero-shot-classification"""     ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,					framework="""pt"""     ,					)
        _UpperCAmelCase          =   zero_shot_classifier(
            """Who are you voting for in 2020?"""     ,					candidate_labels=["""politics""", """public health""", """science"""]     )

        self.assertEqual(
            nested_simplify(__UpperCamelCase     )     ,					{
                """sequence""": """Who are you voting for in 2020?""",
                """labels""": ["""science""", """public health""", """politics"""],
                """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
            }     ,					)







    @require_tf
    def      _snake_case					(			self			:       Any     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline(
            """zero-shot-classification"""     ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,					framework="""tf"""     ,					)
        _UpperCAmelCase          =   zero_shot_classifier(
            """Who are you voting for in 2020?"""     ,					candidate_labels=["""politics""", """public health""", """science"""]     )

        self.assertEqual(
            nested_simplify(__UpperCamelCase     )     ,					{
                """sequence""": """Who are you voting for in 2020?""",
                """labels""": ["""science""", """public health""", """politics"""],
                """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
            }     ,					)







    @slow
    @require_torch
    def      _snake_case					(			self			:       Optional[Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline("""zero-shot-classification"""     ,					model="""roberta-large-mnli"""     ,					framework="""pt"""     )
        _UpperCAmelCase          =   zero_shot_classifier(
            """Who are you voting for in 2020?"""     ,					candidate_labels=["""politics""", """public health""", """science"""]     )

        self.assertEqual(
            nested_simplify(__UpperCamelCase     )     ,					{
                """sequence""": """Who are you voting for in 2020?""",
                """labels""": ["""politics""", """public health""", """science"""],
                """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
            }     ,					)
        _UpperCAmelCase          =   zero_shot_classifier(
            """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
            """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
            """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
            """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
            """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
            """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
            """ English-to-German translation task, improving over the existing best results, including ensembles by"""
            """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
            """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
            """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
            """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
            """ large and limited training data."""     ,					candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,					multi_label=__UpperCamelCase     ,					)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     )     ,					{
                """sequence""": (
                    """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                    """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                    """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                    """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                    """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                    """ superior in quality while being more parallelizable and requiring significantly less time to"""
                    """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                    """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                    """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                    """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                    """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                    """ other tasks by applying it successfully to English constituency parsing both with large and"""
                    """ limited training data."""
                ),
                """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                """scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
            }     ,					)







    @slow
    @require_tf
    def      _snake_case					(			self			:       str     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   pipeline("""zero-shot-classification"""     ,					model="""roberta-large-mnli"""     ,					framework="""tf"""     )
        _UpperCAmelCase          =   zero_shot_classifier(
            """Who are you voting for in 2020?"""     ,					candidate_labels=["""politics""", """public health""", """science"""]     )

        self.assertEqual(
            nested_simplify(__UpperCamelCase     )     ,					{
                """sequence""": """Who are you voting for in 2020?""",
                """labels""": ["""politics""", """public health""", """science"""],
                """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
            }     ,					)
        _UpperCAmelCase          =   zero_shot_classifier(
            """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
            """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
            """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
            """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
            """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
            """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
            """ English-to-German translation task, improving over the existing best results, including ensembles by"""
            """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
            """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
            """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
            """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
            """ large and limited training data."""     ,					candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,					multi_label=__UpperCamelCase     ,					)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     )     ,					{
                """sequence""": (
                    """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                    """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                    """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                    """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                    """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                    """ superior in quality while being more parallelizable and requiring significantly less time to"""
                    """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                    """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                    """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                    """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                    """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                    """ other tasks by applying it successfully to English constituency parsing both with large and"""
                    """ limited training data."""
                ),
                """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                """scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
            }     ,					)

"""simple docstring"""



import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _UpperCAmelCase          =   requests.get(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

    try:
        job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}      )
        _UpperCAmelCase          =   math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

        for i in range(_A      ):
            _UpperCAmelCase          =   requests.get(url + F"""&page={i + 2}"""					,      headers=_A      ).json()
            job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}      )

        return job_links
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""      )

    return {}


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _UpperCAmelCase          =   requests.get(_A					,      headers=_A      ).json()
    _UpperCAmelCase          =   {}

    try:
        artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}      )
        _UpperCAmelCase          =   math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0      )

        for i in range(_A      ):
            _UpperCAmelCase          =   requests.get(url + F"""&page={i + 2}"""					,      headers=_A      ).json()
            artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}      )

        return artifacts
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""      )

    return {}


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   None
    if token is not None:
        _UpperCAmelCase          =   {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase          =   requests.get(_A					,      headers=_A					,      allow_redirects=_A      )
    _UpperCAmelCase          =   result.headers["""Location"""]
    _UpperCAmelCase          =   requests.get(_A					,      allow_redirects=_A      )
    _UpperCAmelCase          =   os.path.join(_A					,      F"""{artifact_name}.zip"""      )
    with open(_A					,      """wb"""      ) as fp:
        fp.write(response.content      )


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   None

    with zipfile.ZipFile(_A      ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_A      ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_A      ) as f:
                        for line in f:
                            _UpperCAmelCase          =   line.decode("""UTF-8"""      ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _UpperCAmelCase          =   line[: line.index(""": """      )]
                                    _UpperCAmelCase          =   line[line.index(""": """      ) + len(""": """      ) :]
                                    errors.append([error_line, error]      )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith("""FAILED """      ):
                                # `test` is the test method that failed
                                _UpperCAmelCase          =   line[len("""FAILED """      ) :]
                                failed_tests.append(_A      )
                            elif filename == "job_name.txt":
                                _UpperCAmelCase          =   line

    if len(_A      ) != len(_A      ):
        raise ValueError(
            F"""`errors` and `failed_tests` should have the same number of elements. Got {len(_A      )} for `errors` """
            F"""and {len(_A      )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            """ problem."""      )

    _UpperCAmelCase          =   None
    if job_name and job_links:
        _UpperCAmelCase          =   job_links.get(_A					,      _A      )

    # A list with elements of the form (line of error, error, failed test)
    _UpperCAmelCase          =   [x + [y] + [job_link] for x, y in zip(_A					,      _A      )]

    return result


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   []

    _UpperCAmelCase          =   [os.path.join(_A					,      _A      ) for p in os.listdir(_A      ) if p.endswith(""".zip"""      )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(_A					,      job_links=_A      )      )

    return errors


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   Counter()
    counter.update([x[1] for x in logs]      )
    _UpperCAmelCase          =   counter.most_common()
    _UpperCAmelCase          =   {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _UpperCAmelCase          =   {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

    _UpperCAmelCase          =   dict(sorted(r.items()					,      key=lambda _A      : item[1]["count"]					,      reverse=_A      )      )
    return r


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   test.split("""::"""      )[0]
    if test.startswith("""tests/models/"""      ):
        _UpperCAmelCase          =   test.split("""/"""      )[2]
    else:
        _UpperCAmelCase          =   None

    return test


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   [(x[0], x[1], get_model(x[2]      )) for x in logs]
    _UpperCAmelCase          =   [x for x in logs if x[2] is not None]
    _UpperCAmelCase          =   {x[2] for x in logs}

    _UpperCAmelCase          =   {}
    for test in tests:
        _UpperCAmelCase          =   Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]      )
        _UpperCAmelCase          =   counter.most_common()
        _UpperCAmelCase          =   {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _UpperCAmelCase          =   sum(error_counts.values()      )
        if n_errors > 0:
            _UpperCAmelCase          =   {"""count""": n_errors, """errors""": error_counts}

    _UpperCAmelCase          =   dict(sorted(r.items()					,      key=lambda _A      : item[1]["count"]					,      reverse=_A      )      )
    return r


def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   """| no. | error | status |"""
    _UpperCAmelCase          =   """|-:|:-|:-|"""
    _UpperCAmelCase          =   [header, sep]
    for error in reduced_by_error:
        _UpperCAmelCase          =   reduced_by_error[error]["""count"""]
        _UpperCAmelCase          =   F"""| {count} | {error[:1_0_0]} |  |"""
        lines.append(_A      )

    return "\n".join(_A      )


def  _UpperCamelCase	(						_A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   """| model | no. of errors | major error | count |"""
    _UpperCAmelCase          =   """|-:|-:|-:|-:|"""
    _UpperCAmelCase          =   [header, sep]
    for model in reduced_by_model:
        _UpperCAmelCase          =   reduced_by_model[model]["""count"""]
        _UpperCAmelCase    ,_UpperCAmelCase          =   list(reduced_by_model[model]["""errors"""].items()      )[0]
        _UpperCAmelCase          =   F"""| {model} | {count} | {error[:6_0]} | {_count} |"""
        lines.append(_A      )

    return "\n".join(_A      )


if __name__ == "__main__":
 a					:	Tuple							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
 parser.add_argument(
     '''--output_dir''',
     type=str,
     required=True,
     help='''Where to store the downloaded artifacts and other result files.''',
 )
 parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
 a					:	Dict							=		parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 a					:	Tuple							=		get_job_links(args.workflow_run_id, token=args.token)
 a					:	Tuple							=		{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    a					:	List[Any]							=		k.find(''' / ''')
    a					:	Tuple							=		k[index + len(''' / ''') :]
   a					:	int							=		v
 with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 a					:	Tuple							=		get_artifacts_links(args.workflow_run_id, token=args.token)
 with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

 for idx, (name, url) in enumerate(artifacts.items()):
  download_artifact(name, url, args.output_dir, args.token)
  # Be gentle to GitHub
  time.sleep(1)

 a					:	Optional[int]							=		get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 a					:	Union[str, Any]							=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 a					:	Optional[int]							=		counter.most_common(3_0)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 a					:	int							=		reduce_by_error(errors)
 a					:	str							=		reduce_by_model(errors)

 a					:	int							=		make_github_table(reduced_by_error)
 a					:	Optional[int]							=		make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)