
"""simple docstring"""





import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertModel,
				)






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=7							,						__a=True							,						__a=True							,						__a=True							,						__a=True							,						__a=99							,						__a=64							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=5_12							,						__a=16							,						__a=2							,						__a=0.02							,						__a=3							,						__a=4							,						__a=None							,						)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    seq_length
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_input_mask
							_UpperCamelCase									=    use_token_type_ids
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    vocab_size
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    embedding_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    max_position_embeddings
							_UpperCamelCase									=    type_vocab_size
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    num_labels
							_UpperCamelCase									=    num_choices
							_UpperCamelCase									=    scope



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size)

							_UpperCamelCase									=    None
							if self.use_input_mask:
												_UpperCamelCase									=    random_attention_mask([self.batch_size, self.seq_length])

							_UpperCamelCase									=    None
							if self.use_token_type_ids:
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size)

							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels)
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.num_choices)

							_UpperCamelCase									=    self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							return MobileBertConfig(
							    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						embedding_size=self.embedding_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						is_decoder=__a							,						initializer_range=self.initializer_range							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    MobileBertModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a							,						token_type_ids=__a)
							_UpperCamelCase									=    model(__a)

							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))
							self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    MobileBertForMaskedLM(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    MobileBertForNextSentencePrediction(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a							,						)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, 2))



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






							'''simple docstring'''
							_UpperCamelCase									=    MobileBertForPreTraining(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a							,						next_sentence_label=__a							,						)
							self.parent.assertEqual(result.prediction_logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size))
							self.parent.assertEqual(result.seq_relationship_logits.shape							,						(self.batch_size, 2))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    MobileBertForQuestionAnswering(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						start_positions=__a							,						end_positions=__a							,						)
							self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length))



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






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    MobileBertForSequenceClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    MobileBertForTokenClassification(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_choices
							_UpperCamelCase									=    MobileBertForMultipleChoice(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    input_ids.unsqueeze(1).expand(-1							,						self.num_choices							,						-1).contiguous()
							_UpperCamelCase									=    token_type_ids.unsqueeze(1).expand(-1							,						self.num_choices							,						-1).contiguous()
							_UpperCamelCase									=    input_mask.unsqueeze(1).expand(-1							,						self.num_choices							,						-1).contiguous()
							_UpperCamelCase									=    model(
							    __a							,						attention_mask=__a							,						token_type_ids=__a							,						labels=__a							,						)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices))




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    
							)									=    config_and_inputs
							_UpperCamelCase									=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (
		        MobileBertModel,
		        MobileBertForMaskedLM,
		        MobileBertForMultipleChoice,
		        MobileBertForNextSentencePrediction,
		        MobileBertForPreTraining,
		        MobileBertForQuestionAnswering,
		        MobileBertForSequenceClassification,
		        MobileBertForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase__         = (
		    {
		        'feature-extraction': MobileBertModel,
		        'fill-mask': MobileBertForMaskedLM,
		        'question-answering': MobileBertForQuestionAnswering,
		        'text-classification': MobileBertForSequenceClassification,
		        'token-classification': MobileBertForTokenClassification,
		        'zero-shot': MobileBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase__         = True



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a=False)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    super()._prepare_for_class(__a							,						__a							,						return_labels=__a)

							if return_labels:
												if model_class in get_values(__a):
																	_UpperCamelCase									=    torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)							,						dtype=torch.long							,						device=__a)
																	_UpperCamelCase									=    torch.zeros(
																	    self.model_tester.batch_size							,						dtype=torch.long							,						device=__a)
							return inputs_dict



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    MobileBertModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_model(*__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_pretraining(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_question_answering(*__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a)




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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_token_classification(*__a)





def  lowerCamelCase__						(     __snake_case      )				->						Tuple:


					"""simple docstring"""





					return torch.tensor(
					    __snake_case,	dtype=torch.long,	device=__snake_case,	)


_a								=						1E-3






@require_torch
@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase(       unittest.TestCase      ):



		@slow
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    MobileBertModel.from_pretrained('''google/mobilebert-uncased''').to(__a)
							_UpperCamelCase									=    _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]])
							with torch.no_grad():
												_UpperCamelCase									=    model(__a)[0]
							_UpperCamelCase									=    torch.Size((1, 9, 5_12))
							self.assertEqual(output.shape							,						__a)
							_UpperCamelCase									=    torch.tensor(
							    [
							        [
							            [-2.4_736_526e07, 8.2_691_656e04, 1.6_521_838e05],
							            [-5.7_541_704e-01, 3.9_056_022e00, 4.4_011_507e00],
							            [2.6_047_359e00, 1.5_677_652e00, -1.7_324_188e-01],
							        ]
							    ]							,						device=__a							,						)

							# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
							# ~1 difference, it's therefore not a good idea to measure using addition.
							# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
							# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
							_UpperCamelCase									=    torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE)
							_UpperCamelCase									=    torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE)

							self.assertTrue(lower_bound and upper_bound)









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

import requests





def 	__a     (      A__			:  str			,  A__			:  List[Any]=None       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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

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

        for i in range(A__       ):
            SCREAMING_SNAKE_CASE										=   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 	__a     (      A__			:  List[Any]			,  A__			:  Optional[int]=None       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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

    try:
        artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}       )
        SCREAMING_SNAKE_CASE										=   math.ceil((result["total_count"] - 100) / 100       )

        for i in range(A__       ):
            SCREAMING_SNAKE_CASE										=   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 	__a     (      A__			:  Any			,  A__			:  str			,  A__			:  List[str]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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





def 	__a     (      A__			:  List[Any]			,  A__			:  List[Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   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:
                            SCREAMING_SNAKE_CASE										=   line.decode("UTF-8"       ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    SCREAMING_SNAKE_CASE										=   line[: line.index(": "       )]
                                    SCREAMING_SNAKE_CASE										=   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
                                SCREAMING_SNAKE_CASE										=   line[len("FAILED "       ) :]
                                failed_tests.append(A__       )
                            elif filename == "job_name.txt":
                                SCREAMING_SNAKE_CASE										=   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."       )

    SCREAMING_SNAKE_CASE										=   None
    if job_name and job_links:
        SCREAMING_SNAKE_CASE										=   job_links.get(A__			,  A__       )

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

    return result





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Union[str, Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []

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

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





def 	__a     (      A__			:  str       ):
    SCREAMING_SNAKE_CASE										=   test.split("::"       )[0]
    if test.startswith("tests/models/"       ):
        SCREAMING_SNAKE_CASE										=   test.split("/"       )[2]
    else:
        SCREAMING_SNAKE_CASE										=   None

    return test





def 	__a     (      A__			:  List[str]			,  A__			:  Dict=None       ):
    SCREAMING_SNAKE_CASE										=   [(x[0], x[1], get_model(x[2]       )) for x in logs]
    SCREAMING_SNAKE_CASE										=   [x for x in logs if x[2] is not None]
    SCREAMING_SNAKE_CASE										=   {x[2] for x in logs}

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

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





def 	__a     (      A__			:  Dict       ):
    SCREAMING_SNAKE_CASE										=   "| no. | error | status |"
    SCREAMING_SNAKE_CASE										=   "|-:|:-|:-|"
    SCREAMING_SNAKE_CASE										=   [header, sep]
    for error in reduced_by_error:
        SCREAMING_SNAKE_CASE										=   reduced_by_error[error]["count"]
        SCREAMING_SNAKE_CASE										=   F"| {count} | {error[:100]} |  |"
        lines.append(A__       )

    return "\n".join(A__       )





def 	__a     (      A__			:  Optional[Any]       ):
    SCREAMING_SNAKE_CASE										=   "| model | no. of errors | major error | count |"
    SCREAMING_SNAKE_CASE										=   "|-:|-:|-:|-:|"
    SCREAMING_SNAKE_CASE										=   [header, sep]
    for model in reduced_by_model:
        SCREAMING_SNAKE_CASE										=   reduced_by_model[model]["count"]
        SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   list(reduced_by_model[model]["errors"].items()       )[0]
        SCREAMING_SNAKE_CASE										=   F"| {model} | {count} | {error[:60]} | {_count} |"
        lines.append(A__       )

    return "\n".join(A__       )


if __name__ == "__main__":
    __A			:					List[str]						=			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			:					Union[str, Any]						=			parser.parse_args()

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

    __A			:					int						=			get_job_links(args.workflow_run_id, token=args.token)
    __A			:					Dict						=			{}
    # 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			:					Union[str, Any]						=			k.find(' / ')
                __A			:					Optional[int]						=			k[index + len(' / ') :]
            __A			:					Optional[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			:					int						=			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			:					Dict						=			Counter()
    counter.update([e[1] for e in errors])

    # print the top 30 most common test errors
    __A			:					Optional[Any]						=			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			:					str						=			reduce_by_error(errors)
    __A			:					int						=			reduce_by_model(errors)

    __A			:					Any						=			make_github_table(reduced_by_error)
    __A			:					List[str]						=			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)