
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTransfoXLForSequenceClassification,
						    TFTransfoXLLMHeadModel,
						    TFTransfoXLModel,
						)

class 						a   :




				def __init__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Dict,    ):
										snake_case       :     Tuple =    parent
										snake_case       :     List[str] =    13
										snake_case       :     Tuple =    7
										snake_case       :     List[Any] =    30
										snake_case       :     Optional[int] =    self.seq_length + self.mem_len
										snake_case       :     List[str] =    15
										snake_case       :     Union[str, Any] =    True
										snake_case       :     Dict =    True
										snake_case       :     List[Any] =    99
										snake_case       :     List[Any] =    [10, 50, 80]
										snake_case       :     int =    32
										snake_case       :     Tuple =    32
										snake_case       :     List[Any] =    4
										snake_case       :     Any =    8
										snake_case       :     str =    1_28
										snake_case       :     Any =    2
										snake_case       :     List[str] =    2
										snake_case       :     Dict =    None
										snake_case       :     List[Any] =    1
										snake_case       :     List[str] =    0
										snake_case       :     str =    3
										snake_case       :     Union[str, Any] =    self.vocab_size - 1
										snake_case       :     str =    0.01




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     int =    ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )
										snake_case       :     Dict =    ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

										snake_case       :     Dict =    None
										if self.use_labels:
																snake_case       :     int =    ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

										snake_case       :     str =    TransfoXLConfig(
										    vocab_size=self.vocab_size,    mem_len=self.mem_len,    clamp_len=self.clamp_len,    cutoffs=self.cutoffs,    d_model=self.hidden_size,    d_embed=self.d_embed,    n_head=self.num_attention_heads,    d_head=self.d_head,    d_inner=self.d_inner,    div_val=self.div_val,    n_layer=self.num_hidden_layers,    eos_token_id=self.eos_token_id,    pad_token_id=self.vocab_size - 1,    init_range=self.init_range,    num_labels=self.num_labels,    )

										return (config, input_ids_a, input_ids_a, lm_labels)




				def 	__snake_case							(							self	:				int      ):
										random.seed(self.seed      )
										tf.random.set_seed(self.seed      )




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     List[Any] =    TFTransfoXLModel(lowercase_      )

										snake_case       :     Union[str, Any] =    model(lowercase_      ).to_tuple()

										snake_case       :     Optional[int] =    {"input_ids": input_ids_a, "mems": mems_a}

										snake_case       :     Dict =    model(lowercase_      ).to_tuple()

										self.parent.assertEqual(hidden_states_a.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )
										self.parent.assertEqual(hidden_states_a.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a],    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,    )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a],    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,    )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     str =    TFTransfoXLLMHeadModel(lowercase_      )

										snake_case       :     int =    model(lowercase_      ).to_tuple()

										snake_case       :     List[str] =    {"input_ids": input_ids_a, "labels": lm_labels}
										snake_case       :     List[Any] =    model(lowercase_      ).to_tuple()

										snake_case       :     str =    model([input_ids_a, mems_a]      ).to_tuple()

										snake_case       :     List[Any] =    {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

										snake_case       :     Dict =    model(lowercase_      ).to_tuple()

										self.parent.assertEqual(lm_logits_a.shape,    (self.batch_size, self.seq_length, self.vocab_size)      )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a],    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,    )

										self.parent.assertEqual(lm_logits_a.shape,    (self.batch_size, self.seq_length, self.vocab_size)      )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a],    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,    )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     Tuple =    TFTransfoXLForSequenceClassification(lowercase_      )
										snake_case       :     Any =    model(lowercase_      )
										self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)      )




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Any =    self.prepare_config_and_inputs()
										(snake_case)       :     Union[str, Any] =    config_and_inputs
										snake_case       :     int =    {"input_ids": input_ids_a}
										return config, inputs_dict




@require_tf
class 						a   (				snake_case__				,snake_case__				,unittest.TestCase    ):
				_snake_case		    = (
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				_snake_case		    = () if is_tf_available() else ()
				_snake_case		    = (
				    {
				        '''feature-extraction''': TFTransfoXLModel,
				        '''text-classification''': TFTransfoXLForSequenceClassification,
				        '''text-generation''': TFTransfoXLLMHeadModel,
				        '''zero-shot''': TFTransfoXLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
										if pipeline_test_casse_name == "TextGenerationPipelineTests":
																# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
																# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
																# tokenizer.
																return True

										return False




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     int =    TFTransfoXLModelTester(self      )
										snake_case       :     Any =    ConfigTester(self,    config_class=lowercase_,    d_embed=37      )




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										self.config_tester.run_common_tests()




				def 	__snake_case							(							self	:				Any      ):
										self.model_tester.set_seed()
										snake_case       :     Optional[int] =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_model(*lowercase_      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										self.model_tester.set_seed()
										snake_case       :     str =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase_      )




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Tuple =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase_      )




				def 	__snake_case							(							self	:				Optional[Any]      ):
										snake_case       :     Optional[Any] =    self.model_tester.prepare_config_and_inputs_for_common()
										snake_case       :     str =    [TFTransfoXLForSequenceClassification]

										for model_class in self.all_model_classes:
																snake_case       :     Dict =    model_class(lowercase_      )
																assert isinstance(model.get_input_embeddings(),    tf.keras.layers.Layer      )
																if model_class in list_other_models_with_output_ebd:
																						snake_case       :     Union[str, Any] =    model.get_output_embeddings()
																						assert isinstance(lowercase_,    tf.keras.layers.Layer      )
																						snake_case       :     Union[str, Any] =    model.get_bias()
																						assert name is None
																else:
																						snake_case       :     Dict =    model.get_output_embeddings()
																						assert x is None
																						snake_case       :     str =    model.get_bias()
																						assert name is None




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										# TODO JP: Make TransfoXL XLA compliant
										pass




				@slow
				def 	__snake_case							(							self	:				List[Any]      ):
										for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case       :     int =    TFTransfoXLModel.from_pretrained(lowercase_      )
																self.assertIsNotNone(lowercase_      )




				@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
				def 	__snake_case							(							self	:				str      ):
										pass




@require_tf
class 						a   (				unittest.TestCase    ):




				@unittest.skip('''Skip test until #12651 is resolved.'''      )
				@slow
				def 	__snake_case							(							self	:				str      ):
										snake_case       :     List[str] =    TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
										# fmt: off
										snake_case       :     Union[str, Any] =    tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]],    dtype=tf.intaa      )  # noqa: E231
										# fmt: on
										#  In 1991 , the remains of Russian Tsar Nicholas II and his family
										#  ( except for Alexei and Maria ) are discovered .
										#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
										#  remainder of the story . 1883 Western Siberia ,
										#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
										#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
										#  father initially slaps him for making such an accusation , Rasputin watches as the
										#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
										#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
										#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

										# fmt: off
										snake_case       :     Union[str, Any] =    [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
										# fmt: on
										#  In 1991, the remains of Russian Tsar Nicholas II and his family (
										#  except for Alexei and Maria ) are discovered. The voice of young son,
										#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
										#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
										#  and a group of men to perform magic. Rasputin has a vision and
										#  denounces one of the men as a horse thief. Although his father initially
										#  slaps him for making such an accusation, Rasputin watches as the man
										#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
										#  of the Virgin Mary, prompting him to become a priest.
										#  Rasputin quickly becomes famous, with people, even a bishop, begging for
										#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
										# Nicholas II and his family were discovered. The voice of <unk> young son,
										# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

										snake_case       :     Dict =    model.generate(lowercase_,    max_length=2_00,    do_sample=lowercase_      )
										self.assertListEqual(output_ids[0].numpy().tolist(),    lowercase_      )



'''simple docstring'''





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

import requests

def      A (       A_							:					List[Any]    ,							A_							:					int=None       ):
						snake_case       :     Optional[int] =    None
						if token is not None:
												snake_case       :     List[Any] =    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}

						snake_case       :     str =    F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						snake_case       :     List[Any] =    requests.get(A_    ,							headers=A_       ).json()
						snake_case       :     List[Any] =    {}

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

												for i in range(A_       ):
																		snake_case       :     Optional[Any] =    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_							:					int    ,							A_							:					Optional[int]=None       ):
						snake_case       :     Any =    None
						if token is not None:
												snake_case       :     str =    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}

						snake_case       :     Tuple =    F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
						snake_case       :     List[Any] =    requests.get(A_    ,							headers=A_       ).json()
						snake_case       :     List[Any] =    {}

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

												for i in range(A_       ):
																		snake_case       :     List[Any] =    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_							:					List[Any]    ,							A_							:					List[Any]    ,							A_							:					Any    ,							A_							:					str       ):
						snake_case       :     Any =    None
						if token is not None:
												snake_case       :     int =    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}

						snake_case       :     Any =    requests.get(A_    ,							headers=A_    ,							allow_redirects=A_       )
						snake_case       :     Union[str, Any] =    result.headers['''Location''']
						snake_case       :     str =    requests.get(A_    ,							allow_redirects=A_       )
						snake_case       :     int =    os.path.join(A_    ,							F"""{artifact_name}.zip"""       )
						with open(A_    ,							'''wb'''       ) as fp:
												fp.write(response.content       )

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

						snake_case       :     int =    None
						if job_name and job_links:
												snake_case       :     int =    job_links.get(A_    ,							A_       )

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

						return result

def      A (       A_							:					Optional[Any]    ,							A_							:					List[str]=None       ):
						snake_case       :     str =    []

						snake_case       :     str =    [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_							:					Dict    ,							A_							:					Optional[Any]=None       ):
						snake_case       :     Any =    Counter()
						counter.update([x[1] for x in logs]       )
						snake_case       :     Optional[Any] =    counter.most_common()
						snake_case       :     Dict =    {}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		snake_case       :     str =    {'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

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

def      A (       A_							:					Tuple       ):
						snake_case       :     Tuple =    test.split('''::'''       )[0]
						if test.startswith('''tests/models/'''       ):
												snake_case       :     Optional[int] =    test.split('''/'''       )[2]
						else:
												snake_case       :     int =    None

						return test

def      A (       A_							:					Optional[Any]    ,							A_							:					Dict=None       ):
						snake_case       :     List[str] =    [(x[0], x[1], get_model(x[2]       )) for x in logs]
						snake_case       :     int =    [x for x in logs if x[2] is not None]
						snake_case       :     int =    {x[2] for x in logs}

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

						snake_case       :     Optional[int] =    dict(sorted(r.items()    ,							key=lambda A_       : item[1]["count"]    ,							reverse=A_       )       )
						return r

def      A (       A_							:					Optional[Any]       ):
						snake_case       :     Dict =    '''| no. | error | status |'''
						snake_case       :     Union[str, Any] =    '''|-:|:-|:-|'''
						snake_case       :     List[Any] =    [header, sep]
						for error in reduced_by_error:
												snake_case       :     Optional[int] =    reduced_by_error[error]['''count''']
												snake_case       :     List[str] =    F"""| {count} | {error[:100]} |  |"""
												lines.append(A_       )

						return "\n".join(A_       )

def      A (       A_							:					Optional[int]       ):
						snake_case       :     Union[str, Any] =    '''| model | no. of errors | major error | count |'''
						snake_case       :     Dict =    '''|-:|-:|-:|-:|'''
						snake_case       :     Optional[Any] =    [header, sep]
						for model in reduced_by_model:
												snake_case       :     str =    reduced_by_model[model]['''count''']
												snake_case,					snake_case       :     Tuple =    list(reduced_by_model[model]['''errors'''].items()       )[0]
												snake_case       :     Dict =    F"""| {model} | {count} | {error[:60]} | {_count} |"""
												lines.append(A_       )

						return "\n".join(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   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.")
						UpperCAmelCase    					=   parser.parse_args()

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

						UpperCAmelCase    					=   get_job_links(args.workflow_run_id, token=args.token)
						UpperCAmelCase    					=   {}
						# 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:
																								UpperCAmelCase    					=   k.find(" / ")
																								UpperCAmelCase    					=   k[index + len(" / ") :]
																		UpperCAmelCase    					=   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)

						UpperCAmelCase    					=   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)

						UpperCAmelCase    					=   get_all_errors(args.output_dir, job_links=job_links)

						# `e[1]` is the error
						UpperCAmelCase    					=   Counter()
						counter.update([e[1] for e in errors])

						# print the top 30 most common test errors
						UpperCAmelCase    					=   counter.most_common(30)
						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)

						UpperCAmelCase    					=   reduce_by_error(errors)
						UpperCAmelCase    					=   reduce_by_model(errors)

						UpperCAmelCase    					=   make_github_table(reduced_by_error)
						UpperCAmelCase    					=   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)

