

import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__A			:					Any						=			os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'{bindir}/../../examples/pytorch/translation'):
    from run_translation import main  # noqa


set_seed(4_2)
__A			:					Tuple						=			'sshleifer/student_marian_en_ro_6_1'
__A			:					List[Any]						=			'sshleifer/tiny-mbart'





@require_torch
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[Any]=False     , __lowerCamelCase					:			int=None     , __lowerCamelCase					:			List[str]=True     , __lowerCamelCase					:			str=True     , __lowerCamelCase					:			Optional[int]=True     , __lowerCamelCase					:			Optional[int]=True     , ):
       SCREAMING_SNAKE_CASE										=   self.run_trainer(
           eval_steps=1     , max_len=12     , model_name=__lowerCamelCase     , num_train_epochs=1     , distributed=__lowerCamelCase     , extra_args_str=__lowerCamelCase     , predict_with_generate=__lowerCamelCase     , do_train=__lowerCamelCase     , do_eval=__lowerCamelCase     , do_predict=__lowerCamelCase     , )
       SCREAMING_SNAKE_CASE										=   TrainerState.load_from_json(os.path.join(__lowerCamelCase     , "trainer_state.json"			)			).log_history

       if not do_eval:
           return

       SCREAMING_SNAKE_CASE										=   [log for log in logs if "eval_loss" in log.keys()]

       SCREAMING_SNAKE_CASE										=   eval_metrics[0]
       if predict_with_generate:
           assert "eval_bleu" in first_step_stats

           SCREAMING_SNAKE_CASE										=   eval_metrics[-1]
           assert isinstance(last_step_stats["eval_bleu"]     , __lowerCamelCase			)
           assert not math.isnan(float(last_step_stats["eval_loss"]			)			), "eval_loss must not be `nan`"





   @require_torch_non_multi_gpu
   def      _snake_case		(       self					:			List[str]			):
       self.run_seqaseq_quick()





   @require_torch_multi_gpu
   def      _snake_case		(       self					:			str			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase			)





   @require_torch_multi_gpu
   def      _snake_case		(       self					:			List[Any]			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase			)





   @unittest.skip("Requires an update of the env running those tests"			)
   @require_torch_multi_gpu
   @require_fairscale
   def      _snake_case		(       self					:			Union[str, Any]			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase     , extra_args_str="--sharded_ddp simple"			)





   @unittest.skip("Requires an update of the env running those tests"			)
   @require_torch_multi_gpu
   @require_fairscale
   def      _snake_case		(       self					:			Dict			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase     , extra_args_str="--sharded_ddp simple --fp16"			)





   @unittest.skip("Requires an update of the env running those tests"			)
   @require_torch_multi_gpu
   @require_fairscale
   def      _snake_case		(       self					:			str			):
       self.run_seqaseq_quick(distributed=__lowerCamelCase     , extra_args_str="--sharded_ddp zero_dp_2"     , predict_with_generate=__lowerCamelCase			)





   @unittest.skip("Requires an update of the env running those tests"			)
   @require_torch_multi_gpu
   @require_fairscale
   def      _snake_case		(       self					:			Optional[int]			):
       self.run_seqaseq_quick(
           distributed=__lowerCamelCase     , extra_args_str="--sharded_ddp zero_dp_2 --fp16"     , predict_with_generate=__lowerCamelCase			)





   @require_apex
   @require_torch_gpu
   def      _snake_case		(       self					:			Tuple			):
       # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
       # program and it breaks other tests that run from the same pytest worker, therefore until this is
       # sorted out it must be run only in an external program, that is distributed=True in this
       # test and only under one or more gpus - if we want cpu will need to make a special test
       #
       # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
       # 2nd main() call it botches the future eval.
       #
       self.run_seqaseq_quick(distributed=__lowerCamelCase     , extra_args_str="--fp16 --fp16_backend=apex"			)
       # test 2nd time - was getting eval_loss': nan'
       # to reproduce the problem set distributed=False
       self.run_seqaseq_quick(distributed=__lowerCamelCase     , extra_args_str="--fp16 --fp16_backend=apex"			)





   @parameterized.expand(["base", "low", "high", "mixed"]			)
   @require_torch_multi_gpu
   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str			):
       # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
       SCREAMING_SNAKE_CASE										=   {
           # test with the default log_level - should be info and thus log info once
           "base": {"extra_args_str": "", "n_matches": 1},
           # test with low log_level and log_level_replica - should be noisy on all processes
           # now the info string should appear twice on 2 processes
           "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
           # test with high log_level and low log_level_replica
           # now the info string should appear once only on the replica
           "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
           # test with high log_level and log_level_replica - should be quiet on all processes
           "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
       }

       SCREAMING_SNAKE_CASE										=   experiments[experiment_id]
       SCREAMING_SNAKE_CASE										=   {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
       SCREAMING_SNAKE_CASE										=   "Running training"
       with CaptureStderr() as cl:
           self.run_seqaseq_quick(**__lowerCamelCase     , extra_args_str=data["extra_args_str"]			)
       SCREAMING_SNAKE_CASE										=   len(re.findall(__lowerCamelCase     , cl.err			)			)
       self.assertEqual(__lowerCamelCase     , data["n_matches"]			)





   @slow
   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.run_trainer(
           eval_steps=2     , max_len=128     , model_name=__lowerCamelCase     , learning_rate=3e-4     , num_train_epochs=10     , distributed=__lowerCamelCase     , )

       # Check metrics
       SCREAMING_SNAKE_CASE										=   TrainerState.load_from_json(os.path.join(__lowerCamelCase     , "trainer_state.json"			)			).log_history
       SCREAMING_SNAKE_CASE										=   [log for log in logs if "eval_loss" in log.keys()]
       SCREAMING_SNAKE_CASE										=   eval_metrics[0]
       SCREAMING_SNAKE_CASE										=   eval_metrics[-1]

       assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
       assert isinstance(last_step_stats["eval_bleu"]     , __lowerCamelCase			)

       # test if do_predict saves generations and metrics
       SCREAMING_SNAKE_CASE										=   os.listdir(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   {os.path.basename(__lowerCamelCase			) for p in contents}
       assert "generated_predictions.txt" in contents
       assert "predict_results.json" in contents





   @slow
   @require_bitsandbytes
   def      _snake_case		(       self					:			List[str]			):
       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(__lowerCamelCase					:			str			) -> Tuple[int, float]:
           SCREAMING_SNAKE_CASE										=   "--skip_memory_metrics 0"

           SCREAMING_SNAKE_CASE										=   self.run_trainer(
               max_len=128     , model_name=__lowerCamelCase     , learning_rate=3e-4     , num_train_epochs=1     , optim=__lowerCamelCase     , distributed=__lowerCamelCase     , extra_args_str=__lowerCamelCase     , do_eval=__lowerCamelCase     , do_predict=__lowerCamelCase     , n_gpus_to_use=1     , )

           # Check metrics
           SCREAMING_SNAKE_CASE										=   TrainerState.load_from_json(Path(__lowerCamelCase     , "trainer_state.json"			)			).log_history
           SCREAMING_SNAKE_CASE										=   int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20			)
           SCREAMING_SNAKE_CASE										=   int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20			)

           SCREAMING_SNAKE_CASE										=   logs[0]["train_loss"]
           return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value			)

       SCREAMING_SNAKE_CASE										=   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       SCREAMING_SNAKE_CASE										=   gpu_peak_mem_orig + gpu_alloc_mem_orig
       SCREAMING_SNAKE_CASE										=   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       SCREAMING_SNAKE_CASE										=   gpu_total_mem_orig - gpu_total_mem_bnb

       # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
       # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
       # in 2 bytes and the diff in optim memory usage is derived as so:
       #
       # - normal 25*8=~200MB (8 bytes per param)
       # - bnb    25*2= ~50MB (2 bytes per param)
       #
       # Thus we should expect ~150MB total memory saved.
       #
       # Peak memory should be the same - the total should be different by about that same margin
       #
       # After leaving a small margin to accommodate for differences between gpus let's check
       # that we have at least 120MB in savings
       SCREAMING_SNAKE_CASE										=   120

       # uncomment the following if this test starts failing - requires py38 for a new print feature
       # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
       # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
       # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
       # print(f"{gpu_alloc_mem_diff=}MB")
       # print(f"{gpu_peak_mem_diff=}MB")
       # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
       # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

       self.assertGreater(
           __lowerCamelCase     , __lowerCamelCase     , "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
           f" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
           f" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"     , )

       self.assertGreater(
           __lowerCamelCase     , __lowerCamelCase     , "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
           f" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
           f" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"     , )

       self.assertEqual(
           __lowerCamelCase     , __lowerCamelCase     , f"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"			)





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float = 3e-3     , __lowerCamelCase					:			str = "adafactor"     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			str = None     , __lowerCamelCase					:			int = 0     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			int = None     , ):
       SCREAMING_SNAKE_CASE										=   self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
       SCREAMING_SNAKE_CASE										=   self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE										=   f"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(__lowerCamelCase			)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(__lowerCamelCase			)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

       SCREAMING_SNAKE_CASE										=   f"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(__lowerCamelCase			)}\n        ".split()

       SCREAMING_SNAKE_CASE										=   "\n            --do_predict\n        ".split()

       SCREAMING_SNAKE_CASE										=   []
       if do_train:
           args += args_train

       if do_eval:
           args += args_eval

       if do_predict:
           args += args_predict

       if predict_with_generate:
           args += "--predict_with_generate".split()

       if do_train:
           if optim == "adafactor":
               args += "--adafactor".split()
           else:
               args += f"--optim {optim}".split()

       if extra_args_str is not None:
           args += extra_args_str.split()

       if distributed:
           if n_gpus_to_use is None:
               SCREAMING_SNAKE_CASE										=   get_gpu_count()
           SCREAMING_SNAKE_CASE										=   get_torch_dist_unique_port()
           SCREAMING_SNAKE_CASE										=   f"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
           SCREAMING_SNAKE_CASE										=   [sys.executable] + distributed_args + args
           # keep for quick debug
           # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
           execute_subprocess_async(__lowerCamelCase     , env=self.get_env()			)
       else:
           SCREAMING_SNAKE_CASE										=   ["run_translation.py"] + args
           with patch.object(__lowerCamelCase     , "argv"     , __lowerCamelCase			):
               main()

       return output_dir


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)