





'''simple docstring'''


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_SCREAMING_SNAKE_CASE									=				"true"


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       str=82		,							SCREAMING_SNAKE_CASE_	:       Dict=16     ):



    '''simple docstring'''





    set_seed(42     )
    _lowerCAmelCase						      =   RegressionModel()
    _lowerCAmelCase						      =   deepcopy(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   RegressionDataset(length=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   DataLoader(SCREAMING_SNAKE_CASE_		,							batch_size=SCREAMING_SNAKE_CASE_     )
    model.to(accelerator.device     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   accelerator.prepare(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    return model, ddp_model, dataloader


def 						__a(SCREAMING_SNAKE_CASE_	:       Accelerator		,							SCREAMING_SNAKE_CASE_	:       int=False     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"     )
    _lowerCAmelCase						      =   load_dataset("glue"		,							"mrpc"		,							split="validation"     )

    def tokenize_function(SCREAMING_SNAKE_CASE_	:       List[str]     ):
        _lowerCAmelCase						      =   tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=SCREAMING_SNAKE_CASE_		,							max_length=SCREAMING_SNAKE_CASE_     )
        return outputs

    with accelerator.main_process_first():
        _lowerCAmelCase						      =   dataset.map(
            SCREAMING_SNAKE_CASE_		,							batched=SCREAMING_SNAKE_CASE_		,							remove_columns=["idx", "sentence1", "sentence2"]		,							)

    _lowerCAmelCase						      =   tokenized_datasets.rename_column("label"		,							"labels"     )

    def collate_fn(SCREAMING_SNAKE_CASE_	:       str     ):
        if use_longest:
            return tokenizer.pad(SCREAMING_SNAKE_CASE_		,							padding="longest"		,							return_tensors="pt"     )
        return tokenizer.pad(SCREAMING_SNAKE_CASE_		,							padding="max_length"		,							max_length=128		,							return_tensors="pt"     )

    return DataLoader(SCREAMING_SNAKE_CASE_		,							shuffle=SCREAMING_SNAKE_CASE_		,							collate_fn=SCREAMING_SNAKE_CASE_		,							batch_size=16     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE_		,							split_batches=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   get_dataloader(SCREAMING_SNAKE_CASE_		,							not dispatch_batches     )
    _lowerCAmelCase						      =   AutoModelForSequenceClassification.from_pretrained(
        "hf-internal-testing/mrpc-bert-base-cased"		,							return_dict=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   accelerator.prepare(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    for batch in dataloader:
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   batch.values()
        with torch.no_grad():
            _lowerCAmelCase						      =   model(SCREAMING_SNAKE_CASE_     )
            _lowerCAmelCase      ,      _lowerCAmelCase						      =   accelerator.gather_for_metrics((logit, target)     )
            logits_and_targets.append((logit, target)     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE_     )
        targs.append(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   torch.cat(SCREAMING_SNAKE_CASE_     ), torch.cat(SCREAMING_SNAKE_CASE_     )
    return logits, targs


def 						__a(SCREAMING_SNAKE_CASE_	:       Accelerator		,							SCREAMING_SNAKE_CASE_	:       List[Any]=82		,							SCREAMING_SNAKE_CASE_	:       Optional[int]=False		,							SCREAMING_SNAKE_CASE_	:       List[Any]=False		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]=16     ):



    '''simple docstring'''





    _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   get_basic_setup(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   generate_predictions(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    assert (
        len(SCREAMING_SNAKE_CASE_     ) == num_samples
    ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE_     )}'''


def 						__a(SCREAMING_SNAKE_CASE_	:       bool = False		,							SCREAMING_SNAKE_CASE_	:       bool = False     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   evaluate.load("glue"		,							"mrpc"     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   get_mrpc_setup(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    # First do baseline
    _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   setup["no"]
    model.to(SCREAMING_SNAKE_CASE_     )
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE_     )
        with torch.inference_mode():
            _lowerCAmelCase						      =   model(**SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   outputs.logits.argmax(dim=-1     )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE_		,							references=batch["labels"]     )
    _lowerCAmelCase						      =   metric.compute()

    # Then do distributed
    _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   setup["ddp"]
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            _lowerCAmelCase						      =   model(**SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   outputs.logits.argmax(dim=-1     )
        _lowerCAmelCase						      =   batch["labels"]
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   accelerator.gather_for_metrics((preds, references)     )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE_		,							references=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   metric.compute()

    for key in "accuracy f1".split():
        assert math.isclose(
            baseline[key]		,							distributed[key]     ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   Accelerator(split_batches=SCREAMING_SNAKE_CASE_		,							dispatch_batches=SCREAMING_SNAKE_CASE_     )
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    # These are a bit slower so they should only be ran on the GPU or TPU
    if torch.cuda.is_available() or is_tpu_available():
        if accelerator.is_local_main_process:
            print("**Testing gather_for_metrics**"     )
        for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                if accelerator.is_local_main_process:
                    print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''     )
                test_mrpc(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
                accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print("**Test torch metrics**"     )
    for split_batches in [True, False]:
        for dispatch_batches in [True, False]:
            _lowerCAmelCase						      =   Accelerator(split_batches=SCREAMING_SNAKE_CASE_		,							dispatch_batches=SCREAMING_SNAKE_CASE_     )
            if accelerator.is_local_main_process:
                print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''     )
            test_torch_metrics(SCREAMING_SNAKE_CASE_		,							99     )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print("**Test last batch is not dropped when perfectly divisible**"     )
    _lowerCAmelCase						      =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE_		,							512     )
    accelerator.state._reset_state()


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    main()


if __name__ == "__main__":
       main()









'''simple docstring'''


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

import requests


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





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

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

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

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   requests.get(url + F'''&page={i + 2}'''		,							headers=SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Dict=None     ):



    '''simple docstring'''





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

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

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

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   requests.get(url + F'''&page={i + 2}'''		,							headers=SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   None

    with zipfile.ZipFile(SCREAMING_SNAKE_CASE_     ) as z:
        for filename in z.namelist():
            if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(SCREAMING_SNAKE_CASE_     ) as f:
                        for line in f:
                            _lowerCAmelCase						      =   line.decode("UTF-8"     ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _lowerCAmelCase						      =   line[: line.index(": "     )]
                                    _lowerCAmelCase						      =   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
                                _lowerCAmelCase						      =   line[len("FAILED "     ) :]
                                failed_tests.append(SCREAMING_SNAKE_CASE_     )
                            elif filename == "job_name.txt":
                                _lowerCAmelCase						      =   line

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

    _lowerCAmelCase						      =   None
    if job_name and job_links:
        _lowerCAmelCase						      =   job_links.get(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

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

    return result


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []

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

    return errors


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       str=None     ):



    '''simple docstring'''





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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





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

    return test


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





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

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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| no. | error | status |"
    _lowerCAmelCase						      =   "|-:|:-|:-|"
    _lowerCAmelCase						      =   [header, sep]
    for error in reduced_by_error:
        _lowerCAmelCase						      =   reduced_by_error[error]["count"]
        _lowerCAmelCase						      =   F'''| {count} | {error[:100]} |  |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| model | no. of errors | major error | count |"
    _lowerCAmelCase						      =   "|-:|-:|-:|-:|"
    _lowerCAmelCase						      =   [header, sep]
    for model in reduced_by_model:
        _lowerCAmelCase						      =   reduced_by_model[model]["count"]
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   list(reduced_by_model[model]["errors"].items()     )[0]
        _lowerCAmelCase						      =   F'''| {model} | {count} | {error[:60]} | {_count} |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


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

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

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

       _SCREAMING_SNAKE_CASE									=				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)

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

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

       # print the top 30 most common test errors
       _SCREAMING_SNAKE_CASE									=				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)

       _SCREAMING_SNAKE_CASE									=				reduce_by_error(errors)
       _SCREAMING_SNAKE_CASE									=				reduce_by_model(errors)

       _SCREAMING_SNAKE_CASE									=				make_github_table(reduced_by_error)
       _SCREAMING_SNAKE_CASE									=				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)


