


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

import requests
def 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  int=None						):
       _A      :       Dict				    = None
       if token is not None:
              _A      :       List[str]				    = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

       _A      :       Union[str, Any]				    = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
       _A      :       str				    = requests.get(UpperCamelCase__				,							headers=UpperCamelCase__						).json()
       _A      :       int				    = {}

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

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

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

       return {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[int]				,							UpperCamelCase__     :  List[str]=None						):
       _A      :       int				    = None
       if token is not None:
              _A      :       Optional[Any]				    = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

       _A      :       Optional[int]				    = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
       _A      :       Dict				    = requests.get(UpperCamelCase__				,							headers=UpperCamelCase__						).json()
       _A      :       str				    = {}

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

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

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

       return {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  str				,							UpperCamelCase__     :  List[Any]						):
       _A      :       str				    = None
       if token is not None:
              _A      :       Tuple				    = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

       _A      :       str				    = requests.get(UpperCamelCase__				,							headers=UpperCamelCase__				,							allow_redirects=UpperCamelCase__						)
       _A      :       str				    = result.headers["Location"]
       _A      :       Any				    = requests.get(UpperCamelCase__				,							allow_redirects=UpperCamelCase__						)
       _A      :       List[str]				    = os.path.join(UpperCamelCase__				,							f"{artifact_name}.zip"						)
       with open(UpperCamelCase__				,							"wb"						) as fp:
              fp.write(response.content						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Optional[Any]=None						):
       _A      :       List[Any]				    = []
       _A      :       Optional[int]				    = []
       _A      :       Optional[Any]				    = None

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

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

       _A      :       List[str]				    = None
       if job_name and job_links:
              _A      :       Optional[Any]				    = job_links.get(UpperCamelCase__				,							UpperCamelCase__						)

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

       return result
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Tuple=None						):
       _A      :       int				    = []

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

       return errors
def 							_UpperCAmelCase				(UpperCamelCase__     :  Dict				,							UpperCamelCase__     :  Dict=None						):
       _A      :       Tuple				    = Counter()
       counter.update([x[1] for x in logs]						)
       _A      :       int				    = counter.most_common()
       _A      :       Any				    = {}
       for error, count in counts:
              if error_filter is None or error not in error_filter:
                     _A      :       Dict				    = {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}

       _A      :       Tuple				    = dict(sorted(r.items()				,							key=lambda UpperCamelCase__						: item[1]["count"]				,							reverse=UpperCamelCase__						)						)
       return r
def 							_UpperCAmelCase				(UpperCamelCase__     :  Dict						):
       _A      :       Dict				    = test.split("::"						)[0]
       if test.startswith("tests/models/"						):
              _A      :       int				    = test.split("/"						)[2]
       else:
              _A      :       List[str]				    = None

       return test
def 							_UpperCAmelCase				(UpperCamelCase__     :  Any				,							UpperCamelCase__     :  Union[str, Any]=None						):
       _A      :       Union[str, Any]				    = [(x[0], x[1], get_model(x[2]						)) for x in logs]
       _A      :       Any				    = [x for x in logs if x[2] is not None]
       _A      :       List[str]				    = {x[2] for x in logs}

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

       _A      :       Optional[int]				    = dict(sorted(r.items()				,							key=lambda UpperCamelCase__						: item[1]["count"]				,							reverse=UpperCamelCase__						)						)
       return r
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A      :       Dict				    = "| no. | error | status |"
       _A      :       int				    = "|-:|:-|:-|"
       _A      :       Tuple				    = [header, sep]
       for error in reduced_by_error:
              _A      :       Tuple				    = reduced_by_error[error]["count"]
              _A      :       Dict				    = f"| {count} | {error[:100]} |  |"
              lines.append(UpperCamelCase__						)

       return "\n".join(UpperCamelCase__						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]						):
       _A      :       Tuple				    = "| model | no. of errors | major error | count |"
       _A      :       Tuple				    = "|-:|-:|-:|-:|"
       _A      :       Optional[Any]				    = [header, sep]
       for model in reduced_by_model:
              _A      :       List[Any]				    = reduced_by_model[model]["count"]
              _A    ,			_A      :       Dict				    = list(reduced_by_model[model]["errors"].items()						)[0]
              _A      :       List[Any]				    = f"| {model} | {count} | {error[:60]} | {_count} |"
              lines.append(UpperCamelCase__						)

       return "\n".join(UpperCamelCase__						)


if __name__ == "__main__":
    lowerCAmelCase__      		= 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.')
    lowerCAmelCase__      		= parser.parse_args()

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

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

    lowerCAmelCase__      		= 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)

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

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

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

    lowerCAmelCase__      		= reduce_by_error(errors)
    lowerCAmelCase__      		= reduce_by_model(errors)

    lowerCAmelCase__      		= make_github_table(reduced_by_error)
    lowerCAmelCase__      		= 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)




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            for model_result in results.values():
                   for batch_size, sequence_length in zip(model_result["bs"] ,						model_result["ss"]):
                          _A      :       Optional[int]				    = model_result["result"][batch_size][sequence_length]
                          self.assertIsNotNone(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Optional[int]				    = "sshleifer/tiny-gpt2"
            _A      :       int				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[str]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Optional[Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       int				    = "sgugger/tiny-distilbert-classification"
            _A      :       str				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						only_pretrain_model=__lowerCamelCase ,						)
            _A      :       Dict				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Tuple				    = "sshleifer/tiny-gpt2"
            _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						torchscript=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Union[str, Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       List[str]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     @unittest.skipIf(torch_device == "cpu" ,						"Cant do half precision")
     def  _lowerCamelCase (						self)			->				int:
            _A      :       Any				    = "sshleifer/tiny-gpt2"
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						fpaa=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Any				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Union[str, Any]				    = "sshleifer/tiny-gpt2"
            _A      :       Any				    = AutoConfig.from_pretrained(__lowerCamelCase)
            # set architectures equal to `None`
            _A      :       Dict				    = None
            _A      :       Any				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Union[str, Any]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       int				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       List[Any]				    = "sshleifer/tiny-gpt2"
            _A      :       int				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Optional[Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       int				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     @unittest.skipIf(torch_device == "cpu" ,						"Can't do half precision")
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Any				    = "sshleifer/tiny-gpt2"
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						fpaa=__lowerCamelCase ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[Any]				    = PyTorchBenchmark(__lowerCamelCase)
            _A      :       Tuple				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				str:
            _A      :       List[str]				    = "sshleifer/tiny-gpt2"
            _A      :       Union[str, Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Any				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Optional[Any]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Tuple				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Tuple				    = "sshleifer/tinier_bart"
            _A      :       Optional[Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Optional[int]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Dict				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Optional[int]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_inference_result)
            self.check_results_dict_not_empty(results.memory_inference_result)


     def  _lowerCamelCase (						self)			->				str:
            _A      :       List[Any]				    = "sshleifer/tiny-gpt2"
            _A      :       Optional[Any]				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       List[str]				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Dict				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       int				    = "sshleifer/tinier_bart"
            _A      :       str				    = AutoConfig.from_pretrained(__lowerCamelCase)
            _A      :       Tuple				    = PyTorchBenchmarkArguments(
                models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=__lowerCamelCase ,						)
            _A      :       Tuple				    = PyTorchBenchmark(__lowerCamelCase ,						configs=[config])
            _A      :       Union[str, Any]				    = benchmark.run()
            self.check_results_dict_not_empty(results.time_train_result)
            self.check_results_dict_not_empty(results.memory_train_result)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[str]				    = "sshleifer/tiny-gpt2"
            with tempfile.TemporaryDirectory() as tmp_dir:
                   _A      :       Optional[Any]				    = PyTorchBenchmarkArguments(
                       models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						save_to_csv=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						inference_time_csv_file=os.path.join(__lowerCamelCase ,						"inf_time.csv") ,						train_memory_csv_file=os.path.join(__lowerCamelCase ,						"train_mem.csv") ,						inference_memory_csv_file=os.path.join(__lowerCamelCase ,						"inf_mem.csv") ,						train_time_csv_file=os.path.join(__lowerCamelCase ,						"train_time.csv") ,						env_info_csv_file=os.path.join(__lowerCamelCase ,						"env.csv") ,						multi_process=__lowerCamelCase ,						)
                   _A      :       Tuple				    = PyTorchBenchmark(__lowerCamelCase)
                   benchmark.run()
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"inf_time.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"train_time.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"inf_mem.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"train_mem.csv")).exists())
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"env.csv")).exists())


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Dict				    = "sshleifer/tiny-gpt2"

            def _check_summary_is_not_empty(__lowerCamelCase):
                   self.assertTrue(hasattr(__lowerCamelCase ,						"sequential"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"cumulative"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"current"))
                   self.assertTrue(hasattr(__lowerCamelCase ,						"total"))

            with tempfile.TemporaryDirectory() as tmp_dir:
                   _A      :       Union[str, Any]				    = PyTorchBenchmarkArguments(
                       models=[MODEL_ID] ,						training=__lowerCamelCase ,						inference=__lowerCamelCase ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						log_filename=os.path.join(__lowerCamelCase ,						"log.txt") ,						log_print=__lowerCamelCase ,						trace_memory_line_by_line=__lowerCamelCase ,						multi_process=__lowerCamelCase ,						)
                   _A      :       Optional[int]				    = PyTorchBenchmark(__lowerCamelCase)
                   _A      :       Dict				    = benchmark.run()
                   _check_summary_is_not_empty(result.inference_summary)
                   _check_summary_is_not_empty(result.train_summary)
                   self.assertTrue(Path(os.path.join(__lowerCamelCase ,						"log.txt")).exists())
