

import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links





def      A__					(			__A  :						Dict     ,				__A  :						List[str]=7			)					->Any:
      __A    =None
      if token is not None:
            __A    ={'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}

      # The id of a workflow (not of a workflow run)
      __A    ='''636036'''

      __A    =F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
      # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
      url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

      __A    =requests.get(__A     ,				headers=__A			).json()

      return result["workflow_runs"]





def      A__					(			__A  :						Tuple			)					->Tuple:
      __A    =get_daily_ci_runs(__A			)
      __A    =None
      for workflow_run in workflow_runs:
            if workflow_run["status"] == "completed":
                  __A    =workflow_run['''id''']
                  break

      return workflow_run_id





def      A__					(			__A  :						List[Any]     ,				__A  :						Any     ,				__A  :						List[str]			)					->Union[str, Any]:
      __A    =get_last_daily_ci_runs(__A			)
      if workflow_run_id is not None:
            __A    =get_artifacts_links(worflow_run_id=__A     ,				token=__A			)
            for artifact_name in artifact_names:
                  if artifact_name in artifacts_links:
                        __A    =artifacts_links[artifact_name]
                        download_artifact(
                            artifact_name=__A     ,				artifact_url=__A     ,				output_dir=__A     ,				token=__A			)





def      A__					(			__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						List[str]			)					->Tuple:
      get_last_daily_ci_artifacts(__A     ,				__A     ,				__A			)

      __A    ={}
      for artifact_name in artifact_names:
            __A    =os.path.join(__A     ,				F'''{artifact_name}.zip'''			)
            if os.path.isfile(__A			):
                  __A    ={}
                  with zipfile.ZipFile(__A			) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(__A			):
                                    # read the file
                                    with z.open(__A			) as f:
                                          __A    =f.read().decode('''UTF-8'''			)

      return results





import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


