


"""simple docstring"""







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

import requests


def 					lowercase      (    A_  ,       A_=None	)->				List[str]:
		'''simple docstring'''







		a						:		Optional[int]											=      None
		if token is not None:
				a						:		Any											=      {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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

		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(A_	):
						a						:		Optional[int]											=      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 					lowercase      (    A_  ,       A_=None	)->				Tuple:
		'''simple docstring'''







		a						:		List[str]											=      None
		if token is not None:
				a						:		Union[str, Any]											=      {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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

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

				for i in range(A_	):
						a						:		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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				List[Any]:
		'''simple docstring'''







		a						:		Tuple											=      None
		if token is not None:
				a						:		Optional[int]											=      {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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


def 					lowercase      (    A_  ,       A_=None	)->				List[Any]:
		'''simple docstring'''







		a						:		int											=      []
		a						:		int											=      []
		a						:		Dict											=      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:
														a						:		int											=      line.decode("UTF-8"	).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		a						:		str											=      line[: line.index(": "	)]
																		a						:		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
																a						:		Optional[int]											=      line[len("FAILED "	) :]
																failed_tests.append(A_	)
														elif filename == "job_name.txt":
																a						:		List[Any]											=      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."	)

		a						:		Union[str, Any]											=      None
		if job_name and job_links:
				a						:		List[Any]											=      job_links.get(A_  ,       A_	)

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

		return result


def 					lowercase      (    A_  ,       A_=None	)->				Dict:
		'''simple docstring'''







		a						:		List[str]											=      []

		a						:		Optional[Any]											=      [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 					lowercase      (    A_  ,       A_=None	)->				List[Any]:
		'''simple docstring'''







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

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


def 					lowercase      (    A_	)->				str:
		'''simple docstring'''







		a						:		Optional[Any]											=      test.split("::"	)[0]
		if test.startswith("tests/models/"	):
				a						:		Any											=      test.split("/"	)[2]
		else:
				a						:		List[Any]											=      None

		return test


def 					lowercase      (    A_  ,       A_=None	)->				str:
		'''simple docstring'''







		a						:		Union[str, Any]											=      [(x[0], x[1], get_model(x[2]	)) for x in logs]
		a						:		Dict											=      [x for x in logs if x[2] is not None]
		a						:		Optional[int]											=      {x[2] for x in logs}

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

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


def 					lowercase      (    A_	)->				int:
		'''simple docstring'''







		a						:		str											=      "| no. | error | status |"
		a						:		List[str]											=      "|-:|:-|:-|"
		a						:		List[str]											=      [header, sep]
		for error in reduced_by_error:
				a						:		Any											=      reduced_by_error[error]["count"]
				a						:		List[str]											=      F'''| {count} | {error[:100]} |  |'''
				lines.append(A_	)

		return "\n".join(A_	)


def 					lowercase      (    A_	)->				Any:
		'''simple docstring'''







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

		return "\n".join(A_	)


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

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

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

	__lowercase    =				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)

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

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

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

	__lowercase    =				reduce_by_error(errors)
	__lowercase    =				reduce_by_model(errors)

	__lowercase    =				make_github_table(reduced_by_error)
	__lowercase    =				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)










"""simple docstring"""







import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment


def 					lowercase      (    )->				Union[str, Any]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      torch.nn.Linear(2  ,       4	)
  a						:		Tuple											=      torch.optim.AdamW(model.parameters()  ,       lr=1.0	)
  a						:		Union[str, Any]											=      torch.optim.lr_scheduler.OneCycleLR(A_  ,       max_lr=0.0_1  ,       steps_per_epoch=2  ,       epochs=1	)
  a						:		List[str]											=      DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
  a						:		int											=      DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

  return model, optimizer, scheduler, train_dl, valid_dl


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  return (model.weight.abs().sum() + model.bias.abs().sum()).item()


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Optional[int]											=      torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
  model.load_state_dict(A_	)







class 						_A  (  _a  ):







    """simple docstring"""




    @require_cuda
    def      __snake_case							( self    :   Any):
      a						:		List[str]											=      Accelerator()
      assert PartialState._shared_state["_cpu"] is False
      assert PartialState._shared_state["device"].type == "cuda"
      with self.assertRaises(__UpperCAmelCase):
        a						:		Any											=      Accelerator(cpu=__UpperCAmelCase)






    def      __snake_case							( self    :   List[Any]):
      a						:		str											=      Accelerator()
      a						:		Optional[Any]											=      GradientState()
      assert state.num_steps == 1
      a						:		Dict											=      4
      assert state.num_steps == 4

      assert state.sync_gradients is True
      a						:		Optional[int]											=      False
      assert state.sync_gradients is False
      GradientState._reset_state()






    def      __snake_case							( self    :   str):
      a						:		int											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Tuple											=      create_components()

      (
          (
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		(
          a
      )							,		
      )						:		Tuple											=      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      self.assertTrue(prepared_model in accelerator._models)
      self.assertTrue(prepared_optimizer in accelerator._optimizers)
      self.assertTrue(prepared_scheduler in accelerator._schedulers)
      self.assertTrue(prepared_train_dl in accelerator._dataloaders)
      self.assertTrue(prepared_valid_dl in accelerator._dataloaders)






    def      __snake_case							( self    :   Dict):
      a						:		Dict											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Any											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      accelerator.free_memory()

      self.assertTrue(len(accelerator._models) == 0)
      self.assertTrue(len(accelerator._optimizers) == 0)
      self.assertTrue(len(accelerator._schedulers) == 0)
      self.assertTrue(len(accelerator._dataloaders) == 0)






    def      __snake_case							( self    :   int):
      PartialState._reset_state()

      # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
      def noop(*__UpperCAmelCase    :   Union[str, Any]    ,			**__UpperCAmelCase    :   Any):
        pass

      with patch("torch.cuda.set_device"    ,			__UpperCAmelCase), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"):
        a						:		int											=      Accelerator()
        self.assertEqual(str(accelerator.state.device)    ,			"cuda:64")






    def      __snake_case							( self    :   List[str]):
      a						:		Tuple											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Optional[Any]											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      get_signature(__UpperCAmelCase)

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # make sure loaded weights match
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)






    def      __snake_case							( self    :   Optional[int]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		Dict											=      create_components()
      accelerator.prepare(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Union[str, Any]											=      get_signature(__UpperCAmelCase)

      # saving hook
      def save_config(__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Union[str, Any]):
        a						:		Tuple											=      {"class_name": models[0].__class__.__name__}

        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"w") as f:
          json.dump(__UpperCAmelCase    ,			__UpperCAmelCase)

        # loading hook
      def load_config(__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]):
        with open(os.path.join(__UpperCAmelCase    ,			"data.json")    ,			"r") as f:
          a						:		Optional[Any]											=      json.load(__UpperCAmelCase)

        a						:		Tuple											=      config["class_name"]

      a						:		Optional[int]											=      accelerator.register_save_state_pre_hook(__UpperCAmelCase)
      a						:		Tuple											=      accelerator.register_load_state_pre_hook(__UpperCAmelCase)

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match with hooks
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # random class name to verify correct one is loaded
        a						:		int											=      "random"

        # make sure loaded weights match with hooks
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)

        # mode.class_name is loaded from config
        self.assertTrue(model.class_name == model.__class__.__name__)

      # remove hooks
      save_hook.remove()
      load_hook.remove()

      with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(__UpperCAmelCase)

        # make sure random weights don't match with hooks removed
        load_random_weights(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)

        # random class name to verify correct one is loaded
        a						:		Dict											=      "random"

        # make sure loaded weights match with hooks removed
        accelerator.load_state(__UpperCAmelCase)
        self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)

        # mode.class_name is NOT loaded from config
        self.assertTrue(model.class_name != model.__class__.__name__)






    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[str]											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		int											=      create_components()
      a						:		Tuple											=      None

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		Any											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertTrue(dummy_obj is None)






    def      __snake_case							( self    :   List[str]):
      a						:		str											=      Accelerator()
      a							,		a							,		a							,		a							,		a						:		List[Any]											=      create_components()
      a						:		Union[str, Any]											=      [1, 2, 3]

      # This should work
      a							,		a							,		a							,		a							,		a							,		a						:		str											=      accelerator.prepare(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Dummy object should have `_is_accelerate_prepared` set to `True`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Model is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,			)
      self.assertEqual(
          getattr(__UpperCAmelCase    ,			"_is_accelerate_prepared"    ,			__UpperCAmelCase)    ,			__UpperCAmelCase    ,			"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,			)






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map={"": 0}    ,			)
      a						:		Tuple											=      Accelerator()

      # This should work
      a						:		List[Any]											=      accelerator.prepare(__UpperCAmelCase)






    @slow
    @require_bnb
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Dict											=      Accelerator()

      with init_empty_weights():
        a						:		Any											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Union[str, Any]											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      "cpu"

      a						:		int											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			device_map=__UpperCAmelCase    ,			load_in_abit=__UpperCAmelCase    ,			llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase)

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)






    @slow
    @require_bnb
    @require_multi_gpu
    def      __snake_case							( self    :   Optional[int]):
      from transformers import AutoModelForCausalLM

      a						:		Union[str, Any]											=      {"distributed_type": DistributedType.MULTI_GPU}

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        model.tie_weights()
        a						:		Any											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		Dict											=      1

      a						:		Any											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		int											=      Accelerator()

      # This should not work and get value error
      with self.assertRaises(__UpperCAmelCase):
        a						:		Optional[int]											=      accelerator.prepare(__UpperCAmelCase)

      PartialState._reset_state()






    @slow
    @require_bnb
    @require_multi_gpu
    def      __snake_case							( self    :   Tuple):
      from transformers import AutoModelForCausalLM

      with init_empty_weights():
        a						:		List[str]											=      AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"    ,			)
        a						:		Tuple											=      infer_auto_device_map(__UpperCAmelCase)
        a						:		str											=      1

      a						:		Optional[int]											=      AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"    ,			load_in_abit=__UpperCAmelCase    ,			device_map=__UpperCAmelCase    ,			)
      a						:		str											=      Accelerator()

      # This should work
      a						:		Any											=      accelerator.prepare(__UpperCAmelCase)






    @require_cuda
    def      __snake_case							( self    :   List[Any]):
      a						:		Tuple											=      torch.nn.Linear(10    ,			10)
      a						:		int											=      torch.optim.SGD(model.parameters()    ,			lr=0.01)
      a						:		Optional[Any]											=      Accelerator(cpu=__UpperCAmelCase)
      a						:		List[str]											=      accelerator.prepare(__UpperCAmelCase)






