

'''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   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = torch.nn.Linear(2 ,       4		)
     __SCREAMING_SNAKE_CASE      = torch.optim.AdamW(model.parameters() ,       lr=1.0		)
     __SCREAMING_SNAKE_CASE      = torch.optim.lr_scheduler.OneCycleLR(a__ ,       max_lr=0.01 ,       steps_per_epoch=2 ,       epochs=1		)
     __SCREAMING_SNAKE_CASE      = DataLoader(TensorDataset(torch.tensor([1, 2, 3]		)		)		)
     __SCREAMING_SNAKE_CASE      = DataLoader(TensorDataset(torch.tensor([4, 5, 6]		)		)		)

     return model, optimizer, scheduler, train_dl, valid_dl







def   a__			(    a__		):




     """simple docstring"""


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







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = torch.nn.Linear(*tuple(model.weight.T.shape		)		).state_dict()
     model.load_state_dict(a__		)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 @require_cuda
 def  UpperCAmelCase__						(						self   :			str     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      assert PartialState._shared_state["_cpu"] is False
      assert PartialState._shared_state["device"].type == "cuda"
      with self.assertRaises(__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = Accelerator(cpu=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      __SCREAMING_SNAKE_CASE      = GradientState()
      assert state.num_steps == 1
      __SCREAMING_SNAKE_CASE      = 4
      assert state.num_steps == 4

      assert state.sync_gradients is True
      __SCREAMING_SNAKE_CASE      = False
      assert state.sync_gradients is False
      GradientState._reset_state()
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = create_components()

      (
          (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     
      )      = accelerator.prepare(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      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  UpperCAmelCase__						(						self   :			int     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = create_components()
      accelerator.prepare(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      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  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      PartialState._reset_state()

      # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
      def noop(*__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				**__SCREAMING_SNAKE_CASE   :			int     ):
           pass

      with patch("""torch.cuda.set_device"""   ,				__SCREAMING_SNAKE_CASE     ), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""     ):
           __SCREAMING_SNAKE_CASE      = Accelerator()
           self.assertEqual(str(accelerator.state.device     )   ,				"""cuda:64"""     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = create_components()
      accelerator.prepare(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = get_signature(__SCREAMING_SNAKE_CASE     )

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

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

           # make sure loaded weights match
           accelerator.load_state(__SCREAMING_SNAKE_CASE     )
           self.assertTrue(abs(model_signature - get_signature(__SCREAMING_SNAKE_CASE     )     ) < 1E-3     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = create_components()
      accelerator.prepare(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = get_signature(__SCREAMING_SNAKE_CASE     )

      # saving hook
      def save_config(__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]     ):
           __SCREAMING_SNAKE_CASE      = {"""class_name""": models[0].__class__.__name__}

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

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

           __SCREAMING_SNAKE_CASE      = config["""class_name"""]

      __SCREAMING_SNAKE_CASE      = accelerator.register_save_state_pre_hook(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = accelerator.register_load_state_pre_hook(__SCREAMING_SNAKE_CASE     )

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

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

           # random class name to verify correct one is loaded
           __SCREAMING_SNAKE_CASE      = """random"""

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

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

           # random class name to verify correct one is loaded
           __SCREAMING_SNAKE_CASE      = """random"""

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

           # mode.class_name is NOT loaded from config
           self.assertTrue(model.class_name != model.__class__.__name__     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = create_components()
      __SCREAMING_SNAKE_CASE      = None

      # This should work
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = accelerator.prepare(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      self.assertTrue(dummy_obj is None     )
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = Accelerator()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = create_components()
      __SCREAMING_SNAKE_CASE      = [1, 2, 3]

      # This should work
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = accelerator.prepare(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      self.assertEqual(
          getattr(__SCREAMING_SNAKE_CASE   ,				"""_is_accelerate_prepared"""   ,				__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE   ,				"""Dummy object should have `_is_accelerate_prepared` set to `True`"""   ,				)
      self.assertEqual(
          getattr(__SCREAMING_SNAKE_CASE   ,				"""_is_accelerate_prepared"""   ,				__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE   ,				"""Model is missing `_is_accelerator_prepared` or is set to `False`"""   ,				)
      self.assertEqual(
          getattr(__SCREAMING_SNAKE_CASE   ,				"""_is_accelerate_prepared"""   ,				__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE   ,				"""Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""   ,				)
      self.assertEqual(
          getattr(__SCREAMING_SNAKE_CASE   ,				"""_is_accelerate_prepared"""   ,				__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE   ,				"""Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""   ,				)
      self.assertEqual(
          getattr(__SCREAMING_SNAKE_CASE   ,				"""_is_accelerate_prepared"""   ,				__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE   ,				"""Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""   ,				)
      self.assertEqual(
          getattr(__SCREAMING_SNAKE_CASE   ,				"""_is_accelerate_prepared"""   ,				__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE   ,				"""Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""   ,				)
 @slow
 @require_bnb
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Dict:


      """simple docstring"""




      from transformers import AutoModelForCausalLM

      __SCREAMING_SNAKE_CASE      = AutoModelForCausalLM.from_pretrained(
          """EleutherAI/gpt-neo-125m"""   ,				load_in_abit=__SCREAMING_SNAKE_CASE   ,				device_map={"""""": 0}   ,				)
      __SCREAMING_SNAKE_CASE      = Accelerator()

      # This should work
      __SCREAMING_SNAKE_CASE      = accelerator.prepare(__SCREAMING_SNAKE_CASE     )
 @slow
 @require_bnb
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      from transformers import AutoModelForCausalLM

      __SCREAMING_SNAKE_CASE      = Accelerator()

      with init_empty_weights():
           __SCREAMING_SNAKE_CASE      = AutoModelForCausalLM.from_pretrained(
               """EleutherAI/gpt-neo-125m"""   ,				)
           model.tie_weights()
           __SCREAMING_SNAKE_CASE      = infer_auto_device_map(__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = """cpu"""

      __SCREAMING_SNAKE_CASE      = AutoModelForCausalLM.from_pretrained(
          """EleutherAI/gpt-neo-125m"""   ,				device_map=__SCREAMING_SNAKE_CASE   ,				load_in_abit=__SCREAMING_SNAKE_CASE   ,				llm_inta_enable_fpaa_cpu_offload=__SCREAMING_SNAKE_CASE     )

      # This should not work and get value error
      with self.assertRaises(__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = accelerator.prepare(__SCREAMING_SNAKE_CASE     )
 @slow
 @require_bnb
 @require_multi_gpu
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					int:


      """simple docstring"""




      from transformers import AutoModelForCausalLM

      __SCREAMING_SNAKE_CASE      = {"""distributed_type""": DistributedType.MULTI_GPU}

      with init_empty_weights():
           __SCREAMING_SNAKE_CASE      = AutoModelForCausalLM.from_pretrained(
               """EleutherAI/gpt-neo-125m"""   ,				)
           model.tie_weights()
           __SCREAMING_SNAKE_CASE      = infer_auto_device_map(__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = 1

      __SCREAMING_SNAKE_CASE      = AutoModelForCausalLM.from_pretrained(
          """EleutherAI/gpt-neo-125m"""   ,				load_in_abit=__SCREAMING_SNAKE_CASE   ,				device_map=__SCREAMING_SNAKE_CASE   ,				)
      __SCREAMING_SNAKE_CASE      = Accelerator()

      # This should not work and get value error
      with self.assertRaises(__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = accelerator.prepare(__SCREAMING_SNAKE_CASE     )

      PartialState._reset_state()
 @slow
 @require_bnb
 @require_multi_gpu
 def  UpperCAmelCase__						(						self   :			str     )    ->					Any:


      """simple docstring"""




      from transformers import AutoModelForCausalLM

      with init_empty_weights():
           __SCREAMING_SNAKE_CASE      = AutoModelForCausalLM.from_pretrained(
               """EleutherAI/gpt-neo-125m"""   ,				)
           __SCREAMING_SNAKE_CASE      = infer_auto_device_map(__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = 1

      __SCREAMING_SNAKE_CASE      = AutoModelForCausalLM.from_pretrained(
          """EleutherAI/gpt-neo-125m"""   ,				load_in_abit=__SCREAMING_SNAKE_CASE   ,				device_map=__SCREAMING_SNAKE_CASE   ,				)
      __SCREAMING_SNAKE_CASE      = Accelerator()

      # This should work
      __SCREAMING_SNAKE_CASE      = accelerator.prepare(__SCREAMING_SNAKE_CASE     )


 @require_cuda
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = torch.nn.Linear(10   ,				10     )
      __SCREAMING_SNAKE_CASE      = torch.optim.SGD(model.parameters()   ,				lr=0.01     )
      __SCREAMING_SNAKE_CASE      = Accelerator(cpu=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = accelerator.prepare(__SCREAMING_SNAKE_CASE     )









'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


UpperCAmelCase					:			Tuple       =	HfArgumentParser(PretokenizationArguments)
UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase					:			List[Any]       =	multiprocessing.cpu_count()
UpperCAmelCase					:			str       =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase					:			Optional[int]       =	time.time()
UpperCAmelCase					:			Any       =	load_dataset(args.dataset_name, split='train')
print(f"""Dataset loaded in {time.time()-t_start:.2f}s""")

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[str]       =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(f"""Dataset tokenized in {time.time()-t_start:.2f}s""")

UpperCAmelCase					:			List[Any]       =	time.time()
ds.push_to_hub(args.tokenized_data_repo)



print(f"""Data pushed to the hub in {time.time()-t_start:.2f}s""")






