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'''
    _lowerCamelCase     :		List[str] 						=						torch.nn.Linear(2   , 4	)
    _lowerCamelCase     :		Optional[int] 						=						torch.optim.AdamW(model.parameters()   , lr=1.0	)
    _lowerCamelCase     :		Optional[Any] 						=						torch.optim.lr_scheduler.OneCycleLR(__A   , max_lr=0.01   , steps_per_epoch=2   , epochs=1	)
    _lowerCamelCase     :		Optional[int] 						=						DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
    _lowerCamelCase     :		Optional[Any] 						=						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'''
    _lowerCamelCase     :		Dict 						=						torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
    model.load_state_dict(__A	)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      @require_cuda
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						Accelerator()
          assert PartialState._shared_state["_cpu"] is False
          assert PartialState._shared_state["device"].type == "cuda"
          with self.assertRaises(_UpperCamelCase):
              _lowerCamelCase     :		str 						=						Accelerator(cpu=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						Accelerator()
          _lowerCamelCase     :		List[Any] 						=						GradientState()
          assert state.num_steps == 1
          _lowerCamelCase     :		Union[str, Any] 						=						4
          assert state.num_steps == 4

          assert state.sync_gradients is True
          _lowerCamelCase     :		Dict 						=						False
          assert state.sync_gradients is False
          GradientState._reset_state()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						Accelerator()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		str 						=						create_components()

          (
              (
              _lowerCamelCase
          )						,							(
              _lowerCamelCase
          )						,							(
              _lowerCamelCase
          )						,							(
              _lowerCamelCase
          )						,							(
              _lowerCamelCase
          )						,							
          )     :		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 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						Accelerator()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		int 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[int]:


          """simple docstring"""


          PartialState._reset_state()

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

          with patch("""torch.cuda.set_device"""    ,  _UpperCamelCase), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""):
              _lowerCamelCase     :		Any 						=						Accelerator()
              self.assertEqual(str(accelerator.state.device)    ,  """cuda:64""")



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						Accelerator()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Optional[int] 						=						create_components()
          accelerator.prepare(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						Accelerator()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						create_components()
          accelerator.prepare(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		List[Any] 						=						get_signature(_UpperCamelCase)

          # saving hook
          def save_config(_UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]):
              _lowerCamelCase     :		int 						=						{"""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     :				Optional[int]    ,  _UpperCamelCase     :				Dict):
              with open(os.path.join(_UpperCamelCase    ,  """data.json""")    ,  """r""") as f:
                  _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)

              _lowerCamelCase     :		Optional[int] 						=						config["""class_name"""]

          _lowerCamelCase     :		Union[str, Any] 						=						accelerator.register_save_state_pre_hook(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						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
              _lowerCamelCase     :		Tuple 						=						"""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
              _lowerCamelCase     :		Optional[int] 						=						"""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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						Accelerator()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						create_components()
          _lowerCamelCase     :		List[str] 						=						None

          # This should work
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						accelerator.prepare(
              _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertTrue(dummy_obj is None)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						Accelerator()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		List[Any] 						=						create_components()
          _lowerCamelCase     :		List[str] 						=						[1, 2, 3]

          # This should work
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          from transformers import AutoModelForCausalLM

          _lowerCamelCase     :		Tuple 						=						AutoModelForCausalLM.from_pretrained(
              """EleutherAI/gpt-neo-125m"""    ,  load_in_abit=_UpperCamelCase    ,  device_map={"""""": 0}    ,  )
          _lowerCamelCase     :		Union[str, Any] 						=						Accelerator()

          # This should work
          _lowerCamelCase     :		int 						=						accelerator.prepare(_UpperCamelCase)



      @slow
      @require_bnb
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          from transformers import AutoModelForCausalLM

          _lowerCamelCase     :		List[str] 						=						Accelerator()

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

          _lowerCamelCase     :		List[str] 						=						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):
              _lowerCamelCase     :		Tuple 						=						accelerator.prepare(_UpperCamelCase)



      @slow
      @require_bnb
      @require_multi_gpu
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->str:


          """simple docstring"""


          from transformers import AutoModelForCausalLM

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

          with init_empty_weights():
              _lowerCamelCase     :		Optional[Any] 						=						AutoModelForCausalLM.from_pretrained(
                  """EleutherAI/gpt-neo-125m"""    ,  )
              model.tie_weights()
              _lowerCamelCase     :		List[Any] 						=						infer_auto_device_map(_UpperCamelCase)
              _lowerCamelCase     :		Optional[Any] 						=						1

          _lowerCamelCase     :		Optional[int] 						=						AutoModelForCausalLM.from_pretrained(
              """EleutherAI/gpt-neo-125m"""    ,  load_in_abit=_UpperCamelCase    ,  device_map=_UpperCamelCase    ,  )
          _lowerCamelCase     :		Optional[Any] 						=						Accelerator()

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

          PartialState._reset_state()



      @slow
      @require_bnb
      @require_multi_gpu
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Union[str, Any]:


          """simple docstring"""


          from transformers import AutoModelForCausalLM

          with init_empty_weights():
              _lowerCamelCase     :		Optional[int] 						=						AutoModelForCausalLM.from_pretrained(
                  """EleutherAI/gpt-neo-125m"""    ,  )
              _lowerCamelCase     :		int 						=						infer_auto_device_map(_UpperCamelCase)
              _lowerCamelCase     :		List[str] 						=						1

          _lowerCamelCase     :		Dict 						=						AutoModelForCausalLM.from_pretrained(
              """EleutherAI/gpt-neo-125m"""    ,  load_in_abit=_UpperCamelCase    ,  device_map=_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[str] 						=						Accelerator()

          # This should work
          _lowerCamelCase     :		List[Any] 						=						accelerator.prepare(_UpperCamelCase)




      @require_cuda
      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						torch.nn.Linear(10    ,  10)
          _lowerCamelCase     :		Dict 						=						torch.optim.SGD(model.parameters()    ,  lr=0.0_1)
          _lowerCamelCase     :		Tuple 						=						Accelerator(cpu=_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						accelerator.prepare(_UpperCamelCase)





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =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""")

lowerCAmelCase			:							Tuple     =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")




