


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       :		str		=torch.nn.Linear(2		,			4     )
       _lowerCamelCase       :		List[str]		=torch.optim.AdamW(model.parameters()		,			lr=1.0     )
       _lowerCamelCase       :		str		=torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE__		,			max_lr=0.01		,			steps_per_epoch=2		,			epochs=1     )
       _lowerCamelCase       :		int		=DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
       _lowerCamelCase       :		Optional[int]		=DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

       return model, optimizer, scheduler, train_dl, valid_dl




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		str		=torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
       model.load_state_dict(SCREAMING_SNAKE_CASE__     )




class A  (   UpperCamelCase_     ):






      @require_cuda
      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=Accelerator()
             _lowerCamelCase       :		Optional[int]		=GradientState()
             assert state.num_steps == 1
             _lowerCamelCase       :		Optional[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        lowerCamelCase       (					self						:     int )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=Accelerator()
             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		Any		=create_components()

             (
                 (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     (
                 _lowerCamelCase
             )	,     
             )       :		Union[str, Any]		=accelerator.prepare(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_			,   lowercase_ )

             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        lowerCamelCase       (					self						:     Optional[int] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=Accelerator()
             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		Any		=create_components()
             accelerator.prepare(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_			,   lowercase_ )
             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        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             PartialState._reset_state()

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

             with patch('torch.cuda.set_device'			,   lowercase_ ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64' ):
                    _lowerCamelCase       :		Tuple		=Accelerator()
                    self.assertEqual(str(accelerator.state.device )			,   'cuda:64' )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		str		=Accelerator()
             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		Tuple		=create_components()
             accelerator.prepare(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_			,   lowercase_ )

             _lowerCamelCase       :		List[str]		=get_signature(lowercase_ )

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

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

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






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




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

             _lowerCamelCase       :		Union[str, Any]		=get_signature(lowercase_ )

             # saving hook
             def save_config(lowercase_						:     List[str]			,   lowercase_						:     int			,   lowercase_						:     Any ):
                    _lowerCamelCase       :		Any		={'class_name': models[0].__class__.__name__}

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

        # loading hook
             def load_config(lowercase_						:     List[str]			,   lowercase_						:     Optional[Any] ):
                    with open(os.path.join(lowercase_			,   'data.json' )			,   'r' ) as f:
                           _lowerCamelCase       :		int		=json.load(lowercase_ )

                    _lowerCamelCase       :		Tuple		=config['class_name']

             _lowerCamelCase       :		Optional[Any]		=accelerator.register_save_state_pre_hook(lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=accelerator.register_load_state_pre_hook(lowercase_ )

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

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

                    # random class name to verify correct one is loaded
                    _lowerCamelCase       :		Optional[int]		='random'

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

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

                    # random class name to verify correct one is loaded
                    _lowerCamelCase       :		Optional[Any]		='random'

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

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






      def        lowerCamelCase       (					self						:     List[str] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=Accelerator()
             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		List[Any]		=create_components()
             _lowerCamelCase       :		Optional[Any]		=None

             # This should work
             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		List[str]		=accelerator.prepare(
                 lowercase_			,   lowercase_			,   lowercase_			,   lowercase_			,   lowercase_			,   lowercase_ )
             self.assertTrue(dummy_obj is None )






      def        lowerCamelCase       (					self						:     int )					->						Dict:
             """simple docstring"""




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

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






      @slow
      @require_bnb
      def        lowerCamelCase       (					self						:     List[str] )					->						Any:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

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

             # This should work
             _lowerCamelCase       :		Union[str, Any]		=accelerator.prepare(lowercase_ )






      @slow
      @require_bnb
      def        lowerCamelCase       (					self						:     Optional[int] )					->						Union[str, Any]:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

             _lowerCamelCase       :		Any		=Accelerator()

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

             _lowerCamelCase       :		Any		=AutoModelForCausalLM.from_pretrained(
                 'EleutherAI/gpt-neo-125m'			,   device_map=lowercase_			,   load_in_abit=lowercase_			,   llm_inta_enable_fpaa_cpu_offload=lowercase_ )

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






      @slow
      @require_bnb
      @require_multi_gpu
      def        lowerCamelCase       (					self						:     Dict )					->						Any:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

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

             with init_empty_weights():
                    _lowerCamelCase       :		int		=AutoModelForCausalLM.from_pretrained(
                        'EleutherAI/gpt-neo-125m'			,   )
                    model.tie_weights()
                    _lowerCamelCase       :		int		=infer_auto_device_map(lowercase_ )
                    _lowerCamelCase       :		List[str]		=1

             _lowerCamelCase       :		Union[str, Any]		=AutoModelForCausalLM.from_pretrained(
                 'EleutherAI/gpt-neo-125m'			,   load_in_abit=lowercase_			,   device_map=lowercase_			,   )
             _lowerCamelCase       :		str		=Accelerator()

             # This should not work and get value error
             with self.assertRaises(lowercase_ ):
                    _lowerCamelCase       :		Union[str, Any]		=accelerator.prepare(lowercase_ )

             PartialState._reset_state()






      @slow
      @require_bnb
      @require_multi_gpu
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Optional[int]:
             """simple docstring"""




             from transformers import AutoModelForCausalLM

             with init_empty_weights():
                    _lowerCamelCase       :		str		=AutoModelForCausalLM.from_pretrained(
                        'EleutherAI/gpt-neo-125m'			,   )
                    _lowerCamelCase       :		Optional[Any]		=infer_auto_device_map(lowercase_ )
                    _lowerCamelCase       :		Tuple		=1

             _lowerCamelCase       :		int		=AutoModelForCausalLM.from_pretrained(
                 'EleutherAI/gpt-neo-125m'			,   load_in_abit=lowercase_			,   device_map=lowercase_			,   )
             _lowerCamelCase       :		List[Any]		=Accelerator()

             # This should work
             _lowerCamelCase       :		str		=accelerator.prepare(lowercase_ )






      @require_cuda
      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=torch.nn.Linear(10			,   10 )
             _lowerCamelCase       :		Any		=torch.optim.SGD(model.parameters()			,   lr=0.01 )
             _lowerCamelCase       :		str		=Accelerator(cpu=lowercase_ )
             _lowerCamelCase       :		Dict		=accelerator.prepare(lowercase_ )






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

lowerCamelCase											=       'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      str=None     ):
       '''simple docstring'''





       require_version(deps[pkg]		,			SCREAMING_SNAKE_CASE__     )


