

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

   return model, optimizer, scheduler, train_dl, valid_dl







def 					_A		(	snake_case     )			->	int:
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()







def 					_A		(	snake_case     )			->	List[Any]:
   _lowercase			:      Optional[int]			     =   torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
   model.load_state_dict(_lowercase     )


class    a__  (							a__  ):


   @require_cuda
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   Accelerator()
      _lowercase			:      str			     =   GradientState()
      assert state.num_steps == 1
      _lowercase			:      str			     =   4
      assert state.num_steps == 4

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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Any			     =   Accelerator()
      _lowercase			:      Tuple			     =   create_components()

      (
          _lowercase       
      )			:      str			     =   accelerator.prepare(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  )

      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  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   Accelerator()
      _lowercase			:      Any			     =   create_components()
      accelerator.prepare(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  )
      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  ):





      """simple docstring"""





      PartialState._reset_state()

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

      with patch("torch.cuda.set_device"			,				lowerCAmelCase__  ), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"  ):
         _lowercase			:      Tuple			     =   Accelerator()
         self.assertEqual(str(accelerator.state.device  )			,				"cuda:64"  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Dict			     =   Accelerator()
      _lowercase			:      str			     =   create_components()
      accelerator.prepare(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  )

      _lowercase			:      Optional[Any]			     =   get_signature(lowerCAmelCase__  )

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

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

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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Union[str, Any]			     =   Accelerator()
      _lowercase			:      Union[str, Any]			     =   create_components()
      accelerator.prepare(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  )

      _lowercase			:      Optional[int]			     =   get_signature(lowerCAmelCase__  )

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

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

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

         _lowercase			:      str			     =   config["class_name"]

      _lowercase			:      List[str]			     =   accelerator.register_save_state_pre_hook(lowerCAmelCase__  )
      _lowercase			:      int			     =   accelerator.register_load_state_pre_hook(lowerCAmelCase__  )

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

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

         # random class name to verify correct one is loaded
         _lowercase			:      Union[str, Any]			     =   "random"

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

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

         # random class name to verify correct one is loaded
         _lowercase			:      List[str]			     =   "random"

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

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


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   Accelerator()
      _lowercase			:      List[str]			     =   create_components()
      _lowercase			:      List[Any]			     =   None

      # This should work
      _lowercase			:      Optional[Any]			     =   accelerator.prepare(
          lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__  )
      self.assertTrue(dummy_obj is None  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Union[str, Any]			     =   Accelerator()
      _lowercase			:      Union[str, Any]			     =   create_components()
      _lowercase			:      List[Any]			     =   [1, 2, 3]

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


   @slow
   @require_bnb
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      from transformers import AutoModelForCausalLM

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

      # This should work
      _lowercase			:      Dict			     =   accelerator.prepare(lowerCAmelCase__  )


   @slow
   @require_bnb
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      from transformers import AutoModelForCausalLM

      _lowercase			:      Dict			     =   Accelerator()

      with init_empty_weights():
         _lowercase			:      Optional[Any]			     =   AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"			,				)
         model.tie_weights()
         _lowercase			:      Optional[Any]			     =   infer_auto_device_map(lowerCAmelCase__  )
         _lowercase			:      Dict			     =   "cpu"

      _lowercase			:      Union[str, Any]			     =   AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"			,				device_map=lowerCAmelCase__			,				load_in_abit=lowerCAmelCase__			,				llm_inta_enable_fpaa_cpu_offload=lowerCAmelCase__  )

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


   @slow
   @require_bnb
   @require_multi_gpu
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      from transformers import AutoModelForCausalLM

      _lowercase			:      str			     =   {"distributed_type": DistributedType.MULTI_GPU}

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

      _lowercase			:      List[Any]			     =   AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"			,				load_in_abit=lowerCAmelCase__			,				device_map=lowerCAmelCase__			,				)
      _lowercase			:      str			     =   Accelerator()

      # This should not work and get value error
      with self.assertRaises(lowerCAmelCase__  ):
         _lowercase			:      List[Any]			     =   accelerator.prepare(lowerCAmelCase__  )

      PartialState._reset_state()


   @slow
   @require_bnb
   @require_multi_gpu
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      from transformers import AutoModelForCausalLM

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

      _lowercase			:      List[str]			     =   AutoModelForCausalLM.from_pretrained(
          "EleutherAI/gpt-neo-125m"			,				load_in_abit=lowerCAmelCase__			,				device_map=lowerCAmelCase__			,				)
      _lowercase			:      Any			     =   Accelerator()

      # This should work
      _lowercase			:      Optional[Any]			     =   accelerator.prepare(lowerCAmelCase__  )






   @require_cuda
   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      str			     =   torch.nn.Linear(10			,				10  )
      _lowercase			:      str			     =   torch.optim.SGD(model.parameters()			,				lr=0.0_1  )
      _lowercase			:      Optional[Any]			     =   Accelerator(cpu=lowerCAmelCase__  )
      _lowercase			:      Any			     =   accelerator.prepare(lowerCAmelCase__  )













"""simple docstring"""




from __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


