





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						(   )    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						int        =							torch.nn.Linear(2      ,  4					)
   lowerCamelCase__       :						Optional[Any]        =							torch.optim.AdamW(model.parameters()      ,  lr=1.0					)
   lowerCamelCase__       :						List[Any]        =							torch.optim.lr_scheduler.OneCycleLR(snake_case_      ,  max_lr=0.01      ,  steps_per_epoch=2      ,  epochs=1					)
   lowerCamelCase__       :						List[Any]        =							DataLoader(TensorDataset(torch.tensor([1, 2, 3]					)					)					)
   lowerCamelCase__       :						Any        =							DataLoader(TensorDataset(torch.tensor([4, 5, 6]					)					)					)

   return model, optimizer, scheduler, train_dl, valid_dl



def 	_a						(   UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 	_a						(   UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						List[str]        =							torch.nn.Linear(*tuple(model.weight.T.shape					)					).state_dict()
   model.load_state_dict(snake_case_					)




class        __SCREAMING_SNAKE_CASE      (			UpperCamelCase__ ):







       @require_cuda
       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          lowerCamelCase__       :						Dict        =							Accelerator()
          assert PartialState._shared_state["_cpu"] is False
          assert PartialState._shared_state["device"].type == "cuda"
          with self.assertRaises(__a      ):
             lowerCamelCase__       :						Optional[Any]        =							Accelerator(cpu=__a      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							Accelerator()
          lowerCamelCase__       :						Optional[int]        =							GradientState()
          assert state.num_steps == 1
          lowerCamelCase__       :						List[Any]        =							4
          assert state.num_steps == 4

          assert state.sync_gradients is True
          lowerCamelCase__       :						Tuple        =							False
          assert state.sync_gradients is False
          GradientState._reset_state()







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Tuple:
          lowerCamelCase__       :						Any        =							Accelerator()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							create_components()

          (
              (
              lowerCamelCase__			
          )       ,							(
              lowerCamelCase__			
          )       ,							(
              lowerCamelCase__			
          )       ,							(
              lowerCamelCase__			
          )       ,							(
              lowerCamelCase__			
          )       ,										
          )       :						Optional[int]        =							accelerator.prepare(__a		, __a		, __a		, __a		, __a      )

          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					:     Any      )       ->Tuple:
          lowerCamelCase__       :						int        =							Accelerator()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Any        =							create_components()
          accelerator.prepare(__a		, __a		, __a		, __a		, __a      )
          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					:     str      )       ->Any:
          PartialState._reset_state()

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

          with patch('''torch.cuda.set_device'''		, __a      ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''      ):
             lowerCamelCase__       :						Any        =							Accelerator()
             self.assertEqual(str(accelerator.state.device      )		, '''cuda:64'''      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->int:
          lowerCamelCase__       :						List[str]        =							Accelerator()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							create_components()
          accelerator.prepare(__a		, __a		, __a		, __a		, __a      )

          lowerCamelCase__       :						Optional[Any]        =							get_signature(__a      )

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

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

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







       def 						__lowerCamelCase   (				self					:     Dict      )       ->int:
          lowerCamelCase__       :						Dict        =							Accelerator()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						str        =							create_components()
          accelerator.prepare(__a		, __a		, __a		, __a		, __a      )

          lowerCamelCase__       :						Tuple        =							get_signature(__a      )

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

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

        # loading hook
          def load_config(A					:     str		, A					:     List[str]      ):
             with open(os.path.join(__a		, '''data.json'''      )		, '''r'''      ) as f:
                lowerCamelCase__       :						Union[str, Any]        =							json.load(__a      )

             lowerCamelCase__       :						Optional[int]        =							config['''class_name''']

          lowerCamelCase__       :						Union[str, Any]        =							accelerator.register_save_state_pre_hook(__a      )
          lowerCamelCase__       :						Tuple        =							accelerator.register_load_state_pre_hook(__a      )

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

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

             # random class name to verify correct one is loaded
             lowerCamelCase__       :						Any        =							'''random'''

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

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

             # random class name to verify correct one is loaded
             lowerCamelCase__       :						int        =							'''random'''

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

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







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[str]:
          lowerCamelCase__       :						str        =							Accelerator()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						str        =							create_components()
          lowerCamelCase__       :						str        =							None

          # This should work
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						List[Any]        =							accelerator.prepare(
              __a		, __a		, __a		, __a		, __a		, __a      )
          self.assertTrue(dummy_obj is None      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          lowerCamelCase__       :						Optional[int]        =							Accelerator()
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						str        =							create_components()
          lowerCamelCase__       :						str        =							[1, 2, 3]

          # This should work
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							accelerator.prepare(
              __a		, __a		, __a		, __a		, __a		, __a      )
          self.assertEqual(
              getattr(__a		, '''_is_accelerate_prepared'''		, __a      )		, __a		, '''Dummy object should have `_is_accelerate_prepared` set to `True`'''		, )
          self.assertEqual(
              getattr(__a		, '''_is_accelerate_prepared'''		, __a      )		, __a		, '''Model is missing `_is_accelerator_prepared` or is set to `False`'''		, )
          self.assertEqual(
              getattr(__a		, '''_is_accelerate_prepared'''		, __a      )		, __a		, '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`'''		, )
          self.assertEqual(
              getattr(__a		, '''_is_accelerate_prepared'''		, __a      )		, __a		, '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`'''		, )
          self.assertEqual(
              getattr(__a		, '''_is_accelerate_prepared'''		, __a      )		, __a		, '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''		, )
          self.assertEqual(
              getattr(__a		, '''_is_accelerate_prepared'''		, __a      )		, __a		, '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''		, )







       @slow
       @require_bnb
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          from transformers import AutoModelForCausalLM

          lowerCamelCase__       :						List[str]        =							AutoModelForCausalLM.from_pretrained(
              '''EleutherAI/gpt-neo-125m'''		, load_in_abit=__a		, device_map={'''''': 0}		, )
          lowerCamelCase__       :						List[str]        =							Accelerator()

          # This should work
          lowerCamelCase__       :						Optional[int]        =							accelerator.prepare(__a      )







       @slow
       @require_bnb
       def 						__lowerCamelCase   (				self					:     Any      )       ->Tuple:
          from transformers import AutoModelForCausalLM

          lowerCamelCase__       :						Union[str, Any]        =							Accelerator()

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

          lowerCamelCase__       :						Optional[int]        =							AutoModelForCausalLM.from_pretrained(
              '''EleutherAI/gpt-neo-125m'''		, device_map=__a		, load_in_abit=__a		, llm_inta_enable_fpaa_cpu_offload=__a      )

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







       @slow
       @require_bnb
       @require_multi_gpu
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          from transformers import AutoModelForCausalLM

          lowerCamelCase__       :						List[str]        =							{'''distributed_type''': DistributedType.MULTI_GPU}

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

          lowerCamelCase__       :						str        =							AutoModelForCausalLM.from_pretrained(
              '''EleutherAI/gpt-neo-125m'''		, load_in_abit=__a		, device_map=__a		, )
          lowerCamelCase__       :						List[str]        =							Accelerator()

          # This should not work and get value error
          with self.assertRaises(__a      ):
             lowerCamelCase__       :						List[str]        =							accelerator.prepare(__a      )

          PartialState._reset_state()







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

          with init_empty_weights():
             lowerCamelCase__       :						Dict        =							AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''		, )
             lowerCamelCase__       :						List[Any]        =							infer_auto_device_map(__a      )
             lowerCamelCase__       :						Optional[int]        =							1

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

          # This should work
          lowerCamelCase__       :						Optional[Any]        =							accelerator.prepare(__a      )







       @require_cuda
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Tuple        =							torch.nn.Linear(1_0		, 1_0      )
          lowerCamelCase__       :						Optional[Any]        =							torch.optim.SGD(model.parameters()		, lr=0.01      )
          lowerCamelCase__       :						Dict        =							Accelerator(cpu=__a      )
          lowerCamelCase__       :						str        =							accelerator.prepare(__a      )











from math import factorial


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]		, A					:     Dict		, A					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							real
          if isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[1] * rank
          else:
             lowerCamelCase__       :						List[Any]        =							rank







       def __repr__(				self					:     Tuple      )       ->str:
          return (
              F"{self.real}+"
              F"{'+'.join(str(A      )+'E'+str(n+1      )for n,dual in enumerate(self.duals      )      )}"
          )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							self.duals.copy()
          while cur[-1] == 0:
             cur.pop(-1      )
          return Dual(self.real		, A      )







       def __add__(				self					:     Union[str, Any]		, A					:     int      )       ->str:
          if not isinstance(A		, A      ):
             return Dual(self.real + other		, self.duals      )
          lowerCamelCase__       :						int        =							self.duals.copy()
          lowerCamelCase__       :						int        =							other.duals.copy()
          if len(A      ) > len(A      ):
             o_dual.extend([1] * (len(A      ) - len(A      ))      )
          elif len(A      ) < len(A      ):
             s_dual.extend([1] * (len(A      ) - len(A      ))      )
          lowerCamelCase__       :						Optional[Any]        =							[]
          for i in range(len(A      )      ):
             new_duals.append(s_dual[i] + o_dual[i]      )
          return Dual(self.real + other.real		, A      )

       _UpperCAmelCase				:  List[Any]			   =   __add__







       def __sub__(				self					:     Any		, A					:     Dict      )       ->int:
          return self + other * -1







       def __mul__(				self					:     Optional[Any]		, A					:     List[Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Optional[int]        =							[]
             for i in self.duals:
                new_duals.append(i * other      )
             return Dual(self.real * other		, A      )
          lowerCamelCase__       :						Tuple        =							[0] * (len(self.duals      ) + len(other.duals      ) + 1)
          for i, item in enumerate(self.duals      ):
             for j, jtem in enumerate(other.duals      ):
                new_duals[i + j + 1] += item * jtem
          for k in range(len(self.duals      )      ):
             new_duals[k] += self.duals[k] * other.real
          for index in range(len(other.duals      )      ):
             new_duals[index] += other.duals[index] * self.real
          return Dual(self.real * other.real		, A      )

       _UpperCAmelCase				:  Optional[Any]			   =   __mul__







       def __truediv__(				self					:     int		, A					:     List[Any]      )       ->Dict:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i / other      )
             return Dual(self.real / other		, A      )
          raise ValueError







       def __floordiv__(				self					:     Dict		, A					:     Union[str, Any]      )       ->Union[str, Any]:
          if not isinstance(A		, A      ):
             lowerCamelCase__       :						Tuple        =							[]
             for i in self.duals:
                new_duals.append(i // other      )
             return Dual(self.real // other		, A      )
          raise ValueError







       def __pow__(				self					:     Any		, A					:     List[Any]      )       ->Tuple:
          if n < 0 or isinstance(A		, A      ):
             raise ValueError('''power must be a positive integer'''      )
          if n == 0:
             return 1
          if n == 1:
             return self
          lowerCamelCase__       :						Union[str, Any]        =							self
          for _ in range(n - 1      ):
             x *= self
          return x



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if not callable(UpperCAmelCase					):
      raise ValueError('''differentiate() requires a function as input for func'''					)
   if not isinstance(UpperCAmelCase      ,  (float, int)					):
      raise ValueError('''differentiate() requires a float as input for position'''					)
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise ValueError('''differentiate() requires an int as input for order'''					)
   lowerCamelCase__       :						List[str]        =							Dual(UpperCAmelCase      ,  1					)
   lowerCamelCase__       :						Any        =							func(UpperCAmelCase					)
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(UpperCAmelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



       def 	_a						(   UpperCAmelCase					)    ->    int:







          """simple docstring"""
          return y**2 * y**4




       print(differentiate(f, 9, 2))



