






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 		lowercase__  (				) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  torch.nn.Linear(2       ,							4)
      UpperCamelCase					       =  torch.optim.AdamW(model.parameters()       ,							lr=1.0)
      UpperCamelCase					       =  torch.optim.lr_scheduler.OneCycleLR(_UpperCamelCase       ,							max_lr=0.0_1       ,							steps_per_epoch=2       ,							epochs=1)
      UpperCamelCase					       =  DataLoader(TensorDataset(torch.tensor([1, 2, 3])))
      UpperCamelCase					       =  DataLoader(TensorDataset(torch.tensor([4, 5, 6])))

      return model, optimizer, scheduler, train_dl, valid_dl
def 		lowercase__  (				_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def 		lowercase__  (				_UpperCamelCase) ->			List[str]:
      """simple docstring"""

      UpperCamelCase					       =  torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
      model.load_state_dict(_UpperCamelCase)





class 					A__     (     __snake_case ):

     '''simple docstring'''






     @require_cuda
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  Accelerator()
           assert PartialState._shared_state["_cpu"] is False
           assert PartialState._shared_state["device"].type == "cuda"
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  Accelerator(cpu=_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




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

           assert state.sync_gradients is True
           UpperCamelCase					       =  False
           assert state.sync_gradients is False
           GradientState._reset_state()







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  Accelerator()
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  create_components()

           (
               (
               UpperCamelCase
           )       ,  (
               UpperCamelCase
           )       ,  (
               UpperCamelCase
           )       ,  (
               UpperCamelCase
           )       ,  (
               UpperCamelCase
           )       ,  
           )					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  Accelerator()
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """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      :    str ):
                 pass

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  Accelerator()
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  create_components()
           accelerator.prepare(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  Accelerator()
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  create_components()
           accelerator.prepare(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  get_signature(_SCREAMING_SNAKE_CASE )

           # saving hook
           def save_config(_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[Any] ):
                 UpperCamelCase					       =  {'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      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):
                 with open(os.path.join(_SCREAMING_SNAKE_CASE   ,						'data.json' )   ,						'r' ) as f:
                       UpperCamelCase					       =  json.load(_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  config['class_name']

           UpperCamelCase					       =  accelerator.register_save_state_pre_hook(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  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
                 UpperCamelCase					       =  '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
                 UpperCamelCase					       =  '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   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  Accelerator()
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  create_components()
           UpperCamelCase					       =  None

           # This should work
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




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

           # This should work
           UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           from transformers import AutoModelForCausalLM

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

           # This should work
           UpperCamelCase					       =  accelerator.prepare(_SCREAMING_SNAKE_CASE )







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






           """simple docstring"""




           from transformers import AutoModelForCausalLM

           UpperCamelCase					       =  Accelerator()

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

           UpperCamelCase					       =  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 ):
                 UpperCamelCase					       =  accelerator.prepare(_SCREAMING_SNAKE_CASE )







     @slow
     @require_bnb
     @require_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           from transformers import AutoModelForCausalLM

           UpperCamelCase					       =  {'distributed_type': DistributedType.MULTI_GPU}

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

           UpperCamelCase					       =  AutoModelForCausalLM.from_pretrained(
               'EleutherAI/gpt-neo-125m'   ,						load_in_abit=_SCREAMING_SNAKE_CASE   ,						device_map=_SCREAMING_SNAKE_CASE   ,						)
           UpperCamelCase					       =  Accelerator()

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

           PartialState._reset_state()







     @slow
     @require_bnb
     @require_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           from transformers import AutoModelForCausalLM

           with init_empty_weights():
                 UpperCamelCase					       =  AutoModelForCausalLM.from_pretrained(
                     'EleutherAI/gpt-neo-125m'   ,						)
                 UpperCamelCase					       =  infer_auto_device_map(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  1

           UpperCamelCase					       =  AutoModelForCausalLM.from_pretrained(
               'EleutherAI/gpt-neo-125m'   ,						load_in_abit=_SCREAMING_SNAKE_CASE   ,						device_map=_SCREAMING_SNAKE_CASE   ,						)
           UpperCamelCase					       =  Accelerator()

           # This should work
           UpperCamelCase					       =  accelerator.prepare(_SCREAMING_SNAKE_CASE )







     @require_cuda
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  torch.nn.Linear(10   ,						10 )
           UpperCamelCase					       =  torch.optim.SGD(model.parameters()   ,						lr=0.0_1 )
           UpperCamelCase					       =  Accelerator(cpu=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  accelerator.prepare(_SCREAMING_SNAKE_CASE )











def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  {
          '^': 3,
          '*': 2,
          '/': 2,
          '%': 2,
          '+': 1,
          '-': 1,
      }  # Priority of each operator
      UpperCamelCase					       =  len(_UpperCamelCase) if (len(_UpperCamelCase) > 7) else 7

      # Print table header for output
      print(
          'Symbol'.center(8)       ,							'Stack'.center(_UpperCamelCase)       ,							'Postfix'.center(_UpperCamelCase)       ,							sep=' | '       ,							)
      print('-' * (print_width * 3 + 7))

      for x in infix:
            if x.isalpha() or x.isdigit():
                  post_fix.append(_UpperCamelCase)  # if x is Alphabet / Digit, add it to Postfix
            elif x == "(":
                  stack.append(_UpperCamelCase)  # if x is "(" push to Stack
            elif x == ")":  # if x is ")" pop stack until "(" is encountered
                  while stack[-1] != "(":
                        post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
                  stack.pop()
            else:
                  if len(_UpperCamelCase) == 0:
                        stack.append(_UpperCamelCase)  # If stack is empty, push x to stack
                  else:  # while priority of x is not > priority of element in the stack
                        while len(_UpperCamelCase) > 0 and priority[x] <= priority[stack[-1]]:
                              post_fix.append(stack.pop())  # pop stack & add to Postfix
                        stack.append(_UpperCamelCase)  # push x to stack

            print(
                x.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      while len(_UpperCamelCase) > 0:  # while stack is not empty
            post_fix.append(stack.pop())  # pop stack & add to Postfix
            print(
                ' '.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      return "".join(_UpperCamelCase)  # return Postfix as str
def 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  list(infix[::-1])  # reverse the infix equation

      for i in range(len(_UpperCamelCase)):
            if infix[i] == "(":
                  UpperCamelCase					       =  ')'  # change "(" to ")"
            elif infix[i] == ")":
                  UpperCamelCase					       =  '('  # change ")" to "("

      return (infix_2_postfix(''.join(_UpperCamelCase)))[
          ::-1
      ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
       __magic_name__   :  int        =							input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
       __magic_name__   :  Optional[Any]        =							''''''.join(Infix.split())  # Remove spaces from the input
       print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



