






import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 			snake_case__   (							UpperCAmelCase_):

							'''simple docstring'''
							lowerCamelCase							:				int 				= 0
							lowerCamelCase							:				bool 				= False
							lowerCamelCase							:				float 				= 3.0




class 			snake_case__   (							unittest.TestCase):

							'''simple docstring'''



							def 					__lowercase						(							self    ) ->      List[str]:






												'''simple docstring'''





												self.assertDictEqual(MockClass().to_kwargs()			,						{}    )
												self.assertDictEqual(MockClass(a=2    ).to_kwargs()			,						{"""a""": 2}    )
												self.assertDictEqual(MockClass(a=2			,						b=_snake_case    ).to_kwargs()			,						{"""a""": 2, """b""": True}    )
												self.assertDictEqual(MockClass(a=2			,						c=2.25    ).to_kwargs()			,						{"""a""": 2, """c""": 2.25}    )



							@require_cuda
							def 					__lowercase						(							self    ) ->      Tuple:






												'''simple docstring'''





												__snake_case			:int          =						GradScalerKwargs(init_scale=10_24			,						growth_factor=2    )
												AcceleratorState._reset_state()
												__snake_case			:Dict          =						Accelerator(mixed_precision="""fp16"""			,						kwargs_handlers=[scaler_handler]    )
												print(accelerator.use_fpaa    )
												__snake_case			:Tuple          =						accelerator.scaler

												# Check the kwargs have been applied
												self.assertEqual(scaler._init_scale			,						10_24.0    )
												self.assertEqual(scaler._growth_factor			,						2.0    )

												# Check the other values are at the default
												self.assertEqual(scaler._backoff_factor			,						0.5    )
												self.assertEqual(scaler._growth_interval			,						20_00    )
												self.assertEqual(scaler._enabled			,						_snake_case    )



							@require_multi_gpu
							def 					__lowercase						(							self    ) ->      str:






												'''simple docstring'''





												__snake_case			:int          =						["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__    )]
												execute_subprocess_async(_snake_case			,						env=os.environ.copy()    )


if __name__ == "__main__":
					lowerCamelCase__							=     DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
					lowerCamelCase__							=     Accelerator(kwargs_handlers=[ddp_scaler])
					lowerCamelCase__							=     torch.nn.Linear(100, 200)
					lowerCamelCase__							=     accelerator.prepare(model)

					# Check the values changed in kwargs
					lowerCamelCase__							=     """"""
					lowerCamelCase__							=     model.bucket_bytes_cap // (1024 * 1024)
					if observed_bucket_cap_map != 15:
										error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
					if model.find_unused_parameters is not True:
										error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

					# Check the values of the defaults
					if model.dim != 0:
										error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
					if model.broadcast_buffers is not True:
										error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
					if model.gradient_as_bucket_view is not False:
										error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

					# Raise error at the end to make sure we don't stop at the first failure.
					if len(error_msg) > 0:
										raise ValueError(error_msg)










import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__=2			,						a__=3			,						a__=64			,						a__=None    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						np.random.default_rng(a__    )
            __snake_case			:List[str]          =						length
            __snake_case			:Optional[Any]          =						rng.normal(size=(length,)    ).astype(np.floataa    )
            __snake_case			:Optional[int]          =						a * self.x + b + rng.normal(scale=0.1			,						size=(length,)    ).astype(np.floataa    )



       def __len__(							self    ) ->      str:






            '''simple docstring'''





            return self.length



       def __getitem__(							self			,						a__    ) ->      int:






            '''simple docstring'''





            return {"x": self.x[i], "y": self.y[i]}




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Any          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:int          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:Dict          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      Optional[Any]:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:Tuple          =						False
            return x * self.a[0] + self.b[0]




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Optional[int]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:List[str]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:str          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      str:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:List[Any]          =						False
            return x * self.a + self.b






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   int = 16  ):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

     __snake_case			:Union[str, Any]          =						AutoTokenizer.from_pretrained("""bert-base-cased"""  )
     __snake_case			:Dict          =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     __snake_case			:Any          =						load_dataset("""csv"""	,data_files=snake_case__  )
     __snake_case			:Dict          =						datasets["""train"""].unique("""label"""  )

     __snake_case			:List[Any]          =						{v: i for i, v in enumerate(snake_case__  )}

     def tokenize_function(snake_case__     :   List[Any]  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:Optional[Any]          =						tokenizer(
              examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""  )
          if "label" in examples:
               __snake_case			:Dict          =						[label_to_id[l] for l in examples["""label"""]]
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __snake_case			:List[str]          =						datasets.map(
         snake_case__	,batched=snake_case__	,remove_columns=["""sentence1""", """sentence2""", """label"""]	,)

     def collate_fn(snake_case__     :   Optional[int]  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(snake_case__	,padding="""max_length"""	,max_length=128	,return_tensors="""pt"""  )
          return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""  )

     # Instantiate dataloaders.
     __snake_case			:List[Any]          =						DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2  )
     __snake_case			:str          =						DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1  )

     return train_dataloader, eval_dataloader


