
import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory




def        __UpperCamelCase					(    ):
 raise RuntimeError('''CUDA out of memory.'''  )



class 		UpperCamelCase__  (	nn.Module   ):
   def __init__(self			: str       ):
    super().__init__()
    __a			:  int		    =  nn.Linear(3 ,    4       )
    __a			:  Union[str, Any]		    =  nn.BatchNormad(4       )
    __a			:  Optional[Any]		    =  nn.Linear(4 ,    5       )







   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: str       ):
    return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase       )       )       )





class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  Any		    =  []

    @find_executable_batch_size(starting_batch_size=1_2_8       )
    def mock_training_loop_function(snake_case_			: Any       ):
     nonlocal batch_sizes
     batch_sizes.append(__lowerCamelCase       )
     if batch_size != 8:
      raise_fake_out_of_memory()

    mock_training_loop_function()
    self.assertListEqual(__lowerCamelCase ,    [1_2_8, 6_4, 3_2, 1_6, 8]       )
   def 				lowerCAmelCase      (self			: Any       ):
    __a			:  List[Any]		    =  []

    @find_executable_batch_size(starting_batch_size=1_2_8       )
    def mock_training_loop_function(snake_case_			: int ,    snake_case_			: str       ):
     nonlocal batch_sizes
     batch_sizes.append(__lowerCamelCase       )
     if batch_size != 8:
      raise_fake_out_of_memory()
     return batch_size, arga

    __a    ,	__a			:  Optional[Any]		    =  mock_training_loop_function('''hello'''       )
    self.assertListEqual(__lowerCamelCase ,    [1_2_8, 6_4, 3_2, 1_6, 8]       )
    self.assertListEqual([bs, arga] ,    [8, '''hello''']       )
   def 				lowerCAmelCase      (self			: List[str]       ):
    @find_executable_batch_size(starting_batch_size=0       )
    def mock_training_loop_function(snake_case_			: Tuple       ):
     pass

    with self.assertRaises(__lowerCamelCase       ) as cm:
     mock_training_loop_function()
     self.assertIn('''No executable batch size found, reached zero.''' ,    cm.exception.args[0]       )
   def 				lowerCAmelCase      (self			: Dict       ):
    @find_executable_batch_size(starting_batch_size=1_6       )
    def mock_training_loop_function(snake_case_			: List[Any]       ):
     if batch_size > 0:
      raise_fake_out_of_memory()
     pass

    with self.assertRaises(__lowerCamelCase       ) as cm:
     mock_training_loop_function()
     self.assertIn('''No executable batch size found, reached zero.''' ,    cm.exception.args[0]       )
   def 				lowerCAmelCase      (self			: str       ):
    @find_executable_batch_size(starting_batch_size=1_2_8       )
    def mock_training_loop_function(snake_case_			: Union[str, Any] ,    snake_case_			: Optional[Any] ,    snake_case_			: Optional[int]       ):
     if batch_size != 8:
      raise raise_fake_out_of_memory()

    with self.assertRaises(__lowerCamelCase       ) as cm:
     mock_training_loop_function(1_2_8 ,    '''hello''' ,    '''world'''       )
     self.assertIn('''Batch size was passed into `f`''' ,    cm.exception.args[0]       )
     self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')''' ,    cm.exception.args[0]       )
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    @find_executable_batch_size(starting_batch_size=1_6       )
    def mock_training_loop_function(snake_case_			: str       ):
     raise ValueError('''Oops, we had an error!'''       )

    with self.assertRaises(__lowerCamelCase       ) as cm:
     mock_training_loop_function()
     self.assertIn('''Oops, we had an error!''' ,    cm.exception.args[0]       )







   @require_cuda
   def 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  List[str]		    =  torch.cuda.memory_allocated()
    __a			:  Tuple		    =  ModelForTest()
    model.cuda()
    self.assertGreater(torch.cuda.memory_allocated() ,    __lowerCamelCase       )
    __a			:  Dict		    =  release_memory(__lowerCamelCase       )
    self.assertEqual(torch.cuda.memory_allocated() ,    __lowerCamelCase       )


from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
