

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      a      (			)    ->      Dict:





      """simple docstring"""



      raise RuntimeError('CUDA out of memory.'     )





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self					)  -> Optional[Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =nn.Linear(3							,   4					)
        _lowercase	       =nn.BatchNormad(4					)
        _lowercase	       =nn.Linear(4							,   5					)




  def   A__     (				self							,   lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase					)					)					)





class  __lowerCAmelCase     (					unittest.TestCase							):




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =[]

        @find_executable_batch_size(starting_batch_size=128					)
        def mock_training_loop_function(lowerCAmelCase					):
              nonlocal batch_sizes
              batch_sizes.append(lowerCAmelCase					)
              if batch_size != 8:
                    raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCAmelCase							,   [128, 64, 32, 16, 8]					)




  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       =[]

        @find_executable_batch_size(starting_batch_size=128					)
        def mock_training_loop_function(lowerCAmelCase							,   lowerCAmelCase					):
              nonlocal batch_sizes
              batch_sizes.append(lowerCAmelCase					)
              if batch_size != 8:
                    raise_fake_out_of_memory()
              return batch_size, arga

        _lowercase       ,       _lowercase	       =mock_training_loop_function('hello'					)
        self.assertListEqual(lowerCAmelCase							,   [128, 64, 32, 16, 8]					)
        self.assertListEqual([bs, arga]							,   [8, 'hello']					)




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        @find_executable_batch_size(starting_batch_size=0					)
        def mock_training_loop_function(lowerCAmelCase					):
              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   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        @find_executable_batch_size(starting_batch_size=16					)
        def mock_training_loop_function(lowerCAmelCase					):
              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   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        @find_executable_batch_size(starting_batch_size=128					)
        def mock_training_loop_function(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					):
              if batch_size != 8:
                    raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCAmelCase					) as cm:
              mock_training_loop_function(128							,   '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   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        @find_executable_batch_size(starting_batch_size=16					)
        def mock_training_loop_function(lowerCAmelCase					):
              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   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =torch.cuda.memory_allocated()
        _lowercase	       =ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()							,   lowerCAmelCase					)
        _lowercase	       =release_memory(lowerCAmelCase					)
        self.assertEqual(torch.cuda.memory_allocated()							,   lowerCAmelCase					)





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE							):




  @register_to_config
  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = False							,   )  -> Tuple:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)
        _lowercase	       =False

        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)

        _lowercase	       =TaConfig(
            vocab_size=lowerCAmelCase							,   d_model=lowerCAmelCase							,   num_heads=lowerCAmelCase							,   d_kv=lowerCAmelCase							,   d_ff=lowerCAmelCase							,   dropout_rate=lowerCAmelCase							,   feed_forward_proj=lowerCAmelCase							,   is_decoder=lowerCAmelCase							,   is_encoder_decoder=lowerCAmelCase							,   )

        _lowercase	       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase					):
              _lowercase	       =TaBlock(lowerCAmelCase					)
              self.encoders.append(lowerCAmelCase					)

        _lowercase	       =TaLayerNorm(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.token_embedder(lowerCAmelCase					)

        _lowercase	       =encoder_input_tokens.shape[1]
        _lowercase	       =torch.arange(lowerCAmelCase							,   device=encoder_input_tokens.device					)
        x += self.position_encoding(lowerCAmelCase					)

        _lowercase	       =self.dropout_pre(lowerCAmelCase					)

        # inverted the attention mask
        _lowercase	       =encoder_input_tokens.size()
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase					)

        for lyr in self.encoders:
              _lowercase	       =lyr(lowerCAmelCase							,   lowerCAmelCase					)[0]
        _lowercase	       =self.layer_norm(lowerCAmelCase					)

        return self.dropout_post(lowerCAmelCase					), encoder_inputs_mask


