


'''simple docstring'''







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        __lowercase						(      )				->					Optional[int]:
     """simple docstring"""




     raise RuntimeError("""CUDA out of memory."""		)


class  lowerCAmelCase_						(       nn.Module      ):





  """simple docstring"""



  def __init__(					self     :							List[Any]						):





       '''simple docstring'''

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


  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE__						)						)						)


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   []

       @find_executable_batch_size(starting_batch_size=1_2_8						)
       def mock_training_loop_function(SCREAMING_SNAKE_CASE__     :							List[str]						):
            nonlocal batch_sizes
            batch_sizes.append(SCREAMING_SNAKE_CASE__						)
            if batch_size != 8:
                 raise_fake_out_of_memory()

       mock_training_loop_function()
       self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[1_2_8, 6_4, 3_2, 1_6, 8]						)

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   []

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

       __a ,						__a       =   mock_training_loop_function("""hello"""						)
       self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[1_2_8, 6_4, 3_2, 1_6, 8]						)
       self.assertListEqual([bs, arga]				,							[8, """hello"""]						)

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       @find_executable_batch_size(starting_batch_size=0						)
       def mock_training_loop_function(SCREAMING_SNAKE_CASE__     :							int						):
            pass

       with self.assertRaises(SCREAMING_SNAKE_CASE__						) as cm:
            mock_training_loop_function()
            self.assertIn("""No executable batch size found, reached zero."""				,							cm.exception.args[0]						)

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       @find_executable_batch_size(starting_batch_size=1_6						)
       def mock_training_loop_function(SCREAMING_SNAKE_CASE__     :							Optional[Any]						):
            if batch_size > 0:
                 raise_fake_out_of_memory()
            pass

       with self.assertRaises(SCREAMING_SNAKE_CASE__						) as cm:
            mock_training_loop_function()
            self.assertIn("""No executable batch size found, reached zero."""				,							cm.exception.args[0]						)

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       @find_executable_batch_size(starting_batch_size=1_2_8						)
       def mock_training_loop_function(SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							str						):
            if batch_size != 8:
                 raise raise_fake_out_of_memory()

       with self.assertRaises(SCREAMING_SNAKE_CASE__						) 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 __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       @find_executable_batch_size(starting_batch_size=1_6						)
       def mock_training_loop_function(SCREAMING_SNAKE_CASE__     :							Tuple						):
            raise ValueError("""Oops, we had an error!"""						)

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


  @require_cuda
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   torch.cuda.memory_allocated()
       __a       =   ModelForTest()
       model.cuda()
       self.assertGreater(torch.cuda.memory_allocated()				,							SCREAMING_SNAKE_CASE__						)
       __a       =   release_memory(SCREAMING_SNAKE_CASE__						)
       self.assertEqual(torch.cuda.memory_allocated()				,							SCREAMING_SNAKE_CASE__						)










'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)






