




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 					snake_case(				) ->							Dict:





     '''simple docstring'''


     raise RuntimeError('''CUDA out of memory.'''    )
class 						_A					(  nn.Module      ):






  def __init__(  self		:	List[Any]      )		->    str:






       """simple docstring"""







       super().__init__()
       lowercase					:   Union[str, Any]								= nn.Linear(3      ,						4      )
       lowercase					:   str								= nn.BatchNormad(4      )
       lowercase					:   Optional[int]								= nn.Linear(4      ,						5      )






  def 					__a			(  self		:	Optional[Any]      ,						_A		:	List[str]      )		->    int:






       """simple docstring"""







       return self.lineara(self.batchnorm(self.lineara(a__      )      )      )


class 						_A					(  unittest.TestCase      ):






  def 					__a			(  self		:	Any      )		->    Dict:






       """simple docstring"""







       lowercase					:   Optional[Any]								= []

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

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






  def 					__a			(  self		:	Tuple      )		->    Optional[int]:






       """simple docstring"""







       lowercase					:   Optional[int]								= []

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

       lowercase			,       lowercase					:   Tuple								= mock_training_loop_function('''hello'''      )
       self.assertListEqual(a__      ,						[128, 64, 32, 16, 8]      )
       self.assertListEqual([bs, arga]      ,						[8, '''hello''']      )






  def 					__a			(  self		:	List[Any]      )		->    Any:






       """simple docstring"""







       @find_executable_batch_size(starting_batch_size=0      )
       def mock_training_loop_function(_A		:	Optional[int]      ):
            pass

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






  def 					__a			(  self		:	Tuple      )		->    Dict:






       """simple docstring"""







       @find_executable_batch_size(starting_batch_size=16      )
       def mock_training_loop_function(_A		:	str      ):
            if batch_size > 0:
                 raise_fake_out_of_memory()
            pass

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






  def 					__a			(  self		:	int      )		->    Tuple:






       """simple docstring"""







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

       with self.assertRaises(a__      ) 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]      )		->    int:






       """simple docstring"""







       @find_executable_batch_size(starting_batch_size=16      )
       def mock_training_loop_function(_A		:	Optional[Any]      ):
            raise ValueError('''Oops, we had an error!'''      )

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






  @require_cuda
  def 					__a			(  self		:	Union[str, Any]      )		->    int:






       """simple docstring"""







       lowercase					:   Union[str, Any]								= torch.cuda.memory_allocated()
       lowercase					:   Union[str, Any]								= ModelForTest()
       model.cuda()
       self.assertGreater(torch.cuda.memory_allocated()      ,						a__      )
       lowercase					:   Any								= release_memory(a__      )
       self.assertEqual(torch.cuda.memory_allocated()      ,						a__      )






import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_				=			get_tests_dir('fixtures/test_sentencepiece_bpe.model')

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    BartphoTokenizer
      A_						:				List[str]							=    False
      A_						:				Optional[Any]							=    True


      def    a (self       :      Tuple      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
         __snake_case							       =   dict(zip(a__				,			range(len(a__      )      )      )      )
         __snake_case							       =   {'''unk_token''': '''<unk>'''}

         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''monolingual_vocab_file''']      )
         with open(self.monolingual_vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as fp:
            for token in vocab_tokens:
               fp.write(f"""{token} {vocab_tokens[token]}\n"""      )

         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         tokenizer.save_pretrained(self.tmpdirname      )


      def    a (self       :      str				,			**a__       :      str      ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return BartphoTokenizer.from_pretrained(self.tmpdirname				,			**a__      )


      def    a (self       :      str				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''This is a<unk><unk> test'''
         return input_text, output_text


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''▁This ▁is ▁a ▁l à ▁t est'''.split()
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )

         __snake_case							       =   tokens + [tokenizer.unk_token]
         __snake_case							       =   [4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

