




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 lowerCamelCase__       (      ):

     '''simple docstring'''


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

class   __a(					nn.Module       ):






    """simple docstring"""





    def __init__(				self  )			->		Tuple:
         super().__init__()
         UpperCAmelCase_      :							Optional[int]       =      nn.Linear(3       ,4  )
         UpperCAmelCase_      :							List[Any]       =      nn.BatchNormad(4  )
         UpperCAmelCase_      :							Union[str, Any]       =      nn.Linear(4       ,5  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE  )  )  )


class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Dict       =      []

         @find_executable_batch_size(starting_batch_size=128  )
         def mock_training_loop_function(_SCREAMING_SNAKE_CASE  ):
              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       ,[128, 64, 32, 16, 8]  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Optional[int]       =      []

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

         UpperCAmelCase_,   UpperCAmelCase_      :							Dict       =      mock_training_loop_function('''hello'''  )
         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,[128, 64, 32, 16, 8]  )
         self.assertListEqual([bs, arga]       ,[8, '''hello''']  )





    def 							a__				(				self  )			->		List[Any]:
         @find_executable_batch_size(starting_batch_size=0  )
         def mock_training_loop_function(_SCREAMING_SNAKE_CASE  ):
              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  )			->		Optional[int]:
         @find_executable_batch_size(starting_batch_size=16  )
         def mock_training_loop_function(_SCREAMING_SNAKE_CASE  ):
              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  )			->		Dict:
         @find_executable_batch_size(starting_batch_size=128  )
         def mock_training_loop_function(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              if batch_size != 8:
                   raise raise_fake_out_of_memory()

         with self.assertRaises(_SCREAMING_SNAKE_CASE  ) 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  )			->		List[Any]:
         @find_executable_batch_size(starting_batch_size=16  )
         def mock_training_loop_function(_SCREAMING_SNAKE_CASE  ):
              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  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      torch.cuda.memory_allocated()
         UpperCAmelCase_      :							Union[str, Any]       =      ModelForTest()
         model.cuda()
         self.assertGreater(torch.cuda.memory_allocated()       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      release_memory(_SCREAMING_SNAKE_CASE  )
         self.assertEqual(torch.cuda.memory_allocated()       ,_SCREAMING_SNAKE_CASE  )





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__a          =			logging.getLogger()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     UpperCAmelCase_      :							Dict       =      parser.parse_args()
     return args.f



class   __a(					_a       ):






    """simple docstring"""





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							int       =      logging.StreamHandler(sys.stdout  )
         logger.addHandler(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							int       =      get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0       ,'''run_glue_deebert.py'''  )
              with patch.object(_SCREAMING_SNAKE_CASE       ,'''argv'''       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(_SCREAMING_SNAKE_CASE       ,0.6_66  )





    @slow
    @require_torch_non_multi_gpu
    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )