





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 	_SCREAMING_SNAKE_CASE		(				)      ->   Optional[Any]:
 raise RuntimeError("""CUDA out of memory."""   )






class        snake_case_    (		nn.Module						):






  def __init__(							self			:					Tuple      )->Dict:






   '''simple docstring'''

   super().__init__()
   __lowerCAmelCase					:	List[str]      =       nn.Linear(3       ,  4      )
   __lowerCAmelCase					:	Any      =       nn.BatchNormad(4      )
   __lowerCAmelCase					:	Any      =       nn.Linear(4       ,  5      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					str      )->List[Any]:






   '''simple docstring'''

   return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase__      )      )      )






class        snake_case_    (		unittest.TestCase						):






  def 				UpperCAmelCase__						(							self			:					Tuple      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       []

   @find_executable_batch_size(starting_batch_size=128      )
   def mock_training_loop_function(_snake_case			:					int      ):
    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 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       []

   @find_executable_batch_size(starting_batch_size=128      )
   def mock_training_loop_function(_snake_case			:					str       ,  _snake_case			:					Optional[Any]      ):
    nonlocal batch_sizes
    batch_sizes.append(lowerCAmelCase__      )
    if batch_size != 8:
     raise_fake_out_of_memory()
    return batch_size, arga

   __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       mock_training_loop_function("""hello"""      )
   self.assertListEqual(lowerCAmelCase__       ,  [128, 64, 32, 16, 8]      )
   self.assertListEqual([bs, arga]       ,  [8, """hello"""]      )






  def 				UpperCAmelCase__						(							self			:					Dict      )->Any:






   '''simple docstring'''

   @find_executable_batch_size(starting_batch_size=0      )
   def mock_training_loop_function(_snake_case			:					Dict      ):
    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 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Dict:






   '''simple docstring'''

   @find_executable_batch_size(starting_batch_size=16      )
   def mock_training_loop_function(_snake_case			:					Tuple      ):
    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 				UpperCAmelCase__						(							self			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

   @find_executable_batch_size(starting_batch_size=128      )
   def mock_training_loop_function(_snake_case			:					Union[str, Any]       ,  _snake_case			:					Dict       ,  _snake_case			:					Dict      ):
    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 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   @find_executable_batch_size(starting_batch_size=16      )
   def mock_training_loop_function(_snake_case			:					List[Any]      ):
    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 				UpperCAmelCase__						(							self			:					Optional[int]      )->Tuple:






   '''simple docstring'''

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        snake_case_    (		__lowercase						):
  A_      							=   'biogpt'






  def __init__(							self			:					int       ,  _snake_case			:					Any=42384       ,  _snake_case			:					Any=1024       ,  _snake_case			:					List[Any]=24       ,  _snake_case			:					Any=16       ,  _snake_case			:					List[str]=4096       ,  _snake_case			:					Dict="gelu"       ,  _snake_case			:					Tuple=0.1       ,  _snake_case			:					str=0.1       ,  _snake_case			:					Tuple=1024       ,  _snake_case			:					Tuple=0.02       ,  _snake_case			:					Tuple=1E-12       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Any=0.0       ,  _snake_case			:					Tuple=0.0       ,  _snake_case			:					str=1       ,  _snake_case			:					Dict=0       ,  _snake_case			:					str=2       ,  **_snake_case			:					Union[str, Any]       ,  )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       vocab_size
   __lowerCAmelCase					:	Dict      =       max_position_embeddings
   __lowerCAmelCase					:	str      =       hidden_size
   __lowerCAmelCase					:	Dict      =       num_hidden_layers
   __lowerCAmelCase					:	List[Any]      =       num_attention_heads
   __lowerCAmelCase					:	Optional[Any]      =       intermediate_size
   __lowerCAmelCase					:	int      =       hidden_act
   __lowerCAmelCase					:	Any      =       hidden_dropout_prob
   __lowerCAmelCase					:	Any      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	Any      =       initializer_range
   __lowerCAmelCase					:	int      =       layer_norm_eps
   __lowerCAmelCase					:	Optional[int]      =       scale_embedding
   __lowerCAmelCase					:	List[Any]      =       use_cache
   __lowerCAmelCase					:	str      =       layerdrop
   __lowerCAmelCase					:	Dict      =       activation_dropout
   super().__init__(pad_token_id=_snake_case       ,  bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  **_snake_case      )