






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



class 							_UpperCamelCase			(nn.Module			):


      def __init__(	self	)->	Any:
           super().__init__()
           __lowerCAmelCase								=					nn.Linear(3					,						4	)
           __lowerCAmelCase								=					nn.BatchNormad(4	)
           __lowerCAmelCase								=					nn.Linear(4					,						5	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Tuple:
           return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase	)	)	)



class 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					[]

           @find_executable_batch_size(starting_batch_size=1_2_8	)
           def mock_training_loop_function(__UpperCamelCase	):
                nonlocal batch_sizes
                batch_sizes.append(__UpperCamelCase	)
                if batch_size != 8:
                     raise_fake_out_of_memory()

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


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					[]

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

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


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           @find_executable_batch_size(starting_batch_size=0	)
           def mock_training_loop_function(__UpperCamelCase	):
                pass

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


      def  __UpperCAmelCase		(	self	)->	str:
           @find_executable_batch_size(starting_batch_size=1_6	)
           def mock_training_loop_function(__UpperCamelCase	):
                if batch_size > 0:
                     raise_fake_out_of_memory()
                pass

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


      def  __UpperCAmelCase		(	self	)->	Any:
           @find_executable_batch_size(starting_batch_size=1_2_8	)
           def mock_training_loop_function(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	):
                if batch_size != 8:
                     raise raise_fake_out_of_memory()

           with self.assertRaises(__UpperCamelCase	) 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  __UpperCAmelCase		(	self	)->	Any:
           @find_executable_batch_size(starting_batch_size=1_6	)
           def mock_training_loop_function(__UpperCamelCase	):
                raise ValueError("Oops, we had an error!"	)

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


      @require_cuda
      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					torch.cuda.memory_allocated()
           __lowerCAmelCase								=					ModelForTest()
           model.cuda()
           self.assertGreater(torch.cuda.memory_allocated()					,						__UpperCamelCase	)
           __lowerCAmelCase								=					release_memory(__UpperCamelCase	)
           self.assertEqual(torch.cuda.memory_allocated()					,						__UpperCamelCase	)














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


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

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



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






