





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 	_a						(   )    ->    Dict:







   """simple docstring"""
   raise RuntimeError('''CUDA out of memory.'''					)


class        __SCREAMING_SNAKE_CASE      (			nn.Module ):







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







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     Optional[Any]      )       ->List[Any]:
          return self.lineara(self.batchnorm(self.lineara(A      )      )      )


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							[]

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

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







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
          lowerCamelCase__       :						str        =							[]

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							mock_training_loop_function('''hello'''      )
          self.assertListEqual(A		, [1_2_8, 6_4, 3_2, 1_6, 8]      )
          self.assertListEqual([bs, arga]		, [8, '''hello''']      )







       def 						__lowerCamelCase   (				self					:     int      )       ->List[Any]:
          @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 						__lowerCamelCase   (				self					:     Any      )       ->Dict:
          @find_executable_batch_size(starting_batch_size=1_6      )
          def mock_training_loop_function(A					:     List[Any]      ):
             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 						__lowerCamelCase   (				self					:     Any      )       ->Optional[Any]:
          @find_executable_batch_size(starting_batch_size=1_2_8      )
          def mock_training_loop_function(A					:     Dict		, A					:     Union[str, Any]		, A					:     List[str]      ):
             if batch_size != 8:
                raise raise_fake_out_of_memory()

          with self.assertRaises(A      ) 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 						__lowerCamelCase   (				self					:     Any      )       ->int:
          @find_executable_batch_size(starting_batch_size=1_6      )
          def mock_training_loop_function(A					:     Tuple      ):
             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 						__lowerCamelCase   (				self					:     Dict      )       ->int:
          lowerCamelCase__       :						List[Any]        =							torch.cuda.memory_allocated()
          lowerCamelCase__       :						Optional[int]        =							ModelForTest()
          model.cuda()
          self.assertGreater(torch.cuda.memory_allocated()		, A      )
          lowerCamelCase__       :						List[Any]        =							release_memory(A      )
          self.assertEqual(torch.cuda.memory_allocated()		, A      )










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


_A :   Any  			=  logging.get_logger(__name__)

_A :   int  			=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "timesformer"







       def __init__(				self					:     List[str]		, A					:     int=2_2_4		, A					:     Optional[Any]=1_6		, A					:     str=3		, A					:     str=8		, A					:     Any=7_6_8		, A					:     Dict=1_2		, A					:     Optional[Any]=1_2		, A					:     Any=3_0_7_2		, A					:     str="gelu"		, A					:     Optional[int]=0.0		, A					:     Union[str, Any]=0.0		, A					:     List[Any]=0.02		, A					:     int=1e-6		, A					:     Tuple=True		, A					:     Any="divided_space_time"		, A					:     Optional[Any]=0		, **A					:     Tuple		, )       ->str:
          super().__init__(**A      )

          lowerCamelCase__       :						Optional[Any]        =							image_size
          lowerCamelCase__       :						int        =							patch_size
          lowerCamelCase__       :						Any        =							num_channels
          lowerCamelCase__       :						Optional[int]        =							num_frames

          lowerCamelCase__       :						Optional[Any]        =							hidden_size
          lowerCamelCase__       :						Optional[int]        =							num_hidden_layers
          lowerCamelCase__       :						Optional[int]        =							num_attention_heads
          lowerCamelCase__       :						Dict        =							intermediate_size
          lowerCamelCase__       :						Optional[Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							hidden_dropout_prob
          lowerCamelCase__       :						Dict        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							initializer_range
          lowerCamelCase__       :						str        =							layer_norm_eps
          lowerCamelCase__       :						Union[str, Any]        =							qkv_bias

          lowerCamelCase__       :						str        =							attention_type
          lowerCamelCase__       :						List[str]        =							drop_path_rate



