'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase	:       int						       =	logging.get_logger(__name__)

__lowerCAmelCase	:       List[Any]						       =	{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 						A   (   UpperCAmelCase   ):
							a_        =      '''beit'''





							def __init__(      self				:							Any     ,		__a				:							int=8_1_9_2     ,		__a				:							Optional[Any]=7_6_8     ,		__a				:							Optional[Any]=1_2     ,		__a				:							Any=1_2     ,		__a				:							List[str]=3_0_7_2     ,		__a				:							str="gelu"     ,		__a				:							Union[str, Any]=0.0     ,		__a				:							Tuple=0.0     ,		__a				:							Optional[int]=0.0_2     ,		__a				:							Union[str, Any]=1e-12     ,		__a				:							str=2_2_4     ,		__a				:							Tuple=1_6     ,		__a				:							Union[str, Any]=3     ,		__a				:							Union[str, Any]=False     ,		__a				:							List[str]=False     ,		__a				:							int=False     ,		__a				:							Union[str, Any]=False     ,		__a				:							Any=0.1     ,		__a				:							Any=0.1     ,		__a				:							List[Any]=True     ,		__a				:							Optional[int]=[3, 5, 7, 1_1]     ,		__a				:							Any=[1, 2, 3, 6]     ,		__a				:							str=True     ,		__a				:							Optional[int]=0.4     ,		__a				:							List[Any]=2_5_6     ,		__a				:							List[Any]=1     ,		__a				:							str=False     ,		__a				:							Any=2_5_5     ,		**__a				:							Optional[Any]     ,		)							->	Union[str, Any]:
								super().__init__(**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps

								__UpperCAmelCase							=			image_size
								__UpperCAmelCase							=			patch_size
								__UpperCAmelCase							=			num_channels
								__UpperCAmelCase							=			use_mask_token
								__UpperCAmelCase							=			use_absolute_position_embeddings
								__UpperCAmelCase							=			use_relative_position_bias
								__UpperCAmelCase							=			use_shared_relative_position_bias
								__UpperCAmelCase							=			layer_scale_init_value
								__UpperCAmelCase							=			drop_path_rate
								__UpperCAmelCase							=			use_mean_pooling
								# decode head attributes (semantic segmentation)
								__UpperCAmelCase							=			out_indices
								__UpperCAmelCase							=			pool_scales
								# auxiliary head attributes (semantic segmentation)
								__UpperCAmelCase							=			use_auxiliary_head
								__UpperCAmelCase							=			auxiliary_loss_weight
								__UpperCAmelCase							=			auxiliary_channels
								__UpperCAmelCase							=			auxiliary_num_convs
								__UpperCAmelCase							=			auxiliary_concat_input
								__UpperCAmelCase							=			semantic_loss_ignore_index






class 						A   (   UpperCAmelCase   ):
							a_        =      version.parse('''1.11'''   )





							@property
							def       snake_case__						(      self				:							Tuple	)							->	Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]	)






							@property
							def       snake_case__						(      self				:							str	)							->	float:
								return 1e-4

'''simple docstring'''


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   ):





							def __init__(      self				:							Optional[Any]	)							->	int:
								super().__init__()
								__UpperCAmelCase							=			nn.Linear(3     ,		4	)
								__UpperCAmelCase							=			nn.BatchNormad(4	)
								__UpperCAmelCase							=			nn.Linear(4     ,		5	)






							def       snake_case__						(      self				:							List[str]     ,		__a				:							Optional[int]	)							->	Optional[int]:
								return self.lineara(self.batchnorm(self.lineara(__a	)	)	)






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							Union[str, Any]	):
									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       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			[]

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

								__UpperCAmelCase				,			__UpperCAmelCase							=			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       snake_case__						(      self				:							Any	)							->	int:
								@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       snake_case__						(      self				:							Any	)							->	List[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Dict	):
									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       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							int	):
									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       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								@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       snake_case__						(      self				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			torch.cuda.memory_allocated()
								__UpperCAmelCase							=			ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()     ,		__a	)
								__UpperCAmelCase							=			release_memory(__a	)
								self.assertEqual(torch.cuda.memory_allocated()     ,		__a	)
