

"""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   __lowercase      (     nn.Module       ):

  def __init__(	self		:      Optional[int]     ):






   '''simple docstring'''
   super().__init__()
   UpperCAmelCase__       :	Union[str, Any]        =       nn.Linear(3       ,4     )
   UpperCAmelCase__       :	List[Any]        =       nn.BatchNormad(4     )
   UpperCAmelCase__       :	Any        =       nn.Linear(4       ,5     )




  def  __lowercase							(	self		:      Tuple       ,A		:      Any     ):






   '''simple docstring'''
   return self.lineara(self.batchnorm(self.lineara(A     )     )     )







class   __lowercase      (     unittest.TestCase       ):

  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       []

   @find_executable_batch_size(starting_batch_size=128     )
   def mock_training_loop_function(A		:      Tuple     ):
    nonlocal batch_sizes
    batch_sizes.append(A     )
    if batch_size != 8:
     raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(A       ,[128, 64, 32, 16, 8]     )

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       []

   @find_executable_batch_size(starting_batch_size=128     )
   def mock_training_loop_function(A		:      int       ,A		:      List[str]     ):
    nonlocal batch_sizes
    batch_sizes.append(A     )
    if batch_size != 8:
     raise_fake_out_of_memory()
    return batch_size, arga

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Dict        =       mock_training_loop_function("""hello"""     )
   self.assertListEqual(A       ,[128, 64, 32, 16, 8]     )
   self.assertListEqual([bs, arga]       ,[8, """hello"""]     )

  def  __lowercase							(	self		:      Optional[int]     ):






   '''simple docstring'''
   @find_executable_batch_size(starting_batch_size=0     )
   def mock_training_loop_function(A		:      str     ):
    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  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   @find_executable_batch_size(starting_batch_size=16     )
   def mock_training_loop_function(A		:      Tuple     ):
    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  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   @find_executable_batch_size(starting_batch_size=128     )
   def mock_training_loop_function(A		:      str       ,A		:      Tuple       ,A		:      int     ):
    if batch_size != 8:
     raise raise_fake_out_of_memory()

   with self.assertRaises(A     ) 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  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   @find_executable_batch_size(starting_batch_size=16     )
   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  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       torch.cuda.memory_allocated()
   UpperCAmelCase__       :	List[str]        =       ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,A     )
   UpperCAmelCase__       :	Optional[Any]        =       release_memory(A     )
   self.assertEqual(torch.cuda.memory_allocated()       ,A     )













"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
			from ... import PreTrainedTokenizerBase, TensorType

lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''vision-encoder-decoder'''
	__magic_name__       =							True





	def __init__( self   :					Union[str, Any]      ,      **lowerCAmelCase_   :					List[Any]		)		->     Union[str, Any]:
				super().__init__(**lowerCAmelCase_		)
				if "encoder" not in kwargs or "decoder" not in kwargs:
							raise ValueError(
							    f"""A configuraton of type {self.model_type} cannot be instantiated because """
							    f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""		)

				UpperCAmelCase_	:					str     	=   kwargs.pop("encoder"		)
				UpperCAmelCase_	:					Dict     	=   encoder_config.pop("model_type"		)
				UpperCAmelCase_	:					List[Any]     	=   kwargs.pop("decoder"		)
				UpperCAmelCase_	:					Dict     	=   decoder_config.pop("model_type"		)

				UpperCAmelCase_	:					Union[str, Any]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   True





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					List[str]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      **lowerCAmelCase_   :					Dict		)		->     PretrainedConfig:
				logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
				UpperCAmelCase_	:					Tuple     	=   True
				UpperCAmelCase_	:					str     	=   True

				return cls(encoder=encoder_config.to_dict()      ,      decoder=decoder_config.to_dict()      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   copy.deepcopy(self.__dict__		)
				UpperCAmelCase_	:					List[str]     	=   self.encoder.to_dict()
				UpperCAmelCase_	:					List[Any]     	=   self.decoder.to_dict()
				UpperCAmelCase_	:					Any     	=   self.__class__.model_type
				return output







class      UpperCamelCase_			(__A	):
	__magic_name__       =							version.parse('''1.11'''	)





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





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     float:
				return 1e-4





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}		)







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_	:					List[Any]     	=   OrderedDict()
				UpperCAmelCase_	:					int     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Dict     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Union[str, Any]     	=   {0: "batch", 1: "encoder_sequence"}

				return common_inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					"PreTrainedTokenizerBase"      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Optional["TensorType"] = None      ,      )		->     Mapping[str, Any]:
				import torch

				UpperCAmelCase_	:					Optional[int]     	=   OrderedDict()

				UpperCAmelCase_	:					Any     	=   super().generate_dummy_inputs(
				    lowerCAmelCase_      ,      batch_size=lowerCAmelCase_      ,      seq_length=lowerCAmelCase_      ,      is_pair=lowerCAmelCase_      ,      framework=lowerCAmelCase_		)

				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   dummy_input["input_ids"].shape
				UpperCAmelCase_	:					List[str]     	=   (batch, encoder_sequence, self._config.encoder_hidden_size)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("input_ids"		)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("attention_mask"		)
				UpperCAmelCase_	:					List[Any]     	=   torch.zeros(lowerCAmelCase_		)

				return common_inputs







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     None:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					PretrainedConfig		)		->     OnnxConfig:
				return VisionEncoderDecoderEncoderOnnxConfig(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					str = "default"		)		->     OnnxConfig:
				UpperCAmelCase_	:					Optional[int]     	=   encoder_config.hidden_size
				return VisionEncoderDecoderDecoderOnnxConfig(lowerCAmelCase_      ,      lowerCAmelCase_		)



