

'''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 				UpperCamelCase(					):
 raise RuntimeError('CUDA out of memory.'     )




class 	A_			(    nn.Module							):






     '''simple docstring'''



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




     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Optional[int]	)						->     Tuple:
      return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase__	)	)	)




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     int:
      UpperCAmelCase	:     Tuple						 =			[]

      @find_executable_batch_size(starting_batch_size=128	)
      def mock_training_loop_function(lowercase_			:					Any	):
       nonlocal batch_sizes
       batch_sizes.append(UpperCAmelCase__	)
       if batch_size != 8:
        raise_fake_out_of_memory()

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


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Optional[Any]:
      UpperCAmelCase	:     Any						 =			[]

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

      UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			mock_training_loop_function('hello'	)
      self.assertListEqual(UpperCAmelCase__					,      [128, 64, 32, 16, 8]	)
      self.assertListEqual([bs, arga]					,      [8, 'hello']	)


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Tuple:
      @find_executable_batch_size(starting_batch_size=0	)
      def mock_training_loop_function(lowercase_			:					Optional[Any]	):
       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			:					Optional[int]	)						->     Optional[Any]:
      @find_executable_batch_size(starting_batch_size=16	)
      def mock_training_loop_function(lowercase_			:					Union[str, Any]	):
       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	)						->     str:
      @find_executable_batch_size(starting_batch_size=128	)
      def mock_training_loop_function(lowercase_			:					Dict					,      lowercase_			:					Tuple					,      lowercase_			:					List[str]	):
       if batch_size != 8:
        raise raise_fake_out_of_memory()

      with self.assertRaises(UpperCAmelCase__	) 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			:					int	)						->     List[str]:
      @find_executable_batch_size(starting_batch_size=16	)
      def mock_training_loop_function(lowercase_			:					Optional[int]	):
       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			:					Tuple	)						->     Dict:
      UpperCAmelCase	:     Any						 =			torch.cuda.memory_allocated()
      UpperCAmelCase	:     Any						 =			ModelForTest()
      model.cuda()
      self.assertGreater(torch.cuda.memory_allocated()					,      UpperCAmelCase__	)
      UpperCAmelCase	:     Optional[int]						 =			release_memory(UpperCAmelCase__	)
      self.assertEqual(torch.cuda.memory_allocated()					,      UpperCAmelCase__	)







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase__         =      {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMForMultipleChoice",
     "XLMForQuestionAnswering",
     "XLMForQuestionAnsweringSimple",
     "XLMForSequenceClassification",
     "XLMForTokenClassification",
     "XLMModel",
     "XLMPreTrainedModel",
     "XLMWithLMHeadModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLMForMultipleChoice",
     "TFXLMForQuestionAnsweringSimple",
     "TFXLMForSequenceClassification",
     "TFXLMForTokenClassification",
     "TFXLMMainLayer",
     "TFXLMModel",
     "TFXLMPreTrainedModel",
     "TFXLMWithLMHeadModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

 lowercase__         =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




