


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__	(	):

      """simple docstring"""




      raise RuntimeError("CUDA out of memory."		)







class     _lowercase				(							nn.Module):


       """simple docstring"""




       def __init__(     self							:    Tuple ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase ) ) )







class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						[]

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

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





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						[]

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

             lowerCamelCase__	:       Tuple					=						mock_training_loop_function("hello" )
             self.assertListEqual(__lowerCamelCase      ,   [128, 64, 32, 16, 8] )
             self.assertListEqual([bs, arga]      ,   [8, "hello"] )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=0 )
             def mock_training_loop_function(__lowerCamelCase							:    List[str] ):
                   pass

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





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=16 )
             def mock_training_loop_function(__lowerCamelCase							:    Optional[int] ):
                   if batch_size > 0:
                         raise_fake_out_of_memory()
                   pass

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





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=128 )
             def mock_training_loop_function(__lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Tuple ):
                   if batch_size != 8:
                         raise raise_fake_out_of_memory()

             with self.assertRaises(__lowerCamelCase ) 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 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             @find_executable_batch_size(starting_batch_size=16 )
             def mock_training_loop_function(__lowerCamelCase							:    List[Any] ):
                   raise ValueError("Oops, we had an error!" )

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





       @require_cuda
       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						torch.cuda.memory_allocated()
             lowerCamelCase__	:       List[str]					=						ModelForTest()
             model.cuda()
             self.assertGreater(torch.cuda.memory_allocated()      ,   __lowerCamelCase )
             lowerCamelCase__	:       Dict					=						release_memory(__lowerCamelCase )
             self.assertEqual(torch.cuda.memory_allocated()      ,   __lowerCamelCase )






from collections import OrderedDict
from typing import Mapping

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


A   :					Union[str, Any]	=      logging.get_logger(__name__)

A   :					Dict	=      {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "ibert"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    List[str]=30522      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    List[Any]=12      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    Any=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Union[str, Any]=0.0_2      ,   __lowerCamelCase							:    Any=1E-1_2      ,   __lowerCamelCase							:    int=1      ,   __lowerCamelCase							:    Optional[Any]=0      ,   __lowerCamelCase							:    int=2      ,   __lowerCamelCase							:    int="absolute"      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Dict="none"      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Any					=						vocab_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       int					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						hidden_act
             lowerCamelCase__	:       List[str]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       Any					=						type_vocab_size
             lowerCamelCase__	:       Optional[int]					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       List[str]					=						quant_mode
             lowerCamelCase__	:       int					=						force_dequant







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


