




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





class    __lowercase						(  nn.Module ):







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Optional[int]:
     return self.lineara(self.batchnorm(self.lineara(UpperCamelCase      )      )      )





class    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					[]

     @find_executable_batch_size(starting_batch_size=128      )
     def mock_training_loop_function(UpperCamelCase      ):
       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      )				->    Optional[Any]:
     __a			=					[]

     @find_executable_batch_size(starting_batch_size=128      )
     def mock_training_loop_function(UpperCamelCase      ,							UpperCamelCase      ):
       nonlocal batch_sizes
       batch_sizes.append(UpperCamelCase      )
       if batch_size != 8:
         raise_fake_out_of_memory()
       return batch_size, arga

     __a	,       __a			=					mock_training_loop_function('hello'      )
     self.assertListEqual(UpperCamelCase      ,							[128, 64, 32, 16, 8]      )
     self.assertListEqual([bs, arga]      ,							[8, 'hello']      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     @find_executable_batch_size(starting_batch_size=0      )
     def mock_training_loop_function(UpperCamelCase      ):
       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]:
     @find_executable_batch_size(starting_batch_size=16      )
     def mock_training_loop_function(UpperCamelCase      ):
       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      )				->    List[str]:
     @find_executable_batch_size(starting_batch_size=128      )
     def mock_training_loop_function(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ):
       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      )				->    Dict:
     @find_executable_batch_size(starting_batch_size=16      )
     def mock_training_loop_function(UpperCamelCase      ):
       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      )				->    int:
     __a			=					torch.cuda.memory_allocated()
     __a			=					ModelForTest()
     model.cuda()
     self.assertGreater(torch.cuda.memory_allocated()      ,							UpperCamelCase      )
     __a			=					release_memory(UpperCamelCase      )
     self.assertEqual(torch.cuda.memory_allocated()      ,							UpperCamelCase      )









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}





class    __lowercase						(  __magic_name__ ):
   _a							=      """distilbert"""
   _a							=      {
       """hidden_size""": """dim""",
       """num_attention_heads""": """n_heads""",
       """num_hidden_layers""": """n_layers""",
   }







   def __init__(	self      ,							UpperCamelCase=3_0522      ,							UpperCamelCase=512      ,							UpperCamelCase=False      ,							UpperCamelCase=6      ,							UpperCamelCase=12      ,							UpperCamelCase=768      ,							UpperCamelCase=4 * 768      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.02      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.2      ,							UpperCamelCase=0      ,							**UpperCamelCase      ,							)				->    int:
     __a			=					vocab_size
     __a			=					max_position_embeddings
     __a			=					sinusoidal_pos_embds
     __a			=					n_layers
     __a			=					n_heads
     __a			=					dim
     __a			=					hidden_dim
     __a			=					dropout
     __a			=					attention_dropout
     __a			=					activation
     __a			=					initializer_range
     __a			=					qa_dropout
     __a			=					seq_classif_dropout
     super().__init__(**UpperCamelCase      ,							pad_token_id=UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):







   @property
   def 		UpperCamelCase__			(	self      )				->    Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
       __a			=					{0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
       __a			=					{0: 'batch', 1: 'sequence'}
     return OrderedDict(
         [
             ('input_ids', dynamic_axis),
             ('attention_mask', dynamic_axis),
         ]      )



