


'''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 __snake_case					(      ):
 raise RuntimeError("CUDA out of memory."		)





class      snake_case    (    nn.Module			):





  """simple docstring"""




  def __init__(    self     ):







   """simple docstring"""
   super().__init__()
   lowerCamelCase_      =  nn.Linear(3			,  4     )
   lowerCamelCase_      =  nn.BatchNormad(4     )
   lowerCamelCase_      =  nn.Linear(4			,  5     )







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.lineara(self.batchnorm(self.lineara(UpperCamelCase     )     )     )





class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  []

   @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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  []

   @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

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






  def   snake_case		(    self     ):







   """simple docstring"""
   @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   snake_case		(    self     ):







   """simple docstring"""
   @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   snake_case		(    self     ):







   """simple docstring"""
   @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   snake_case		(    self     ):







   """simple docstring"""
   @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   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch.cuda.memory_allocated()
   lowerCamelCase_      =  ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()			,  UpperCamelCase     )
   lowerCamelCase_      =  release_memory(UpperCamelCase     )
   self.assertEqual(torch.cuda.memory_allocated()			,  UpperCamelCase     )







'''simple docstring'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser





def __snake_case					(      ):
 lowerCamelCase_      =  ArgumentParser("Accelerate CLI tool"					,				usage="accelerate <command> [<args>]"					,				allow_abbrev=UpperCAmelCase_		)
 lowerCamelCase_      =  parser.add_subparsers(help="accelerate command helpers"		)

 # Register commands
 get_config_parser(subparsers=UpperCAmelCase_		)
 env_command_parser(subparsers=UpperCAmelCase_		)
 launch_command_parser(subparsers=UpperCAmelCase_		)
 tpu_command_parser(subparsers=UpperCAmelCase_		)
 test_command_parser(subparsers=UpperCAmelCase_		)

 # Let's go
 lowerCamelCase_      =  parser.parse_args()

 if not hasattr(UpperCAmelCase_					,				"func"		):
  parser.print_help()
  exit(1		)

 # Run
 args.func(UpperCAmelCase_		)


if __name__ == "__main__":
     main()



