"""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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__magic_name__     					=       """Create a default config file for Accelerate with only a few flags set."""





def 	_lowerCamelCase			(			UpperCAmelCase__="no",UpperCAmelCase__ = default_json_config_file,UpperCAmelCase__ = False       )				->  Any:


   '''simple docstring'''


   a__  					=		Path(lowercase_       )
   path.parent.mkdir(parents=lowercase_,exist_ok=lowercase_       )
   if path.exists():
      print(
          f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''       )
      return False
   a__  					=		mixed_precision.lower()
   if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
      raise ValueError(
          f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''       )
   a__  					=		{
       """compute_environment""": """LOCAL_MACHINE""",
       """mixed_precision""": mixed_precision,
   }
   if torch.cuda.is_available():
      a__  					=		torch.cuda.device_count()
      a__  					=		num_gpus
      a__  					=		False
      if num_gpus > 1:
         a__  					=		"""MULTI_GPU"""
      else:
         a__  					=		"""NO"""
   elif is_xpu_available() and use_xpu:
      a__  					=		torch.xpu.device_count()
      a__  					=		num_xpus
      a__  					=		False
      if num_xpus > 1:
         a__  					=		"""MULTI_XPU"""
      else:
         a__  					=		"""NO"""
   elif is_npu_available():
      a__  					=		torch.npu.device_count()
      a__  					=		num_npus
      a__  					=		False
      if num_npus > 1:
         a__  					=		"""MULTI_NPU"""
      else:
         a__  					=		"""NO"""
   else:
      a__  					=		0
      a__  					=		True
      a__  					=		1
      a__  					=		"""NO"""
   a__  					=		ClusterConfig(**lowercase_       )
   config.to_json_file(lowercase_       )
   return path





def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__       )				->  Optional[Any]:


   '''simple docstring'''


   a__  					=		parser.add_parser('default',parents=lowercase_,help=lowercase_,formatter_class=lowercase_       )
   parser.add_argument(
       '--config_file',default=lowercase_,help=(
           'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
           'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
           'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
           'with \'huggingface\'.'
       ),dest='save_location',)

   parser.add_argument(
       '--mixed_precision',choices=['no', 'fp16', 'bf16'],type=lowercase_,help='Whether or not to use mixed precision training. '
       'Choose between FP16 and BF16 (bfloat16) training. '
       'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.',default='no',)
   parser.set_defaults(func=lowercase_       )
   return parser





def 	_lowerCamelCase			(			UpperCAmelCase__       )				->  Any:


   '''simple docstring'''


   a__  					=		write_basic_config(args.mixed_precision,args.save_location       )
   if config_file:
      print(f'''accelerate configuration saved at {config_file}'''       )




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						(   )		->						List[Any]:


  '''simple docstring'''






  raise RuntimeError("""CUDA out of memory.""" )



class    _snake_case (						nn.Module				):


 def __init__(       self):


   '''simple docstring'''




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



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_)))



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowercase__		,     lowercase__	: int     		=   mock_training_loop_function("""hello""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])
   self.assertListEqual([bs, arga]       ,	[8, """hello"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=0)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     pass

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


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     if batch_size > 0:
       raise_fake_out_of_memory()
     pass

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


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function(1_28       ,	"""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):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     raise ValueError("""Oops, we had an error!""")

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



 @require_cuda
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch.cuda.memory_allocated()
   lowercase__	: str     		=   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   release_memory(SCREAMING_SNAKE_CASE_)
   self.assertEqual(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)






