



import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: int          =		0
   snake_case_					: bool          =		False
   snake_case_					: float          =		3.0







class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			List[Any])				->				Any:

    """simple docstring"""







    self.assertDictEqual(MockClass().to_kwargs()				,		{})
    self.assertDictEqual(MockClass(a=2).to_kwargs()				,		{"""a""": 2})
    self.assertDictEqual(MockClass(a=2				,		b=lowerCAmelCase).to_kwargs()				,		{"""a""": 2, """b""": True})
    self.assertDictEqual(MockClass(a=2				,		c=2.25).to_kwargs()				,		{"""a""": 2, """c""": 2.25})

   @require_cuda
   def      UpperCamelCase_  (					self							:			str)				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					GradScalerKwargs(init_scale=1024				,		growth_factor=2)
    AcceleratorState._reset_state()
    _snake_case    :  Optional[int]					=					Accelerator(mixed_precision="""fp16"""				,		kwargs_handlers=[scaler_handler])
    print(accelerator.use_fpaa)
    _snake_case    :  Dict					=					accelerator.scaler

    # Check the kwargs have been applied
    self.assertEqual(scaler._init_scale				,		1_024.0)
    self.assertEqual(scaler._growth_factor				,		2.0)

    # Check the other values are at the default
    self.assertEqual(scaler._backoff_factor				,		0.5)
    self.assertEqual(scaler._growth_interval				,		2000)
    self.assertEqual(scaler._enabled				,		lowerCAmelCase)


   @require_multi_gpu
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  int					=					["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__)]
    execute_subprocess_async(lowerCAmelCase				,		env=os.environ.copy())


if __name__ == "__main__":
  a__    =	DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
  a__    =	Accelerator(kwargs_handlers=[ddp_scaler])
  a__    =	torch.nn.Linear(1_00, 2_00)
  a__    =	accelerator.prepare(model)

  # Check the values changed in kwargs
  a__    =	""""""
  a__    =	model.bucket_bytes_cap // (10_24 * 10_24)
  if observed_bucket_cap_map != 15:
    error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
  if model.find_unused_parameters is not True:
    error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

  # Check the values of the defaults
  if model.dim != 0:
    error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
  if model.broadcast_buffers is not True:
    error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
  if model.gradient_as_bucket_view is not False:
    error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)





import argparse
import os
import re


a__    =	"""src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a__    =	re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
a__    =	re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	bool = False				)  ->     Optional[Any]:
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				) as f:
  _snake_case    :  int					=					f.read()

 _snake_case    :  str					=					content.split("""\n"""				)
 _snake_case    :  List[str]					=					[]
 _snake_case    :  int					=					0
 while line_idx < len(SCREAMING_SNAKE_CASE__				):
  if _re_intro_mapping.search(lines[line_idx]				) is not None:
   _snake_case    :  Union[str, Any]					=					len(re.search(R"""^(\s*)\S"""				,       lines[line_idx]				).groups()[0]				) + 8
   # Start of a new mapping!
   while not lines[line_idx].startswith(""" """ * indent + """("""				):
    new_lines.append(lines[line_idx]				)
    line_idx += 1

   _snake_case    :  int					=					[]
   while lines[line_idx].strip() != "]":
    # Blocks either fit in one line or not
    if lines[line_idx].strip() == "(":
     _snake_case    :  Optional[Any]					=					line_idx
     while not lines[line_idx].startswith(""" """ * indent + """)"""				):
      line_idx += 1
     blocks.append("""\n""".join(lines[start_idx : line_idx + 1]				)				)
    else:
     blocks.append(lines[line_idx]				)
    line_idx += 1

   # Sort blocks by their identifiers
   _snake_case    :  int					=					sorted(SCREAMING_SNAKE_CASE__				,       key=lambda SCREAMING_SNAKE_CASE__				: _re_identifier.search(SCREAMING_SNAKE_CASE__				).groups()[0]				)
   new_lines += blocks
  else:
   new_lines.append(lines[line_idx]				)
   line_idx += 1

 if overwrite:
  with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as f:
   f.write("""\n""".join(SCREAMING_SNAKE_CASE__				)				)
 elif "\n".join(SCREAMING_SNAKE_CASE__				) != content:
  return True



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	bool = False				)  ->     int:
 _snake_case    :  List[Any]					=					[os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for f in os.listdir(SCREAMING_SNAKE_CASE__				) if f.endswith(""".py"""				)]
 _snake_case    :  str					=					[sort_auto_mapping(SCREAMING_SNAKE_CASE__				,       overwrite=SCREAMING_SNAKE_CASE__				) for fname in fnames]

 if not overwrite and any(SCREAMING_SNAKE_CASE__				):
  _snake_case    :  List[Any]					=					[f for f, d in zip(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) if d]
  raise ValueError(
      F'''The following files have auto mappings that need sorting: {', '.join(SCREAMING_SNAKE_CASE__				)}. Run `make style` to fix'''
      """ this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  a__    =	parser.parse_args()

  sort_all_auto_mappings(not args.check_only)
