
"""simple docstring"""




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  inspect.getfile(accelerate.test_utils							)
         _lowercase:      List[Any]   			=  os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_script.py''']							)
         _lowercase:      Optional[int]   			=  os.path.sep.join(
             mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']							)
         _lowercase:      str   			=  os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_ops.py''']							)
       @require_multi_gpu
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         print(f'''Found {torch.cuda.device_count()} devices.'''							)
         _lowercase:      List[Any]   			=  ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
         with patch_environment(omp_num_threads=1							):
           execute_subprocess_async(A_			,					env=os.environ.copy()							)
       @require_multi_gpu
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         print(f'''Found {torch.cuda.device_count()} devices.'''							)
         _lowercase:      Union[str, Any]   			=  ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
         print(f'''Command: {cmd}'''							)
         with patch_environment(omp_num_threads=1							):
           execute_subprocess_async(A_			,					env=os.environ.copy()							)
       @require_multi_gpu
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      int   			=  ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__							)]
         with patch_environment(omp_num_threads=1							):
           execute_subprocess_async(A_			,					env=os.environ.copy()							)



       @require_multi_gpu
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''							)
         _lowercase:      Optional[Any]   			=  ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
         with patch_environment(omp_num_threads=1			,					cuda_visible_devices='''0,1'''							):
           execute_subprocess_async(A_			,					env=os.environ.copy()							)


if __name__ == "__main__":
  A__ :       List[Any]				= Accelerator()
  A__ :       Optional[int]				= (accelerator.state.process_index + 2, 1_0)
  A__ :       str				= torch.randint(0, 1_0, shape).to(accelerator.device)

  A__ :       Dict				= ''

  A__ :       int				= accelerator.pad_across_processes(tensor)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
    error_msg += "Padding was not done with the right value (0)."

  A__ :       Any				= accelerator.pad_across_processes(tensor, pad_first=True)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  A__ :       str				= accelerator.state.num_processes - accelerator.state.process_index - 1
  if not torch.equal(tensora[index:], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[:index] == 0):
    error_msg += "Padding was not done with the right value (0)."

  # 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)


"""simple docstring"""




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A__ :       Optional[int]				= [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
  A__ :       Optional[int]				= parser.parse_args()
  if args.check_lib:
    A__ :       Union[str, Any]				= importlib.import_module('transformers')
    A__ :       Optional[Any]				= Path(transformers_module.__file__).parent
  else:
    A__ :       str				= Path.cwd() / 'build/lib/transformers'
  if not test_custom_files_are_present(transformers_path):
    raise ValueError('The built release does not contain the custom files. Fix this before going further!')
