





"""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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						inspect.getfile(accelerate.test_utils )
        __UpperCAmelCase    :						Optional[Any]       =						os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
        __UpperCAmelCase    :						Optional[Any]       =						os.path.sep.join(
            mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
        __UpperCAmelCase    :						Optional[int]       =						os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )

      @require_multi_gpu
      def       A_					(							self       :      int ):


        '''simple docstring'''




        print(f'''Found {torch.cuda.device_count()} devices.''' )
        __UpperCAmelCase    :						str       =						["""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       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        print(f'''Found {torch.cuda.device_count()} devices.''' )
        __UpperCAmelCase    :						Dict       =						["""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       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						["""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       A_					(							self       :      str ):


        '''simple docstring'''




        print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
        __UpperCAmelCase    :						List[str]       =						["""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__":
      lowercase__				:Tuple				   =       Accelerator()
      lowercase__				:Optional[int]				   =       (accelerator.state.process_index + 2, 1_0)
      lowercase__				:Any				   =       torch.randint(0, 1_0, shape).to(accelerator.device)

      lowercase__				:List[Any]				   =       ""

      lowercase__				:List[str]				   =       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)."

      lowercase__				:List[str]				   =       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."
      lowercase__				:Union[str, Any]				   =       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"""





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


lowercase__				:int				   =       '3'

print('Python version:', sys.version)
print('transformers version:', transformers.__version__)

try:
  import torch

  print('Torch version:', torch.__version__)
  print('Cuda available:', torch.cuda.is_available())
  print('Cuda version:', torch.version.cuda)
  print('CuDNN version:', torch.backends.cudnn.version())
  print('Number of GPUs available:', torch.cuda.device_count())
  print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
  print('Torch version:', None)

try:
  import deepspeed

  print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
  print('DeepSpeed version:', None)

try:
  import tensorflow as tf

  print('TensorFlow version:', tf.__version__)
  print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
  print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
  print('TensorFlow version:', None)