

'''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     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




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






  @require_multi_gpu
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   print(F"Found {torch.cuda.device_count()} devices."    )
   __lowercase													=				['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
   with patch_environment(omp_num_threads=1    ):
    execute_subprocess_async(lowercase__      ,env=os.environ.copy()    )






  @require_multi_gpu
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   print(F"Found {torch.cuda.device_count()} devices."    )
   __lowercase													=				['''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(lowercase__      ,env=os.environ.copy()    )






  @require_multi_gpu
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__    )]
   with patch_environment(omp_num_threads=1    ):
    execute_subprocess_async(lowercase__      ,env=os.environ.copy()    )






  @require_multi_gpu
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"    )
   __lowercase													=				['''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(lowercase__      ,env=os.environ.copy()    )


if __name__ == "__main__":
   lowerCAmelCase__      		=			Accelerator()
   lowerCAmelCase__      		=			(accelerator.state.process_index + 2, 10)
   lowerCAmelCase__      		=			torch.randint(0, 10, shape).to(accelerator.device)

   lowerCAmelCase__      		=			''''''

   lowerCAmelCase__      		=			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)."

   lowerCAmelCase__      		=			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."
   lowerCAmelCase__      		=			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'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__      		=			{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTMAEForPreTraining''',
     '''ViTMAELayer''',
     '''ViTMAEModel''',
     '''ViTMAEPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''TFViTMAEForPreTraining''',
     '''TFViTMAEModel''',
     '''TFViTMAEPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_mae import (
      VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTMAEForPreTraining,
      ViTMAELayer,
      ViTMAEModel,
      ViTMAEPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
 import sys

 lowerCAmelCase__      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

