





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   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Tuple      		=  inspect.getfile(accelerate.test_utils      )
      a__	:						List[str]      		=  os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_script.py"]      )
      a__	:						List[Any]      		=  os.path.sep.join(
          mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_distributed_data_loop.py"]      )
      a__	:						Tuple      		=  os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_ops.py"]      )





   @require_multi_gpu
   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      print(F"""Found {torch.cuda.device_count()} devices."""      )
      a__	:						Optional[Any]      		=  ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1      ):
         execute_subprocess_async(snake_case					,			env=os.environ.copy()      )





   @require_multi_gpu
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      print(F"""Found {torch.cuda.device_count()} devices."""      )
      a__	:						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(snake_case					,			env=os.environ.copy()      )





   @require_multi_gpu
   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						str      		=  ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      with patch_environment(omp_num_threads=1      ):
         execute_subprocess_async(snake_case					,			env=os.environ.copy()      )





   @require_multi_gpu
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""      )
      a__	:						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(snake_case					,			env=os.environ.copy()      )


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

  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						""""""

  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						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)."

  SCREAMING_SNAKE_CASE__       :			Tuple			      =						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."
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						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)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__       :			int			      =						logging.get_logger(__name__)







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						int     		=				"""upernet"""





   def __init__( self					,			snake_case=None					,			snake_case=512					,			snake_case=0.02					,			snake_case=[1, 2, 3, 6]					,			snake_case=True					,			snake_case=0.4					,			snake_case=384					,			snake_case=256					,			snake_case=1					,			snake_case=False					,			snake_case=255					,			**snake_case					,			)				->  Optional[Any]:
      """simple docstring"""





      super().__init__(**snake_case      )

      if backbone_config is None:
         logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."      )
         a__	:						List[str]      		=  CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]      )
      elif isinstance(snake_case					,			snake_case      ):
         a__	:						Optional[int]      		=  backbone_config.get("model_type"      )
         a__	:						str      		=  CONFIG_MAPPING[backbone_model_type]
         a__	:						str      		=  config_class.from_dict(snake_case      )

      a__	:						int      		=  backbone_config
      a__	:						Optional[Any]      		=  hidden_size
      a__	:						Optional[Any]      		=  initializer_range
      a__	:						Tuple      		=  pool_scales
      a__	:						Optional[Any]      		=  use_auxiliary_head
      a__	:						Optional[Any]      		=  auxiliary_loss_weight
      a__	:						Dict      		=  auxiliary_in_channels
      a__	:						Optional[int]      		=  auxiliary_channels
      a__	:						Any      		=  auxiliary_num_convs
      a__	:						Any      		=  auxiliary_concat_input
      a__	:						int      		=  loss_ignore_index





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Tuple      		=  copy.deepcopy(self.__dict__      )
      a__	:						Optional[Any]      		=  self.backbone_config.to_dict()
      a__	:						List[Any]      		=  self.__class__.model_type
      return output

