





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

   @require_multi_gpu
   def 		__UpperCamelCase						(							self       ):
    print(f"Found {torch.cuda.device_count()} devices."       )
    snake_case__     :					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(__SCREAMING_SNAKE_CASE	,		env=os.environ.copy()       )

   @require_multi_gpu
   def 		__UpperCamelCase						(							self       ):
    print(f"Found {torch.cuda.device_count()} devices."       )
    snake_case__     :					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(__SCREAMING_SNAKE_CASE	,		env=os.environ.copy()       )

   @require_multi_gpu
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Tuple    =  ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
    with patch_environment(omp_num_threads=1       ):
     execute_subprocess_async(__SCREAMING_SNAKE_CASE	,		env=os.environ.copy()       )


   @require_multi_gpu
   def 		__UpperCamelCase						(							self       ):
    print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
    snake_case__     :					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(__SCREAMING_SNAKE_CASE	,		env=os.environ.copy()       )


if __name__ == "__main__":
    A_		:  Union[str, Any]		 =						Accelerator()
    A_		:  List[Any]		 =						(accelerator.state.process_index + 2, 10)
    A_		:  Tuple		 =						torch.randint(0, 10, shape).to(accelerator.device)

    A_		:  List[Any]		 =						""

    A_		:  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)."

    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_		:  Optional[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)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """vit_msn"""


      def __init__(		self   ,				a__=768   ,				a__=12   ,				a__=12   ,				a__=3072   ,				a__="gelu"   ,				a__=0.0   ,				a__=0.0   ,				a__=0.02   ,				a__=1e-06   ,				a__=224   ,				a__=16   ,				a__=3   ,				a__=True   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(**a__)

             _lowerCamelCase				:  Tuple						=		hidden_size
             _lowerCamelCase				:  str						=		num_hidden_layers
             _lowerCamelCase				:  int						=		num_attention_heads
             _lowerCamelCase				:  Optional[int]						=		intermediate_size
             _lowerCamelCase				:  Union[str, Any]						=		hidden_act
             _lowerCamelCase				:  str						=		hidden_dropout_prob
             _lowerCamelCase				:  List[Any]						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Optional[int]						=		initializer_range
             _lowerCamelCase				:  Optional[Any]						=		layer_norm_eps
             _lowerCamelCase				:  Dict						=		image_size
             _lowerCamelCase				:  Tuple						=		patch_size
             _lowerCamelCase				:  List[str]						=		num_channels
             _lowerCamelCase				:  Optional[int]						=		qkv_bias

