


"""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	):






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Tuple				=      inspect.getfile(accelerate.test_utils )
           lowerCAmelCase    :		Optional[Any]				=      os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
           lowerCAmelCase    :		Optional[int]				=      os.path.sep.join(
               mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
           lowerCAmelCase    :		Union[str, Any]				=      os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    int ):
           print(F'''Found {torch.cuda.device_count()} devices.''' )
           lowerCAmelCase    :		str				=      ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
           with patch_environment(omp_num_threads=1 ):
                 execute_subprocess_async(UpperCamelCase_    , env=os.environ.copy() )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    List[Any] ):
           print(F'''Found {torch.cuda.device_count()} devices.''' )
           lowerCAmelCase    :		List[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(UpperCamelCase_    , env=os.environ.copy() )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Optional[int]				=      ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
           with patch_environment(omp_num_threads=1 ):
                 execute_subprocess_async(UpperCamelCase_    , env=os.environ.copy() )






     @require_multi_gpu
     def    lowerCamelCase__   (		self     :    Dict ):
           print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
           lowerCAmelCase    :		Tuple				=      ['''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(UpperCamelCase_    , env=os.environ.copy() )


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

 snake_case__						:   Any			 = ''''''

 snake_case__						:   Tuple			 = 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)."

 snake_case__						:   Dict			 = 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."
 snake_case__						:   int			 = 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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__      :    str    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Optional[int]    							= {
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						int					       =   '''yolos'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=[5_12, 8_64]   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=1_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=0.1   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Optional[Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       int           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       str           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       List[str]           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       Dict           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       List[str]           =   image_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   patch_size
											SCREAMING_SNAKE_CASE__						:       List[str]           =   num_channels
											SCREAMING_SNAKE_CASE__						:       List[str]           =   qkv_bias
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   num_detection_tokens
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   use_mid_position_embeddings
											SCREAMING_SNAKE_CASE__						:       List[str]           =   auxiliary_loss
											# Hungarian matcher
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   class_cost
											SCREAMING_SNAKE_CASE__						:       List[str]           =   bbox_cost
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   giou_cost
											# Loss coefficients
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   bbox_loss_coefficient
											SCREAMING_SNAKE_CASE__						:       List[str]           =   giou_loss_coefficient
											SCREAMING_SNAKE_CASE__						:       int           =   eos_coefficient






class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Dict					       =   version.parse('''1.11'''  )



				@property
				def 	__magic_name__    (self    )					->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]    )



				@property
				def 	__magic_name__    (self    )					->		float:



											"""simple docstring"""




											return 1E-4



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return 12




