





"""simple docstring"""


import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      __SCREAMING_SNAKE_CASE    (						UpperCAmelCase_   ):







		'''simple docstring'''


		SCREAMING_SNAKE_CASE__							:int	     =					0
		SCREAMING_SNAKE_CASE__							:bool	     =					False
		SCREAMING_SNAKE_CASE__							:float	     =					3.0





class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







		'''simple docstring'''


		def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Any:
								self.assertDictEqual(MockClass().to_kwargs()					,   {}	)
								self.assertDictEqual(MockClass(a=2	).to_kwargs()					,   {"a": 2}	)
								self.assertDictEqual(MockClass(a=2					,   b=_snake_case	).to_kwargs()					,   {"a": 2, "b": True}	)
								self.assertDictEqual(MockClass(a=2					,   c=2.25	).to_kwargs()					,   {"a": 2, "c": 2.25}	)

		@require_cuda
		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Any:
								_UpperCamelCase      :	List[str]						=       GradScalerKwargs(init_scale=1024					,   growth_factor=2	)
								AcceleratorState._reset_state()
								_UpperCamelCase      :	List[Any]						=       Accelerator(mixed_precision="fp16"					,   kwargs_handlers=[scaler_handler]	)
								print(accelerator.use_fpaa	)
								_UpperCamelCase      :	Union[str, Any]						=       accelerator.scaler

								# Check the kwargs have been applied
								self.assertEqual(scaler._init_scale					,   10_24.0	)
								self.assertEqual(scaler._growth_factor					,   2.0	)

								# Check the other values are at the default
								self.assertEqual(scaler._backoff_factor					,   0.5	)
								self.assertEqual(scaler._growth_interval					,   2000	)
								self.assertEqual(scaler._enabled					,   _snake_case	)


		@require_multi_gpu
		def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				List[str]:
								_UpperCamelCase      :	Tuple						=       ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__	)]
								execute_subprocess_async(_snake_case					,   env=os.environ.copy()	)


if __name__ == "__main__":
		lowerCamelCase__				  =   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
		lowerCamelCase__				  =   Accelerator(kwargs_handlers=[ddp_scaler])
		lowerCamelCase__				  =   torch.nn.Linear(100, 200)
		lowerCamelCase__				  =   accelerator.prepare(model)

		# Check the values changed in kwargs
		lowerCamelCase__				  =   ""
		lowerCamelCase__				  =   model.bucket_bytes_cap // (1024 * 1024)
		if observed_bucket_cap_map != 15:
				error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
		if model.find_unused_parameters is not True:
				error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

		# Check the values of the defaults
		if model.dim != 0:
				error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
		if model.broadcast_buffers is not True:
				error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
		if model.gradient_as_bucket_view is not False:
				error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

		# 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


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "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      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					"yolos"

  def __init__(		self   :       Dict					,   __a   :       Optional[Any]=768					,   __a   :       List[Any]=12					,   __a   :       Any=12					,   __a   :       List[Any]=3072					,   __a   :       Optional[int]="gelu"					,   __a   :       Dict=0.0					,   __a   :       Optional[Any]=0.0					,   __a   :       Any=0.02					,   __a   :       Optional[int]=1e-1_2					,   __a   :       List[Any]=[512, 864]					,   __a   :       List[str]=16					,   __a   :       str=3					,   __a   :       Optional[Any]=True					,   __a   :       Optional[Any]=100					,   __a   :       List[str]=True					,   __a   :       Any=False					,   __a   :       List[str]=1					,   __a   :       str=5					,   __a   :       Optional[Any]=2					,   __a   :       Tuple=5					,   __a   :       Any=2					,   __a   :       Union[str, Any]=0.1					,   **__a   :       List[str]					,   )		->				List[str]:
        super().__init__(**__a	)

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





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]	     =					version.parse("1.11"   )

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				float:
        return 1e-4


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				int:
        return 12
