



'''simple docstring'''

from typing import List

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}


class 	a__     (				_lowercase	):
 __magic_name__		:    Dict	    =     "efficientformer"


 def __init__(self : Any,					__UpperCAmelCase : Optional[Any] = [3, 2, 6, 4],					__UpperCAmelCase : Union[str, Any] = [48, 96, 224, 448],					__UpperCAmelCase : Tuple = [True, True, True, True],					__UpperCAmelCase : Optional[Any] = 448,					__UpperCAmelCase : List[Any] = 32,					__UpperCAmelCase : Any = 4,					__UpperCAmelCase : List[str] = 7,					__UpperCAmelCase : str = 5,					__UpperCAmelCase : Any = 8,					__UpperCAmelCase : Any = 4,					__UpperCAmelCase : Dict = 0.0,					__UpperCAmelCase : int = 16,					__UpperCAmelCase : str = 3,					__UpperCAmelCase : Any = 3,					__UpperCAmelCase : int = 3,					__UpperCAmelCase : str = 2,					__UpperCAmelCase : Dict = 1,					__UpperCAmelCase : str = 0.0,					__UpperCAmelCase : str = 1,					__UpperCAmelCase : int = True,					__UpperCAmelCase : Tuple = True,					__UpperCAmelCase : int = 1e-5,					__UpperCAmelCase : Tuple = "gelu",					__UpperCAmelCase : Union[str, Any] = 0.02,					__UpperCAmelCase : Optional[int] = 1e-12,					__UpperCAmelCase : Any = 224,					__UpperCAmelCase : str = 1e-05,					**__UpperCAmelCase : List[Any],					)     ->			int:

     """simple docstring"""

     super().__init__(**__lowerCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= hidden_act
     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= hidden_dropout_prob
     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_sizes
     SCREAMING_SNAKE_CASE  :						int      					= num_hidden_layers
     SCREAMING_SNAKE_CASE  :						int      					= num_attention_heads
     SCREAMING_SNAKE_CASE  :						str      					= initializer_range
     SCREAMING_SNAKE_CASE  :						int      					= layer_norm_eps
     SCREAMING_SNAKE_CASE  :						List[str]      					= patch_size
     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= num_channels
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= depths
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= mlp_expansion_ratio
     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= downsamples
     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= dim
     SCREAMING_SNAKE_CASE  :						str      					= key_dim
     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= attention_ratio
     SCREAMING_SNAKE_CASE  :						Dict      					= resolution
     SCREAMING_SNAKE_CASE  :						List[Any]      					= pool_size
     SCREAMING_SNAKE_CASE  :						Dict      					= downsample_patch_size
     SCREAMING_SNAKE_CASE  :						str      					= downsample_stride
     SCREAMING_SNAKE_CASE  :						str      					= downsample_pad
     SCREAMING_SNAKE_CASE  :						List[Any]      					= drop_path_rate
     SCREAMING_SNAKE_CASE  :						Dict      					= num_metaad_blocks
     SCREAMING_SNAKE_CASE  :						List[Any]      					= distillation
     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= use_layer_scale
     SCREAMING_SNAKE_CASE  :						str      					= layer_scale_init_value
     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= image_size
     SCREAMING_SNAKE_CASE  :						Tuple      					= batch_norm_eps









'''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 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     False
	__magic_name__		:    float	    =     3.0



class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : str      )     ->			Union[str, Any]:

					"""simple docstring"""

					self.assertDictEqual(MockClass().to_kwargs(),					{}      )
					self.assertDictEqual(MockClass(a=2      ).to_kwargs(),					{'''a''': 2}      )
					self.assertDictEqual(MockClass(a=2,					b=__UpperCAmelCase      ).to_kwargs(),					{'''a''': 2, '''b''': True}      )
					self.assertDictEqual(MockClass(a=2,					c=2.25      ).to_kwargs(),					{'''a''': 2, '''c''': 2.25}      )


	@require_cuda
	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= GradScalerKwargs(init_scale=1024,					growth_factor=2      )
					AcceleratorState._reset_state()
					SCREAMING_SNAKE_CASE  :						Tuple      					= Accelerator(mixed_precision='''fp16''',					kwargs_handlers=[scaler_handler]      )
					print(accelerator.use_fpaa      )
					SCREAMING_SNAKE_CASE  :						str      					= accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale,					1024.0      )
					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,					__UpperCAmelCase      )






	@require_multi_gpu
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
					execute_subprocess_async(__UpperCAmelCase,					env=os.environ.copy()      )


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

	# Check the values changed in kwargs
	snake_case_			=     """"""
	snake_case_			=     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)




