






'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase   :					int   							=					logging.get_logger(__name__)

__lowerCamelCase   :					List[Any]   							=					{
    """microsoft/swin-tiny-patch4-window7-224""": (
        """https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}







class    A_	(UpperCamelCase_						,					UpperCamelCase_							):
							"""simple docstring"""




							a__   		=	'''swin'''

							a__   		=	{
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}

							def __init__(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[int]=224       ,							lowerCAmelCase__			:Optional[int]=4       ,							lowerCAmelCase__			:Any=3       ,							lowerCAmelCase__			:List[Any]=96       ,							lowerCAmelCase__			:Union[str, Any]=[2, 2, 6, 2]       ,							lowerCAmelCase__			:List[Any]=[3, 6, 12, 24]       ,							lowerCAmelCase__			:str=7       ,							lowerCAmelCase__			:Any=4.0       ,							lowerCAmelCase__			:int=True       ,							lowerCAmelCase__			:int=0.0       ,							lowerCAmelCase__			:Union[str, Any]=0.0       ,							lowerCAmelCase__			:Union[str, Any]=0.1       ,							lowerCAmelCase__			:Optional[Any]="gelu"       ,							lowerCAmelCase__			:Dict=False       ,							lowerCAmelCase__			:List[Any]=0.0_2       ,							lowerCAmelCase__			:Any=1E-5       ,							lowerCAmelCase__			:Optional[int]=32       ,							lowerCAmelCase__			:Optional[int]=None       ,							lowerCAmelCase__			:str=None       ,							**lowerCAmelCase__			:Dict       ,							)				->  Optional[int]:


										'''simple docstring'''




										super().__init__(**__A				)

										snake_case_				:					str   =       image_size
										snake_case_				:					Optional[Any]   =       patch_size
										snake_case_				:					Tuple   =       num_channels
										snake_case_				:					Any   =       embed_dim
										snake_case_				:					Optional[Any]   =       depths
										snake_case_				:					Tuple   =       len(__A				)
										snake_case_				:					int   =       num_heads
										snake_case_				:					str   =       window_size
										snake_case_				:					Dict   =       mlp_ratio
										snake_case_				:					List[str]   =       qkv_bias
										snake_case_				:					str   =       hidden_dropout_prob
										snake_case_				:					Optional[Any]   =       attention_probs_dropout_prob
										snake_case_				:					str   =       drop_path_rate
										snake_case_				:					List[str]   =       hidden_act
										snake_case_				:					Union[str, Any]   =       use_absolute_embeddings
										snake_case_				:					Union[str, Any]   =       layer_norm_eps
										snake_case_				:					Optional[Any]   =       initializer_range
										snake_case_				:					Union[str, Any]   =       encoder_stride
										# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										snake_case_				:					Optional[int]   =       int(embed_dim * 2 ** (len(__A				) - 1)				)
										snake_case_				:					List[Any]   =       ["stem"] + [F'''stage{idx}''' for idx in range(1       ,							len(__A				) + 1				)]
										snake_case_				:					int   =       get_aligned_output_features_output_indices(
										    out_features=__A       ,							out_indices=__A       ,							stage_names=self.stage_names				)







class    A_	(UpperCamelCase_							):
							"""simple docstring"""




							a__   		=	version.parse('''1.11'''							)

							@property
							def 							_A	(		self			:str				)				->  Optional[int]:


										'''simple docstring'''




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






							@property
							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										return 1E-4












'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




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


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       torch.nn.Linear(10       ,							10				)
										snake_case_				:					Dict   =       torch.optim.SGD(model.parameters()       ,							0.1				)
										snake_case_				:					Tuple   =       Accelerator()
										snake_case_				:					Optional[Any]   =       accelerator.prepare(lowerCAmelCase__				)
										try:
													pickle.loads(pickle.dumps(lowerCAmelCase__				)				)
										except Exception as e:
													self.fail(F'''Accelerated optimizer pickling failed with {e}'''				)
										AcceleratorState._reset_state()




