





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


__UpperCAmelCase	:       Union[str, Any]    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       Optional[int]    	=							{
    "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    __snake_case	(							__lowerCamelCase       ,      __lowerCamelCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					'''swin'''

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




							def __init__(  self			:   Tuple							,    A			:   Union[str, Any]=224							,    A			:   Optional[int]=4							,    A			:   List[str]=3							,    A			:   Dict=96							,    A			:   int=[2, 2, 6, 2]							,    A			:   List[Any]=[3, 6, 12, 24]							,    A			:   Dict=7							,    A			:   List[str]=4.0							,    A			:   List[Any]=True							,    A			:   Any=0.0							,    A			:   int=0.0							,    A			:   Tuple=0.1							,    A			:   str="gelu"							,    A			:   Tuple=False							,    A			:   Tuple=0.02							,    A			:   Optional[int]=1E-5							,    A			:   List[Any]=32							,    A			:   int=None							,    A			:   Tuple=None							,    **A			:   str							,    ):
									super().__init__(**_snake_case		)

									__snake_case:						Tuple			  =						image_size
									__snake_case:						Any			  =						patch_size
									__snake_case:						Optional[Any]			  =						num_channels
									__snake_case:						List[str]			  =						embed_dim
									__snake_case:						int			  =						depths
									__snake_case:						Tuple			  =						len(_snake_case		)
									__snake_case:						List[str]			  =						num_heads
									__snake_case:						List[str]			  =						window_size
									__snake_case:						Any			  =						mlp_ratio
									__snake_case:						Optional[Any]			  =						qkv_bias
									__snake_case:						Any			  =						hidden_dropout_prob
									__snake_case:						List[Any]			  =						attention_probs_dropout_prob
									__snake_case:						Tuple			  =						drop_path_rate
									__snake_case:						Optional[int]			  =						hidden_act
									__snake_case:						int			  =						use_absolute_embeddings
									__snake_case:						Any			  =						layer_norm_eps
									__snake_case:						str			  =						initializer_range
									__snake_case:						Dict			  =						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[Any]			  =						int(embed_dim * 2 ** (len(_snake_case		) - 1)		)
									__snake_case:						Optional[Any]			  =						["""stem"""] + [f'''stage{idx}''' for idx in range(1							,    len(_snake_case		) + 1		)]
									__snake_case  ,       __snake_case:						Any			  =						get_aligned_output_features_output_indices(
									    out_features=_snake_case							,    out_indices=_snake_case							,    stage_names=self.stage_names		)







class    __snake_case	(							__lowerCamelCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					version.parse("""1.11"""      )




							@property
							def 							UpperCAmelCase__			(  self			:   Any		):
									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]		)






							@property
							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									return 1E-4













import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)






