





"""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


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

__magic_name__       :					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 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[int]										=  """swin"""

			__lowerCAmelCase     :				Any										=  {
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}







			def __init__(      self						,   _A=2_2_4						,   _A=4						,   _A=3						,   _A=9_6						,   _A=[2, 2, 6, 2]						,   _A=[3, 6, 1_2, 2_4]						,   _A=7						,   _A=4.0						,   _A=True						,   _A=0.0						,   _A=0.0						,   _A=0.1						,   _A="gelu"						,   _A=False						,   _A=0.02						,   _A=1e-5						,   _A=3_2						,   _A=None						,   _A=None						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )

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






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Dict										=  version.parse("""1.11"""	)







			@property
			def   _a      (      self    ):






									'''simple docstring'''







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







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return 1e-4










"""simple docstring"""


from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



