



"""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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """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_						(						a_     ,a_				):
       __lowerCAmelCase    					=					"swin"

       __lowerCAmelCase    					=					{
           "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_			,     ):
         super().__init__(**a_      )

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




class 			snake_case_						(						a_				):
       __lowerCAmelCase    					=					version.parse("1.11"				)






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






       @property
       def snake_case_  (   self      ):
         return 1e-4




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




@slow
@require_torch_gpu
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2