


'''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      :Dict    	=			logging.get_logger(__name__)

lowerCAmelCase      :Tuple    	=			{
    '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 _lowerCamelCase       (						lowercase__		,     lowercase__							):



		'''simple docstring'''







		A_	:			Optional[int]          =				"""swin"""

		A_	:			int          =				{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}







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

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







class _lowerCamelCase       (						lowercase__							):



		'''simple docstring'''







		A_	:			Optional[Any]          =				version.parse("""1.11"""							)







		@property
		def    __lowerCAmelCase		(					self		:						List[Any]			)  -> Mapping[str, Mapping[int, str]]:
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ]			)







		@property
		def    __lowerCAmelCase		(					self		:						Dict			)  -> float:
			return 1E-4




"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



@nightly
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, 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)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    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)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    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)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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


