"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCAmelCase__						:						str		  =			{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"donut-swin"

					snake_case__       					=				{
					    "num_attention_heads": "num_heads",
					    "num_hidden_layers": "num_layers",
					}







					def __init__(       self     :			Tuple					,lowerCamelCase__     :			int=224					,lowerCamelCase__     :			Union[str, Any]=4					,lowerCamelCase__     :			List[Any]=3					,lowerCamelCase__     :			str=96					,lowerCamelCase__     :			List[str]=[2, 2, 6, 2]					,lowerCamelCase__     :			List[str]=[3, 6, 12, 24]					,lowerCamelCase__     :			Dict=7					,lowerCamelCase__     :			Any=4.0					,lowerCamelCase__     :			Union[str, Any]=True					,lowerCamelCase__     :			int=0.0					,lowerCamelCase__     :			List[Any]=0.0					,lowerCamelCase__     :			Optional[Any]=0.1					,lowerCamelCase__     :			int="gelu"					,lowerCamelCase__     :			Optional[Any]=False					,lowerCamelCase__     :			Any=0.0_2					,lowerCamelCase__     :			List[str]=1e-5					,**lowerCamelCase__     :			str					,):
									super().__init__(**lowerCamelCase__    )

									UpperCAmelCase__    =							image_size
									UpperCAmelCase__    =							patch_size
									UpperCAmelCase__    =							num_channels
									UpperCAmelCase__    =							embed_dim
									UpperCAmelCase__    =							depths
									UpperCAmelCase__    =							len(lowerCamelCase__    )
									UpperCAmelCase__    =							num_heads
									UpperCAmelCase__    =							window_size
									UpperCAmelCase__    =							mlp_ratio
									UpperCAmelCase__    =							qkv_bias
									UpperCAmelCase__    =							hidden_dropout_prob
									UpperCAmelCase__    =							attention_probs_dropout_prob
									UpperCAmelCase__    =							drop_path_rate
									UpperCAmelCase__    =							hidden_act
									UpperCAmelCase__    =							use_absolute_embeddings
									UpperCAmelCase__    =							layer_norm_eps
									UpperCAmelCase__    =							initializer_range
									# 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__    =							int(embed_dim * 2 ** (len(lowerCamelCase__    ) - 1)    )




"""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__						:						Union[str, Any]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






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




					"""simple docstring"""

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







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )

									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

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

									UpperCAmelCase__    =							generator.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,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     :			Optional[int]    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'cyberpunk 2077'
									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]    )

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

									UpperCAmelCase__    =							'A painting of a squirrel eating a burger '
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.text_to_image(
									    prompt=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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

									UpperCAmelCase__    =							pipe.image_variation(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]    )

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



