





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


a__:      Dict									=		logging.get_logger(__name__)

a__:      Union[str, Any]									=		{
    '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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''donut-swin'''

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



 def __init__(							self,__lowerCamelCase=224,__lowerCamelCase=4,__lowerCamelCase=3,__lowerCamelCase=96,__lowerCamelCase=[2, 2, 6, 2],__lowerCamelCase=[3, 6, 12, 24],__lowerCamelCase=7,__lowerCamelCase=4.0,__lowerCamelCase=True,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.1,__lowerCamelCase="gelu",__lowerCamelCase=False,__lowerCamelCase=0.02,__lowerCamelCase=1E-5,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase )

        A__											=						image_size
        A__											=						patch_size
        A__											=						num_channels
        A__											=						embed_dim
        A__											=						depths
        A__											=						len(__lowerCamelCase )
        A__											=						num_heads
        A__											=						window_size
        A__											=						mlp_ratio
        A__											=						qkv_bias
        A__											=						hidden_dropout_prob
        A__											=						attention_probs_dropout_prob
        A__											=						drop_path_rate
        A__											=						hidden_act
        A__											=						use_absolute_embeddings
        A__											=						layer_norm_eps
        A__											=						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
        A__											=						int(embed_dim * 2 ** (len(__lowerCamelCase ) - 1) )












import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a__:      List[str]									=		False




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 pass




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



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



 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__lowerCamelCase )
               A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						generator.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,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 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(
            '''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

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

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

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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





