

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


_lowerCAmelCase      :    Dict						       =    logging.get_logger(__name__)

_lowerCAmelCase      :    Union[str, Any]						       =    {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		'nystromformer'





			def __init__(	self      ,						__snake_case=3_0000      ,						__snake_case=768      ,						__snake_case=12      ,						__snake_case=12      ,						__snake_case=3072      ,						__snake_case="gelu_new"      ,						__snake_case=0.1      ,						__snake_case=0.1      ,						__snake_case=510      ,						__snake_case=2      ,						__snake_case=64      ,						__snake_case=64      ,						__snake_case=65      ,						__snake_case=False      ,						__snake_case=0.02      ,						__snake_case=1e-5      ,						__snake_case=1      ,						__snake_case=0      ,						__snake_case=2      ,						**__snake_case      ,						)     ->				int:




						'''simple docstring'''
						__a						   =vocab_size
						__a						   =max_position_embeddings
						__a						   =hidden_size
						__a						   =num_hidden_layers
						__a						   =num_attention_heads
						__a						   =intermediate_size
						__a						   =hidden_act
						__a						   =hidden_dropout_prob
						__a						   =attention_probs_dropout_prob
						__a						   =initializer_range
						__a						   =type_vocab_size
						__a						   =segment_means_seq_len
						__a						   =num_landmarks
						__a						   =conv_kernel_size
						__a						   =inv_coeff_init_option
						__a						   =layer_norm_eps
						super().__init__(pad_token_id=__snake_case      ,						bos_token_id=__snake_case      ,						eos_token_id=__snake_case      ,						**__snake_case      )









import gc
import tempfile
import unittest

import numpy as np
import torch

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


_lowerCAmelCase      :    List[str]						       =    False






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





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

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

						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(__snake_case      )
									__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained(__snake_case      )

						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =generator.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						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 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   ='cyberpunk 2077'
						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt=__snake_case      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						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([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]      )

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






