





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


A_												=     False

class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					pass

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





					"""simple docstring"""


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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,  torch_dtype=torch.floataa	)
										pipe.to(_lowerCAmelCase	)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

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

										lowerCamelCase__       =       torch.manual_seed(0	)
										lowerCamelCase__       =       pipe.dual_guided(
										    prompt="first prompt"	,  image=_lowerCAmelCase	,  text_to_image_strength=0.75	,  generator=_lowerCAmelCase	,  guidance_scale=7.5	,  num_inference_steps=2	,  output_type="numpy"	,  ).images

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

										lowerCamelCase__       =       generator.manual_seed(0	)
										lowerCamelCase__       =       pipe.dual_guided(
										    prompt="first prompt"	,  image=_lowerCAmelCase	,  text_to_image_strength=0.75	,  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 					__magic_name__ ( self	):
										lowerCamelCase__       =       VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,  torch_dtype=torch.floataa	)
										pipe.to(_lowerCAmelCase	)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

										lowerCamelCase__       =       "cyberpunk 2077"
										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"	)
										lowerCamelCase__       =       torch.manual_seed(0	)
										lowerCamelCase__       =       pipe.dual_guided(
										    prompt=_lowerCAmelCase	,  image=_lowerCAmelCase	,  text_to_image_strength=0.75	,  generator=_lowerCAmelCase	,  guidance_scale=7.5	,  num_inference_steps=50	,  output_type="numpy"	,  ).images

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__       =       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

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

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__       =       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

										lowerCamelCase__       =       pipe.image_variation(_lowerCAmelCase	,  generator=_lowerCAmelCase	,  output_type="numpy"	).images

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__       =       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








from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_												=     {"tokenization_byt5": ["ByT5Tokenizer"]}


if TYPE_CHECKING:
						from .tokenization_byta import ByTaTokenizer
else:
						import sys

						A_												=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

