






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_						:  str									=			False

class 						_snake_case     (     unittest.TestCase    ):
			pass



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






			def       SCREAMING_SNAKE_CASE__			(				self)					->				Tuple:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def       SCREAMING_SNAKE_CASE__			(				self)					->				Union[str, Any]:
						SCREAMING_SNAKE_CASE						 =       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion')
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

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

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

						SCREAMING_SNAKE_CASE						 =       generator.manual_seed(0)
						SCREAMING_SNAKE_CASE						 =       pipe(
						    prompt=a						,    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       SCREAMING_SNAKE_CASE__			(				self)					->				Any:
						SCREAMING_SNAKE_CASE						 =       VersatileDiffusionTextToImagePipeline.from_pretrained(
						    'shi-labs/versatile-diffusion'						,    torch_dtype=torch.floataa)
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

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

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

						assert image.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE						 =       np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78])

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

