




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


UpperCamelCase__						=		False


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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )

						lowerCamelCase_				:						int				  =				torch.manual_seed(0   )
						lowerCamelCase_				:						List[Any]				  =				pipe(
						    prompt='first prompt'					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			guidance_scale=7.5					,			num_inference_steps=2					,			output_type='numpy'					,			).images

						with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(__lowercase   )
							lowerCamelCase_				:						Dict				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained(__lowercase   )

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

						lowerCamelCase_				:						Optional[Any]				  =				generator.manual_seed(0   )
						lowerCamelCase_				:						List[Any]				  =				pipe(
						    prompt='first prompt'					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			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       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
						pipe.remove_unused_weights()
						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Union[str, Any]				  =				'cyberpunk 2077'
						lowerCamelCase_				:						Optional[Any]				  =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
						lowerCamelCase_				:						str				  =				torch.manual_seed(0   )
						lowerCamelCase_				:						Dict				  =				pipe(
						    prompt=__lowercase					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			guidance_scale=7.5					,			num_inference_steps=50					,			output_type='numpy'					,			).images

						lowerCamelCase_				:						Dict				  =				image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCamelCase_				:						List[str]				  =				np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]   )

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



"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__			:						str							=			{
    """configuration_rwkv""": ["""RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RwkvConfig""", """RwkvOnnxConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[int]							=			[
							    """RWKV_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RwkvForCausalLM""",
							    """RwkvModel""",
							    """RwkvPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rwkv import (
														    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RwkvForCausalLM,
														    RwkvModel,
														    RwkvPreTrainedModel,
														)
else:
							import sys

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


