



'''simple docstring'''




import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

@skip_mps
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				VideoToVideoSDPipeline
	lowercase    =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'}    ) - {'image', 'width', 'height'}
	lowercase    =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'}    ) - {'image'}
	lowercase    =				PipelineTesterMixin.required_optional_params - {'latents'}
	lowercase    =				False

	# No `output_type`.
	lowercase    =				frozenset(
	    [
	        'num_inference_steps',
	        'generator',
	        'latents',
	        'return_dict',
	        'callback',
	        'callback_steps',
	    ]    )





	def 				_lowercase(						self					)    ->    int:
								torch.manual_seed(0					)
								UpperCAmelCase  :	Union[str, Any]							  =	UNetaDConditionModel(
								    block_out_channels=(32, 64, 64, 64)	,	layers_per_block=2	,	sample_size=32	,	in_channels=4	,	out_channels=4	,	down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")	,	up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")	,	cross_attention_dim=32	,	attention_head_dim=4	,	)
								UpperCAmelCase  :	Any							  =	DDIMScheduler(
								    beta_start=0.0_0_0_8_5	,	beta_end=0.0_1_2	,	beta_schedule="""scaled_linear"""	,	clip_sample=A	,	set_alpha_to_one=A	,	)
								torch.manual_seed(0					)
								UpperCAmelCase  :	int							  =	AutoencoderKL(
								    block_out_channels=[32, 64]	,	in_channels=3	,	out_channels=3	,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,	latent_channels=4	,	sample_size=128	,	)
								torch.manual_seed(0					)
								UpperCAmelCase  :	Any							  =	CLIPTextConfig(
								    bos_token_id=0	,	eos_token_id=2	,	hidden_size=32	,	intermediate_size=37	,	layer_norm_eps=1e-05	,	num_attention_heads=4	,	num_hidden_layers=5	,	pad_token_id=1	,	vocab_size=1000	,	hidden_act="""gelu"""	,	projection_dim=512	,	)
								UpperCAmelCase  :	str							  =	CLIPTextModel(A					)
								UpperCAmelCase  :	Optional[Any]							  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

								UpperCAmelCase  :	Union[str, Any]							  =	{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								}
								return components





	def 				_lowercase(						self	,	A	,	A=0					)    ->    Optional[Any]:
								# 3 frames
								UpperCAmelCase  :	int							  =	floats_tensor((1, 3, 3, 32, 32)	,	rng=random.Random(A					)					).to(A					)

								if str(A					).startswith("""mps"""					):
															UpperCAmelCase  :	str							  =	torch.manual_seed(A					)
								else:
															UpperCAmelCase  :	Optional[Any]							  =	torch.Generator(device=A					).manual_seed(A					)
								UpperCAmelCase  :	Tuple							  =	{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """video""": video,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """pt""",
								}
								return inputs





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Any							  =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase  :	Optional[Any]							  =	self.get_dummy_components()
								UpperCAmelCase  :	Any							  =	VideoToVideoSDPipeline(**A					)
								UpperCAmelCase  :	Tuple							  =	sd_pipe.to(A					)
								sd_pipe.set_progress_bar_config(disable=A					)

								UpperCAmelCase  :	Any							  =	self.get_dummy_inputs(A					)
								UpperCAmelCase  :	List[Any]							  =	"""np"""
								UpperCAmelCase  :	str							  =	sd_pipe(**A					).frames
								UpperCAmelCase  :	Optional[Any]							  =	frames[0][-3:, -3:, -1]

								assert frames[0].shape == (32, 32, 3)
								UpperCAmelCase  :	List[Any]							  =	np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]					)

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





	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()	,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""	,	)
	def 				_lowercase(						self					)    ->    Tuple:
								self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A	,	expected_max_diff=5e-3					)





	@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""					)
	def 				_lowercase(						self					)    ->    Any:
								pass





	@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""					)
	def 				_lowercase(						self					)    ->    List[str]:
								pass





	@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""					)
	def 				_lowercase(						self					)    ->    Any:
								pass






	def 				_lowercase(						self					)    ->    int:
								return super().test_progress_bar()




@slow
@skip_mps
class     UpperCamelCase_				(							unittest.TestCase    ):





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Optional[int]							  =	VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""	,	torch_dtype=torch.floataa					)
								pipe.enable_model_cpu_offload()

								# 10 frames
								UpperCAmelCase  :	List[str]							  =	torch.Generator(device="""cpu"""					).manual_seed(0					)
								UpperCAmelCase  :	List[str]							  =	torch.randn((1, 10, 3, 1024, 576)	,	generator=A					)
								UpperCAmelCase  :	Union[str, Any]							  =	video.to("""cuda"""					)

								UpperCAmelCase  :	int							  =	"""Spiderman is surfing"""

								UpperCAmelCase  :	Optional[int]							  =	pipe(A	,	video=A	,	generator=A	,	num_inference_steps=3	,	output_type="""pt"""					).frames

								UpperCAmelCase  :	List[str]							  =	np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]					)
								assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array					).sum() < 1e-2






'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a     :    Tuple      						=					False

class     UpperCamelCase_				(							unittest.TestCase    ):
	pass




@slow
@require_torch_gpu
class     UpperCamelCase_				(							unittest.TestCase    ):





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Any							  =	VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""					)
								pipe.to(A					)
								pipe.set_progress_bar_config(disable=A					)

								UpperCAmelCase  :	List[str]							  =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""					)
								UpperCAmelCase  :	List[Any]							  =	torch.manual_seed(0					)
								UpperCAmelCase  :	List[str]							  =	pipe(
								    image=A	,	generator=A	,	guidance_scale=7.5	,	num_inference_steps=50	,	output_type="""numpy"""	,	).images

								UpperCAmelCase  :	Tuple							  =	image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase  :	List[str]							  =	np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]					)

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

