

import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      List[str]:




				'''simple docstring'''





				A__        =			[tensor.shape for tensor in tensor_list]
				return all(shape == shapes[0] for shape in shapes[1:]      )

class 					a__						(					snake_case							,	snake_case							,	snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       StableDiffusionLatentUpscalePipeline
			__lowerCamelCase        =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    'height',
			    'width',
			    'cross_attention_kwargs',
			    'negative_prompt_embeds',
			    'prompt_embeds',
			}
			__lowerCamelCase        =       PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
			__lowerCamelCase        =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__lowerCamelCase        =       frozenset(
			    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__lowerCamelCase        =       frozenset([]			)

			__lowerCamelCase        =       True


			@property
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			1
							A__        =			4
							A__        =			(16, 16)

							A__        =			floats_tensor((batch_size, num_channels) + sizes      ,      rng=random.Random(0       )       ).to(lowercase       )
							return image


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							torch.manual_seed(0       )
							A__        =			UNetaDConditionModel(
							    act_fn="gelu"      ,      attention_head_dim=8      ,      norm_num_groups=lowercase      ,      block_out_channels=[32, 32, 64, 64]      ,      time_cond_proj_dim=160      ,      conv_in_kernel=1      ,      conv_out_kernel=1      ,      cross_attention_dim=32      ,      down_block_types=(
							        "KDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							    )      ,      in_channels=8      ,      mid_block_type=lowercase      ,      only_cross_attention=lowercase      ,      out_channels=5      ,      resnet_time_scale_shift="scale_shift"      ,      time_embedding_type="fourier"      ,      timestep_post_act="gelu"      ,      up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")      ,      )
							A__        =			AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]      ,      in_channels=3      ,      out_channels=3      ,      down_block_types=[
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							    ]      ,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]      ,      latent_channels=4      ,      )
							A__        =			EulerDiscreteScheduler(prediction_type="sample"       )
							A__        =			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="quick_gelu"      ,      projection_dim=512      ,      )
							A__        =			CLIPTextModel(lowercase       )
							A__        =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

							A__        =			{
							    "unet": model.eval(),
							    "vae": vae.eval(),
							    "scheduler": scheduler,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}

							return components


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=0       )  ->						Optional[Any]:
							'''simple docstring'''






							if str(lowercase       ).startswith("mps"       ):
											A__        =			torch.manual_seed(lowercase       )
							else:
											A__        =			torch.Generator(device=lowercase       ).manual_seed(lowercase       )
							A__        =			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": self.dummy_image.cpu(),
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			"cpu"

							A__        =			self.get_dummy_components()
							A__        =			self.pipeline_class(**lowercase       )
							pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			self.get_dummy_inputs(lowercase       )
							A__        =			pipe(**lowercase       ).images
							A__        =			image[0, -3:, -3:, -1]

							self.assertEqual(image.shape      ,      (1, 256, 256, 3)       )
							A__        =			np.array(
							    [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]       )
							A__        =			np.abs(image_slice.flatten() - expected_slice       ).max()
							self.assertLessEqual(lowercase      ,      1e-3       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							super().test_attention_slicing_forward_pass(expected_max_diff=7e-3       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							super().test_cpu_offload_forward_pass(expected_max_diff=3e-3       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							super().test_inference_batch_single_identical(expected_max_diff=7e-3       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3       )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							super().test_save_load_local(expected_max_difference=3e-3       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							super().test_save_load_optional_components(expected_max_difference=3e-3       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			[
							    "DDIMScheduler",
							    "DDPMScheduler",
							    "PNDMScheduler",
							    "HeunDiscreteScheduler",
							    "EulerAncestralDiscreteScheduler",
							    "KDPM2DiscreteScheduler",
							    "KDPM2AncestralDiscreteScheduler",
							    "DPMSolverSDEScheduler",
							]
							A__        =			self.get_dummy_components()
							A__        =			self.pipeline_class(**lowercase       )

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=lowercase       )

							pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )
							A__        =			self.get_dummy_inputs(lowercase       )
							A__        =			2

							A__        =			[]
							for scheduler_enum in KarrasDiffusionSchedulers:
											if scheduler_enum.name in skip_schedulers:
															# no sigma schedulers are not supported
															# no schedulers
															continue

											A__        =			getattr(lowercase      ,      scheduler_enum.name       )
											A__        =			scheduler_cls.from_config(pipe.scheduler.config       )
											A__        =			pipe(**lowercase       )[0]
											outputs.append(lowercase       )

							assert check_same_shape(lowercase       )

@require_torch_gpu
@slow
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			torch.manual_seed(33       )

							A__        =			StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      ,      torch_dtype=torch.floataa       )
							pipe.to("cuda"       )

							A__        =			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"      ,      torch_dtype=torch.floataa       )
							upscaler.to("cuda"       )

							A__        =			"a photo of an astronaut high resolution, unreal engine, ultra realistic"

							A__        =			pipe(lowercase      ,      generator=lowercase      ,      output_type="latent"       ).images

							A__        =			upscaler(
							    prompt=lowercase      ,      image=lowercase      ,      num_inference_steps=20      ,      guidance_scale=0      ,      generator=lowercase      ,      output_type="np"      ,      ).images[0]

							A__        =			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"       )
							assert np.abs((expected_image - image).mean()       ) < 5e-2


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			torch.manual_seed(33       )

							A__        =			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"      ,      torch_dtype=torch.floataa       )
							upscaler.to("cuda"       )

							A__        =			"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

							A__        =			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"       )

							A__        =			upscaler(
							    prompt=lowercase      ,      image=lowercase      ,      num_inference_steps=20      ,      guidance_scale=0      ,      generator=lowercase      ,      output_type="np"      ,      ).images[0]

							A__        =			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"       )
							assert np.abs((expected_image - image).max()       ) < 5e-2




import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       DanceDiffusionPipeline
			__lowerCamelCase        =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
			__lowerCamelCase        =       PipelineTesterMixin.required_optional_params - {
			    'callback',
			    'latents',
			    'callback_steps',
			    'output_type',
			    'num_images_per_prompt',
			}
			__lowerCamelCase        =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							torch.manual_seed(0       )
							A__        =			UNetaDModel(
							    block_out_channels=(32, 32, 64)      ,      extra_in_channels=16      ,      sample_size=512      ,      sample_rate=16000      ,      in_channels=2      ,      out_channels=2      ,      flip_sin_to_cos=lowercase      ,      use_timestep_embedding=lowercase      ,      time_embedding_type="fourier"      ,      mid_block_type="UNetMidBlock1D"      ,      down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,      up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,      )
							A__        =			IPNDMScheduler()

							A__        =			{
							    "unet": unet,
							    "scheduler": scheduler,
							}
							return components


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=0       )  ->						Union[str, Any]:
							'''simple docstring'''






							if str(lowercase       ).startswith("mps"       ):
											A__        =			torch.manual_seed(lowercase       )
							else:
											A__        =			torch.Generator(device=lowercase       ).manual_seed(lowercase       )
							A__        =			{
							    "batch_size": 1,
							    "generator": generator,
							    "num_inference_steps": 4,
							}
							return inputs


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							A__        =			self.get_dummy_components()
							A__        =			DanceDiffusionPipeline(**lowercase       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			self.get_dummy_inputs(lowercase       )
							A__        =			pipe(**lowercase       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, components["unet"].sample_size)
							A__        =			np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]       )
							assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							return super().test_save_load_local()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							return super().test_save_load_optional_components()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return super().test_attention_slicing_forward_pass()


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							super().test_inference_batch_single_identical(expected_max_diff=3e-3       )

@slow
@require_torch_gpu
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			torch_device

							A__        =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			torch.manual_seed(0       )
							A__        =			pipe(generator=lowercase      ,      num_inference_steps=100      ,      audio_length_in_s=4.096       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							A__        =			np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]       )

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


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			torch_device

							A__        =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"      ,      torch_dtype=torch.floataa       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			torch.manual_seed(0       )
							A__        =			pipe(generator=lowercase      ,      num_inference_steps=100      ,      audio_length_in_s=4.096       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							A__        =			np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]       )

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

