





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()

class      A       (	snake_case__		,      snake_case__		,      unittest.TestCase						):
      _snake_case							     =StableDiffusionXLImgaImgPipeline
      _snake_case							     =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
      _snake_case							     =PipelineTesterMixin.required_optional_params - {'''latents'''}
      _snake_case							     =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      _snake_case							     =IMAGE_TO_IMAGE_IMAGE_PARAMS
      _snake_case							     =IMAGE_TO_IMAGE_IMAGE_PARAMS



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

         torch.manual_seed(0       )
         UpperCAmelCase_											=UNetaDConditionModel(
             block_out_channels=(32, 64)		,  layers_per_block=2		,  sample_size=32		,  in_channels=4		,  out_channels=4		,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,  attention_head_dim=(2, 4)		,  use_linear_projection=lowercase_		,  addition_embed_type="text_time"		,  addition_time_embed_dim=8		,  transformer_layers_per_block=(1, 2)		,  projection_class_embeddings_input_dim=80		,  cross_attention_dim=64		,  )
         UpperCAmelCase_											=EulerDiscreteScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  steps_offset=1		,  beta_schedule="scaled_linear"		,  timestep_spacing="leading"		,  )
         torch.manual_seed(0       )
         UpperCAmelCase_											=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_											=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=32		,  )
         UpperCAmelCase_											=CLIPTextModel(lowercase_       )
         UpperCAmelCase_											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		,  local_files_only=lowercase_       )

         UpperCAmelCase_											=CLIPTextModelWithProjection(lowercase_       )
         UpperCAmelCase_											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		,  local_files_only=lowercase_       )

         UpperCAmelCase_											={
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_encoder_2": text_encoder_a,
             "tokenizer_2": tokenizer_a,
             # "safety_checker": None,
             # "feature_extractor": None,
         }
         return components



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[int]=0       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=floats_tensor((1, 3, 32, 32)		,  rng=random.Random(lowercase_       )       ).to(lowercase_       )
         UpperCAmelCase_											=image / 2 + 0.5
         if str(lowercase_       ).startswith("mps"       ):
            UpperCAmelCase_											=torch.manual_seed(lowercase_       )
         else:
            UpperCAmelCase_											=torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )
         UpperCAmelCase_											={
             "prompt": "A painting of a squirrel eating a burger",
             "image": image,
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 5.0,
             "output_type": "numpy",
             "strength": 0.75,
         }
         return inputs



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

         UpperCAmelCase_											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_											=self.get_dummy_components()
         UpperCAmelCase_											=StableDiffusionXLImgaImgPipeline(**lowercase_       )
         UpperCAmelCase_											=sd_pipe.to(lowercase_       )
         sd_pipe.set_progress_bar_config(disable=lowercase_       )

         UpperCAmelCase_											=self.get_dummy_inputs(lowercase_       )
         UpperCAmelCase_											=sd_pipe(**lowercase_       ).images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)

         UpperCAmelCase_											=np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]       )

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



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

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



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

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



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

         pass






      def 	lowerCAmelCase__    (	self:						Any       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.get_dummy_components()
         UpperCAmelCase_											=StableDiffusionXLImgaImgPipeline(**lowercase_       )
         UpperCAmelCase_											=sd_pipe.to(lowercase_       )
         UpperCAmelCase_											=sd_pipe.to(lowercase_       )
         sd_pipe.set_progress_bar_config(disable=lowercase_       )

         # forward without prompt embeds
         UpperCAmelCase_											=self.get_dummy_inputs(lowercase_       )
         UpperCAmelCase_											=3 * ["this is a negative prompt"]
         UpperCAmelCase_											=negative_prompt
         UpperCAmelCase_											=3 * [inputs["prompt"]]

         UpperCAmelCase_											=sd_pipe(**lowercase_       )
         UpperCAmelCase_											=output.images[0, -3:, -3:, -1]

         # forward with prompt embeds
         UpperCAmelCase_											=self.get_dummy_inputs(lowercase_       )
         UpperCAmelCase_											=3 * ["this is a negative prompt"]
         UpperCAmelCase_											=3 * [inputs.pop("prompt"       )]

         (
             UpperCAmelCase_ 
         )											=sd_pipe.encode_prompt(lowercase_		,  negative_prompt=lowercase_       )

         UpperCAmelCase_											=sd_pipe(
             **lowercase_		,  prompt_embeds=lowercase_		,  negative_prompt_embeds=lowercase_		,  pooled_prompt_embeds=lowercase_		,  negative_pooled_prompt_embeds=lowercase_		,  )
         UpperCAmelCase_											=output.images[0, -3:, -3:, -1]

         # make sure that it's equal
         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1e-4

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



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

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



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						List[Any]="cpu"		,  _lowerCAmelCase:						str=torch.floataa		,  _lowerCAmelCase:						Optional[Any]=0       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )
         UpperCAmelCase_											=np.random.RandomState(lowercase_       ).standard_normal((1, 4, 64, 64)       )
         UpperCAmelCase_											=torch.from_numpy(lowercase_       ).to(device=lowercase_		,  dtype=lowercase_       )
         UpperCAmelCase_											={
             "prompt": "a photograph of an astronaut riding a horse",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs






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

         UpperCAmelCase_											=DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"       )
         pipe.to(lowercase_       )
         pipe.set_progress_bar_config(disable=lowercase_       )

         UpperCAmelCase_											=self.get_inputs(lowercase_       )
         UpperCAmelCase_											=pipe(**lowercase_       ).images
         UpperCAmelCase_											=image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_											=np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]       )
         assert np.abs(image_slice - expected_slice       ).max() < 7e-3











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       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =DanceDiffusionPipeline
      _snake_case							     =UNCONDITIONAL_AUDIO_GENERATION_PARAMS
      _snake_case							     =PipelineTesterMixin.required_optional_params - {
          '''callback''',
          '''latents''',
          '''callback_steps''',
          '''output_type''',
          '''num_images_per_prompt''',
      }
      _snake_case							     =UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
      _snake_case							     =False
      _snake_case							     =False



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

         torch.manual_seed(0       )
         UpperCAmelCase_											=UNetaDModel(
             block_out_channels=(32, 32, 64)		,  extra_in_channels=16		,  sample_size=512		,  sample_rate=1_6000		,  in_channels=2		,  out_channels=2		,  flip_sin_to_cos=_lowerCAmelCase		,  use_timestep_embedding=_lowerCAmelCase		,  time_embedding_type="fourier"		,  mid_block_type="UNetMidBlock1D"		,  down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,  up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,  )
         UpperCAmelCase_											=IPNDMScheduler()

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



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[str]=0       )						->    str:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Tuple:
         '''simple docstring'''

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

         UpperCAmelCase_											=self.get_dummy_inputs(_lowerCAmelCase       )
         UpperCAmelCase_											=pipe(**_lowerCAmelCase       )
         UpperCAmelCase_											=output.audios

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

         assert audio.shape == (1, 2, components["unet"].sample_size)
         UpperCAmelCase_											=np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]       )
         assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2



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

         return super().test_save_load_local()



      @skip_mps
      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    int:
         '''simple docstring'''

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



      @skip_mps
      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[int]:
         '''simple docstring'''

         return super().test_save_load_optional_components()



      @skip_mps
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         return super().test_attention_slicing_forward_pass()






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

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

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



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

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



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

         UpperCAmelCase_											=torch_device

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

         UpperCAmelCase_											=torch.manual_seed(0       )
         UpperCAmelCase_											=pipe(generator=_lowerCAmelCase		,  num_inference_steps=100		,  audio_length_in_s=4.0_96       )
         UpperCAmelCase_											=output.audios

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

         assert audio.shape == (1, 2, pipe.unet.sample_size)
         UpperCAmelCase_											=np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]       )

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






      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=torch_device

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

         UpperCAmelCase_											=torch.manual_seed(0       )
         UpperCAmelCase_											=pipe(generator=_lowerCAmelCase		,  num_inference_steps=100		,  audio_length_in_s=4.0_96       )
         UpperCAmelCase_											=output.audios

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

         assert audio.shape == (1, 2, pipe.unet.sample_size)
         UpperCAmelCase_											=np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]       )

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




