


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class   a__	(						UpperCamelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Any							    =     UnCLIPImageVariationPipeline
       lowerCamelCase__:     List[str]							    =     IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
       lowerCamelCase__:     Optional[Any]							    =     IMAGE_VARIATION_BATCH_PARAMS

       lowerCamelCase__:     str							    =     [
           """generator""",
           """return_dict""",
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]
       lowerCamelCase__:     List[str]							    =     False

       @property
       def 						UpperCAmelCase(   self      :     str    ):
            return 3_2

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            return 3_2

       @property
       def 						UpperCAmelCase(   self      :     Any    ):
            return self.time_input_dim

       @property
       def 						UpperCAmelCase(   self      :     int    ):
            return self.time_input_dim * 4

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            return 1_0_0

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     int										=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )
            return tokenizer

       @property
       def 						UpperCAmelCase(   self      :     Dict    ):
            torch.manual_seed(0    )
            a_    :     List[Any]										=    CLIPTextConfig(
                bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=self.text_embedder_hidden_size      ,							projection_dim=self.text_embedder_hidden_size      ,							intermediate_size=3_7      ,							layer_norm_eps=1E-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1_0_0_0      ,							)
            return CLIPTextModelWithProjection(lowerCamelCase_    )

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            torch.manual_seed(0    )
            a_    :     str										=    CLIPVisionConfig(
                hidden_size=self.text_embedder_hidden_size      ,							projection_dim=self.text_embedder_hidden_size      ,							num_hidden_layers=5      ,							num_attention_heads=4      ,							image_size=3_2      ,							intermediate_size=3_7      ,							patch_size=1      ,							)
            return CLIPVisionModelWithProjection(lowerCamelCase_    )

       @property
       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            torch.manual_seed(0    )

            a_    :     Any										=    {
                '''clip_embeddings_dim''': self.text_embedder_hidden_size,
                '''time_embed_dim''': self.time_embed_dim,
                '''cross_attention_dim''': self.cross_attention_dim,
            }

            a_    :     Union[str, Any]										=    UnCLIPTextProjModel(**lowerCamelCase_    )
            return model

       @property
       def 						UpperCAmelCase(   self      :     Tuple    ):
            torch.manual_seed(0    )

            a_    :     str										=    {
                '''sample_size''': 3_2,
                # RGB in channels
                '''in_channels''': 3,
                # Out channels is double in channels because predicts mean and variance
                '''out_channels''': 6,
                '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
                '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
                '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
                '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
                '''layers_per_block''': 1,
                '''cross_attention_dim''': self.cross_attention_dim,
                '''attention_head_dim''': 4,
                '''resnet_time_scale_shift''': '''scale_shift''',
                '''class_embed_type''': '''identity''',
            }

            a_    :     Union[str, Any]										=    UNetaDConditionModel(**lowerCamelCase_    )
            return model

       @property
       def 						UpperCAmelCase(   self      :     int    ):
            return {
                "sample_size": 6_4,
                "layers_per_block": 1,
                "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
                "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
                "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                "in_channels": 6,
                "out_channels": 3,
            }

       @property
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            torch.manual_seed(0    )

            a_    :     Any										=    UNetaDModel(**self.dummy_super_res_kwargs    )
            return model

       @property
       def 						UpperCAmelCase(   self      :     Any    ):
            # seeded differently to get different unet than `self.dummy_super_res_first`
            torch.manual_seed(1    )

            a_    :     Union[str, Any]										=    UNetaDModel(**self.dummy_super_res_kwargs    )
            return model

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     Optional[Any]										=    self.dummy_decoder
            a_    :     Dict										=    self.dummy_text_proj
            a_    :     Any										=    self.dummy_text_encoder
            a_    :     Any										=    self.dummy_tokenizer
            a_    :     Union[str, Any]										=    self.dummy_super_res_first
            a_    :     int										=    self.dummy_super_res_last

            a_    :     Any										=    UnCLIPScheduler(
                variance_type="""learned_range"""      ,							prediction_type="""epsilon"""      ,							num_train_timesteps=1_0_0_0      ,							)

            a_    :     int										=    UnCLIPScheduler(
                variance_type="""fixed_small_log"""      ,							prediction_type="""epsilon"""      ,							num_train_timesteps=1_0_0_0      ,							)

            a_    :     Dict										=    CLIPImageProcessor(crop_size=3_2      ,							size=3_2    )

            a_    :     List[str]										=    self.dummy_image_encoder

            return {
                "decoder": decoder,
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "text_proj": text_proj,
                "feature_extractor": feature_extractor,
                "image_encoder": image_encoder,
                "super_res_first": super_res_first,
                "super_res_last": super_res_last,
                "decoder_scheduler": decoder_scheduler,
                "super_res_scheduler": super_res_scheduler,
            }

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     List[str]=0      ,							lowerCamelCase_      :     Tuple=True    ):
            a_    :     Tuple										=    floats_tensor((1, 3, 3_2, 3_2)      ,							rng=random.Random(lowerCamelCase_    )    ).to(lowerCamelCase_    )
            if str(lowerCamelCase_    ).startswith("""mps"""    ):
                 a_    :     Optional[Any]										=    torch.manual_seed(lowerCamelCase_    )
            else:
                 a_    :     Tuple										=    torch.Generator(device=lowerCamelCase_    ).manual_seed(lowerCamelCase_    )

            if pil_image:
                 a_    :     Optional[int]										=    input_image * 0.5 + 0.5
                 a_    :     Optional[int]										=    input_image.clamp(0      ,							1    )
                 a_    :     int										=    input_image.cpu().permute(0      ,							2      ,							3      ,							1    ).float().numpy()
                 a_    :     Union[str, Any]										=    DiffusionPipeline.numpy_to_pil(lowerCamelCase_    )[0]

            return {
                "image": input_image,
                "generator": generator,
                "decoder_num_inference_steps": 2,
                "super_res_num_inference_steps": 2,
                "output_type": "np",
            }

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Optional[Any]										=    '''cpu'''

            a_    :     Any										=    self.get_dummy_components()

            a_    :     Union[str, Any]										=    self.pipeline_class(**lowerCamelCase_    )
            a_    :     str										=    pipe.to(lowerCamelCase_    )

            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     int										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )

            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    )
            a_    :     Tuple										=    output.images

            a_    :     Optional[int]										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )

            a_    :     Optional[Any]										=    pipe(
                **lowerCamelCase_      ,							return_dict=lowerCamelCase_      ,							)[0]

            a_    :     Any										=    image[0, -3:, -3:, -1]
            a_    :     List[str]										=    image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 6_4, 6_4, 3)

            a_    :     Union[str, Any]										=    np.array(
                [
                    0.9_9_9_7,
                    0.0_0_0_2,
                    0.9_9_9_7,
                    0.9_9_9_7,
                    0.9_9_6_9,
                    0.0_0_2_3,
                    0.9_9_9_7,
                    0.9_9_6_9,
                    0.9_9_7_0,
                ]    )

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

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Any										=    '''cpu'''

            a_    :     Union[str, Any]										=    self.get_dummy_components()

            a_    :     Union[str, Any]										=    self.pipeline_class(**lowerCamelCase_    )
            a_    :     Dict										=    pipe.to(lowerCamelCase_    )

            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     List[Any]										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )

            a_    :     int										=    pipe(**lowerCamelCase_    )
            a_    :     Dict										=    output.images

            a_    :     List[str]										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )

            a_    :     Any										=    pipe(
                **lowerCamelCase_      ,							return_dict=lowerCamelCase_      ,							)[0]

            a_    :     Optional[int]										=    image[0, -3:, -3:, -1]
            a_    :     Union[str, Any]										=    image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 6_4, 6_4, 3)

            a_    :     str										=    np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]    )

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

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Any										=    '''cpu'''

            a_    :     Optional[int]										=    self.get_dummy_components()

            a_    :     Union[str, Any]										=    self.pipeline_class(**lowerCamelCase_    )
            a_    :     int										=    pipe.to(lowerCamelCase_    )

            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Optional[Any]										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )
            a_    :     Optional[int]										=    [
                pipeline_inputs['''image'''],
                pipeline_inputs['''image'''],
            ]

            a_    :     Any										=    pipe(**lowerCamelCase_    )
            a_    :     Tuple										=    output.images

            a_    :     Tuple										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )
            a_    :     Union[str, Any]										=    [
                tuple_pipeline_inputs['''image'''],
                tuple_pipeline_inputs['''image'''],
            ]

            a_    :     Optional[Any]										=    pipe(
                **lowerCamelCase_      ,							return_dict=lowerCamelCase_      ,							)[0]

            a_    :     Any										=    image[0, -3:, -3:, -1]
            a_    :     Dict										=    image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (2, 6_4, 6_4, 3)

            a_    :     str										=    np.array(
                [
                    0.9_9_9_7,
                    0.9_9_8_9,
                    0.0_0_0_8,
                    0.0_0_2_1,
                    0.9_9_6_0,
                    0.0_0_1_8,
                    0.0_0_1_4,
                    0.0_0_0_2,
                    0.9_9_3_3,
                ]    )

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

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[int]										=    torch.device("""cpu"""    )


            class   a__	:
                   lowerCamelCase__:     Optional[int]							    =     1

            a_    :     Optional[int]										=    self.get_dummy_components()

            a_    :     int										=    self.pipeline_class(**lowerCamelCase_    )
            a_    :     List[Any]										=    pipe.to(lowerCamelCase_    )

            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Any										=    torch.Generator(device=lowerCamelCase_    ).manual_seed(0    )
            a_    :     Any										=    pipe.decoder.dtype
            a_    :     Dict										=    1

            a_    :     Optional[int]										=    (
                batch_size,
                pipe.decoder.config.in_channels,
                pipe.decoder.config.sample_size,
                pipe.decoder.config.sample_size,
            )
            a_    :     Any										=    pipe.prepare_latents(
                lowerCamelCase_      ,							dtype=lowerCamelCase_      ,							device=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							latents=lowerCamelCase_      ,							scheduler=DummyScheduler()    )

            a_    :     Any										=    (
                batch_size,
                pipe.super_res_first.config.in_channels // 2,
                pipe.super_res_first.config.sample_size,
                pipe.super_res_first.config.sample_size,
            )
            a_    :     Dict										=    pipe.prepare_latents(
                lowerCamelCase_      ,							dtype=lowerCamelCase_      ,							device=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							latents=lowerCamelCase_      ,							scheduler=DummyScheduler()    )

            a_    :     List[str]										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )

            a_    :     int										=    pipe(
                **lowerCamelCase_      ,							decoder_latents=lowerCamelCase_      ,							super_res_latents=lowerCamelCase_    ).images

            a_    :     List[Any]										=    self.get_dummy_inputs(lowerCamelCase_      ,							pil_image=lowerCamelCase_    )
            # Don't pass image, instead pass embedding
            a_    :     Optional[int]										=    pipeline_inputs.pop("""image"""    )
            a_    :     Dict										=    pipe.image_encoder(lowerCamelCase_    ).image_embeds

            a_    :     Optional[int]										=    pipe(
                **lowerCamelCase_      ,							decoder_latents=lowerCamelCase_      ,							super_res_latents=lowerCamelCase_      ,							image_embeddings=lowerCamelCase_      ,							).images

            # make sure passing text embeddings manually is identical
            assert np.abs(img_out_a - img_out_a    ).max() < 1E-4

       @skip_mps
       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     List[Any]										=    torch_device == '''cpu'''

            # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
            a_    :     Optional[int]										=    1E-2

            self._test_attention_slicing_forward_pass(
                test_max_difference=lowerCamelCase_      ,							expected_max_diff=lowerCamelCase_    )

       @skip_mps
       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     List[Any]										=    torch_device == '''cpu'''
            a_    :     Optional[int]										=    True
            a_    :     Any										=    [
                '''decoder_num_inference_steps''',
                '''super_res_num_inference_steps''',
            ]

            self._test_inference_batch_single_identical(
                test_max_difference=lowerCamelCase_      ,							relax_max_difference=lowerCamelCase_      ,							additional_params_copy_to_batched_inputs=lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     int										=    [
                '''decoder_num_inference_steps''',
                '''super_res_num_inference_steps''',
            ]

            if torch_device == "mps":
                 # TODO: MPS errors with larger batch sizes
                 a_    :     List[Any]										=    [2, 3]
                 self._test_inference_batch_consistent(
                     batch_sizes=lowerCamelCase_      ,							additional_params_copy_to_batched_inputs=lowerCamelCase_      ,							)
            else:
                 self._test_inference_batch_consistent(
                     additional_params_copy_to_batched_inputs=lowerCamelCase_    )

       @skip_mps
       def 						UpperCAmelCase(   self      :     Dict    ):
            return super().test_dict_tuple_outputs_equivalent()

       @skip_mps
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return super().test_save_load_local()

       @skip_mps
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            return super().test_save_load_optional_components()


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

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     Dict										=    load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""    )
            a_    :     int										=    load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""    )

            a_    :     Tuple										=    UnCLIPImageVariationPipeline.from_pretrained(
                """kakaobrain/karlo-v1-alpha-image-variations"""      ,							torch_dtype=torch.floataa    )
            a_    :     Any										=    pipeline.to(lowerCamelCase_    )
            pipeline.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Any										=    torch.Generator(device="""cpu"""    ).manual_seed(0    )
            a_    :     Any										=    pipeline(
                lowerCamelCase_      ,							generator=lowerCamelCase_      ,							output_type="""np"""      ,							)

            a_    :     Tuple										=    output.images[0]

            assert image.shape == (2_5_6, 2_5_6, 3)

            assert_mean_pixel_difference(lowerCamelCase_      ,							lowerCamelCase_      ,							1_5    )







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[str]							    =     ["""vqvae"""]

       def __init__(   self      :     str      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Mel      ,							lowerCamelCase_      :     Union[DDIMScheduler, DDPMScheduler]      ,							):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							mel=lowerCamelCase_      ,							vqvae=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            return 5_0 if isinstance(self.scheduler      ,							lowerCamelCase_    ) else 1_0_0_0

       @torch.no_grad()
       def __call__(   self      :     List[str]      ,							lowerCamelCase_      :     int = 1      ,							lowerCamelCase_      :     str = None      ,							lowerCamelCase_      :     np.ndarray = None      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = None      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     List[Any]=True      ,							):
            a_    :     Optional[Any]										=    steps or self.get_default_steps()
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Optional[int]										=    step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size    ) == int:
                 a_    :     str										=    (self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                 a_    :     Dict										=    randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )      ,							generator=lowerCamelCase_      ,							device=self.device      ,							)
            a_    :     Tuple										=    noise
            a_    :     Union[str, Any]										=    None

            if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    self.mel.audio_slice_to_image(lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    np.frombuffer(input_image.tobytes()      ,							dtype="""uint8"""    ).reshape(
                     (input_image.height, input_image.width)    )
                 a_    :     Union[str, Any]										=    (input_image / 2_5_5) * 2 - 1
                 a_    :     Tuple										=    torch.tensor(input_image[np.newaxis, :, :]      ,							dtype=torch.float    ).to(self.device    )

                 if self.vqvae is not None:
                      a_    :     Dict										=    self.vqvae.encode(torch.unsqueeze(lowerCamelCase_      ,							0    )    ).latent_dist.sample(
                          generator=lowerCamelCase_    )[0]
                      a_    :     List[Any]										=    self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                      a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							self.scheduler.timesteps[start_step - 1]    )

                 a_    :     Union[str, Any]										=    (
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 a_    :     int										=    int(mask_start_secs * pixels_per_second    )
                 a_    :     Tuple										=    int(mask_end_secs * pixels_per_second    )
                 a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							torch.tensor(self.scheduler.timesteps[start_step:]    )    )

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
                 if isinstance(self.unet      ,							lowerCamelCase_    ):
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 else:
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]

                 if isinstance(self.scheduler      ,							lowerCamelCase_    ):
                      a_    :     Any										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]
                 else:
                      a_    :     Optional[Any]										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]

                 if mask is not None:
                      if mask_start > 0:
                           a_    :     Any										=    mask[:, step, :, :mask_start]
                      if mask_end > 0:
                           a_    :     Dict										=    mask[:, step, :, -mask_end:]

            if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 a_    :     str										=    1 / self.vqvae.config.scaling_factor * images
                 a_    :     List[Any]										=    self.vqvae.decode(lowerCamelCase_    )["""sample"""]

            a_    :     Union[str, Any]										=    (images / 2 + 0.5).clamp(0      ,							1    )
            a_    :     Dict										=    images.cpu().permute(0      ,							2      ,							3      ,							1    ).numpy()
            a_    :     Any										=    (images * 2_5_5).round().astype("""uint8"""    )
            a_    :     int										=    list(
                (Image.fromarray(_[:, :, 0]    ) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(lowerCamelCase_      ,							mode="""RGB"""    ).convert("""L"""    ) for _ in images)    )

            a_    :     str										=    [self.mel.image_to_audio(lowerCamelCase_    ) for _ in images]
            if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

            return BaseOutput(**AudioPipelineOutput(np.array(lowerCamelCase_    )[:, np.newaxis, :]    )      ,							**ImagePipelineOutput(lowerCamelCase_    )    )

       @torch.no_grad()
       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[Image.Image]      ,							lowerCamelCase_      :     int = 5_0    ):
            assert isinstance(self.scheduler      ,							lowerCamelCase_    )
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Any										=    np.array(
                [np.frombuffer(image.tobytes()      ,							dtype="""uint8"""    ).reshape((1, image.height, image.width)    ) for image in images]    )
            a_    :     Union[str, Any]										=    (sample / 2_5_5) * 2 - 1
            a_    :     Optional[Any]										=    torch.Tensor(lowerCamelCase_    ).to(self.device    )

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,							(0,)    )    ):
                 a_    :     str										=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 a_    :     Any										=    self.scheduler.alphas_cumprod[t]
                 a_    :     Dict										=    (
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 a_    :     Optional[Any]										=    1 - alpha_prod_t
                 a_    :     Optional[int]										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 a_    :     Tuple										=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
                 a_    :     Tuple										=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 a_    :     Tuple										=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     float    ):
            a_    :     Dict										=    acos(torch.dot(torch.flatten(lowerCamelCase_    )      ,							torch.flatten(lowerCamelCase_    )    ) / torch.norm(lowerCamelCase_    ) / torch.norm(lowerCamelCase_    )    )
            return sin((1 - alpha) * theta    ) * xa / sin(lowerCamelCase_    ) + sin(alpha * theta    ) * xa / sin(lowerCamelCase_    )



