





"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     KandinskyVaaControlnetImgaImgPipeline
 _lowerCamelCase        =     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _lowerCamelCase        =     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 _lowerCamelCase        =     [
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCamelCase        =     False

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 32

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 32

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.time_input_dim

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.time_input_dim * 4

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 100

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )

     __A    :						Tuple						 =     {
         '''in_channels''': 8,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 8,
         '''addition_embed_type''': '''image_hint''',
         '''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,
         '''encoder_hid_dim''': self.text_embedder_hidden_size,
         '''encoder_hid_dim_type''': '''image_proj''',
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': None,
     }

     __A    :						Union[str, Any]						 =     UNetaDConditionModel(**__lowerCamelCase  )
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return {
         "block_out_channels": [32, 32, 64, 64],
         "down_block_types": [
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "AttnDownEncoderBlock2D",
         ],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
         "vq_embed_dim": 4,
     }

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Any						 =     VQModel(**self.dummy_movq_kwargs  )
     return model

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Any						 =     self.dummy_unet
     __A    :						Union[str, Any]						 =     self.dummy_movq

     __A    :						Optional[Any]						 =     {
         '''num_train_timesteps''': 1000,
         '''beta_schedule''': '''linear''',
         '''beta_start''': 0.0_0_0_8_5,
         '''beta_end''': 0.0_1_2,
         '''clip_sample''': False,
         '''set_alpha_to_one''': False,
         '''steps_offset''': 0,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
     }

     __A    :						Optional[int]						 =     DDIMScheduler(**__lowerCamelCase  )

     __A    :						Tuple						 =     {
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }

     return components

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=0  ):



     '''simple docstring'''




     __A    :						Tuple						 =     floats_tensor((1, self.text_embedder_hidden_size)					,						rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
     __A    :						str						 =     floats_tensor((1, self.text_embedder_hidden_size)					,						rng=random.Random(seed + 1  )  ).to(
         __lowerCamelCase  )
     # create init_image
     __A    :						List[Any]						 =     floats_tensor((1, 3, 64, 64)					,						rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
     __A    :						List[str]						 =     image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						Optional[Any]						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((256, 256)  )
     # create hint
     __A    :						Union[str, Any]						 =     floats_tensor((1, 3, 64, 64)					,						rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )

     if str(__lowerCamelCase  ).startswith('''mps'''  ):
         __A    :						Any						 =     torch.manual_seed(__lowerCamelCase  )
     else:
         __A    :						Union[str, Any]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(__lowerCamelCase  )
     __A    :						Union[str, Any]						 =     {
         '''image''': init_image,
         '''image_embeds''': image_embeds,
         '''negative_image_embeds''': negative_image_embeds,
         '''hint''': hint,
         '''generator''': generator,
         '''height''': 64,
         '''width''': 64,
         '''num_inference_steps''': 10,
         '''guidance_scale''': 7.0,
         '''strength''': 0.2,
         '''output_type''': '''np''',
     }
     return inputs







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     '''cpu'''

     __A    :						int						 =     self.get_dummy_components()

     __A    :						List[Any]						 =     self.pipeline_class(**__lowerCamelCase  )
     __A    :						str						 =     pipe.to(__lowerCamelCase  )

     pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						str						 =     pipe(**self.get_dummy_inputs(__lowerCamelCase  )  )
     __A    :						Any						 =     output.images

     __A    :						List[str]						 =     pipe(
         **self.get_dummy_inputs(__lowerCamelCase  )					,						return_dict=__lowerCamelCase					,						)[0]

     __A    :						Union[str, Any]						 =     image[0, -3:, -3:, -1]
     __A    :						Tuple						 =     image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     __A    :						Dict						 =     np.array(
         [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]  )
     assert (
         np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






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





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''  )

     __A    :						List[str]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
     __A    :						Union[str, Any]						 =     init_image.resize((512, 512)  )

     __A    :						int						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/hint_image_cat.png'''  )
     __A    :						Any						 =     torch.from_numpy(np.array(__lowerCamelCase  )  ).float() / 2_5_5.0
     __A    :						int						 =     hint.permute(2					,						0					,						1  ).unsqueeze(0  )

     __A    :						List[str]						 =     '''A robot, 4k photo'''

     __A    :						str						 =     KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-prior'''					,						torch_dtype=torch.floataa  )
     pipe_prior.to(__lowerCamelCase  )

     __A    :						Dict						 =     KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-controlnet-depth'''					,						torch_dtype=torch.floataa  )
     __A    :						Dict						 =     pipeline.to(__lowerCamelCase  )

     pipeline.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     torch.Generator(device='''cpu'''  ).manual_seed(0  )

     __A ,       __A    :						Optional[Any]						 =     pipe_prior(
         __lowerCamelCase					,						image=__lowerCamelCase					,						strength=0.8_5					,						generator=__lowerCamelCase					,						negative_prompt=''''''					,						).to_tuple()

     __A    :						Any						 =     pipeline(
         image=__lowerCamelCase					,						image_embeds=__lowerCamelCase					,						negative_image_embeds=__lowerCamelCase					,						hint=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=100					,						height=512					,						width=512					,						strength=0.5					,						output_type='''np'''					,						)

     __A    :						Optional[Any]						 =     output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(__lowerCamelCase					,						__lowerCamelCase  )











"""simple docstring"""



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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     ["""vqvae"""]

 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						mel=__lowerCamelCase					,						vqvae=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 50 if isinstance(self.scheduler					,						__lowerCamelCase  ) else 1000

 @torch.no_grad()
 def __call__(    self					,						__lowerCamelCase = 1					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase=True					,						):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     steps or self.get_default_steps()
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						Any						 =     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size  ) == int:
         __A    :						Optional[Any]						 =     (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    :						Optional[int]						 =     noise
     __A    :						int						 =     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						int						 =     self.mel.audio_slice_to_image(__lowerCamelCase  )
         __A    :						Optional[Any]						 =     np.frombuffer(input_image.tobytes()					,						dtype='''uint8'''  ).reshape(
             (input_image.height, input_image.width)  )
         __A    :						Union[str, Any]						 =     (input_image / 255) * 2 - 1
         __A    :						Any						 =     torch.tensor(input_image[np.newaxis, :, :]					,						dtype=torch.float  ).to(self.device  )

         if self.vqvae is not None:
             __A    :						Any						 =     self.vqvae.encode(torch.unsqueeze(__lowerCamelCase					,						0  )  ).latent_dist.sample(
                 generator=__lowerCamelCase  )[0]
             __A    :						Optional[Any]						 =     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             __A    :						Union[str, Any]						 =     self.scheduler.add_noise(__lowerCamelCase					,						__lowerCamelCase					,						self.scheduler.timesteps[start_step - 1]  )

         __A    :						Optional[int]						 =     (
             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    :						Dict						 =     int(mask_end_secs * pixels_per_second  )
         __A    :						int						 =     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    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         else:
             __A    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']

         if isinstance(self.scheduler					,						__lowerCamelCase  ):
             __A    :						Optional[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    :						Optional[Any]						 =     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 __A    :						Tuple						 =     mask[:, step, :, -mask_end:]

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

     __A    :						Any						 =     (images / 2 + 0.5).clamp(0					,						1  )
     __A    :						Any						 =     images.cpu().permute(0					,						2					,						3					,						1  ).numpy()
     __A    :						Any						 =     (images * 255).round().astype('''uint8'''  )
     __A    :						List[Any]						 =     list(
         (Image.fromarray(_[:, :, 0]  ) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(__lowerCamelCase					,						mode='''RGB'''  ).convert('''L'''  ) for _ in images)  )

     __A    :						Dict						 =     [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					,						__lowerCamelCase					,						__lowerCamelCase = 50  ):



     '''simple docstring'''




     assert isinstance(self.scheduler					,						__lowerCamelCase  )
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						str						 =     np.array(
         [np.frombuffer(image.tobytes()					,						dtype='''uint8'''  ).reshape((1, image.height, image.width)  ) for image in images]  )
     __A    :						Any						 =     (sample / 255) * 2 - 1
     __A    :						Dict						 =     torch.Tensor(__lowerCamelCase  ).to(self.device  )

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,						(0,)  )  ):
         __A    :						Tuple						 =     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         __A    :						List[Any]						 =     self.scheduler.alphas_cumprod[t]
         __A    :						Optional[int]						 =     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         __A    :						List[Any]						 =     1 - alpha_prod_t
         __A    :						int						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         __A    :						Union[str, Any]						 =     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         __A    :						List[Any]						 =     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         __A    :						Union[str, Any]						 =     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample







 @staticmethod
 def 					UpperCamelCase__(    __lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     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  )




