






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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""vqvae"""]




				def __init__(     self						:    Dict,  lowerCamelCase						:    AutoencoderKL,  lowerCamelCase						:    UNetaDConditionModel,  lowerCamelCase						:    Mel,  lowerCamelCase						:    Union[DDIMScheduler, DDPMScheduler],  ):

								'''simple docstring'''
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase,  mel=lowerCamelCase,  vqvae=lowerCamelCase  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								return 50 if isinstance(self.scheduler,  lowerCamelCase  ) else 1_000




				@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						:    Union[str, Any]=True,  ):

								'''simple docstring'''
								lowercase__    =			steps or self.get_default_steps()
								self.scheduler.set_timesteps(lowerCamelCase  )
								lowercase__    =			step_generator or generator
								# For backwards compatibility
								if type(self.unet.config.sample_size  ) == int:
												lowercase__    =			(self.unet.config.sample_size, self.unet.config.sample_size)
								if noise is None:
												lowercase__    =			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,  )
								lowercase__    =			noise
								lowercase__    =			None

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

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

												if start_step > 0:
																lowercase__    =			self.scheduler.add_noise(lowerCamelCase,  lowerCamelCase,  self.scheduler.timesteps[start_step - 1]  )

												lowercase__    =			(
												    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
												)
												lowercase__    =			int(mask_start_secs * pixels_per_second  )
												lowercase__    =			int(mask_end_secs * pixels_per_second  )
												lowercase__    =			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  ):
																lowercase__    =			self.unet(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )['''sample''']
												else:
																lowercase__    =			self.unet(lowerCamelCase,  lowerCamelCase  )['''sample''']

												if isinstance(self.scheduler,  lowerCamelCase  ):
																lowercase__    =			self.scheduler.step(
																    model_output=lowerCamelCase,  timestep=lowerCamelCase,  sample=lowerCamelCase,  eta=lowerCamelCase,  generator=lowerCamelCase,  )['''prev_sample''']
												else:
																lowercase__    =			self.scheduler.step(
																    model_output=lowerCamelCase,  timestep=lowerCamelCase,  sample=lowerCamelCase,  generator=lowerCamelCase,  )['''prev_sample''']

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

								if self.vqvae is not None:
												# 0.18215 was scaling factor used in training to ensure unit variance
												lowercase__    =			1 / self.vqvae.config.scaling_factor * images
												lowercase__    =			self.vqvae.decode(lowerCamelCase  )['''sample''']

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

								lowercase__    =			[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 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    List[Image.Image],  lowerCamelCase						:    int = 50  ):

								'''simple docstring'''
								assert isinstance(self.scheduler,  lowerCamelCase  )
								self.scheduler.set_timesteps(lowerCamelCase  )
								lowercase__    =			np.array(
								    [np.frombuffer(image.tobytes(),  dtype='''uint8'''  ).reshape((1, image.height, image.width)  ) for image in images]  )
								lowercase__    =			(sample / 255) * 2 - 1
								lowercase__    =			torch.Tensor(lowerCamelCase  ).to(self.device  )

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps,  (0,)  )  ):
												lowercase__    =			t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
												lowercase__    =			self.scheduler.alphas_cumprod[t]
												lowercase__    =			(
												    self.scheduler.alphas_cumprod[prev_timestep]
												    if prev_timestep >= 0
												    else self.scheduler.final_alpha_cumprod
												)
												lowercase__    =			1 - alpha_prod_t
												lowercase__    =			self.unet(lowerCamelCase,  lowerCamelCase  )['''sample''']
												lowercase__    =			(1 - alpha_prod_t_prev) ** 0.5 * model_output
												lowercase__    =			(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
												lowercase__    =			sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample




				@staticmethod
				def 				lowercase__   (     lowerCamelCase						:    torch.Tensor,  lowerCamelCase						:    torch.Tensor,  lowerCamelCase						:    float  ):

								'''simple docstring'''
								lowercase__    =			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  )












import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       TextToVideoSDPipeline
				lowercase__											=       TEXT_TO_IMAGE_PARAMS
				lowercase__											=       TEXT_TO_IMAGE_BATCH_PARAMS
				# No `output_type`.
				lowercase__											=       frozenset(
				    [
				        """num_inference_steps""",
				        """generator""",
				        """latents""",
				        """return_dict""",
				        """callback""",
				        """callback_steps""",
				    ]	)




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			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,  )
								lowercase__    =			DDIMScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								torch.manual_seed(0  )
								lowercase__    =			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  )
								lowercase__    =			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=1_000,  hidden_act='''gelu''',  projection_dim=512,  )
								lowercase__    =			CLIPTextModel(lowerCamelCase  )
								lowercase__    =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowercase__    =			{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								}
								return components




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    int=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__    =			torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowercase__    =			{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''pt''',
								}
								return inputs




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			TextToVideoSDPipeline(**lowerCamelCase  )
								lowercase__    =			sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			'''np'''
								lowercase__    =			sd_pipe(**lowerCamelCase  ).frames
								lowercase__    =			frames[0][-3:, -3:, -1]

								assert frames[0].shape == (64, 64, 3)
								lowercase__    =			np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]  )

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




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase,  expected_max_diff=3E-3  )




				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available(),  reason='''XFormers attention is only available with CUDA and `xformers` installed''',  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase,  expected_max_diff=1E-2  )




				@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''  )
				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.'''  )
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								return super().test_progress_bar()



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

				"""simple docstring"""




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'''  )

								lowercase__    =			TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''  )
								lowercase__    =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
								lowercase__    =			pipe.to('''cuda'''  )

								lowercase__    =			'''Spiderman is surfing'''
								lowercase__    =			torch.Generator(device='''cpu'''  ).manual_seed(0  )

								lowercase__    =			pipe(lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=25,  output_type='''pt'''  ).frames
								lowercase__    =			video_frames.cpu().numpy()

								assert np.abs(expected_video - video  ).mean() < 5E-2




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'''  )

								lowercase__    =			TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''  )
								lowercase__    =			pipe.to('''cuda'''  )

								lowercase__    =			'''Spiderman is surfing'''
								lowercase__    =			torch.Generator(device='''cpu'''  ).manual_seed(0  )

								lowercase__    =			pipe(lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=2,  output_type='''pt'''  ).frames
								lowercase__    =			video_frames.cpu().numpy()

								assert np.abs(expected_video - video  ).mean() < 5E-2




