


"""simple docstring"""


import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

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


enable_full_determinism()



@skip_mps
class   _a      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Optional[Any]             =    VideoToVideoSDPipeline
    a_    :				Tuple             =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'} ) - {'image', 'width', 'height'}
    a_    :				str             =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'} ) - {'image'}
    a_    :				int             =    PipelineTesterMixin.required_optional_params - {'latents'}
    a_    :				List[str]             =    False

    # No `output_type`.
    a_    :				List[Any]             =    frozenset(
        [
            'num_inference_steps',
            'generator',
            'latents',
            'return_dict',
            'callback',
            'callback_steps',
        ] )
    def      _UpperCamelCase       ( self							:					int				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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							,					)
          lowerCamelCase__           =		DDIMScheduler(
              beta_start=0.0_00_85							,					beta_end=0.0_12							,					beta_schedule='scaled_linear'							,					clip_sample=SCREAMING_SNAKE_CASE__							,					set_alpha_to_one=SCREAMING_SNAKE_CASE__							,					)
          torch.manual_seed(0				)
          lowerCamelCase__           =		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=1_28							,					)
          torch.manual_seed(0				)
          lowerCamelCase__           =		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=10_00							,					hidden_act='gelu'							,					projection_dim=5_12							,					)
          lowerCamelCase__           =		CLIPTextModel(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

          lowerCamelCase__           =		{
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
          }
          return components
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0				):
          # 3 frames
          lowerCamelCase__           =		floats_tensor((1, 3, 3, 32, 32)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)

          if str(SCREAMING_SNAKE_CASE__				).startswith('mps'				):
                lowerCamelCase__           =		torch.manual_seed(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'A painting of a squirrel eating a burger',
              'video': video,
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'pt',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		'cpu'  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		VideoToVideoSDPipeline(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		sd_pipe.to(SCREAMING_SNAKE_CASE__				)
          sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

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

          assert frames[0].shape == (32, 32, 3)
          lowerCamelCase__           =		np.array([1_06, 1_17, 1_13, 1_74, 1_37, 1_12, 1_48, 1_51, 1_31]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()							,					reason='XFormers attention is only available with CUDA and `xformers` installed'							,					)
    def      _UpperCamelCase       ( self							:					Dict				):
          self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__							,					expected_max_diff=5e-3				)
    @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          pass
    @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'				)
    def      _UpperCamelCase       ( self							:					str				):
          pass
    @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'				)
    def      _UpperCamelCase       ( self							:					Any				):
          pass


    def      _UpperCamelCase       ( self							:					str				):
          return super().test_progress_bar()





@slow
@skip_mps
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'							,					torch_dtype=torch.floataa				)
          pipe.enable_model_cpu_offload()

          # 10 frames
          lowerCamelCase__           =		torch.Generator(device='cpu'				).manual_seed(0				)
          lowerCamelCase__           =		torch.randn((1, 10, 3, 10_24, 5_76)							,					generator=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		video.to('cuda'				)

          lowerCamelCase__           =		'Spiderman is surfing'

          lowerCamelCase__           =		pipe(SCREAMING_SNAKE_CASE__							,					video=SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=3							,					output_type='pt'				).frames

          lowerCamelCase__           =		np.array([-1.0_45_89_84, -1.1_27_92_97, -0.9_66_30_86, -0.91_50_39_06, -0.75_09_76_56]				)
          assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array				).sum() < 1e-2







"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


_snake_case          =							logging.get_logger(__name__)  # pylint: disable=invalid-name

_snake_case          =							"\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"



@dataclass
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[PIL.Image.Image, np.ndarray]





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					PriorTransformer							,					SCREAMING_SNAKE_CASE__							:					CLIPVisionModel							,					SCREAMING_SNAKE_CASE__							:					CLIPImageProcessor							,					SCREAMING_SNAKE_CASE__							:					HeunDiscreteScheduler							,					SCREAMING_SNAKE_CASE__							:					ShapERenderer							,					):
          super().__init__()

          self.register_modules(
              prior=SCREAMING_SNAKE_CASE__							,					image_encoder=SCREAMING_SNAKE_CASE__							,					image_processor=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__							,					renderer=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if latents is None:
                lowerCamelCase__           =		randn_tensor(SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          else:
                if latents.shape != shape:
                      raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
                lowerCamelCase__           =		latents.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		latents * scheduler.init_noise_sigma
          return latents
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0				):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError('Please install accelerate via `pip install accelerate`'				)

          lowerCamelCase__           =		torch.device(F'cuda:{gpu_id}'				)

          lowerCamelCase__           =		[self.image_encoder, self.prior]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          if self.device != torch.device('meta'				) or not hasattr(self.image_encoder							,					'_hf_hook'				):
                return self.device
          for module in self.image_encoder.modules():
                if (
                    hasattr(SCREAMING_SNAKE_CASE__							,					'_hf_hook'				)
                    and hasattr(module._hf_hook							,					'execution_device'				)
                    and module._hf_hook.execution_device is not None
                ):
                      return torch.device(module._hf_hook.execution_device				)
          return self.device
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					):
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					torch.Tensor				):
                lowerCamelCase__           =		torch.cat(SCREAMING_SNAKE_CASE__							,					axis=0				) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__							,					axis=0				)

          if not isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		self.image_processor(SCREAMING_SNAKE_CASE__							,					return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

          lowerCamelCase__           =		image.to(dtype=self.image_encoder.dtype							,					device=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.image_encoder(SCREAMING_SNAKE_CASE__				)['last_hidden_state']
          lowerCamelCase__           =		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          lowerCamelCase__           =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__							,					dim=0				)

          if do_classifier_free_guidance:
                lowerCamelCase__           =		torch.zeros_like(SCREAMING_SNAKE_CASE__				)

                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                lowerCamelCase__           =		torch.cat([negative_image_embeds, image_embeds]				)

          return image_embeds


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[PIL.Image.Image, List[PIL.Image.Image]]							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					int = 25							,					SCREAMING_SNAKE_CASE__							:					Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[torch.FloatTensor] = None							,					SCREAMING_SNAKE_CASE__							:					float = 4.0							,					SCREAMING_SNAKE_CASE__							:					int = 64							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = "pil"							,					SCREAMING_SNAKE_CASE__							:					bool = True							,					):

          if isinstance(SCREAMING_SNAKE_CASE__							,					PIL.Image.Image				):
                lowerCamelCase__           =		1
          elif isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		image.shape[0]
          elif isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					(torch.Tensor, PIL.Image.Image)				):
                lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          else:
                raise ValueError(
                    F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(SCREAMING_SNAKE_CASE__				)}'				)

          lowerCamelCase__           =		self._execution_device

          lowerCamelCase__           =		batch_size * num_images_per_prompt

          lowerCamelCase__           =		guidance_scale > 1.0
          lowerCamelCase__           =		self._encode_image(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          # prior

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.scheduler.timesteps

          lowerCamelCase__           =		self.prior.config.num_embeddings
          lowerCamelCase__           =		self.prior.config.embedding_dim

          lowerCamelCase__           =		self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)							,					image_embeds.dtype							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					self.scheduler							,					)

          # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
          lowerCamelCase__           =		latents.reshape(latents.shape[0]							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__				)				):
                # expand the latents if we are doing classifier free guidance
                lowerCamelCase__           =		torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                lowerCamelCase__           =		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		self.prior(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					proj_embedding=SCREAMING_SNAKE_CASE__							,					).predicted_image_embedding

                # remove the variance
                lowerCamelCase__    ,  lowerCamelCase__           =		noise_pred.split(
                    scaled_model_input.shape[2]							,					dim=2				)  # batch_size, num_embeddings, embedding_dim

                if do_classifier_free_guidance is not None:
                      lowerCamelCase__    ,  lowerCamelCase__           =		noise_pred.chunk(2				)
                      lowerCamelCase__           =		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                lowerCamelCase__           =		self.scheduler.step(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					sample=SCREAMING_SNAKE_CASE__							,					).prev_sample

          if output_type == "latent":
                return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		[]
          for i, latent in enumerate(SCREAMING_SNAKE_CASE__				):
                print()
                lowerCamelCase__           =		self.renderer.decode(
                    latent[None, :]							,					SCREAMING_SNAKE_CASE__							,					size=SCREAMING_SNAKE_CASE__							,					ray_batch_size=40_96							,					n_coarse_samples=64							,					n_fine_samples=1_28							,					)

                images.append(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.stack(SCREAMING_SNAKE_CASE__				)

          if output_type not in ["np", "pil"]:
                raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'				)

          lowerCamelCase__           =		images.cpu().numpy()

          if output_type == "pil":
                lowerCamelCase__           =		[self.numpy_to_pil(SCREAMING_SNAKE_CASE__				) for image in images]

          # Offload last model to CPU
          if hasattr(self							,					'final_offload_hook'				) and self.final_offload_hook is not None:
                self.final_offload_hook.offload()

          if not return_dict:
                return (images,)

          return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE__				)



