


"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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   _a      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Any             =    KandinskyInpaintPipeline
    a_    :				List[str]             =    ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
    a_    :				Dict             =    [
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
        'mask_image',
    ]
    a_    :				Optional[int]             =    [
        'generator',
        'height',
        'width',
        'latents',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    a_    :				Tuple             =    False
    @property
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					int				):
          return self.time_input_dim
    @property
    def      _UpperCamelCase       ( self							:					str				):
          return self.time_input_dim * 4
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return 1_00
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'				)
          return tokenizer
    @property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		MCLIPConfig(
              numDims=self.cross_attention_dim							,					transformerDimensions=self.text_embedder_hidden_size							,					hidden_size=self.text_embedder_hidden_size							,					intermediate_size=37							,					num_attention_heads=4							,					num_hidden_layers=5							,					vocab_size=10_05							,					)

          lowerCamelCase__           =		MultilingualCLIP(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		text_encoder.eval()

          return text_encoder
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          torch.manual_seed(0				)

          lowerCamelCase__           =		{
              'in_channels': 9,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              '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': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          lowerCamelCase__           =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE__				)
          return model
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["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",
              ],
              "vq_embed_dim": 4,
          }
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		VQModel(**self.dummy_movq_kwargs				)
          return model
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.dummy_text_encoder
          lowerCamelCase__           =		self.dummy_tokenizer
          lowerCamelCase__           =		self.dummy_unet
          lowerCamelCase__           =		self.dummy_movq

          lowerCamelCase__           =		DDIMScheduler(
              num_train_timesteps=10_00							,					beta_schedule='linear'							,					beta_start=0.0_00_85							,					beta_end=0.0_12							,					clip_sample=SCREAMING_SNAKE_CASE__							,					set_alpha_to_one=SCREAMING_SNAKE_CASE__							,					steps_offset=1							,					prediction_type='epsilon'							,					thresholding=SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0				):
          lowerCamelCase__           =		floats_tensor((1, self.cross_attention_dim)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		floats_tensor((1, self.cross_attention_dim)							,					rng=random.Random(seed + 1				)				).to(SCREAMING_SNAKE_CASE__				)
          # create init_image
          lowerCamelCase__           =		floats_tensor((1, 3, 64, 64)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		image.cpu().permute(0							,					2							,					3							,					1				)[0]
          lowerCamelCase__           =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__				)				).convert('RGB'				).resize((2_56, 2_56)				)
          # create mask
          lowerCamelCase__           =		np.ones((64, 64)							,					dtype=np.floataa				)
          lowerCamelCase__           =		0

          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': 'horse',
              'image': init_image,
              'mask_image': mask,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 2,
              'guidance_scale': 4.0,
              'output_type': 'np',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		'cpu'

          lowerCamelCase__           =		self.get_dummy_components()

          lowerCamelCase__           =		self.pipeline_class(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		pipe.to(SCREAMING_SNAKE_CASE__				)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		output.images

          lowerCamelCase__           =		pipe(
              **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)							,					return_dict=SCREAMING_SNAKE_CASE__							,					)[0]

          lowerCamelCase__           =		image[0, -3:, -3:, -1]
          lowerCamelCase__           =		image_from_tuple[0, -3:, -3:, -1]

          print(F'image.shape {image.shape}'				)

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

          lowerCamelCase__           =		np.array(
              [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]				)

          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()}'


    def      _UpperCamelCase       ( self							:					Dict				):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3				)





@slow
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					str				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'				)

          lowerCamelCase__           =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
          lowerCamelCase__           =		np.ones((7_68, 7_68)							,					dtype=np.floataa				)
          lowerCamelCase__           =		0

          lowerCamelCase__           =		'a hat'

          lowerCamelCase__           =		KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'							,					torch_dtype=torch.floataa				)
          pipe_prior.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		KandinskyInpaintPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-inpaint'							,					torch_dtype=torch.floataa				)
          lowerCamelCase__           =		pipeline.to(SCREAMING_SNAKE_CASE__				)
          pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.Generator(device='cpu'				).manual_seed(0				)
          lowerCamelCase__    ,  lowerCamelCase__           =		pipe_prior(
              SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=5							,					negative_prompt=''							,					).to_tuple()

          lowerCamelCase__           =		pipeline(
              SCREAMING_SNAKE_CASE__							,					image=SCREAMING_SNAKE_CASE__							,					mask_image=SCREAMING_SNAKE_CASE__							,					image_embeds=SCREAMING_SNAKE_CASE__							,					negative_image_embeds=SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=1_00							,					height=7_68							,					width=7_68							,					output_type='np'							,					)

          lowerCamelCase__           =		output.images[0]

          assert image.shape == (7_68, 7_68, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)







"""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__				)



