"""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


lowerCAmelCase__						:						Optional[int]		  =			logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase__						:						Optional[int]		  =			'\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        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				42






class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def __init__(       self     :			Dict					,lowerCamelCase__     :			PriorTransformer					,lowerCamelCase__     :			CLIPVisionModel					,lowerCamelCase__     :			CLIPImageProcessor					,lowerCamelCase__     :			HeunDiscreteScheduler					,lowerCamelCase__     :			ShapERenderer					,):
									super().__init__()

									self.register_modules(
									    prior=lowerCamelCase__					,image_encoder=lowerCamelCase__					,image_processor=lowerCamelCase__					,scheduler=lowerCamelCase__					,renderer=lowerCamelCase__					,)







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			int					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Optional[int]    ):
									if latents is None:
													UpperCAmelCase__    =							randn_tensor(lowerCamelCase__					,generator=lowerCamelCase__					,device=lowerCamelCase__					,dtype=lowerCamelCase__    )
									else:
													if latents.shape != shape:
																	raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
													UpperCAmelCase__    =							latents.to(lowerCamelCase__    )

									UpperCAmelCase__    =							latents * scheduler.init_noise_sigma
									return latents







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=0    ):
									if is_accelerate_available():
													from accelerate import cpu_offload
									else:
													raise ImportError('Please install accelerate via `pip install accelerate`'    )

									UpperCAmelCase__    =							torch.device(f'''cuda:{gpu_id}'''    )

									UpperCAmelCase__    =							[self.image_encoder, self.prior]
									for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																	cpu_offload(lowerCamelCase__					,lowerCamelCase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									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(lowerCamelCase__					,'_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 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[Any]					,):
									if isinstance(lowerCamelCase__					,lowerCamelCase__    ) and isinstance(image[0]					,torch.Tensor    ):
													UpperCAmelCase__    =							torch.cat(lowerCamelCase__					,axis=0    ) if image[0].ndim == 4 else torch.stack(lowerCamelCase__					,axis=0    )

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

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

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

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

									if do_classifier_free_guidance:
													UpperCAmelCase__    =							torch.zeros_like(lowerCamelCase__    )

													# 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
													UpperCAmelCase__    =							torch.cat([negative_image_embeds, image_embeds]    )

									return image_embeds







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

									if isinstance(lowerCamelCase__					,PIL.Image.Image    ):
													UpperCAmelCase__    =							1
									elif isinstance(lowerCamelCase__					,torch.Tensor    ):
													UpperCAmelCase__    =							image.shape[0]
									elif isinstance(lowerCamelCase__					,lowerCamelCase__    ) and isinstance(image[0]					,(torch.Tensor, PIL.Image.Image)    ):
													UpperCAmelCase__    =							len(lowerCamelCase__    )
									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(lowerCamelCase__    )}'''    )

									UpperCAmelCase__    =							self._execution_device

									UpperCAmelCase__    =							batch_size * num_images_per_prompt

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

									# prior

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

									UpperCAmelCase__    =							self.prior.config.num_embeddings
									UpperCAmelCase__    =							self.prior.config.embedding_dim

									UpperCAmelCase__    =							self.prepare_latents(
									    (batch_size, num_embeddings * embedding_dim)					,image_embeds.dtype					,lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,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
									UpperCAmelCase__    =							latents.reshape(latents.shape[0]					,lowerCamelCase__					,lowerCamelCase__    )

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

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

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

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

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

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

									UpperCAmelCase__    =							[]
									for i, latent in enumerate(lowerCamelCase__    ):
													print()
													UpperCAmelCase__    =							self.renderer.decode(
													    latent[None, :]					,lowerCamelCase__					,size=lowerCamelCase__					,ray_batch_size=4_096					,n_coarse_samples=64					,n_fine_samples=128					,)

													images.append(lowerCamelCase__    )

									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )

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

									UpperCAmelCase__    =							images.cpu().numpy()

									if output_type == "pil":
													UpperCAmelCase__    =							[self.numpy_to_pil(lowerCamelCase__    ) 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=lowerCamelCase__    )




"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf
@require_vision
@require_torchvision
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							tempfile.mkdtemp()
									UpperCAmelCase__    =							SamImageProcessor()
									UpperCAmelCase__    =							SamProcessor(lowerCamelCase__    )
									processor.save_pretrained(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			str					,**lowerCamelCase__     :			Dict    ):
									return AutoProcessor.from_pretrained(self.tmpdirname					,**lowerCamelCase__    ).image_processor







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									shutil.rmtree(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							[np.random.randint(255					,size=(3, 30, 400)					,dtype=np.uinta    )]

									UpperCAmelCase__    =							[Image.fromarray(np.moveaxis(lowerCamelCase__					,0					,-1    )    ) for x in image_inputs]

									return image_inputs







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							SamProcessor(image_processor=self.get_image_processor()    )
									processor.save_pretrained(self.tmpdirname    )

									UpperCAmelCase__    =							self.get_image_processor(do_normalize=lowerCamelCase__					,padding_value=1.0    )

									UpperCAmelCase__    =							SamProcessor.from_pretrained(self.tmpdirname					,do_normalize=lowerCamelCase__					,padding_value=1.0    )

									self.assertEqual(processor.image_processor.to_json_string()					,image_processor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.image_processor					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.get_image_processor()

									UpperCAmelCase__    =							SamProcessor(image_processor=lowerCamelCase__    )

									UpperCAmelCase__    =							self.prepare_image_inputs()

									UpperCAmelCase__    =							image_processor(lowerCamelCase__					,return_tensors='np'    )
									UpperCAmelCase__    =							processor(images=lowerCamelCase__					,return_tensors='np'    )

									input_feat_extract.pop('original_sizes'    )  # pop original_sizes as it is popped in the processor
									input_feat_extract.pop('reshaped_input_sizes'    )  # pop original_sizes as it is popped in the processor

									for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()					,input_processor[key].sum()					,delta=1e-2    )







					@require_torch
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							self.get_image_processor()

									UpperCAmelCase__    =							SamProcessor(image_processor=lowerCamelCase__    )
									UpperCAmelCase__    =							[torch.ones((1, 3, 5, 5)    )]

									UpperCAmelCase__    =							[[1_764, 2_646]]

									UpperCAmelCase__    =							[[683, 1_024]]
									UpperCAmelCase__    =							processor.post_process_masks(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )
									self.assertEqual(masks[0].shape					,(1, 3, 1_764, 2_646)    )

									UpperCAmelCase__    =							processor.post_process_masks(
									    lowerCamelCase__					,torch.tensor(lowerCamelCase__    )					,torch.tensor(lowerCamelCase__    )    )
									self.assertEqual(masks[0].shape					,(1, 3, 1_764, 2_646)    )

									# should also work with np
									UpperCAmelCase__    =							[np.ones((1, 3, 5, 5)    )]
									UpperCAmelCase__    =							processor.post_process_masks(lowerCamelCase__					,np.array(lowerCamelCase__    )					,np.array(lowerCamelCase__    )    )

									self.assertEqual(masks[0].shape					,(1, 3, 1_764, 2_646)    )

									UpperCAmelCase__    =							[[1, 0], [0, 1]]
									with self.assertRaises(lowerCamelCase__    ):
													UpperCAmelCase__    =							processor.post_process_masks(lowerCamelCase__					,np.array(lowerCamelCase__    )					,np.array(lowerCamelCase__    )    )






@require_vision
@require_tf
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							tempfile.mkdtemp()
									UpperCAmelCase__    =							SamImageProcessor()
									UpperCAmelCase__    =							SamProcessor(lowerCamelCase__    )
									processor.save_pretrained(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			str					,**lowerCamelCase__     :			Union[str, Any]    ):
									return AutoProcessor.from_pretrained(self.tmpdirname					,**lowerCamelCase__    ).image_processor







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									shutil.rmtree(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							[np.random.randint(255					,size=(3, 30, 400)					,dtype=np.uinta    )]

									UpperCAmelCase__    =							[Image.fromarray(np.moveaxis(lowerCamelCase__					,0					,-1    )    ) for x in image_inputs]

									return image_inputs







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							SamProcessor(image_processor=self.get_image_processor()    )
									processor.save_pretrained(self.tmpdirname    )

									UpperCAmelCase__    =							self.get_image_processor(do_normalize=lowerCamelCase__					,padding_value=1.0    )

									UpperCAmelCase__    =							SamProcessor.from_pretrained(self.tmpdirname					,do_normalize=lowerCamelCase__					,padding_value=1.0    )

									self.assertEqual(processor.image_processor.to_json_string()					,image_processor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.image_processor					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.get_image_processor()

									UpperCAmelCase__    =							SamProcessor(image_processor=lowerCamelCase__    )

									UpperCAmelCase__    =							self.prepare_image_inputs()

									UpperCAmelCase__    =							image_processor(lowerCamelCase__					,return_tensors='np'    )
									UpperCAmelCase__    =							processor(images=lowerCamelCase__					,return_tensors='np'    )

									input_feat_extract.pop('original_sizes'    )  # pop original_sizes as it is popped in the processor
									input_feat_extract.pop('reshaped_input_sizes'    )  # pop reshaped_input_sizes as it is popped in the processor

									for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()					,input_processor[key].sum()					,delta=1e-2    )







					@require_tf
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							self.get_image_processor()

									UpperCAmelCase__    =							SamProcessor(image_processor=lowerCamelCase__    )
									UpperCAmelCase__    =							[tf.ones((1, 3, 5, 5)    )]

									UpperCAmelCase__    =							[[1_764, 2_646]]

									UpperCAmelCase__    =							[[683, 1_024]]
									UpperCAmelCase__    =							processor.post_process_masks(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,return_tensors='tf'    )
									self.assertEqual(masks[0].shape					,(1, 3, 1_764, 2_646)    )

									UpperCAmelCase__    =							processor.post_process_masks(
									    lowerCamelCase__					,tf.convert_to_tensor(lowerCamelCase__    )					,tf.convert_to_tensor(lowerCamelCase__    )					,return_tensors='tf'					,)
									self.assertEqual(masks[0].shape					,(1, 3, 1_764, 2_646)    )

									# should also work with np
									UpperCAmelCase__    =							[np.ones((1, 3, 5, 5)    )]
									UpperCAmelCase__    =							processor.post_process_masks(
									    lowerCamelCase__					,np.array(lowerCamelCase__    )					,np.array(lowerCamelCase__    )					,return_tensors='tf'    )

									self.assertEqual(masks[0].shape					,(1, 3, 1_764, 2_646)    )

									UpperCAmelCase__    =							[[1, 0], [0, 1]]
									with self.assertRaises(tf.errors.InvalidArgumentError    ):
													UpperCAmelCase__    =							processor.post_process_masks(
													    lowerCamelCase__					,np.array(lowerCamelCase__    )					,np.array(lowerCamelCase__    )					,return_tensors='tf'    )






@require_vision
@require_torchvision
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							tempfile.mkdtemp()
									UpperCAmelCase__    =							SamImageProcessor()
									UpperCAmelCase__    =							SamProcessor(lowerCamelCase__    )
									processor.save_pretrained(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			Dict					,**lowerCamelCase__     :			Any    ):
									return AutoProcessor.from_pretrained(self.tmpdirname					,**lowerCamelCase__    ).image_processor







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									shutil.rmtree(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							[np.random.randint(255					,size=(3, 30, 400)					,dtype=np.uinta    )]

									UpperCAmelCase__    =							[Image.fromarray(np.moveaxis(lowerCamelCase__					,0					,-1    )    ) for x in image_inputs]

									return image_inputs







					@is_pt_tf_cross_test
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							self.get_image_processor()

									UpperCAmelCase__    =							SamProcessor(image_processor=lowerCamelCase__    )
									UpperCAmelCase__    =							np.random.randint(0					,2					,size=(1, 3, 5, 5)    ).astype(np.floataa    )
									UpperCAmelCase__    =							[tf.convert_to_tensor(lowerCamelCase__    )]
									UpperCAmelCase__    =							[torch.tensor(lowerCamelCase__    )]

									UpperCAmelCase__    =							[[1_764, 2_646]]

									UpperCAmelCase__    =							[[683, 1_024]]
									UpperCAmelCase__    =							processor.post_process_masks(
									    lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,return_tensors='tf'    )
									UpperCAmelCase__    =							processor.post_process_masks(
									    lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,return_tensors='pt'    )

									self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()    )    )







					@is_pt_tf_cross_test
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							self.get_image_processor()

									UpperCAmelCase__    =							SamProcessor(image_processor=lowerCamelCase__    )

									UpperCAmelCase__    =							self.prepare_image_inputs()

									UpperCAmelCase__    =							image_processor(lowerCamelCase__					,return_tensors='pt'    )['pixel_values'].numpy()
									UpperCAmelCase__    =							processor(images=lowerCamelCase__					,return_tensors='pt'    )['pixel_values'].numpy()

									UpperCAmelCase__    =							image_processor(lowerCamelCase__					,return_tensors='tf'    )['pixel_values'].numpy()
									UpperCAmelCase__    =							processor(images=lowerCamelCase__					,return_tensors='tf'    )['pixel_values'].numpy()

									self.assertTrue(np.allclose(lowerCamelCase__					,lowerCamelCase__    )    )
									self.assertTrue(np.allclose(lowerCamelCase__					,lowerCamelCase__    )    )
									self.assertTrue(np.allclose(lowerCamelCase__					,lowerCamelCase__    )    )



