



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

lowerCAmelCase__		:					Tuple							='''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''

@dataclass
class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Union[PIL.Image.Image, np.ndarray]







class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							):







													'''simple docstring'''

													super().__init__()

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






						def 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													if latents is None:
																				__SCREAMING_SNAKE_CASE									= randn_tensor(_A				,							generator=_A				,							device=_A				,							dtype=_A		)
													else:
																				if latents.shape != shape:
																											raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
																				__SCREAMING_SNAKE_CASE									= latents.to(_A		)

													__SCREAMING_SNAKE_CASE									= latents * scheduler.init_noise_sigma
													return latents






						def 	_A     (   self				,							_A=0		):







													'''simple docstring'''

													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError('Please install accelerate via `pip install accelerate`'		)

													__SCREAMING_SNAKE_CASE									= torch.device(f"""cuda:{gpu_id}"""		)

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






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													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(_A				,							'_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 	_A     (   self				,							_A				,							_A				,							_A				,							_A				,							):







													'''simple docstring'''

													if isinstance(_A				,							_A		) and isinstance(image[0]				,							torch.Tensor		):
																				__SCREAMING_SNAKE_CASE									= torch.cat(_A				,							axis=0		) if image[0].ndim == 4 else torch.stack(_A				,							axis=0		)

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

													__SCREAMING_SNAKE_CASE									= image.to(dtype=self.image_encoder.dtype				,							device=_A		)

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

													__SCREAMING_SNAKE_CASE									= image_embeds.repeat_interleave(_A				,							dim=0		)

													if do_classifier_free_guidance:
																				__SCREAMING_SNAKE_CASE									= torch.zeros_like(_A		)

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

													return image_embeds






						@torch.no_grad()
						@replace_example_docstring(_A		)
						def __call__(   self				,							_A				,							_A = 1				,							_A = 25				,							_A = None				,							_A = None				,							_A = 4.0				,							_A = 64				,							_A = "pil"				,							_A = True				,							):







													'''simple docstring'''

													if isinstance(_A				,							PIL.Image.Image		):
																				__SCREAMING_SNAKE_CASE									= 1
													elif isinstance(_A				,							torch.Tensor		):
																				__SCREAMING_SNAKE_CASE									= image.shape[0]
													elif isinstance(_A				,							_A		) and isinstance(image[0]				,							(torch.Tensor, PIL.Image.Image)		):
																				__SCREAMING_SNAKE_CASE									= len(_A		)
													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(_A		)}"""		)

													__SCREAMING_SNAKE_CASE									= self._execution_device

													__SCREAMING_SNAKE_CASE									= batch_size * num_images_per_prompt

													__SCREAMING_SNAKE_CASE									= guidance_scale > 1.0
													__SCREAMING_SNAKE_CASE									= self._encode_image(_A				,							_A				,							_A				,							_A		)

													# prior

													self.scheduler.set_timesteps(_A				,							device=_A		)
													__SCREAMING_SNAKE_CASE									= self.scheduler.timesteps

													__SCREAMING_SNAKE_CASE									= self.prior.config.num_embeddings
													__SCREAMING_SNAKE_CASE									= self.prior.config.embedding_dim

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

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

																				__SCREAMING_SNAKE_CASE									= self.prior(
																				    _A				,							timestep=_A				,							proj_embedding=_A				,							).predicted_image_embedding

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

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

																				__SCREAMING_SNAKE_CASE									= self.scheduler.step(
																				    _A				,							timestep=_A				,							sample=_A				,							).prev_sample

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

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

																				images.append(_A		)

													__SCREAMING_SNAKE_CASE									= torch.stack(_A		)

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

													__SCREAMING_SNAKE_CASE									= images.cpu().numpy()

													if output_type == "pil":
																				__SCREAMING_SNAKE_CASE									= [self.numpy_to_pil(_A		) 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=_A		)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__		:					List[str]							={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TimesformerModel''',
			    '''TimesformerForVideoClassification''',
			    '''TimesformerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_timesformer import (
						    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TimesformerForVideoClassification,
						    TimesformerModel,
						    TimesformerPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase__		:					Union[str, Any]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






