



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


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

lowercase_	       =			'''
    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 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       42






class 						__a (     SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    Tuple				,	snake_case_							:    PriorTransformer				,	snake_case_							:    CLIPVisionModel				,	snake_case_							:    CLIPImageProcessor				,	snake_case_							:    HeunDiscreteScheduler				,	snake_case_							:    ShapERenderer				,	)->							Dict:
						super().__init__()

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



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Optional[int]				,	snake_case_							:    Optional[int]				,	snake_case_							:    Tuple				,	snake_case_							:    int				,	snake_case_							:    List[str]				,	snake_case_							:    int)->							List[str]:
						if latents is None:
											__lowerCAmelCase	   =randn_tensor(snake_case_				,	generator=snake_case_				,	device=snake_case_				,	dtype=snake_case_)
						else:
											if latents.shape != shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
											__lowerCAmelCase	   =latents.to(snake_case_)

						__lowerCAmelCase	   =latents * scheduler.init_noise_sigma
						return latents



	def 		UpperCamelCase							(						self							:    List[str]				,	snake_case_							:    str=0)->							Optional[int]:
						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(snake_case_				,	snake_case_)



	@property
	def 		UpperCamelCase							(						self							:    Dict)->							List[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(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							:    int				,	snake_case_							:    Dict				,	snake_case_							:    Optional[Any]				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	)->							str:
						if isinstance(snake_case_				,	snake_case_) and isinstance(image[0]				,	torch.Tensor):
											__lowerCAmelCase	   =torch.cat(snake_case_				,	axis=0) if image[0].ndim == 4 else torch.stack(snake_case_				,	axis=0)

						if not isinstance(snake_case_				,	torch.Tensor):
											__lowerCAmelCase	   =self.image_processor(snake_case_				,	return_tensors="""pt""").pixel_values[0].unsqueeze(0)

						__lowerCAmelCase	   =image.to(dtype=self.image_encoder.dtype				,	device=snake_case_)

						__lowerCAmelCase	   =self.image_encoder(snake_case_)["""last_hidden_state"""]
						__lowerCAmelCase	   =image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

						__lowerCAmelCase	   =image_embeds.repeat_interleave(snake_case_				,	dim=0)

						if do_classifier_free_guidance:
											__lowerCAmelCase	   =torch.zeros_like(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(snake_case_)
	def __call__(						self							:    Optional[int]				,	snake_case_							:    Union[PIL.Image.Image, List[PIL.Image.Image]]				,	snake_case_							:    int = 1				,	snake_case_							:    int = 25				,	snake_case_							:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,	snake_case_							:    Optional[torch.FloatTensor] = None				,	snake_case_							:    float = 4.0				,	snake_case_							:    int = 64				,	snake_case_							:    Optional[str] = "pil"				,	snake_case_							:    bool = True				,	)->							int:

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

						__lowerCAmelCase	   =self._execution_device

						__lowerCAmelCase	   =batch_size * num_images_per_prompt

						__lowerCAmelCase	   =guidance_scale > 1.0
						__lowerCAmelCase	   =self._encode_image(snake_case_				,	snake_case_				,	snake_case_				,	snake_case_)

						# prior

						self.scheduler.set_timesteps(snake_case_				,	device=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				,	snake_case_				,	snake_case_				,	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]				,	snake_case_				,	snake_case_)

						for i, t in enumerate(self.progress_bar(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(snake_case_				,	snake_case_)

											__lowerCAmelCase	   =self.prior(
											    snake_case_				,	timestep=snake_case_				,	proj_embedding=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(
											    snake_case_				,	timestep=snake_case_				,	sample=snake_case_				,	).prev_sample

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

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

											images.append(snake_case_)

						__lowerCAmelCase	   =torch.stack(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(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=snake_case_)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =set({"""(""", """[""", """{"""}	)
					__lowerCAmelCase	   =set({""")""", """]""", """}"""}	)
					__lowerCAmelCase	   ={"""{""": """}""", """[""": """]""", """(""": """)"""}

					for i in range(len(__lowerCamelCase	)	):
										if s[i] in open_brackets:
															stack.append(s[i]	)

										elif s[i] in closed_brackets and (
										    len(__lowerCamelCase	) == 0 or (len(__lowerCamelCase	) > 0 and open_to_closed[stack.pop()] != s[i])
										):
															return False

					return len(__lowerCamelCase	) == 0






def 					__lowerCAmelCase		(  )  ->						List[str]:
					__lowerCAmelCase	   =input("""Enter sequence of brackets: """	)
					if is_balanced(__lowerCamelCase	):
										print(__lowerCamelCase				,   """is balanced"""	)
					else:
										print(__lowerCamelCase				,   """is not balanced"""	)


if __name__ == "__main__":
				main()




