
'''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


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

lowercase__											='\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_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Union[PIL.Image.Image, np.ndarray]







class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   ):
				super().__init__()

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				if latents is None:
					a_      				=						randn_tensor(UpperCAmelCase					,   generator=UpperCAmelCase					,   device=UpperCAmelCase					,   dtype=UpperCAmelCase	)
				else:
					if latents.shape != shape:
						raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
					a_      				=						latents.to(UpperCAmelCase	)

				a_      				=						latents * scheduler.init_noise_sigma
				return latents




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=0	):
				if is_accelerate_available():
					from accelerate import cpu_offload
				else:
					raise ImportError("""Please install accelerate via `pip install accelerate`"""	)

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

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




			@property
			def 					lowerCAmelCase__					(       self	):
				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(UpperCAmelCase					,   """_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					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   ):
				if isinstance(UpperCAmelCase					,   UpperCAmelCase	) and isinstance(image[0]					,   torch.Tensor	):
					a_      				=						torch.cat(UpperCAmelCase					,   axis=0	) if image[0].ndim == 4 else torch.stack(UpperCAmelCase					,   axis=0	)

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

				a_      				=						image.to(dtype=self.image_encoder.dtype					,   device=UpperCAmelCase	)

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

				a_      				=						image_embeds.repeat_interleave(UpperCAmelCase					,   dim=0	)

				if do_classifier_free_guidance:
					a_      				=						torch.zeros_like(UpperCAmelCase	)

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

				return image_embeds






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

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

				a_      				=						self._execution_device

				a_      				=						batch_size * num_images_per_prompt

				a_      				=						guidance_scale > 1.0
				a_      				=						self._encode_image(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

				# prior

				self.scheduler.set_timesteps(UpperCAmelCase					,   device=UpperCAmelCase	)
				a_      				=						self.scheduler.timesteps

				a_      				=						self.prior.config.num_embeddings
				a_      				=						self.prior.config.embedding_dim

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

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

					a_      				=						self.prior(
					    UpperCAmelCase					,   timestep=UpperCAmelCase					,   proj_embedding=UpperCAmelCase					,   ).predicted_image_embedding

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

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

					a_      				=						self.scheduler.step(
					    UpperCAmelCase					,   timestep=UpperCAmelCase					,   sample=UpperCAmelCase					,   ).prev_sample

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

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

					images.append(UpperCAmelCase	)

				a_      				=						torch.stack(UpperCAmelCase	)

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

				a_      				=						images.cpu().numpy()

				if output_type == "pil":
					a_      				=						[self.numpy_to_pil(UpperCAmelCase	) 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=UpperCAmelCase	)


'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):

	def count_of_possible_combinations(A__						) -> int:
		if target < 0:
			return 0
		if target == 0:
			return 1
		return sum(count_of_possible_combinations(target - item						) for item in array						)

	return count_of_possible_combinations(A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):

	def count_of_possible_combinations_with_dp_array(
	    A__  ,      A__						) -> int:
		if target < 0:
			return 0
		if target == 0:
			return 1
		if dp_array[target] != -1:
			return dp_array[target]
		a_      				=						sum(
		    count_of_possible_combinations_with_dp_array(target - item  ,      A__						)
		    for item in array						)
		a_      				=						answer
		return answer

	a_      				=						[-1] * (target + 1)
	return count_of_possible_combinations_with_dp_array(A__  ,      A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						[0] * (target + 1)
	a_      				=						1

	for i in range(1  ,      target + 1						):
		for j in range(A__						):
			if i - array[j] >= 0:
				dp_array[i] += dp_array[i - array[j]]

	return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowercase__											=3
		lowercase__											=5
		lowercase__											=[1, 2, 5]
		print(combination_sum_iv(n, array, target))
