


def        UpperCamelCase_(				_A   :list				,     _A   :list				,     _A   :int				,     _A   :int				,     _A   :int  )->		int:
			if index == number_of_items:
						return 0
			UpperCamelCase__					=       0
			UpperCamelCase__					=       0
			UpperCamelCase__					=       knapsack(_A				,     _A				,     _A				,     _A				,     index + 1  )
			if weights[index] <= max_weight:
						UpperCamelCase__					=       values[index] + knapsack(
						    _A				,     _A				,     _A				,     max_weight - weights[index]				,     index + 1  )
			return max(_A				,     _A  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()






from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

__UpperCamelCase    		=       '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def        UpperCamelCase_(				_A   :Dict				,     _A   :Union[str, Any]				,     _A   :Optional[int]=8  )->		Dict:
			UpperCamelCase__					=       h // scale_factor**2
			if h % scale_factor**2 != 0:
						new_h += 1
			UpperCamelCase__					=       w // scale_factor**2
			if w % scale_factor**2 != 0:
						new_w += 1
			return new_h * scale_factor, new_w * scale_factor







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						):

						'''simple docstring'''


						super().__init__()

						self.register_modules(
						    text_encoder=snake_case      ,						tokenizer=snake_case      ,						unet=snake_case      ,						scheduler=snake_case      ,						movq=snake_case      ,						)
						UpperCamelCase__					=       2 ** (len(self.movq.config.block_out_channels     ) - 1)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if latents is None:
									UpperCamelCase__					=       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}'''     )
									UpperCamelCase__					=       latents.to(snake_case     )

						UpperCamelCase__					=       latents * scheduler.init_noise_sigma
						return latents




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case=None      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       len(snake_case     ) if isinstance(snake_case      ,						snake_case     ) else 1
						# get prompt text embeddings
						UpperCamelCase__					=       self.tokenizer(
						    snake_case      ,						padding="max_length"      ,						truncation=snake_case      ,						max_length=77      ,						return_attention_mask=snake_case      ,						add_special_tokens=snake_case      ,						return_tensors="pt"      ,						)

						UpperCamelCase__					=       text_inputs.input_ids
						UpperCamelCase__					=       self.tokenizer(snake_case      ,						padding="longest"      ,						return_tensors="pt"     ).input_ids

						if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case      ,						snake_case     ):
									UpperCamelCase__					=       self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
									logger.warning(
									    "The following part of your input was truncated because CLIP can only handle sequences up to"
									    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )

						UpperCamelCase__					=       text_input_ids.to(snake_case     )
						UpperCamelCase__					=       text_inputs.attention_mask.to(snake_case     )

						UpperCamelCase__,      UpperCamelCase__					=       self.text_encoder(
						    input_ids=snake_case      ,						attention_mask=snake_case     )

						UpperCamelCase__					=       prompt_embeds.repeat_interleave(snake_case      ,						dim=0     )
						UpperCamelCase__					=       text_encoder_hidden_states.repeat_interleave(snake_case      ,						dim=0     )
						UpperCamelCase__					=       text_mask.repeat_interleave(snake_case      ,						dim=0     )

						if do_classifier_free_guidance:
									UpperCamelCase__					=       42
									if negative_prompt is None:
												UpperCamelCase__					=       [""] * batch_size
									elif type(snake_case     ) is not type(snake_case     ):
												raise TypeError(
												    F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case     )} !='''
												    F''' {type(snake_case     )}.'''     )
									elif isinstance(snake_case      ,						snake_case     ):
												UpperCamelCase__					=       [negative_prompt]
									elif batch_size != len(snake_case     ):
												raise ValueError(
												    F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case     )}, but `prompt`:'''
												    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
												    " the batch size of `prompt`."     )
									else:
												UpperCamelCase__					=       negative_prompt

									UpperCamelCase__					=       self.tokenizer(
									    snake_case      ,						padding="max_length"      ,						max_length=77      ,						truncation=snake_case      ,						return_attention_mask=snake_case      ,						add_special_tokens=snake_case      ,						return_tensors="pt"      ,						)
									UpperCamelCase__					=       uncond_input.input_ids.to(snake_case     )
									UpperCamelCase__					=       uncond_input.attention_mask.to(snake_case     )

									UpperCamelCase__,      UpperCamelCase__					=       self.text_encoder(
									    input_ids=snake_case      ,						attention_mask=snake_case     )

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

									UpperCamelCase__					=       negative_prompt_embeds.shape[1]
									UpperCamelCase__					=       negative_prompt_embeds.repeat(1      ,						snake_case     )
									UpperCamelCase__					=       negative_prompt_embeds.view(batch_size * num_images_per_prompt      ,						snake_case     )

									UpperCamelCase__					=       uncond_text_encoder_hidden_states.shape[1]
									UpperCamelCase__					=       uncond_text_encoder_hidden_states.repeat(1      ,						snake_case      ,						1     )
									UpperCamelCase__					=       uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt      ,						snake_case      ,						-1     )
									UpperCamelCase__					=       uncond_text_mask.repeat_interleave(snake_case      ,						dim=0     )

									# done duplicates

									# 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_prompt_embeds, prompt_embeds]     )
									UpperCamelCase__					=       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

									UpperCamelCase__					=       torch.cat([uncond_text_mask, text_mask]     )

						return prompt_embeds, text_encoder_hidden_states, text_mask




			def 				snake_case__				(							self      ,						snake_case=0     ):

						'''simple docstring'''


						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.unet,
						    self.text_encoder,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(snake_case      ,						snake_case     )




			def 				snake_case__				(							self      ,						snake_case=0     ):

						'''simple docstring'''


						if is_accelerate_available() and is_accelerate_version(">="      ,						"0.17.0.dev0"     ):
									from accelerate import cpu_offload_with_hook
						else:
									raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."     )

						UpperCamelCase__					=       torch.device(F'''cuda:{gpu_id}'''     )

						if self.device.type != "cpu":
									self.to("cpu"      ,						silence_dtype_warnings=snake_case     )
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						UpperCamelCase__					=       None
						for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
									UpperCamelCase__,      UpperCamelCase__					=       cpu_offload_with_hook(snake_case      ,						snake_case      ,						prev_module_hook=snake_case     )

						if self.safety_checker is not None:
									UpperCamelCase__,      UpperCamelCase__					=       cpu_offload_with_hook(self.safety_checker      ,						snake_case      ,						prev_module_hook=snake_case     )

						# We'll offload the last model manually.
						UpperCamelCase__					=       hook




			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if not hasattr(self.unet      ,						"_hf_hook"     ):
									return self.device
						for module in self.unet.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







			@torch.no_grad()
			@replace_example_docstring(snake_case     )
			def __call__(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						snake_case = 512      ,						snake_case = 512      ,						snake_case = 100      ,						snake_case = 4.0      ,						snake_case = 1      ,						snake_case = None      ,						snake_case = None      ,						snake_case = "pil"      ,						snake_case = True      ,						):

						'''simple docstring'''


						if isinstance(snake_case      ,						snake_case     ):
									UpperCamelCase__					=       1
						elif isinstance(snake_case      ,						snake_case     ):
									UpperCamelCase__					=       len(snake_case     )
						else:
									raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case     )}'''     )

						UpperCamelCase__					=       self._execution_device

						UpperCamelCase__					=       batch_size * num_images_per_prompt
						UpperCamelCase__					=       guidance_scale > 1.0

						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       self._encode_prompt(
						    snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     )

						if isinstance(snake_case      ,						snake_case     ):
									UpperCamelCase__					=       torch.cat(snake_case      ,						dim=0     )
						if isinstance(snake_case      ,						snake_case     ):
									UpperCamelCase__					=       torch.cat(snake_case      ,						dim=0     )

						if do_classifier_free_guidance:
									UpperCamelCase__					=       image_embeds.repeat_interleave(snake_case      ,						dim=0     )
									UpperCamelCase__					=       negative_image_embeds.repeat_interleave(snake_case      ,						dim=0     )

						UpperCamelCase__					=       torch.cat([negative_image_embeds, image_embeds]      ,						dim=0     ).to(
						    dtype=prompt_embeds.dtype      ,						device=snake_case     )

						self.scheduler.set_timesteps(snake_case      ,						device=snake_case     )
						UpperCamelCase__					=       self.scheduler.timesteps

						UpperCamelCase__					=       self.unet.config.in_channels

						UpperCamelCase__,      UpperCamelCase__					=       get_new_h_w(snake_case      ,						snake_case      ,						self.movq_scale_factor     )

						# create initial latent
						UpperCamelCase__					=       self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)      ,						text_encoder_hidden_states.dtype      ,						snake_case      ,						snake_case      ,						snake_case      ,						self.scheduler      ,						)

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

									UpperCamelCase__					=       {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
									UpperCamelCase__					=       self.unet(
									    sample=snake_case      ,						timestep=snake_case      ,						encoder_hidden_states=snake_case      ,						added_cond_kwargs=snake_case      ,						return_dict=snake_case      ,						)[0]

									if do_classifier_free_guidance:
												UpperCamelCase__,      UpperCamelCase__					=       noise_pred.split(latents.shape[1]      ,						dim=1     )
												UpperCamelCase__,      UpperCamelCase__					=       noise_pred.chunk(2     )
												UpperCamelCase__,      UpperCamelCase__					=       variance_pred.chunk(2     )
												UpperCamelCase__					=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												UpperCamelCase__					=       torch.cat([noise_pred, variance_pred_text]      ,						dim=1     )

									if not (
									    hasattr(self.scheduler.config      ,						"variance_type"     )
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
												UpperCamelCase__,      UpperCamelCase__					=       noise_pred.split(latents.shape[1]      ,						dim=1     )

									# compute the previous noisy sample x_t -> x_t-1
									UpperCamelCase__					=       self.scheduler.step(
									    snake_case      ,						snake_case      ,						snake_case      ,						generator=snake_case      ,						).prev_sample
						# post-processing
						UpperCamelCase__					=       self.movq.decode(snake_case      ,						force_not_quantize=snake_case     )["sample"]

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

						if output_type in ["np", "pil"]:
									UpperCamelCase__					=       image * 0.5 + 0.5
									UpperCamelCase__					=       image.clamp(0      ,						1     )
									UpperCamelCase__					=       image.cpu().permute(0      ,						2      ,						3      ,						1     ).float().numpy()

						if output_type == "pil":
									UpperCamelCase__					=       self.numpy_to_pil(snake_case     )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=snake_case     )


