






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

__UpperCAmelCase							:   str							=  "\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 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							List[str]=8      ):





				'''simple docstring'''


				snake_case__      :str         =   h // scale_factor**2
				if h % scale_factor**2 != 0:
								new_h += 1
				snake_case__      :int         =   w // scale_factor**2
				if w % scale_factor**2 != 0:
								new_w += 1
				return new_h * scale_factor, new_w * scale_factor


class 					_snake_case						(			__A ):






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,)						->    int:
					super().__init__()

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Any:
					if latents is None:
									snake_case__      :Optional[int]         =   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}'       )
									snake_case__      :str         =   latents.to(UpperCamelCase__       )

					snake_case__      :Tuple         =   latents * scheduler.init_noise_sigma
					return latents






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None					,)						->    str:
					snake_case__      :Dict         =   len(UpperCamelCase__       ) if isinstance(UpperCamelCase__					,UpperCamelCase__       ) else 1
					# get prompt text embeddings
					snake_case__      :Union[str, Any]         =   self.tokenizer(
					    UpperCamelCase__					,padding="max_length"					,truncation=UpperCamelCase__					,max_length=77					,return_attention_mask=UpperCamelCase__					,add_special_tokens=UpperCamelCase__					,return_tensors="pt"					,)

					snake_case__      :Optional[Any]         =   text_inputs.input_ids
					snake_case__      :int         =   self.tokenizer(UpperCamelCase__					,padding="longest"					,return_tensors="pt"       ).input_ids

					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCamelCase__					,UpperCamelCase__       ):
									snake_case__      :List[str]         =   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}'       )

					snake_case__      :Tuple         =   text_input_ids.to(UpperCamelCase__       )
					snake_case__      :str         =   text_inputs.attention_mask.to(UpperCamelCase__       )

					snake_case__      :Optional[int]         =   self.text_encoder(
					    input_ids=UpperCamelCase__					,attention_mask=UpperCamelCase__       )

					snake_case__      :List[str]         =   prompt_embeds.repeat_interleave(UpperCamelCase__					,dim=0       )
					snake_case__      :int         =   text_encoder_hidden_states.repeat_interleave(UpperCamelCase__					,dim=0       )
					snake_case__      :str         =   text_mask.repeat_interleave(UpperCamelCase__					,dim=0       )

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

									snake_case__      :Dict         =   self.tokenizer(
									    UpperCamelCase__					,padding="max_length"					,max_length=77					,truncation=UpperCamelCase__					,return_attention_mask=UpperCamelCase__					,add_special_tokens=UpperCamelCase__					,return_tensors="pt"					,)
									snake_case__      :List[str]         =   uncond_input.input_ids.to(UpperCamelCase__       )
									snake_case__      :Dict         =   uncond_input.attention_mask.to(UpperCamelCase__       )

									snake_case__      :List[Any]         =   self.text_encoder(
									    input_ids=UpperCamelCase__					,attention_mask=UpperCamelCase__       )

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

									snake_case__      :List[Any]         =   negative_prompt_embeds.shape[1]
									snake_case__      :str         =   negative_prompt_embeds.repeat(1					,UpperCamelCase__       )
									snake_case__      :str         =   negative_prompt_embeds.view(batch_size * num_images_per_prompt					,UpperCamelCase__       )

									snake_case__      :Any         =   uncond_text_encoder_hidden_states.shape[1]
									snake_case__      :Union[str, Any]         =   uncond_text_encoder_hidden_states.repeat(1					,UpperCamelCase__					,1       )
									snake_case__      :int         =   uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt					,UpperCamelCase__					,-1       )
									snake_case__      :str         =   uncond_text_mask.repeat_interleave(UpperCamelCase__					,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
									snake_case__      :int         =   torch.cat([negative_prompt_embeds, prompt_embeds]       )
									snake_case__      :Any         =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]       )

									snake_case__      :str         =   torch.cat([uncond_text_mask, text_mask]       )

					return prompt_embeds, text_encoder_hidden_states, text_mask






	def    lowerCAmelCase_							(		self					,UpperCamelCase=0       )						->    int:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`"       )

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

					snake_case__      :Any         =   [
					    self.unet,
					    self.text_encoder,
					    self.movq,
					]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(UpperCamelCase__					,UpperCamelCase__       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase=0       )						->    str:
					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."       )

					snake_case__      :Optional[Any]         =   torch.device(f'cuda:{gpu_id}'       )

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

					snake_case__      :Optional[int]         =   None
					for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
									snake_case__      :List[Any]         =   cpu_offload_with_hook(UpperCamelCase__					,UpperCamelCase__					,prev_module_hook=UpperCamelCase__       )

					if self.safety_checker is not None:
									snake_case__      :List[Any]         =   cpu_offload_with_hook(self.safety_checker					,UpperCamelCase__					,prev_module_hook=UpperCamelCase__       )

					# We'll offload the last model manually.
					snake_case__      :List[str]         =   hook






	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    lowerCAmelCase_							(		self       )						->    Any:
					if not hasattr(self.unet					,"_hf_hook"       ):
									return self.device
					for module in self.unet.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






	@torch.no_grad()
	@replace_example_docstring(UpperCamelCase__       )
	def __call__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = 512					,UpperCamelCase = 512					,UpperCamelCase = 100					,UpperCamelCase = 4.0					,UpperCamelCase = 1					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = "pil"					,UpperCamelCase = True					,)						->    int:

					if isinstance(UpperCamelCase__					,UpperCamelCase__       ):
									snake_case__      :Optional[int]         =   1
					elif isinstance(UpperCamelCase__					,UpperCamelCase__       ):
									snake_case__      :Union[str, Any]         =   len(UpperCamelCase__       )
					else:
									raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__       )}'       )

					snake_case__      :str         =   self._execution_device

					snake_case__      :str         =   batch_size * num_images_per_prompt
					snake_case__      :int         =   guidance_scale > 1.0

					snake_case__      :int         =   self._encode_prompt(
					    UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__       )

					if isinstance(UpperCamelCase__					,UpperCamelCase__       ):
									snake_case__      :Optional[Any]         =   torch.cat(UpperCamelCase__					,dim=0       )
					if isinstance(UpperCamelCase__					,UpperCamelCase__       ):
									snake_case__      :Optional[Any]         =   torch.cat(UpperCamelCase__					,dim=0       )

					if do_classifier_free_guidance:
									snake_case__      :Dict         =   image_embeds.repeat_interleave(UpperCamelCase__					,dim=0       )
									snake_case__      :Optional[int]         =   negative_image_embeds.repeat_interleave(UpperCamelCase__					,dim=0       )

					snake_case__      :List[Any]         =   torch.cat([negative_image_embeds, image_embeds]					,dim=0       ).to(
					    dtype=prompt_embeds.dtype					,device=UpperCamelCase__       )

					self.scheduler.set_timesteps(UpperCamelCase__					,device=UpperCamelCase__       )
					snake_case__      :List[Any]         =   self.scheduler.timesteps

					snake_case__      :Any         =   self.unet.config.in_channels

					snake_case__      :Optional[Any]         =   get_new_h_w(UpperCamelCase__					,UpperCamelCase__					,self.movq_scale_factor       )

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

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

									snake_case__      :List[str]         =   {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
									snake_case__      :str         =   self.unet(
									    sample=UpperCamelCase__					,timestep=UpperCamelCase__					,encoder_hidden_states=UpperCamelCase__					,added_cond_kwargs=UpperCamelCase__					,return_dict=UpperCamelCase__					,)[0]

									if do_classifier_free_guidance:
													snake_case__      :int         =   noise_pred.split(latents.shape[1]					,dim=1       )
													snake_case__      :Dict         =   noise_pred.chunk(2       )
													snake_case__      :str         =   variance_pred.chunk(2       )
													snake_case__      :Optional[int]         =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													snake_case__      :Tuple         =   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"]
									):
													snake_case__      :Union[str, Any]         =   noise_pred.split(latents.shape[1]					,dim=1       )

									# compute the previous noisy sample x_t -> x_t-1
									snake_case__      :Any         =   self.scheduler.step(
									    UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,generator=UpperCamelCase__					,).prev_sample
					# post-processing
					snake_case__      :int         =   self.movq.decode(UpperCamelCase__					,force_not_quantize=UpperCamelCase__       )['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"]:
									snake_case__      :Union[str, Any]         =   image * 0.5 + 0.5
									snake_case__      :Optional[int]         =   image.clamp(0					,1       )
									snake_case__      :List[Any]         =   image.cpu().permute(0					,2					,3					,1       ).float().numpy()

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

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=UpperCamelCase__       )







def 					lowercase_ (    __snake_case     :							int = 10_00      )     -> int:





				'''simple docstring'''


				snake_case__      :int         =   3
				snake_case__      :int         =   0
				while a < n:
								if a % 3 == 0 or a % 5 == 0:
												result += a
								elif a % 15 == 0:
												result -= a
								a += 1
				return result


if __name__ == "__main__":
	print(F'''{solution() = }''')