


'''simple docstring'''





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__  			=				'''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Dict,_SCREAMING_SNAKE_CASE					: Optional[int]=8 ):






			"""simple docstring"""




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






class 						a__					(					a_   ):





			'''simple docstring'''

			def __init__(  self							:			int			,    lowerCAmelCase_							:			MultilingualCLIP			,    lowerCAmelCase_							:			XLMRobertaTokenizer			,    lowerCAmelCase_							:			UNetaDConditionModel			,    lowerCAmelCase_							:			Union[DDIMScheduler, DDPMScheduler]			,    lowerCAmelCase_							:			VQModel			,    )      ->		str:
						super().__init__()

						self.register_modules(
						    text_encoder=lowerCAmelCase_			,    tokenizer=lowerCAmelCase_			,    unet=lowerCAmelCase_			,    scheduler=lowerCAmelCase_			,    movq=lowerCAmelCase_			,    )
						__A=	2 ** (len(self.movq.config.block_out_channels	) - 1)
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		int:
						if latents is None:
									__A=	randn_tensor(lowerCAmelCase_			,    generator=lowerCAmelCase_			,    device=lowerCAmelCase_			,    dtype=lowerCAmelCase_	)
						else:
									if latents.shape != shape:
												raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
									__A=	latents.to(lowerCAmelCase_	)

						__A=	latents * scheduler.init_noise_sigma
						return latents
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			List[str]=None			,    )      ->		Union[str, Any]:
						__A=	len(lowerCAmelCase_	) if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	) else 1
						# get prompt text embeddings
						__A=	self.tokenizer(
						    lowerCAmelCase_			,    padding='max_length'			,    truncation=lowerCAmelCase_			,    max_length=77			,    return_attention_mask=lowerCAmelCase_			,    add_special_tokens=lowerCAmelCase_			,    return_tensors='pt'			,    )

						__A=	text_inputs.input_ids
						__A=	self.tokenizer(lowerCAmelCase_			,    padding='longest'			,    return_tensors='pt'	).input_ids

						if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase_			,    lowerCAmelCase_	):
									__A=	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}"""	)

						__A=	text_input_ids.to(lowerCAmelCase_	)
						__A=	text_inputs.attention_mask.to(lowerCAmelCase_	)

						__A,	__A=	self.text_encoder(
						    input_ids=lowerCAmelCase_			,    attention_mask=lowerCAmelCase_	)

						__A=	prompt_embeds.repeat_interleave(lowerCAmelCase_			,    dim=0	)
						__A=	text_encoder_hidden_states.repeat_interleave(lowerCAmelCase_			,    dim=0	)
						__A=	text_mask.repeat_interleave(lowerCAmelCase_			,    dim=0	)

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

									__A=	self.tokenizer(
									    lowerCAmelCase_			,    padding='max_length'			,    max_length=77			,    truncation=lowerCAmelCase_			,    return_attention_mask=lowerCAmelCase_			,    add_special_tokens=lowerCAmelCase_			,    return_tensors='pt'			,    )
									__A=	uncond_input.input_ids.to(lowerCAmelCase_	)
									__A=	uncond_input.attention_mask.to(lowerCAmelCase_	)

									__A,	__A=	self.text_encoder(
									    input_ids=lowerCAmelCase_			,    attention_mask=lowerCAmelCase_	)

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

									__A=	negative_prompt_embeds.shape[1]
									__A=	negative_prompt_embeds.repeat(1			,    lowerCAmelCase_	)
									__A=	negative_prompt_embeds.view(batch_size * num_images_per_prompt			,    lowerCAmelCase_	)

									__A=	uncond_text_encoder_hidden_states.shape[1]
									__A=	uncond_text_encoder_hidden_states.repeat(1			,    lowerCAmelCase_			,    1	)
									__A=	uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt			,    lowerCAmelCase_			,    -1	)
									__A=	uncond_text_mask.repeat_interleave(lowerCAmelCase_			,    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
									__A=	torch.cat([negative_prompt_embeds, prompt_embeds]	)
									__A=	torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)

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

						return prompt_embeds, text_encoder_hidden_states, text_mask
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			List[Any]=0	)      ->		int:
						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.unet,
						    self.text_encoder,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			int=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.'	)

						__A=	torch.device(F"""cuda:{gpu_id}"""	)

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

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

						if self.safety_checker is not None:
									__A,	__A=	cpu_offload_with_hook(self.safety_checker			,    lowerCAmelCase_			,    prev_module_hook=lowerCAmelCase_	)

						# We'll offload the last model manually.
						__A=	hook
			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[Any]:
						if not hasattr(self.unet			,    '_hf_hook'	):
									return self.device
						for module in self.unet.modules():
									if (
									    hasattr(lowerCAmelCase_			,    '_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(lowerCAmelCase_	)
			def __call__(  self							:			Optional[int]			,    lowerCAmelCase_							:			Union[str, List[str]]			,    lowerCAmelCase_							:			Union[torch.FloatTensor, List[torch.FloatTensor]]			,    lowerCAmelCase_							:			Union[torch.FloatTensor, List[torch.FloatTensor]]			,    lowerCAmelCase_							:			Optional[Union[str, List[str]]] = None			,    lowerCAmelCase_							:			int = 512			,    lowerCAmelCase_							:			int = 512			,    lowerCAmelCase_							:			int = 100			,    lowerCAmelCase_							:			float = 4.0			,    lowerCAmelCase_							:			int = 1			,    lowerCAmelCase_							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    lowerCAmelCase_							:			Optional[torch.FloatTensor] = None			,    lowerCAmelCase_							:			Optional[str] = "pil"			,    lowerCAmelCase_							:			bool = True			,    )      ->		Optional[Any]:

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

						__A=	self._execution_device

						__A=	batch_size * num_images_per_prompt
						__A=	guidance_scale > 1.0

						__A,	__A,	__A=	self._encode_prompt(
						    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_	)

						if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	):
									__A=	torch.cat(lowerCAmelCase_			,    dim=0	)
						if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	):
									__A=	torch.cat(lowerCAmelCase_			,    dim=0	)

						if do_classifier_free_guidance:
									__A=	image_embeds.repeat_interleave(lowerCAmelCase_			,    dim=0	)
									__A=	negative_image_embeds.repeat_interleave(lowerCAmelCase_			,    dim=0	)

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

						self.scheduler.set_timesteps(lowerCAmelCase_			,    device=lowerCAmelCase_	)
						__A=	self.scheduler.timesteps

						__A=	self.unet.config.in_channels

						__A,	__A=	get_new_h_w(lowerCAmelCase_			,    lowerCAmelCase_			,    self.movq_scale_factor	)

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

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

									__A=	{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
									__A=	self.unet(
									    sample=lowerCAmelCase_			,    timestep=lowerCAmelCase_			,    encoder_hidden_states=lowerCAmelCase_			,    added_cond_kwargs=lowerCAmelCase_			,    return_dict=lowerCAmelCase_			,    )[0]

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

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

						if output_type == "pil":
									__A=	self.numpy_to_pil(lowerCAmelCase_	)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=lowerCAmelCase_	)







'''simple docstring'''





class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict	)      ->		List[str]:
						__A=	name
						__A=	val
			def __str__(  self							:			int	)      ->		List[Any]:
						return F"""{self.__class__.__name__}({self.name}, {self.val})"""

			def __lt__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			str	)      ->		List[Any]:
						return self.val < other.val


class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			int	)      ->		Any:
						__A=	{}
						__A=	{}
						__A=	self.build_heap(lowerCAmelCase_	)
			def __getitem__(  self							:			int			,    lowerCAmelCase_							:			Dict	)      ->		Tuple:
						return self.get_value(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						return (idx - 1) // 2
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		List[str]:
						return idx * 2 + 1
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Any:
						return idx * 2 + 2
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Dict:
						return self.heap_dict[key]
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Tuple:
						__A=	len(lowerCAmelCase_	) - 1
						__A=	self.get_parent_idx(lowerCAmelCase_	)

						for idx, i in enumerate(lowerCAmelCase_	):
									__A=	idx
									__A=	i.val

						for i in range(lowerCAmelCase_			,    -1			,    -1	):
									self.sift_down(lowerCAmelCase_			,    lowerCAmelCase_	)
						return array
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			Tuple	)      ->		Dict:
						while True:
									__A=	self.get_left_child_idx(lowerCAmelCase_	)  # noqa: E741
									__A=	self.get_right_child_idx(lowerCAmelCase_	)

									__A=	idx
									if l < len(lowerCAmelCase_	) and array[l] < array[idx]:
												__A=	l
									if r < len(lowerCAmelCase_	) and array[r] < array[smallest]:
												__A=	r

									if smallest != idx:
												__A,	__A=	array[smallest], array[idx]
												(
												    (
												    __A
												),	(
												    __A
												),	
												)=	(
												    self.idx_of_element[array[smallest]],
												    self.idx_of_element[array[idx]],
												)
												__A=	smallest
									else:
												break
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			Dict	)      ->		str:
						__A=	self.get_parent_idx(lowerCAmelCase_	)
						while p >= 0 and self.heap[p] > self.heap[idx]:
									__A,	__A=	self.heap[idx], self.heap[p]
									__A,	__A=	(
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									__A=	p
									__A=	self.get_parent_idx(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return self.heap[0]
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		int:
						__A,	__A=	self.heap[-1], self.heap[0]
						__A,	__A=	(
						    self.idx_of_element[self.heap[-1]],
						    self.idx_of_element[self.heap[0]],
						)

						__A=	self.heap.pop()
						del self.idx_of_element[x]
						self.sift_down(0			,    self.heap	)
						return x
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Optional[int]:
						self.heap.append(lowerCAmelCase_	)
						__A=	len(self.heap	) - 1
						__A=	node.val
						self.sift_up(len(self.heap	) - 1	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return len(self.heap	) == 0

			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Optional[Any]:
						assert (
						    self.heap[self.idx_of_element[node]].val > new_value
						), "newValue must be less that current value"
						__A=	new_value
						__A=	new_value
						self.sift_up(self.idx_of_element[node]	)


UpperCAmelCase__  			=				Node('''R''', -1)
UpperCAmelCase__  			=				Node('''B''', 6)
UpperCAmelCase__  			=				Node('''A''', 3)
UpperCAmelCase__  			=				Node('''X''', 1)
UpperCAmelCase__  			=				Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
UpperCAmelCase__  			=				MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
					print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()



