




from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


lowercase_      :							List[Any]    							=					getLogger(__name__)


lowercase_      :							Optional[Any]    							=					4
lowercase_      :							Optional[Any]    							=					7

lowercase_      :							List[str]    							=					{
    'float16': torch.floataa,
    'float32': torch.floataa,
}


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						if isinstance(snake_case_      ,       torch.Tensor		):
												return image
						elif isinstance(snake_case_      ,       PIL.Image.Image		):
												_UpperCAmelCase						=	[image]

						if isinstance(image[0]      ,       PIL.Image.Image		):
												_UpperCAmelCase				,					_UpperCAmelCase						=	image[0].size
												_UpperCAmelCase				,					_UpperCAmelCase						=	(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

												_UpperCAmelCase						=	[np.array(i.resize((w, h)		)		)[None, :] for i in image]
												_UpperCAmelCase						=	np.concatenate(snake_case_      ,       axis=0		)
												_UpperCAmelCase						=	np.array(snake_case_		).astype(np.floataa		) / 255.0
												_UpperCAmelCase						=	image.transpose(0      ,       3      ,       1      ,       2		)
												_UpperCAmelCase						=	2.0 * image - 1.0
												_UpperCAmelCase						=	torch.from_numpy(snake_case_		)
						elif isinstance(image[0]      ,       torch.Tensor		):
												_UpperCAmelCase						=	torch.cat(snake_case_      ,       dim=0		)

						return image






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     Optional[int]       ,       snake_case__       :     OnnxRuntimeModel       ,       snake_case__       :     OnnxRuntimeModel       ,       snake_case__       :     Any       ,       snake_case__       :     OnnxRuntimeModel       ,       snake_case__       :     DDPMScheduler       ,       snake_case__       :     Any       ,       snake_case__       :     int = 350       ,       ):




									"""simple docstring"""


									super().__init__(
									    vae=snake_case__       ,       text_encoder=snake_case__       ,       tokenizer=snake_case__       ,       unet=snake_case__       ,       low_res_scheduler=snake_case__       ,       scheduler=snake_case__       ,       safety_checker=snake_case__       ,       feature_extractor=snake_case__       ,       watermarker=snake_case__       ,       max_noise_level=snake_case__       ,       )







			def __call__(			self       :     Any       ,       snake_case__       :     Union[str, List[str]]       ,       snake_case__       :     Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]       ,       snake_case__       :     int = 75       ,       snake_case__       :     float = 9.0       ,       snake_case__       :     int = 20       ,       snake_case__       :     Optional[Union[str, List[str]]] = None       ,       snake_case__       :     Optional[int] = 1       ,       snake_case__       :     float = 0.0       ,       snake_case__       :     Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,       snake_case__       :     Optional[torch.FloatTensor] = None       ,       snake_case__       :     Optional[np.ndarray] = None       ,       snake_case__       :     Optional[np.ndarray] = None       ,       snake_case__       :     Optional[str] = "pil"       ,       snake_case__       :     bool = True       ,       snake_case__       :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,       snake_case__       :     Optional[int] = 1       ,       ):




									"""simple docstring"""


									self.check_inputs(snake_case__       ,       snake_case__       ,       snake_case__       ,       snake_case__ )

									# 2. Define call parameters
									if prompt is not None and isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	1
									elif prompt is not None and isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	len(snake_case__ )
									else:
															_UpperCAmelCase						=	prompt_embeds.shape[0]

									_UpperCAmelCase						=	self._execution_device
									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									_UpperCAmelCase						=	guidance_scale > 1.0

									# 3. Encode input prompt
									_UpperCAmelCase						=	self._encode_prompt(
									    snake_case__       ,       snake_case__       ,       snake_case__       ,       snake_case__       ,       snake_case__       ,       prompt_embeds=snake_case__       ,       negative_prompt_embeds=snake_case__       ,       )

									_UpperCAmelCase						=	ORT_TO_PT_TYPE[str(text_embeddings.dtype )]

									# 4. Preprocess image
									_UpperCAmelCase						=	preprocess(snake_case__ )
									_UpperCAmelCase						=	image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(snake_case__       ,       device=snake_case__ )
									_UpperCAmelCase						=	self.scheduler.timesteps

									# 5. Add noise to image
									_UpperCAmelCase						=	torch.tensor([noise_level]       ,       dtype=torch.long       ,       device=snake_case__ )
									_UpperCAmelCase						=	torch.randn(image.shape       ,       generator=snake_case__       ,       device=snake_case__       ,       dtype=snake_case__ )
									_UpperCAmelCase						=	self.low_res_scheduler.add_noise(snake_case__       ,       snake_case__       ,       snake_case__ )

									_UpperCAmelCase						=	2 if do_classifier_free_guidance else 1
									_UpperCAmelCase						=	np.concatenate([image] * batch_multiplier * num_images_per_prompt )
									_UpperCAmelCase						=	np.concatenate([noise_level] * image.shape[0] )

									# 6. Prepare latent variables
									_UpperCAmelCase				,					_UpperCAmelCase						=	image.shape[2:]
									_UpperCAmelCase						=	self.prepare_latents(
									    batch_size * num_images_per_prompt       ,       snake_case__       ,       snake_case__       ,       snake_case__       ,       snake_case__       ,       snake_case__       ,       snake_case__       ,       snake_case__       ,       )

									# 7. Check that sizes of image and latents match
									_UpperCAmelCase						=	image.shape[1]
									if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
															raise ValueError(
															    "Incorrect configuration settings! The config of `pipeline.unet` expects"
															    F""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
															    F""" `num_channels_image`: {num_channels_image} """
															    F""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
															    " `pipeline.unet` or your `image` input." )

									# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
									_UpperCAmelCase						=	self.prepare_extra_step_kwargs(snake_case__       ,       snake_case__ )

									_UpperCAmelCase						=	next(
									    (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")       ,       "tensor(float)" )
									_UpperCAmelCase						=	ORT_TO_NP_TYPE[timestep_dtype]

									# 9. Denoising loop
									_UpperCAmelCase						=	len(snake_case__ ) - num_inference_steps * self.scheduler.order
									with self.progress_bar(total=snake_case__ ) as progress_bar:
															for i, t in enumerate(snake_case__ ):
																					# expand the latents if we are doing classifier free guidance
																					_UpperCAmelCase						=	np.concatenate([latents] * 2 ) if do_classifier_free_guidance else latents

																					# concat latents, mask, masked_image_latents in the channel dimension
																					_UpperCAmelCase						=	self.scheduler.scale_model_input(snake_case__       ,       snake_case__ )
																					_UpperCAmelCase						=	np.concatenate([latent_model_input, image]       ,       axis=1 )

																					# timestep to tensor
																					_UpperCAmelCase						=	np.array([t]       ,       dtype=snake_case__ )

																					# predict the noise residual
																					_UpperCAmelCase						=	self.unet(
																					    sample=snake_case__       ,       timestep=snake_case__       ,       encoder_hidden_states=snake_case__       ,       class_labels=noise_level.astype(np.intaa )       ,       )[0]

																					# perform guidance
																					if do_classifier_free_guidance:
																											_UpperCAmelCase				,					_UpperCAmelCase						=	np.split(snake_case__       ,       2 )
																					_UpperCAmelCase						=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					# compute the previous noisy sample x_t -> x_t-1
																					_UpperCAmelCase						=	self.scheduler.step(
																					    torch.from_numpy(snake_case__ )       ,       snake_case__       ,       snake_case__       ,       **snake_case__ ).prev_sample

																					# call the callback, if provided
																					if i == len(snake_case__ ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																											progress_bar.update()
																											if callback is not None and i % callback_steps == 0:
																																	callback(snake_case__       ,       snake_case__       ,       snake_case__ )

        # 10. Post-processing
									_UpperCAmelCase						=	self.decode_latents(latents.float() )

									# 11. Convert to PIL
									if output_type == "pil":
															_UpperCAmelCase						=	self.numpy_to_pil(snake_case__ )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=snake_case__ )







			def UpperCamelCase     (			self       :     Union[str, Any]       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	1 / 0.08_333 * latents
									_UpperCAmelCase						=	self.vae(latent_sample=snake_case__ )[0]
									_UpperCAmelCase						=	np.clip(image / 2 + 0.5       ,       0       ,       1 )
									_UpperCAmelCase						=	image.transpose((0, 2, 3, 1) )
									return image







			def UpperCamelCase     (			self       :     List[Any]       ,       snake_case__       :     Union[str, List[str]]       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     Optional[int]       ,       snake_case__       :     bool       ,       snake_case__       :     Optional[str]       ,       snake_case__       :     Optional[torch.FloatTensor] = None       ,       snake_case__       :     Optional[torch.FloatTensor] = None       ,       ):




									"""simple docstring"""


									if prompt is not None and isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	1
									elif prompt is not None and isinstance(snake_case__       ,       snake_case__ ):
															_UpperCAmelCase						=	len(snake_case__ )
									else:
															_UpperCAmelCase						=	prompt_embeds.shape[0]

									if prompt_embeds is None:
															_UpperCAmelCase						=	self.tokenizer(
															    snake_case__       ,       padding="max_length"       ,       max_length=self.tokenizer.model_max_length       ,       truncation=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}""" )

															# no positional arguments to text_encoder
															_UpperCAmelCase						=	self.text_encoder(
															    input_ids=text_input_ids.int().to(snake_case__ )       ,       )
															_UpperCAmelCase						=	prompt_embeds[0]

									_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									_UpperCAmelCase						=	prompt_embeds.repeat(1       ,       snake_case__ )
									_UpperCAmelCase						=	prompt_embeds.reshape(bs_embed * num_images_per_prompt       ,       snake_case__       ,       -1 )

									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance and negative_prompt_embeds is None:
															_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						=	text_input_ids.shape[-1]
															_UpperCAmelCase						=	self.tokenizer(
															    snake_case__       ,       padding="max_length"       ,       max_length=snake_case__       ,       truncation=snake_case__       ,       return_tensors="pt"       ,       )

															# if hasattr(uncond_input, "attention_mask"):
															#     attention_mask = uncond_input.attention_mask.to(device)
															# else:
															#     attention_mask = None

															_UpperCAmelCase						=	self.text_encoder(
															    input_ids=uncond_input.input_ids.int().to(snake_case__ )       ,       )
															_UpperCAmelCase						=	uncond_embeddings[0]

									if do_classifier_free_guidance:
															_UpperCAmelCase						=	uncond_embeddings.shape[1]
															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															_UpperCAmelCase						=	uncond_embeddings.repeat(1       ,       snake_case__ )
															_UpperCAmelCase						=	uncond_embeddings.reshape(batch_size * num_images_per_prompt       ,       snake_case__       ,       -1 )

															# 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						=	np.concatenate([uncond_embeddings, prompt_embeds] )

									return prompt_embeds










import math
from itertools import permutations


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5      ,       int(math.sqrt(snake_case_		) + 1		)      ,       6		):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase				,					_UpperCAmelCase						=	0, len(snake_case_		) - 1
						while left <= right:
												_UpperCAmelCase						=	(left + right) // 2
												if prime_list[middle] == target:
																		return True
												elif prime_list[middle] < target:
																		_UpperCAmelCase						=	middle + 1
												else:
																		_UpperCAmelCase						=	middle - 1

						return False


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	[n for n in range(1001      ,       1_0000      ,       2		) if is_prime(snake_case_		)]
						_UpperCAmelCase						=	[]

						for number in prime_list:
												_UpperCAmelCase						=	[]

												for prime_member in permutations(list(str(snake_case_		)		)		):
																		_UpperCAmelCase						=	int("".join(snake_case_		)		)

																		if prime % 2 == 0:
																								continue

																		if search(snake_case_      ,       snake_case_		):
																								tmp_numbers.append(snake_case_		)

												tmp_numbers.sort()
												if len(snake_case_		) >= 3:
																		candidates.append(snake_case_		)

						_UpperCAmelCase						=	[]
						for candidate in candidates:
												_UpperCAmelCase						=	len(snake_case_		)
												_UpperCAmelCase						=	False

												for i in range(snake_case_		):
																		for j in range(i + 1      ,       snake_case_		):
																								for k in range(j + 1      ,       snake_case_		):
																														if (
																														    abs(candidate[i] - candidate[j]		)
																														    == abs(candidate[j] - candidate[k]		)
																														    and len({candidate[i], candidate[j], candidate[k]}		) == 3
																														):
																																				passed.append(
																																				    sorted([candidate[i], candidate[j], candidate[k]]		)		)
																																				_UpperCAmelCase						=	True

																														if found:
																																				break
																								if found:
																														break
																		if found:
																								break

						_UpperCAmelCase						=	set()
						for seq in passed:
												answer.add("".join([str(snake_case_		) for i in seq]		)		)

						return max(int(snake_case_		) for x in answer		)


if __name__ == "__main__":
				print(solution())




