


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


__a			 =   getLogger(__name__)


__a			 =   4
__a			 =   7

__a			 =   {
    "float16": torch.floataa,
    "float32": torch.floataa,
}






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if isinstance(_UpperCamelCase	,			torch.Tensor ):
												return image
						elif isinstance(_UpperCamelCase	,			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(_UpperCamelCase	,			axis=0 )
												UpperCAmelCase__		=    np.array(_UpperCamelCase ).astype(np.floataa ) / 255.0
												UpperCAmelCase__		=    image.transpose(0	,			3	,			1	,			2 )
												UpperCAmelCase__		=    2.0 * image - 1.0
												UpperCAmelCase__		=    torch.from_numpy(_UpperCamelCase )
						elif isinstance(image[0]	,			torch.Tensor ):
												UpperCAmelCase__		=    torch.cat(_UpperCamelCase	,			dim=0 )

						return image


class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""






							def __init__(						self :     int		, lowerCamelCase_ :     OnnxRuntimeModel		, lowerCamelCase_ :     OnnxRuntimeModel		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     OnnxRuntimeModel		, lowerCamelCase_ :     DDPMScheduler		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     int = 350		, ):
													super().__init__(
													    vae=lowerCamelCase_		, text_encoder=lowerCamelCase_		, tokenizer=lowerCamelCase_		, unet=lowerCamelCase_		, low_res_scheduler=lowerCamelCase_		, scheduler=lowerCamelCase_		, safety_checker=lowerCamelCase_		, feature_extractor=lowerCamelCase_		, watermarker=lowerCamelCase_		, max_noise_level=lowerCamelCase_		, )






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

													# 2. Define call parameters
													if prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    1
													elif prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    len(lowerCamelCase_				)
													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(
													    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, prompt_embeds=lowerCamelCase_		, negative_prompt_embeds=lowerCamelCase_		, )

													UpperCAmelCase__		=    ORT_TO_PT_TYPE[str(text_embeddings.dtype				)]

													# 4. Preprocess image
													UpperCAmelCase__		=    preprocess(lowerCamelCase_				)
													UpperCAmelCase__		=    image.cpu()

													# 5. set timesteps
													self.scheduler.set_timesteps(lowerCamelCase_		, device=lowerCamelCase_				)
													UpperCAmelCase__		=    self.scheduler.timesteps

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

													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		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

													# 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(lowerCamelCase_		, lowerCamelCase_				)

													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(lowerCamelCase_				) - num_inference_steps * self.scheduler.order
													with self.progress_bar(total=lowerCamelCase_				) as progress_bar:
																			for i, t in enumerate(lowerCamelCase_				):
																									# 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(lowerCamelCase_		, lowerCamelCase_				)
																									UpperCAmelCase__		=    np.concatenate([latent_model_input, image]		, axis=1				)

																									# timestep to tensor
																									UpperCAmelCase__		=    np.array([t]		, dtype=lowerCamelCase_				)

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

																									# perform guidance
																									if do_classifier_free_guidance:
																															UpperCAmelCase__	,				UpperCAmelCase__		=    np.split(lowerCamelCase_		, 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(lowerCamelCase_				)		, lowerCamelCase_		, lowerCamelCase_		, **lowerCamelCase_				).prev_sample

																									# call the callback, if provided
																									if i == len(lowerCamelCase_				) - 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(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_				)

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

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

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     Any				):
													UpperCAmelCase__		=    1 / 0.08333 * latents
													UpperCAmelCase__		=    self.vae(latent_sample=lowerCamelCase_				)[0]
													UpperCAmelCase__		=    np.clip(image / 2 + 0.5		, 0		, 1				)
													UpperCAmelCase__		=    image.transpose((0, 2, 3, 1)				)
													return image






							def 					lowerCAmelCase     (						self :     int		, lowerCamelCase_ :     Union[str, List[str]]		, lowerCamelCase_ :     Optional[int]		, lowerCamelCase_ :     Optional[int]		, lowerCamelCase_ :     bool		, lowerCamelCase_ :     Optional[str]		, lowerCamelCase_ :     Optional[torch.FloatTensor] = None		, lowerCamelCase_ :     Optional[torch.FloatTensor] = None		, ):
													if prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    1
													elif prompt is not None and isinstance(lowerCamelCase_		, lowerCamelCase_				):
																			UpperCAmelCase__		=    len(lowerCamelCase_				)
													else:
																			UpperCAmelCase__		=    prompt_embeds.shape[0]

													if prompt_embeds is None:
																			UpperCAmelCase__		=    self.tokenizer(
																			    lowerCamelCase_		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, truncation=lowerCamelCase_		, return_tensors='''pt'''		, )
																			UpperCAmelCase__		=    text_inputs.input_ids
																			UpperCAmelCase__		=    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_				):
																									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(lowerCamelCase_				)		, )
																			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		, lowerCamelCase_				)
													UpperCAmelCase__		=    prompt_embeds.reshape(bs_embed * num_images_per_prompt		, lowerCamelCase_		, -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(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_				):
																									UpperCAmelCase__		=    [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:
																									UpperCAmelCase__		=    negative_prompt

																			UpperCAmelCase__		=    text_input_ids.shape[-1]
																			UpperCAmelCase__		=    self.tokenizer(
																			    lowerCamelCase_		, padding='''max_length'''		, max_length=lowerCamelCase_		, truncation=lowerCamelCase_		, 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(lowerCamelCase_				)		, )
																			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		, lowerCamelCase_				)
																			UpperCAmelCase__		=    uncond_embeddings.reshape(batch_size * num_images_per_prompt		, lowerCamelCase_		, -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



"""simple docstring"""







from collections import defaultdict






def 					a__ (       _UpperCamelCase = 5 ):





						"""simple docstring"""

						UpperCAmelCase__		=    defaultdict(_UpperCamelCase )
						UpperCAmelCase__		=    0

						while True:
												UpperCAmelCase__		=    get_digits(_UpperCamelCase )
												freqs[digits].append(_UpperCamelCase )

												if len(freqs[digits] ) == max_base:
																		UpperCAmelCase__		=    freqs[digits][0] ** 3
																		return base

												num += 1






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return "".join(sorted(str(num**3 ) ) )


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