



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


UpperCAmelCase__  =			getLogger(__name__)


UpperCAmelCase__  =			4
UpperCAmelCase__  =			7

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





def 				A      (	_UpperCAmelCase							:  int       )							->					Dict:







			'''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       (     A		):



			def __init__(		self  :    List[str]					,						A  :    OnnxRuntimeModel					,						A  :    OnnxRuntimeModel					,						A  :    Any					,						A  :    OnnxRuntimeModel					,						A  :    DDPMScheduler					,						A  :    Any					,						A  :    int = 3_50					,						)  ->       Dict:



						"""simple docstring"""






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



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



						"""simple docstring"""






						self.check_inputs(A					,						A					,						A					,						A)

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

						_UpperCAmelCase									=  ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

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

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

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

						_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					,						A					,						A					,						A					,						A					,						A					,						A					,						A					,						)

						# 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(A					,						A)

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

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

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

												# perform guidance
												if do_classifier_free_guidance:
															_UpperCAmelCase			,	_UpperCAmelCase									=  np.split(A					,						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(A)					,						A					,						A					,						**A).prev_sample

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

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

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=A)



			def        _lowerCamelCase			(		self  :    Union[str, Any]					,						A  :    Any)  ->       Dict:



						"""simple docstring"""






						_UpperCAmelCase									=  1 / 0.0_8_3_3_3 * latents
						_UpperCAmelCase									=  self.vae(latent_sample=A)[0]
						_UpperCAmelCase									=  np.clip(image / 2 + 0.5					,						0					,						1)
						_UpperCAmelCase									=  image.transpose((0, 2, 3, 1))
						return image



			def        _lowerCamelCase			(		self  :    str					,						A  :    Union[str, List[str]]					,						A  :    Union[str, Any]					,						A  :    Optional[int]					,						A  :    bool					,						A  :    Optional[str]					,						A  :    Optional[torch.FloatTensor] = None					,						A  :    Optional[torch.FloatTensor] = None					,						)  ->       List[str]:



						"""simple docstring"""






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

						if prompt_embeds is None:
									_UpperCAmelCase									=  self.tokenizer(
									    A					,						padding='max_length'					,						max_length=self.tokenizer.model_max_length					,						truncation=A					,						return_tensors='pt'					,						)
									_UpperCAmelCase									=  text_inputs.input_ids
									_UpperCAmelCase									=  self.tokenizer(A					,						padding='longest'					,						return_tensors='pt').input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
									    A					,						A):
												_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(A)					,						)
									_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					,						A)
						_UpperCAmelCase									=  prompt_embeds.reshape(bs_embed * num_images_per_prompt					,						A					,						-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(A) is not type(A):
												raise TypeError(
												    F"`negative_prompt` should be the same type to `prompt`, but got {type(A)} !="
												    F" {type(A)}.")
									elif isinstance(A					,						A):
												_UpperCAmelCase									=  [negative_prompt]
									elif batch_size != len(A):
												raise ValueError(
												    F"`negative_prompt`: {negative_prompt} has batch size {len(A)}, 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(
									    A					,						padding='max_length'					,						max_length=A					,						truncation=A					,						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(A)					,						)
									_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					,						A)
									_UpperCAmelCase									=  uncond_embeddings.reshape(batch_size * num_images_per_prompt					,						A					,						-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





def 				A      (	_UpperCAmelCase							:  List[Any]       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  len(_UpperCAmelCase       )
			for _ in range(
			    0				,  int(((no_of_elements - 1) / 2) + 1       )       ):  # we don't need to traverse to end of list as
						for j in range(0				,  no_of_elements - 1       ):
									if (
									    lst[j + 1] < lst[j]
									):  # applying bubble sort algorithm from left to right (or forwards)
												_UpperCAmelCase									=  lst[j + 1]
												_UpperCAmelCase									=  lst[j]
												_UpperCAmelCase									=  temp
									if (
									    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
									):  # applying bubble sort algorithm from right to left (or backwards)
												_UpperCAmelCase									=  lst[no_of_elements - 1 - j]
												_UpperCAmelCase									=  lst[no_of_elements - 2 - j]
												_UpperCAmelCase									=  temp
			return lst


if __name__ == "__main__":
							print("enter the list to be sorted")
							UpperCAmelCase__  =			[int(x) for x in input().split()]  # inputing elements of the list in one line
							UpperCAmelCase__  =			double_sort(lst)
							print("the sorted list is")
							print(sorted_lst)
