
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  :			List[Any]     				=				getLogger(__name__)


A  :			List[Any]     				=				4
A  :			List[str]     				=				7

A  :			Any     				=				{
    "float16": torch.floataa,
    "float32": torch.floataa,
}


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:







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

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

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

				return image







class     __A(				SCREAMING_SNAKE_CASE__    ):




				def __init__(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case = 350	,  )					->   int:


								'''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	,  _snake_case	,  _snake_case	,  _snake_case = 75	,  _snake_case = 9.0	,  _snake_case = 20	,  _snake_case = None	,  _snake_case = 1	,  _snake_case = 0.0	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = "pil"	,  _snake_case = True	,  _snake_case = None	,  _snake_case = 1	,  )					->   List[Any]:


								'''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       ):
												__a    							=					1
								elif prompt is not None and isinstance(_snake_case	,  _snake_case       ):
												__a    							=					len(_snake_case       )
								else:
												__a    							=					prompt_embeds.shape[0]

								__a    							=					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.
								__a    							=					guidance_scale > 1.0

								# 3. Encode input prompt
								__a    							=					self._encode_prompt(
								    _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  prompt_embeds=_snake_case	,  negative_prompt_embeds=_snake_case	,  )

								__a    							=					ORT_TO_PT_TYPE[str(text_embeddings.dtype       )]

								# 4. Preprocess image
								__a    							=					preprocess(_snake_case       )
								__a    							=					image.cpu()

								# 5. set timesteps
								self.scheduler.set_timesteps(_snake_case	,  device=_snake_case       )
								__a    							=					self.scheduler.timesteps

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

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

								# 6. Prepare latent variables
								__a							,  __a    							=					image.shape[2:]
								__a    							=					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
								__a    							=					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
								__a    							=					self.prepare_extra_step_kwargs(_snake_case	,  _snake_case       )

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

								# 9. Denoising loop
								__a    							=					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
																__a    							=					np.concatenate([latents] * 2       ) if do_classifier_free_guidance else latents

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

																# timestep to tensor
																__a    							=					np.array([t]	,  dtype=_snake_case       )

																# predict the noise residual
																__a    							=					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:
																				__a							,  __a    							=					np.split(_snake_case	,  2       )
																__a    							=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																# compute the previous noisy sample x_t -> x_t-1
																__a    							=					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
								__a    							=					self.decode_latents(latents.float()       )

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

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					1 / 0.0_8333 * latents
								__a    							=					self.vae(latent_sample=_snake_case       )[0]
								__a    							=					np.clip(image / 2 + 0.5	,  0	,  1       )
								__a    							=					image.transpose((0, 2, 3, 1)       )
								return image




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case = None	,  _snake_case = None	,  )					->   Optional[int]:


								'''simple docstring'''




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

								if prompt_embeds is None:
												__a    							=					self.tokenizer(
												    _snake_case	,  padding='''max_length'''	,  max_length=self.tokenizer.model_max_length	,  truncation=_snake_case	,  return_tensors='''pt'''	,  )
												__a    							=					text_inputs.input_ids
												__a    							=					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       ):
																__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}"""       )

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

								__a							,  __a							,  __a    							=					prompt_embeds.shape
								# duplicate text embeddings for each generation per prompt, using mps friendly method
								__a    							=					prompt_embeds.repeat(1	,  _snake_case       )
								__a    							=					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:
												__a    							=					42
												if negative_prompt is None:
																__a    							=					[''''''] * 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       ):
																__a    							=					[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:
																__a    							=					negative_prompt

												__a    							=					text_input_ids.shape[-1]
												__a    							=					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

												__a    							=					self.text_encoder(
												    input_ids=uncond_input.input_ids.int().to(_snake_case       )	,  )
												__a    							=					uncond_embeddings[0]

								if do_classifier_free_guidance:
												__a    							=					uncond_embeddings.shape[1]
												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												__a    							=					uncond_embeddings.repeat(1	,  _snake_case       )
												__a    							=					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
												__a    							=					np.concatenate([uncond_embeddings, prompt_embeds]       )

								return prompt_embeds








from itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()