'''simple docstring'''




def      _UpperCAmelCase    (							_UpperCamelCase    :	float,   _UpperCamelCase    :	float,   _UpperCamelCase    :	int   )	->      float:
    A_     =   [1 / first_term]
    A_     =   1 / first_term
    for _ in range(number_of_terms - 1   ):
        first_term += common_difference
        arithmetic_progression.append(_UpperCamelCase   )
    A_     =   [1 / step for step in arithmetic_progression]
    return sum(_UpperCamelCase   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(sum_of_harmonic_progression(1 / 2, 2, 2))












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 					snake_case					(							A			)							->							Optional[int]:




	'''simple docstring'''


	if isinstance(A						,   torch.Tensor			):
		return image
	elif isinstance(A						,   PIL.Image.Image			):
		lowerCamelCase_							:		List[str]							   =							[image]

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

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

	return image



class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = 350 ,       ):







						'''simple docstring'''




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

					def __call__(				self ,       __lowercase ,       __lowercase ,       __lowercase = 75 ,       __lowercase = 9.0 ,       __lowercase = 20 ,       __lowercase = None ,       __lowercase = 1 ,       __lowercase = 0.0 ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = None ,       __lowercase = "pil" ,       __lowercase = True ,       __lowercase = None ,       __lowercase = 1 ,       ):







						'''simple docstring'''




						self.check_inputs(__lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

						# 2. Define call parameters
						if prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Any							   =							1
						elif prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		List[Any]							   =							len(__lowercase					)
						else:
							lowerCamelCase_							:		Optional[Any]							   =							prompt_embeds.shape[0]

						lowerCamelCase_							:		List[Any]							   =							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.
						lowerCamelCase_							:		int							   =							guidance_scale > 1.0

						# 3. Encode input prompt
						lowerCamelCase_							:		Optional[int]							   =							self._encode_prompt(
						    __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       prompt_embeds=__lowercase ,       negative_prompt_embeds=__lowercase ,       )

						lowerCamelCase_							:		str							   =							ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]

						# 4. Preprocess image
						lowerCamelCase_							:		List[str]							   =							preprocess(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							image.cpu()

						# 5. set timesteps
						self.scheduler.set_timesteps(__lowercase ,       device=__lowercase					)
						lowerCamelCase_							:		int							   =							self.scheduler.timesteps

						# 5. Add noise to image
						lowerCamelCase_							:		Optional[Any]							   =							torch.tensor([noise_level] ,       dtype=torch.long ,       device=__lowercase					)
						lowerCamelCase_							:		List[Any]							   =							torch.randn(image.shape ,       generator=__lowercase ,       device=__lowercase ,       dtype=__lowercase					)
						lowerCamelCase_							:		Tuple							   =							self.low_res_scheduler.add_noise(__lowercase ,       __lowercase ,       __lowercase					)

						lowerCamelCase_							:		Dict							   =							2 if do_classifier_free_guidance else 1
						lowerCamelCase_							:		Any							   =							np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
						lowerCamelCase_							:		str							   =							np.concatenate([noise_level] * image.shape[0]					)

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

						# 7. Check that sizes of image and latents match
						lowerCamelCase_							:		List[str]							   =							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
						lowerCamelCase_							:		Dict							   =							self.prepare_extra_step_kwargs(__lowercase ,       __lowercase					)

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

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

								# concat latents, mask, masked_image_latents in the channel dimension
								lowerCamelCase_							:		List[Any]							   =							self.scheduler.scale_model_input(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		Optional[Any]							   =							np.concatenate([latent_model_input, image] ,       axis=1					)

								# timestep to tensor
								lowerCamelCase_							:		Optional[int]							   =							np.array([t] ,       dtype=__lowercase					)

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

								# perform guidance
								if do_classifier_free_guidance:
									lowerCamelCase_   ,			lowerCamelCase_							:		int							   =							np.split(__lowercase ,       2					)
								lowerCamelCase_							:		Tuple							   =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# compute the previous noisy sample x_t -> x_t-1
								lowerCamelCase_							:		str							   =							self.scheduler.step(
								    torch.from_numpy(__lowercase					) ,       __lowercase ,       __lowercase ,       **__lowercase					).prev_sample

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

        # 10. Post-processing
						lowerCamelCase_							:		Dict							   =							self.decode_latents(latents.float()					)

						# 11. Convert to PIL
						if output_type == "pil":
							lowerCamelCase_							:		List[str]							   =							self.numpy_to_pil(__lowercase					)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							1 / 0.0_83_33 * latents
						lowerCamelCase_							:		List[Any]							   =							self.vae(latent_sample=__lowercase					)[0]
						lowerCamelCase_							:		List[Any]							   =							np.clip(image / 2 + 0.5 ,       0 ,       1					)
						lowerCamelCase_							:		List[Any]							   =							image.transpose((0, 2, 3, 1)					)
						return image





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase = None ,       __lowercase = None ,       ):







						'''simple docstring'''




						if prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Tuple							   =							1
						elif prompt is not None and isinstance(__lowercase ,       __lowercase					):
							lowerCamelCase_							:		Union[str, Any]							   =							len(__lowercase					)
						else:
							lowerCamelCase_							:		Any							   =							prompt_embeds.shape[0]

						if prompt_embeds is None:
							lowerCamelCase_							:		Dict							   =							self.tokenizer(
							    __lowercase ,       padding='max_length' ,       max_length=self.tokenizer.model_max_length ,       truncation=__lowercase ,       return_tensors='pt' ,       )
							lowerCamelCase_							:		List[str]							   =							text_inputs.input_ids
							lowerCamelCase_							:		Tuple							   =							self.tokenizer(__lowercase ,       padding='longest' ,       return_tensors='pt'					).input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
							    __lowercase ,       __lowercase					):
								lowerCamelCase_							:		Any							   =							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
							lowerCamelCase_							:		Tuple							   =							self.text_encoder(
							    input_ids=text_input_ids.int().to(__lowercase					) ,       )
							lowerCamelCase_							:		List[str]							   =							prompt_embeds[0]

						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Optional[Any]							   =							prompt_embeds.shape
						# duplicate text embeddings for each generation per prompt, using mps friendly method
						lowerCamelCase_							:		str							   =							prompt_embeds.repeat(1 ,       __lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							prompt_embeds.reshape(bs_embed * num_images_per_prompt ,       __lowercase ,       -1					)

						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance and negative_prompt_embeds is None:
							lowerCamelCase_							:		List[str]
							if negative_prompt is None:
								lowerCamelCase_							:		Dict							   =							[''] * batch_size
							elif type(__lowercase					) is not type(__lowercase					):
								raise TypeError(
								    f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowercase					)} !="
								    f" {type(__lowercase					)}."					)
							elif isinstance(__lowercase ,       __lowercase					):
								lowerCamelCase_							:		Union[str, Any]							   =							[negative_prompt]
							elif batch_size != len(__lowercase					):
								raise ValueError(
								    f"`negative_prompt`: {negative_prompt} has batch size {len(__lowercase					)}, but `prompt`:"
								    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
								    ' the batch size of `prompt`.'					)
							else:
								lowerCamelCase_							:		Any							   =							negative_prompt

							lowerCamelCase_							:		str							   =							text_input_ids.shape[-1]
							lowerCamelCase_							:		str							   =							self.tokenizer(
							    __lowercase ,       padding='max_length' ,       max_length=__lowercase ,       truncation=__lowercase ,       return_tensors='pt' ,       )

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

							lowerCamelCase_							:		Dict							   =							self.text_encoder(
							    input_ids=uncond_input.input_ids.int().to(__lowercase					) ,       )
							lowerCamelCase_							:		Optional[Any]							   =							uncond_embeddings[0]

						if do_classifier_free_guidance:
							lowerCamelCase_							:		Union[str, Any]							   =							uncond_embeddings.shape[1]
							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							lowerCamelCase_							:		Any							   =							uncond_embeddings.repeat(1 ,       __lowercase					)
							lowerCamelCase_							:		Union[str, Any]							   =							uncond_embeddings.reshape(batch_size * num_images_per_prompt ,       __lowercase ,       -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
							lowerCamelCase_							:		Any							   =							np.concatenate([uncond_embeddings, prompt_embeds]					)

						return prompt_embeds
