




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_     :    Optional[Any]      =			getLogger(__name__)


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

lowercase_     :    int      =			{
    'float16': torch.floataa,
    'float32': torch.floataa,
}


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


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

						return image






class    __lowerCAmelCase			(	UpperCAmelCase__			):







						def __init__(	self   :      str   ,   snake_case__   :      Optional[int]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Tuple   ,   snake_case__   :      int   ,   snake_case__   :      Dict   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      Optional[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   :      List[str]   ,   snake_case__   :      Any   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      int = 75   ,   snake_case__   :      Any = 9.0   ,   snake_case__   :      Optional[int] = 20   ,   snake_case__   :      List[str] = None   ,   snake_case__   :      str = 1   ,   snake_case__   :      Any = 0.0   ,   snake_case__   :      Tuple = None   ,   snake_case__   :      Dict = None   ,   snake_case__   :      Tuple = None   ,   snake_case__   :      List[str] = None   ,   snake_case__   :      Dict = "pil"   ,   snake_case__   :      str = True   ,   snake_case__   :      Any = None   ,   snake_case__   :      str = 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__   :      List[str]				):




												"""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   :      Union[str, Any]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      Tuple = None   ,   snake_case__   :      int = 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










from __future__ import annotations


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					{}
						for index, val in enumerate(snake_case_						):
												_UpperCAmelCase													=					target - val
												if compl in chk_map:
																		return [chk_map[compl], index]
												_UpperCAmelCase													=					index
						return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_sum([2, 7, 11, 15], 9) = }""")




