import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig







class        UpperCAmelCase_					(				__SCREAMING_SNAKE_CASE     ):
    UpperCAmelCase_      :							str    			=				CustomConfig




    def __init__(       self	: Optional[Any]   ,   lowerCAmelCase	: Optional[Any] )						->			Optional[Any]:
      super().__init__(lowerCamelCase_ )
      a__  		=  torch.nn.Linear(config.hidden_size   ,   config.hidden_size )




    def     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: Dict )						->			Optional[Any]:
      return self.linear(lowerCamelCase_ )




    def     __lowerCamelCase			(       self	: List[Any]   ,   lowerCAmelCase	: List[Any] )						->			str:
      pass







class        UpperCAmelCase_					(				__SCREAMING_SNAKE_CASE     ):
    UpperCAmelCase_      :							Optional[Any]    			=				NoSuperInitConfig




    def __init__(       self	: Optional[int]   ,   lowerCAmelCase	: Tuple )						->			Any:
      super().__init__(lowerCamelCase_ )
      a__  		=  torch.nn.Linear(config.attribute   ,   config.attribute )




    def     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: Optional[Any] )						->			Dict:
      return self.linear(lowerCamelCase_ )




    def     __lowerCamelCase			(       self	: Optional[int]   ,   lowerCAmelCase	: int )						->			List[str]:
      pass





"""simple docstring"""







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			:  Optional[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			:  Optional[int]		,			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			:  Tuple		,			lowerCamelCase_			:  List[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			:  Optional[Any]		,			lowerCamelCase_			:  Union[str, List[str]]		,			lowerCamelCase_			:  Union[str, Any]		,			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