



'''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


lowerCamelCase__	  =		getLogger(__name__)


lowerCamelCase__	  =		4
lowerCamelCase__	  =		7

lowerCamelCase__	  =		{
    'float16': torch.floataa,
    'float32': torch.floataa,
}





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Any  )		->str:






							'''simple docstring'''
							if isinstance(snake_case_ ,       torch.Tensor  ):
														return image
							elif isinstance(snake_case_ ,       PIL.Image.Image  ):
														_lowercase							:		Union[str, Any]				=   [image]

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

														_lowercase							:		Optional[int]				=   [np.array(i.resize((w, h)  )  )[None, :] for i in image]
														_lowercase							:		List[str]				=   np.concatenate(snake_case_ ,       axis=0  )
														_lowercase							:		Optional[int]				=   np.array(snake_case_  ).astype(np.floataa  ) / 2_5_5.0
														_lowercase							:		List[str]				=   image.transpose(0 ,       3 ,       1 ,       2  )
														_lowercase							:		List[Any]				=   2.0 * image - 1.0
														_lowercase							:		int				=   torch.from_numpy(snake_case_  )
							elif isinstance(image[0] ,       torch.Tensor  ):
														_lowercase							:		str				=   torch.cat(snake_case_ ,       dim=0  )

							return image




class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		def __init__(						self							:       Optional[int]      ,		UpperCamelCase_							:       OnnxRuntimeModel      ,		UpperCamelCase_							:       OnnxRuntimeModel      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       OnnxRuntimeModel      ,		UpperCamelCase_							:       DDPMScheduler      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       int = 350      ,		)      ->			str:







									'''simple docstring'''





									super().__init__(
									    vae=UpperCamelCase_      ,		text_encoder=UpperCamelCase_      ,		tokenizer=UpperCamelCase_      ,		unet=UpperCamelCase_      ,		low_res_scheduler=UpperCamelCase_      ,		scheduler=UpperCamelCase_      ,		safety_checker=UpperCamelCase_      ,		feature_extractor=UpperCamelCase_      ,		watermarker=UpperCamelCase_      ,		max_noise_level=UpperCamelCase_      ,		)
		def __call__(						self							:       List[str]      ,		UpperCamelCase_							:       Union[str, List[str]]      ,		UpperCamelCase_							:       Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]      ,		UpperCamelCase_							:       int = 75      ,		UpperCamelCase_							:       float = 9.0      ,		UpperCamelCase_							:       int = 20      ,		UpperCamelCase_							:       Optional[Union[str, List[str]]] = None      ,		UpperCamelCase_							:       Optional[int] = 1      ,		UpperCamelCase_							:       float = 0.0      ,		UpperCamelCase_							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,		UpperCamelCase_							:       Optional[torch.FloatTensor] = None      ,		UpperCamelCase_							:       Optional[np.ndarray] = None      ,		UpperCamelCase_							:       Optional[np.ndarray] = None      ,		UpperCamelCase_							:       Optional[str] = "pil"      ,		UpperCamelCase_							:       bool = True      ,		UpperCamelCase_							:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,		UpperCamelCase_							:       Optional[int] = 1      ,		)      ->			int:







									'''simple docstring'''





									self.check_inputs(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)

									# 2. Define call parameters
									if prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_		):
																_lowercase							:		Dict				=   1
									elif prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_		):
																_lowercase							:		Dict				=   len(UpperCamelCase_		)
									else:
																_lowercase							:		List[str]				=   prompt_embeds.shape[0]

									_lowercase							:		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.
									_lowercase							:		Dict				=   guidance_scale > 1.0

									# 3. Encode input prompt
									_lowercase							:		str				=   self._encode_prompt(
									    UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		prompt_embeds=UpperCamelCase_      ,		negative_prompt_embeds=UpperCamelCase_      ,		)

									_lowercase							:		Dict				=   ORT_TO_PT_TYPE[str(text_embeddings.dtype		)]

									# 4. Preprocess image
									_lowercase							:		str				=   preprocess(UpperCamelCase_		)
									_lowercase							:		int				=   image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(UpperCamelCase_      ,		device=UpperCamelCase_		)
									_lowercase							:		Tuple				=   self.scheduler.timesteps

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

									_lowercase							:		int				=   2 if do_classifier_free_guidance else 1
									_lowercase							:		Union[str, Any]				=   np.concatenate([image] * batch_multiplier * num_images_per_prompt		)
									_lowercase							:		Optional[Any]				=   np.concatenate([noise_level] * image.shape[0]		)

									# 6. Prepare latent variables
									_lowercase			, _lowercase							:		Optional[int]				=   image.shape[2:]
									_lowercase							:		Optional[Any]				=   self.prepare_latents(
									    batch_size * num_images_per_prompt      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		)

									# 7. Check that sizes of image and latents match
									_lowercase							:		Union[str, Any]				=   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
									_lowercase							:		Dict				=   self.prepare_extra_step_kwargs(UpperCamelCase_      ,		UpperCamelCase_		)

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

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

																							# concat latents, mask, masked_image_latents in the channel dimension
																							_lowercase							:		Union[str, Any]				=   self.scheduler.scale_model_input(UpperCamelCase_      ,		UpperCamelCase_		)
																							_lowercase							:		Union[str, Any]				=   np.concatenate([latent_model_input, image]      ,		axis=1		)

																							# timestep to tensor
																							_lowercase							:		Dict				=   np.array([t]      ,		dtype=UpperCamelCase_		)

																							# predict the noise residual
																							_lowercase							:		Optional[int]				=   self.unet(
																							    sample=UpperCamelCase_      ,		timestep=UpperCamelCase_      ,		encoder_hidden_states=UpperCamelCase_      ,		class_labels=noise_level.astype(np.intaa		)      ,		)[0]

																							# perform guidance
																							if do_classifier_free_guidance:
																														_lowercase			, _lowercase							:		Union[str, Any]				=   np.split(UpperCamelCase_      ,		2		)
																							_lowercase							:		int				=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																							# compute the previous noisy sample x_t -> x_t-1
																							_lowercase							:		Union[str, Any]				=   self.scheduler.step(
																							    torch.from_numpy(UpperCamelCase_		)      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		**UpperCamelCase_		).prev_sample

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

        # 10. Post-processing
									_lowercase							:		str				=   self.decode_latents(latents.float()		)

									# 11. Convert to PIL
									if output_type == "pil":
																_lowercase							:		int				=   self.numpy_to_pil(UpperCamelCase_		)

									if not return_dict:
																return (image,)

									return ImagePipelineOutput(images=UpperCamelCase_		)
		def 						__lowercase      (						self							:       Tuple      ,		UpperCamelCase_							:       List[str]		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		List[str]				=   1 / 0.08_333 * latents
									_lowercase							:		List[str]				=   self.vae(latent_sample=UpperCamelCase_		)[0]
									_lowercase							:		List[Any]				=   np.clip(image / 2 + 0.5      ,		0      ,		1		)
									_lowercase							:		Any				=   image.transpose((0, 2, 3, 1)		)
									return image





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







									'''simple docstring'''





									if prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_		):
																_lowercase							:		Union[str, Any]				=   1
									elif prompt is not None and isinstance(UpperCamelCase_      ,		UpperCamelCase_		):
																_lowercase							:		int				=   len(UpperCamelCase_		)
									else:
																_lowercase							:		Optional[Any]				=   prompt_embeds.shape[0]

									if prompt_embeds is None:
																_lowercase							:		Tuple				=   self.tokenizer(
																    UpperCamelCase_      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=UpperCamelCase_      ,		return_tensors='''pt'''      ,		)
																_lowercase							:		str				=   text_inputs.input_ids
																_lowercase							:		Any				=   self.tokenizer(UpperCamelCase_      ,		padding='''longest'''      ,		return_tensors='''pt'''		).input_ids

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

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

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

																_lowercase							:		List[Any]				=   text_input_ids.shape[-1]
																_lowercase							:		Optional[int]				=   self.tokenizer(
																    UpperCamelCase_      ,		padding='''max_length'''      ,		max_length=UpperCamelCase_      ,		truncation=UpperCamelCase_      ,		return_tensors='''pt'''      ,		)

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

																_lowercase							:		Tuple				=   self.text_encoder(
																    input_ids=uncond_input.input_ids.int().to(UpperCamelCase_		)      ,		)
																_lowercase							:		Union[str, Any]				=   uncond_embeddings[0]

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

									return prompt_embeds








'''simple docstring'''






import warnings

from .generation import GenerationMixin

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		# warning at import time
		warnings.warn(
		    'Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'     ,		__A     ,		)



