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 	__lowerCamelCase							(				__a    : List[str]		)      ->       Optional[int]:
	if isinstance(__a      ,							torch.Tensor		):
		return image
	elif isinstance(__a      ,							PIL.Image.Image		):
		_lowercase   =[image]

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

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

	return image
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 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       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 75       ,  lowerCAmelCase_ = 9.0       ,  lowerCAmelCase_ = 20       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = 0.0       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = "pil"       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = 1       ,  ):
		self.check_inputs(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

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

		_lowercase   =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   =guidance_scale > 1.0

		# 3. Encode input prompt
		_lowercase   =self._encode_prompt(
		    lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  prompt_embeds=lowerCAmelCase_       ,  negative_prompt_embeds=lowerCAmelCase_       ,  )

		_lowercase   =ORT_TO_PT_TYPE[str(text_embeddings.dtype       )]

		# 4. Preprocess image
		_lowercase   =preprocess(lowerCAmelCase_       )
		_lowercase   =image.cpu()

		# 5. set timesteps
		self.scheduler.set_timesteps(lowerCAmelCase_       ,  device=lowerCAmelCase_       )
		_lowercase   =self.scheduler.timesteps

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

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

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

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

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

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

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

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

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

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

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =1 / 0.0_8_3_3_3 * latents
		_lowercase   =self.vae(latent_sample=lowerCAmelCase_       )[0]
		_lowercase   =np.clip(image / 2 + 0.5       ,  0       ,  1       )
		_lowercase   =image.transpose((0, 2, 3, 1)       )
		return image


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  ):
		if prompt is not None and isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =1
		elif prompt is not None and isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =len(lowerCAmelCase_       )
		else:
			_lowercase   =prompt_embeds.shape[0]

		if prompt_embeds is None:
			_lowercase   =self.tokenizer(
			    lowerCAmelCase_       ,  padding="max_length"       ,  max_length=self.tokenizer.model_max_length       ,  truncation=lowerCAmelCase_       ,  return_tensors="pt"       ,  )
			_lowercase   =text_inputs.input_ids
			_lowercase   =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_       ):
				_lowercase   =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   =self.text_encoder(
			    input_ids=text_input_ids.int().to(lowerCAmelCase_       )       ,  )
			_lowercase   =prompt_embeds[0]

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

			_lowercase   =text_input_ids.shape[-1]
			_lowercase   =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

			_lowercase   =self.text_encoder(
			    input_ids=uncond_input.input_ids.int().to(lowerCAmelCase_       )       ,  )
			_lowercase   =uncond_embeddings[0]

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

		return prompt_embeds





'''simple docstring'''

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A		,    __A		,    __A		,    __A		,    )			->  Any:
								super().__init__()

								self.register_modules(vae=__A		,    text_encoder=__A		,    tokenizer=__A		,    unet=__A		,    scheduler=__A					)






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.encode(tfms.ToTensor()(__A					).unsqueeze(0					).to(self.device					) * 2 - 1					)
														_lowerCAmelCase					    =0.18_215 * latent.latent_dist.sample()
								return latent






		def 		UpperCamelCase__							( self		,    __A					)			->  Dict:
								_lowerCAmelCase					    =(1 / 0.18_215) * latent
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.decode(__A					).sample
								_lowerCAmelCase					    =(img / 2 + 0.5).clamp(0		,    1					)
								_lowerCAmelCase					    =img.detach().cpu().permute(0		,    2		,    3		,    1					).numpy()
								_lowerCAmelCase					    =(img * 255).round().astype('uint8'					)
								return Image.fromarray(img[0]					)






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =self.tokenizer(
								    __A		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

								_lowerCAmelCase					    =self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

								_lowerCAmelCase					    =self.tokenizer(
								    ''		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

								_lowerCAmelCase					    =self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

								return torch.cat([uncond_embedding, text_embedding]					)






		def __call__( self		,    __A		,    __A		,    __A = 0.3		,    __A = 0.6		,    __A = 0.5		,    __A = 42		,    __A = 50		,    __A = 7.5		,    )			->  Image.Image:
								_lowerCAmelCase					    =steps - int(kmin * steps					)
								_lowerCAmelCase					    =steps - int(kmax * steps					)

								_lowerCAmelCase					    =self.prep_text(__A					)

								self.scheduler.set_timesteps(__A					)

								_lowerCAmelCase    ,					_lowerCAmelCase					    =img.size
								_lowerCAmelCase					    =self.encode(__A					)

								torch.manual_seed(__A					)
								_lowerCAmelCase					    =torch.randn(
								    (1, self.unet.config.in_channels, height // 8, width // 8)		,    ).to(self.device					)

								_lowerCAmelCase					    =self.scheduler.add_noise(
								    __A		,    __A		,    timesteps=self.scheduler.timesteps[tmax]		,    )

								_lowerCAmelCase					    =torch.cat([latents] * 2					)

								_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    self.scheduler.timesteps[tmax]					)

								with torch.no_grad():
														_lowerCAmelCase					    =self.unet(
														    __A		,    self.scheduler.timesteps[tmax]		,    encoder_hidden_states=__A		,    ).sample

								_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
								_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

								_lowerCAmelCase					    =self.scheduler.step(__A		,    self.scheduler.timesteps[tmax]		,    __A					).prev_sample

								for i, t in enumerate(tqdm(self.scheduler.timesteps					)					):
														if i > tmax:
																				if i < tmin:  # layout generation phase
																										_lowerCAmelCase					    =self.scheduler.add_noise(
																										    __A		,    __A		,    timesteps=__A		,    )

																										_lowerCAmelCase					    =(mix_factor * latents) + (
																										    1 - mix_factor
																										) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																										_lowerCAmelCase					    =torch.cat([input] * 2					)

																				else:  # content generation phase
																										_lowerCAmelCase					    =torch.cat([latents] * 2					)

																				_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    __A					)

																				with torch.no_grad():
																										_lowerCAmelCase					    =self.unet(
																										    __A		,    __A		,    encoder_hidden_states=__A		,    ).sample

																				_lowerCAmelCase    ,					_lowerCAmelCase					    =pred.chunk(2					)
																				_lowerCAmelCase					    =pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				_lowerCAmelCase					    =self.scheduler.step(__A		,    __A		,    __A					).prev_sample

								return self.decode(__A					)





