

"""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__				:  List[Any]													=  getLogger(__name__)


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

lowerCamelCase__				:  Any													=  {
    "float16": torch.floataa,
    "float32": torch.floataa,
}
def       UpperCamelCase					(			_lowerCAmelCase     :						Union[str, Any] )				->			Optional[int]:







	if isinstance(_lowerCAmelCase,  torch.Tensor ):
		return image
	elif isinstance(_lowerCAmelCase,  PIL.Image.Image ):
		_UpperCAmelCase    :							Dict									=					[image]

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

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

	return image






class     _UpperCAmelCase			(		SCREAMING_SNAKE_CASE__):
			def __init__(		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = 3_50      ,		)    ->    int:






				'''simple docstring'''







				super().__init__(
				    vae=_A      ,		text_encoder=_A      ,		tokenizer=_A      ,		unet=_A      ,		low_res_scheduler=_A      ,		scheduler=_A      ,		safety_checker=_A      ,		feature_extractor=_A      ,		watermarker=_A      ,		max_noise_level=_A      ,		)
			def __call__(		self      ,		_A      ,		_A      ,		_A = 75      ,		_A = 9.0      ,		_A = 20      ,		_A = None      ,		_A = 1      ,		_A = 0.0      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = "pil"      ,		_A = True      ,		_A = None      ,		_A = 1      ,		)    ->    List[Any]:






				'''simple docstring'''







				self.check_inputs(_A      ,		_A      ,		_A      ,		_A     )

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

				_UpperCAmelCase    :							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.
				_UpperCAmelCase    :							int									=					guidance_scale > 1.0

				# 3. Encode input prompt
				_UpperCAmelCase    :							Dict									=					self._encode_prompt(
				    _A      ,		_A      ,		_A      ,		_A      ,		_A      ,		prompt_embeds=_A      ,		negative_prompt_embeds=_A      ,		)

				_UpperCAmelCase    :							List[str]									=					ORT_TO_PT_TYPE[str(text_embeddings.dtype     )]

				# 4. Preprocess image
				_UpperCAmelCase    :							Dict									=					preprocess(_A     )
				_UpperCAmelCase    :							Optional[int]									=					image.cpu()

				# 5. set timesteps
				self.scheduler.set_timesteps(_A      ,		device=_A     )
				_UpperCAmelCase    :							Tuple									=					self.scheduler.timesteps

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

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

				# 6. Prepare latent variables
				_UpperCAmelCase						,   _UpperCAmelCase    :							Optional[Any]									=					image.shape[2:]
				_UpperCAmelCase    :							Tuple									=					self.prepare_latents(
				    batch_size * num_images_per_prompt      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		)

				# 7. Check that sizes of image and latents match
				_UpperCAmelCase    :							Tuple									=					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    :							List[Any]									=					self.prepare_extra_step_kwargs(_A      ,		_A     )

				_UpperCAmelCase    :							Optional[Any]									=					next(
				    (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")      ,		"""tensor(float)"""     )
				_UpperCAmelCase    :							Union[str, Any]									=					ORT_TO_NP_TYPE[timestep_dtype]

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

						# concat latents, mask, masked_image_latents in the channel dimension
						_UpperCAmelCase    :							List[str]									=					self.scheduler.scale_model_input(_A      ,		_A     )
						_UpperCAmelCase    :							Dict									=					np.concatenate([latent_model_input, image]      ,		axis=1     )

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

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

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

						# compute the previous noisy sample x_t -> x_t-1
						_UpperCAmelCase    :							Any									=					self.scheduler.step(
						    torch.from_numpy(_A     )      ,		_A      ,		_A      ,		**_A     ).prev_sample

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

        # 10. Post-processing
				_UpperCAmelCase    :							Union[str, Any]									=					self.decode_latents(latents.float()     )

				# 11. Convert to PIL
				if output_type == "pil":
					_UpperCAmelCase    :							Any									=					self.numpy_to_pil(_A     )

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=_A     )
			def 							__snake_case (		self      ,		_A     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Optional[Any]									=					1 / 0.08333 * latents
				_UpperCAmelCase    :							List[str]									=					self.vae(latent_sample=_A     )[0]
				_UpperCAmelCase    :							Any									=					np.clip(image / 2 + 0.5      ,		0      ,		1     )
				_UpperCAmelCase    :							Union[str, Any]									=					image.transpose((0, 2, 3, 1)     )
				return image
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = None      ,		_A = None      ,		)    ->    Optional[int]:






				'''simple docstring'''







				if prompt is not None and isinstance(_A      ,		_A     ):
					_UpperCAmelCase    :							str									=					1
				elif prompt is not None and isinstance(_A      ,		_A     ):
					_UpperCAmelCase    :							List[Any]									=					len(_A     )
				else:
					_UpperCAmelCase    :							Dict									=					prompt_embeds.shape[0]

				if prompt_embeds is None:
					_UpperCAmelCase    :							Dict									=					self.tokenizer(
					    _A      ,		padding="""max_length"""      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors="""pt"""      ,		)
					_UpperCAmelCase    :							Any									=					text_inputs.input_ids
					_UpperCAmelCase    :							str									=					self.tokenizer(_A      ,		padding="""longest"""      ,		return_tensors="""pt"""     ).input_ids

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

				_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							str									=					prompt_embeds.shape
				# duplicate text embeddings for each generation per prompt, using mps friendly method
				_UpperCAmelCase    :							Tuple									=					prompt_embeds.repeat(1      ,		_A     )
				_UpperCAmelCase    :							Tuple									=					prompt_embeds.reshape(bs_embed * num_images_per_prompt      ,		_A      ,		-1     )

				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance and negative_prompt_embeds is None:
					_UpperCAmelCase    :							Tuple									=					42
					if negative_prompt is None:
						_UpperCAmelCase    :							Any									=					[""""""] * batch_size
					elif type(_A     ) is not type(_A     ):
						raise TypeError(
						    f'''`negative_prompt` should be the same type to `prompt`, but got {type(_A     )} !='''
						    f''' {type(_A     )}.'''     )
					elif isinstance(_A      ,		_A     ):
						_UpperCAmelCase    :							Optional[int]									=					[negative_prompt]
					elif batch_size != len(_A     ):
						raise ValueError(
						    f'''`negative_prompt`: {negative_prompt} has batch size {len(_A     )}, but `prompt`:'''
						    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
						    """ the batch size of `prompt`."""     )
					else:
						_UpperCAmelCase    :							int									=					negative_prompt

					_UpperCAmelCase    :							Dict									=					text_input_ids.shape[-1]
					_UpperCAmelCase    :							List[Any]									=					self.tokenizer(
					    _A      ,		padding="""max_length"""      ,		max_length=_A      ,		truncation=_A      ,		return_tensors="""pt"""      ,		)

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

					_UpperCAmelCase    :							Any									=					self.text_encoder(
					    input_ids=uncond_input.input_ids.int().to(_A     )      ,		)
					_UpperCAmelCase    :							Optional[Any]									=					uncond_embeddings[0]

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

				return prompt_embeds






"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int = 1000 )				->			str:
	if (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (digit_position <= 0):
		raise ValueError("""Digit position must be a positive integer""" )
	elif (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (precision < 0):
		raise ValueError("""Precision must be a nonnegative integer""" )

	# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
	# accurate
	_UpperCAmelCase    :							List[Any]									=					(
	    4 * _subsum(_lowerCAmelCase,  1,  _lowerCAmelCase )
	    - 2 * _subsum(_lowerCAmelCase,  4,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  5,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  6,  _lowerCAmelCase )
	)

	# return the first hex digit of the fractional part of the result
	return hex(int((sum_result % 1) * 16 ) )[2:]
def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						int )				->			float:
	_UpperCAmelCase    :							Optional[int]									=					0.0
	for sum_index in range(digit_pos_to_extract + precision ):
		_UpperCAmelCase    :							Optional[Any]									=					8 * sum_index + denominator_addend
		if sum_index < digit_pos_to_extract:
			# if the exponential term is an integer and we mod it by the denominator
			# before dividing, only the integer part of the sum will change;
			# the fractional part will not
			_UpperCAmelCase    :							Optional[Any]									=					pow(
			    16,  digit_pos_to_extract - 1 - sum_index,  _lowerCAmelCase )
		else:
			_UpperCAmelCase    :							int									=					pow(16,  digit_pos_to_extract - 1 - sum_index )
		total += exponential_term / denominator
	return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()





