


"""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



"""simple docstring"""







import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__a      			= [nn.LayerNorm]

__a      			= logging.get_logger(__name__)

__a      			= version.parse(version.parse(torch.__version__).base_version)

__a      			= parsed_torch_version_base >= version.parse("2.0")
__a      			= parsed_torch_version_base >= version.parse("1.12")
__a      			= parsed_torch_version_base >= version.parse("1.11")
__a      			= parsed_torch_version_base < version.parse("1.11")






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      self			):





						"""simple docstring"""

						from torch import _softmax_backward_data

						if is_torch_less_than_1_11:
												return _softmax_backward_data(_UpperCamelCase						,      _UpperCamelCase						,      parent.dim						,      self			)
						else:
												return _softmax_backward_data(_UpperCamelCase						,      _UpperCamelCase						,      parent.dim						,      self.dtype			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = 0			):





						"""simple docstring"""

						UpperCAmelCase__												=     index.to(layer.weight.device			)
						UpperCAmelCase__												=     layer.weight.index_select(_UpperCamelCase						,      _UpperCamelCase			).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		UpperCAmelCase__												=     layer.bias.clone().detach()
												else:
																		UpperCAmelCase__												=     layer.bias[index].clone().detach()
						UpperCAmelCase__												=     list(layer.weight.size()			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     nn.Linear(new_size[1]						,      new_size[0]						,      bias=layer.bias is not None			).to(layer.weight.device			)
						UpperCAmelCase__												=     False
						new_layer.weight.copy_(W.contiguous()			)
						UpperCAmelCase__												=     True
						if layer.bias is not None:
												UpperCAmelCase__												=     False
												new_layer.bias.copy_(b.contiguous()			)
												UpperCAmelCase__												=     True
						return new_layer
class 				lowerCamelCase							(      nn.Module			):


							"""simple docstring"""






							def __init__(    self			:  str		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Dict					):
													super().__init__()
													UpperCAmelCase__												=     nf
													UpperCAmelCase__												=     nn.Parameter(torch.empty(lowerCamelCase_		,			lowerCamelCase_					)					)
													UpperCAmelCase__												=     nn.Parameter(torch.zeros(lowerCamelCase_					)					)
													nn.init.normal_(self.weight		,			std=0.02					)






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Tuple					):
													UpperCAmelCase__												=     x.size()[:-1] + (self.nf,)
													UpperCAmelCase__												=     torch.addmm(self.bias		,			x.view(-1		,			x.size(-1					)					)		,			self.weight					)
													UpperCAmelCase__												=     x.view(lowerCamelCase_					)
													return x






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = 1			):





						"""simple docstring"""

						UpperCAmelCase__												=     index.to(layer.weight.device			)
						UpperCAmelCase__												=     layer.weight.index_select(_UpperCamelCase						,      _UpperCamelCase			).clone().detach()
						if dim == 0:
												UpperCAmelCase__												=     layer.bias.clone().detach()
						else:
												UpperCAmelCase__												=     layer.bias[index].clone().detach()
						UpperCAmelCase__												=     list(layer.weight.size()			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     ConvaD(new_size[1]						,      new_size[0]			).to(layer.weight.device			)
						UpperCAmelCase__												=     False
						new_layer.weight.copy_(W.contiguous()			)
						UpperCAmelCase__												=     True
						UpperCAmelCase__												=     False
						new_layer.bias.copy_(b.contiguous()			)
						UpperCAmelCase__												=     True
						return new_layer






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = None			):





						"""simple docstring"""

						if isinstance(_UpperCamelCase						,      nn.Linear			):
												return prune_linear_layer(_UpperCamelCase						,      _UpperCamelCase						,      dim=0 if dim is None else dim			)
						elif isinstance(_UpperCamelCase						,      _UpperCamelCase			):
												return prune_convad_layer(_UpperCamelCase						,      _UpperCamelCase						,      dim=1 if dim is None else dim			)
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}"""			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      *_UpperCamelCase			):





						"""simple docstring"""

						assert len(_UpperCamelCase			) > 0, F"""{input_tensors} has to be a tuple/list of tensors"""

						# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
						UpperCAmelCase__												=     len(inspect.signature(_UpperCamelCase			).parameters			)
						if num_args_in_forward_chunk_fn != len(_UpperCamelCase			):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_UpperCamelCase			)} input """
												    '''tensors are given'''			)

						if chunk_size > 0:
												UpperCAmelCase__												=     input_tensors[0].shape[chunk_dim]
												for input_tensor in input_tensors:
																		if input_tensor.shape[chunk_dim] != tensor_shape:
																								raise ValueError(
																								    F"""All input tenors have to be of the same shape: {tensor_shape}, """
																								    F"""found shape {input_tensor.shape[chunk_dim]}"""			)

												if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
																		raise ValueError(
																		    F"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
																		    F"""size {chunk_size}"""			)

												UpperCAmelCase__												=     input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												UpperCAmelCase__												=     tuple(input_tensor.chunk(_UpperCamelCase						,      dim=_UpperCamelCase			) for input_tensor in input_tensors			)
												# apply forward fn to every tuple
												UpperCAmelCase__												=     tuple(forward_fn(*_UpperCamelCase			) for input_tensors_chunk in zip(*_UpperCamelCase			)			)
												# concatenate output at same dimension
												return torch.cat(_UpperCamelCase						,      dim=_UpperCamelCase			)

						return forward_fn(*_UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     torch.ones(_UpperCamelCase						,      _UpperCamelCase			)
						UpperCAmelCase__												=     set(_UpperCamelCase			) - already_pruned_heads  # Convert to set and remove already pruned heads
						for head in heads:
												# Compute how many pruned heads are before the head and move the index accordingly
												UpperCAmelCase__												=     head - sum(1 if h < head else 0 for h in already_pruned_heads			)
												UpperCAmelCase__												=     0
						UpperCAmelCase__												=     mask.view(-1			).contiguous().eq(1			)
						UpperCAmelCase__												=     torch.arange(len(_UpperCamelCase			)			)[mask].long()
						return heads, index






def        a__			(							*_UpperCamelCase						,      _UpperCamelCase = None			):





						"""simple docstring"""

						return torch.meshgrid(*_UpperCamelCase						,      indexing=_UpperCamelCase			)






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						return tensor.device, storage_ptr(_UpperCamelCase			), storage_size(_UpperCamelCase			)