



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__						:						List[Any]          =      getLogger(__name__)


A__						:						Dict          =      4
A__						:						Optional[Any]          =      7

A__						:						Union[str, Any]          =      {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}


def     UpperCamelCase(						__UpperCamelCase							:	List[str]				):
		if isinstance(__UpperCamelCase  ,torch.Tensor				):
				return image
		elif isinstance(__UpperCamelCase  ,PIL.Image.Image				):
				lowerCAmelCase_    :  str             =				[image]

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

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

		return image





class        __snake_case		(  UpperCamelCase_		):




							def __init__( self  : int       ,       A_  : OnnxRuntimeModel       ,       A_  : OnnxRuntimeModel       ,       A_  : Any       ,       A_  : OnnxRuntimeModel       ,       A_  : DDPMScheduler       ,       A_  : Any       ,       A_  : int = 3_5_0       ,       ):
									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  : Optional[int]       ,       A_  : Union[str, List[str]]       ,       A_  : Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]       ,       A_  : int = 7_5       ,       A_  : float = 9.0       ,       A_  : int = 2_0       ,       A_  : Optional[Union[str, List[str]]] = None       ,       A_  : Optional[int] = 1       ,       A_  : float = 0.0       ,       A_  : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,       A_  : Optional[torch.FloatTensor] = None       ,       A_  : Optional[np.ndarray] = None       ,       A_  : Optional[np.ndarray] = None       ,       A_  : Optional[str] = "pil"       ,       A_  : bool = True       ,       A_  : Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,       A_  : Optional[int] = 1       ,       ):
									self.check_inputs(A_       ,       A_       ,       A_       ,       A_)

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

									lowerCAmelCase_    :  List[str]             =				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.
									lowerCAmelCase_    :  Dict             =				guidance_scale > 1.0

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

									lowerCAmelCase_    :  int             =				ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

									# 4. Preprocess image
									lowerCAmelCase_    :  Optional[Any]             =				preprocess(A_)
									lowerCAmelCase_    :  List[str]             =				image.cpu()

									# 5. set timesteps
									self.scheduler.set_timesteps(A_       ,       device=A_)
									lowerCAmelCase_    :  int             =				self.scheduler.timesteps

									# 5. Add noise to image
									lowerCAmelCase_    :  Union[str, Any]             =				torch.tensor([noise_level]       ,       dtype=torch.long       ,       device=A_)
									lowerCAmelCase_    :  Tuple             =				torch.randn(image.shape       ,       generator=A_       ,       device=A_       ,       dtype=A_)
									lowerCAmelCase_    :  str             =				self.low_res_scheduler.add_noise(A_       ,       A_       ,       A_)

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

									# 6. Prepare latent variables
									lowerCAmelCase_      ,						lowerCAmelCase_    :  Tuple             =				image.shape[2:]
									lowerCAmelCase_    :  Optional[Any]             =				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
									lowerCAmelCase_    :  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
									lowerCAmelCase_    :  Union[str, Any]             =				self.prepare_extra_step_kwargs(A_       ,       A_)

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

									# 9. Denoising loop
									lowerCAmelCase_    :  Union[str, Any]             =				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
													lowerCAmelCase_    :  int             =				np.concatenate([latents] * 2) if do_classifier_free_guidance else latents

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

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

													# predict the noise residual
													lowerCAmelCase_    :  Union[str, 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:
															lowerCAmelCase_      ,						lowerCAmelCase_    :  List[Any]             =				np.split(A_       ,       2)
													lowerCAmelCase_    :  Optional[int]             =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													lowerCAmelCase_    :  int             =				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
									lowerCAmelCase_    :  Optional[Any]             =				self.decode_latents(latents.float())

									# 11. Convert to PIL
									if output_type == "pil":
											lowerCAmelCase_    :  List[Any]             =				self.numpy_to_pil(A_)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=A_)




							def      UpperCAmelCase__    ( self  : Optional[int]       ,       A_  : Any):
									lowerCAmelCase_    :  Optional[int]             =				1 / 0.0_8333 * latents
									lowerCAmelCase_    :  str             =				self.vae(latent_sample=A_)[0]
									lowerCAmelCase_    :  List[Any]             =				np.clip(image / 2 + 0.5       ,       0       ,       1)
									lowerCAmelCase_    :  Optional[int]             =				image.transpose((0, 2, 3, 1))
									return image







							def      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : Union[str, List[str]]       ,       A_  : Any       ,       A_  : Optional[int]       ,       A_  : bool       ,       A_  : Optional[str]       ,       A_  : Optional[torch.FloatTensor] = None       ,       A_  : Optional[torch.FloatTensor] = None       ,       ):
									if prompt is not None and isinstance(A_       ,       A_):
											lowerCAmelCase_    :  Dict             =				1
									elif prompt is not None and isinstance(A_       ,       A_):
											lowerCAmelCase_    :  Optional[Any]             =				len(A_)
									else:
											lowerCAmelCase_    :  Dict             =				prompt_embeds.shape[0]

									if prompt_embeds is None:
											lowerCAmelCase_    :  int             =				self.tokenizer(
											    A_       ,       padding='''max_length'''       ,       max_length=self.tokenizer.model_max_length       ,       truncation=A_       ,       return_tensors='''pt'''       ,       )
											lowerCAmelCase_    :  Tuple             =				text_inputs.input_ids
											lowerCAmelCase_    :  Union[str, Any]             =				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_):
													lowerCAmelCase_    :  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
											lowerCAmelCase_    :  Optional[Any]             =				self.text_encoder(
											    input_ids=text_input_ids.int().to(A_)       ,       )
											lowerCAmelCase_    :  Union[str, Any]             =				prompt_embeds[0]

									lowerCAmelCase_      ,						lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				prompt_embeds.shape
									# duplicate text embeddings for each generation per prompt, using mps friendly method
									lowerCAmelCase_    :  Optional[Any]             =				prompt_embeds.repeat(1       ,       A_)
									lowerCAmelCase_    :  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:
											lowerCAmelCase_    :  List[str]
											if negative_prompt is None:
													lowerCAmelCase_    :  Tuple             =				[''''''] * 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_):
													lowerCAmelCase_    :  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:
													lowerCAmelCase_    :  Union[str, Any]             =				negative_prompt

											lowerCAmelCase_    :  List[Any]             =				text_input_ids.shape[-1]
											lowerCAmelCase_    :  Dict             =				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

											lowerCAmelCase_    :  Tuple             =				self.text_encoder(
											    input_ids=uncond_input.input_ids.int().to(A_)       ,       )
											lowerCAmelCase_    :  Any             =				uncond_embeddings[0]

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

									return prompt_embeds










from __future__ import annotations

from typing import Generic, TypeVar

A__						:						Dict          =      TypeVar('''T''')



class        __snake_case		(  Generic[T]		):




							def __init__( self  : List[Any]       ,       A_  : T):
									lowerCAmelCase_    :  Optional[Any]             =				data  # Assign data
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize next as null
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize prev as null





class        __snake_case		(  Generic[T]		):




							def __init__( self  : Union[str, Any]):
									lowerCAmelCase_    :  Node[T] | None             =				None




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : T):
									if self.head is None:
											lowerCAmelCase_    :  List[Any]             =				Node(A_)
									else:
											lowerCAmelCase_    :  Tuple             =				Node(A_)
											lowerCAmelCase_    :  Optional[int]             =				new_node
											lowerCAmelCase_    :  Optional[int]             =				self.head
											lowerCAmelCase_    :  Optional[Any]             =				None
											lowerCAmelCase_    :  Dict             =				new_node




							def      UpperCAmelCase__    ( self  : List[str]):
									if self.head is None:
											return None
									else:
											assert self.head is not None
											lowerCAmelCase_    :  Tuple             =				self.head.data
											lowerCAmelCase_    :  Any             =				self.head.next
											if self.head is not None:
													lowerCAmelCase_    :  Optional[Any]             =				None
											return temp




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									return self.head.data if self.head is not None else None




							def __len__( self  : Dict):
									lowerCAmelCase_    :  List[str]             =				self.head
									lowerCAmelCase_    :  str             =				0
									while temp is not None:
											count += 1
											lowerCAmelCase_    :  Union[str, Any]             =				temp.next
									return count




							def      UpperCAmelCase__    ( self  : Dict):
									return self.head is None







							def      UpperCAmelCase__    ( self  : Dict):
									print('''stack elements are:''')
									lowerCAmelCase_    :  Optional[Any]             =				self.head
									while temp is not None:
											print(temp.data       ,       end='''->''')
											lowerCAmelCase_    :  List[str]             =				temp.next


# Code execution starts here
if __name__ == "__main__":
						# Start with the empty stack
						A__						:						Stack[int]          =      Stack()

						# Insert 4 at the beginning. So stack becomes 4->None
						print('''Stack operations using Doubly LinkedList''')
						stack.push(4)

						# Insert 5 at the beginning. So stack becomes 4->5->None
						stack.push(5)

						# Insert 6 at the beginning. So stack becomes 4->5->6->None
						stack.push(6)

						# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
						stack.push(7)

						# Print the stack
						stack.print_stack()

						# Print the top element
						print('''\nTop element is ''', stack.top())

						# Print the stack size
						print('''Size of the stack is ''', len(stack))

						# pop the top element
						stack.pop()

						# pop the top element
						stack.pop()

						# two elements have now been popped off
						stack.print_stack()

						# Print True if the stack is empty else False
						print('''\nstack is empty:''', stack.is_empty())





