






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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_UpperCAmelCase			:		Tuple   				=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   AutoencoderKL ,							SCREAMING_SNAKE_CASE_     :   CLIPTextModel ,							SCREAMING_SNAKE_CASE_     :   CLIPTokenizer ,							SCREAMING_SNAKE_CASE_     :   UNetaDConditionModel ,							SCREAMING_SNAKE_CASE_     :   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,							SCREAMING_SNAKE_CASE_     :   StableDiffusionSafetyChecker ,							SCREAMING_SNAKE_CASE_     :   CLIPImageProcessor ,							):
											super().__init__()
											self.register_modules(
											    vae=SCREAMING_SNAKE_CASE_ ,							text_encoder=SCREAMING_SNAKE_CASE_ ,							tokenizer=SCREAMING_SNAKE_CASE_ ,							unet=SCREAMING_SNAKE_CASE_ ,							scheduler=SCREAMING_SNAKE_CASE_ ,							safety_checker=SCREAMING_SNAKE_CASE_ ,							feature_extractor=SCREAMING_SNAKE_CASE_ ,							)




							def 						__snake_case					(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[Union[str, int]] = "auto"	):
											if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															lowerCAmelCase__      			=						self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											self.enable_attention_slicing(SCREAMING_SNAKE_CASE_	)




							@torch.no_grad()
							def __call__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, List[str]] ,							SCREAMING_SNAKE_CASE_     :   int = 512 ,							SCREAMING_SNAKE_CASE_     :   int = 512 ,							SCREAMING_SNAKE_CASE_     :   int = 50 ,							SCREAMING_SNAKE_CASE_     :   float = 7.5 ,							SCREAMING_SNAKE_CASE_     :   Optional[Union[str, List[str]]] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[int] = 1 ,							SCREAMING_SNAKE_CASE_     :   float = 0.0 ,							SCREAMING_SNAKE_CASE_     :   Optional[torch.Generator] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[torch.FloatTensor] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = "pil" ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							SCREAMING_SNAKE_CASE_     :   int = 1 ,							SCREAMING_SNAKE_CASE_     :   Optional[torch.FloatTensor] = None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):

											if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
															lowerCAmelCase__      			=						1
											elif isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
															lowerCAmelCase__      			=						len(SCREAMING_SNAKE_CASE_	)
											else:
															raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_	)}'	)

											if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'	)

											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	) or callback_steps <= 0)
											):
															raise ValueError(
															    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
															    f' {type(SCREAMING_SNAKE_CASE_	)}.'	)

											# get prompt text embeddings
											lowerCAmelCase__      			=						self.tokenizer(
											    SCREAMING_SNAKE_CASE_ ,							padding='''max_length''' ,							max_length=self.tokenizer.model_max_length ,							return_tensors='''pt''' ,							)
											lowerCAmelCase__      			=						text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															lowerCAmelCase__      			=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
															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}'	)
															lowerCAmelCase__      			=						text_input_ids[:, : self.tokenizer.model_max_length]

											if text_embeddings is None:
															lowerCAmelCase__      			=						self.text_encoder(text_input_ids.to(self.device	)	)[0]

											# duplicate text embeddings for each generation per prompt, using mps friendly method
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						text_embeddings.shape
											lowerCAmelCase__      			=						text_embeddings.repeat(1 ,							SCREAMING_SNAKE_CASE_ ,							1	)
											lowerCAmelCase__      			=						text_embeddings.view(bs_embed * num_images_per_prompt ,							SCREAMING_SNAKE_CASE_ ,							-1	)

											# 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__      			=						guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
															lowerCAmelCase__      			=						42
															if negative_prompt is None:
																			lowerCAmelCase__      			=						['''''']
															elif type(SCREAMING_SNAKE_CASE_	) is not type(SCREAMING_SNAKE_CASE_	):
																			raise TypeError(
																			    f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_	)} !='
																			    f' {type(SCREAMING_SNAKE_CASE_	)}.'	)
															elif isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																			lowerCAmelCase__      			=						[negative_prompt]
															elif batch_size != len(SCREAMING_SNAKE_CASE_	):
																			raise ValueError(
																			    f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_	)}, but `prompt`:'
																			    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																			    ''' the batch size of `prompt`.'''	)
															else:
																			lowerCAmelCase__      			=						negative_prompt

															lowerCAmelCase__      			=						text_input_ids.shape[-1]
															lowerCAmelCase__      			=						self.tokenizer(
															    SCREAMING_SNAKE_CASE_ ,							padding='''max_length''' ,							max_length=SCREAMING_SNAKE_CASE_ ,							truncation=SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt''' ,							)
															lowerCAmelCase__      			=						self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															lowerCAmelCase__      			=						uncond_embeddings.shape[1]
															lowerCAmelCase__      			=						uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							1	)
															lowerCAmelCase__      			=						uncond_embeddings.view(batch_size * num_images_per_prompt ,							SCREAMING_SNAKE_CASE_ ,							-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__      			=						torch.cat([uncond_embeddings, text_embeddings]	)

											# get the initial random noise unless the user supplied it

											# Unlike in other pipelines, latents need to be generated in the target device
											# for 1-to-1 results reproducibility with the CompVis implementation.
											# However this currently doesn't work in `mps`.
											lowerCAmelCase__      			=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											lowerCAmelCase__      			=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
											lowerCAmelCase__      			=						text_embeddings.dtype
											if latents is None:
															if self.device.type == "mps":
																			# randn does not exist on mps
																			lowerCAmelCase__      			=						torch.randn(
																			    SCREAMING_SNAKE_CASE_ ,							generator=SCREAMING_SNAKE_CASE_ ,							device='''cpu''' ,							dtype=SCREAMING_SNAKE_CASE_	).to(self.device	)
																			lowerCAmelCase__      			=						torch.randn(SCREAMING_SNAKE_CASE_ ,							generator=SCREAMING_SNAKE_CASE_ ,							device='''cpu''' ,							dtype=SCREAMING_SNAKE_CASE_	).to(
																			    self.device	)
															else:
																			lowerCAmelCase__      			=						torch.randn(
																			    SCREAMING_SNAKE_CASE_ ,							generator=SCREAMING_SNAKE_CASE_ ,							device=self.device ,							dtype=SCREAMING_SNAKE_CASE_	)
																			lowerCAmelCase__      			=						torch.randn(SCREAMING_SNAKE_CASE_ ,							generator=SCREAMING_SNAKE_CASE_ ,							device=self.device ,							dtype=SCREAMING_SNAKE_CASE_	)
											else:
															if latents_reference.shape != latents_shape:
																			raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'	)
															lowerCAmelCase__      			=						latents_reference.to(self.device	)
															lowerCAmelCase__      			=						latents.to(self.device	)

											# This is the key part of the pipeline where we
											# try to ensure that the generated images w/ the same seed
											# but different sizes actually result in similar images
											lowerCAmelCase__      			=						(latents_shape[3] - latents_shape_reference[3]) // 2
											lowerCAmelCase__      			=						(latents_shape[2] - latents_shape_reference[2]) // 2
											lowerCAmelCase__      			=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
											lowerCAmelCase__      			=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
											lowerCAmelCase__      			=						0 if dx < 0 else dx
											lowerCAmelCase__      			=						0 if dy < 0 else dy
											lowerCAmelCase__      			=						max(-dx ,							0	)
											lowerCAmelCase__      			=						max(-dy ,							0	)
											# import pdb
											# pdb.set_trace()
											lowerCAmelCase__      			=						latents_reference[:, :, dy : dy + h, dx : dx + w]

											# set timesteps
											self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

											# Some schedulers like PNDM have timesteps as arrays
											# It's more optimized to move all timesteps to correct device beforehand
											lowerCAmelCase__      			=						self.scheduler.timesteps.to(self.device	)

											# scale the initial noise by the standard deviation required by the scheduler
											lowerCAmelCase__      			=						latents * self.scheduler.init_noise_sigma

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
											# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
											# and should be between [0, 1]
											lowerCAmelCase__      			=						'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
											lowerCAmelCase__      			=						{}
											if accepts_eta:
															lowerCAmelCase__      			=						eta

											for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_	)	):
															# expand the latents if we are doing classifier free guidance
															lowerCAmelCase__      			=						torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
															lowerCAmelCase__      			=						self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

															# predict the noise residual
															lowerCAmelCase__      			=						self.unet(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_	).sample

															# perform guidance
															if do_classifier_free_guidance:
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						noise_pred.chunk(2	)
																			lowerCAmelCase__      			=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															lowerCAmelCase__      			=						self.scheduler.step(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	).prev_sample

															# call the callback, if provided
															if callback is not None and i % callback_steps == 0:
																			callback(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						1 / 0.18_215 * latents
											lowerCAmelCase__      			=						self.vae.decode(SCREAMING_SNAKE_CASE_	).sample

											lowerCAmelCase__      			=						(image / 2 + 0.5).clamp(0 ,							1	)

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											lowerCAmelCase__      			=						image.cpu().permute(0 ,							2 ,							3 ,							1	).float().numpy()

											if self.safety_checker is not None:
															lowerCAmelCase__      			=						self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_	) ,							return_tensors='''pt'''	).to(
															    self.device	)
															lowerCAmelCase__			,		lowerCAmelCase__      			=						self.safety_checker(
															    images=SCREAMING_SNAKE_CASE_ ,							clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
											else:
															lowerCAmelCase__      			=						None

											if output_type == "pil":
															lowerCAmelCase__      			=						self.numpy_to_pil(SCREAMING_SNAKE_CASE_	)

											if not return_dict:
															return (image, has_nsfw_concept)

											return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ ,							nsfw_content_detected=SCREAMING_SNAKE_CASE_	)













import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class  lowerCAmelCase_       :




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   int	):
											if dst_width < 0 or dst_height < 0:
															raise ValueError('''Destination width/height should be > 0'''	)

											lowerCAmelCase__      			=						img
											lowerCAmelCase__      			=						img.shape[1]
											lowerCAmelCase__      			=						img.shape[0]
											lowerCAmelCase__      			=						dst_width
											lowerCAmelCase__      			=						dst_height

											lowerCAmelCase__      			=						self.src_w / self.dst_w
											lowerCAmelCase__      			=						self.src_h / self.dst_h

											lowerCAmelCase__      			=						lowerCAmelCase__      			=						(
											    np.ones((self.dst_h, self.dst_w, 3) ,							np.uinta	) * 255
											)




							def 						__snake_case					(   self     :   List[str]	):
											for i in range(self.dst_h	):
															for j in range(self.dst_w	):
																			lowerCAmelCase__      			=						self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_x * x	)




							def 						__snake_case					(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   int	):
											return int(self.ratio_y * y	)


if __name__ == "__main__":
			_UpperCAmelCase				,   _UpperCAmelCase			:		List[str]   				=    800, 600
			_UpperCAmelCase			:		Tuple   				=    imread("image_data/lena.jpg", 1)
			_UpperCAmelCase			:		str   				=    NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
			)
			waitKey(0)
			destroyAllWindows()





