






import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if isinstance(snake_case,  torch.Tensor ):
								return image
				elif isinstance(snake_case,  PIL.Image.Image ):
								__magic_name__		:int        =      [image]

				if isinstance(image[0],  PIL.Image.Image ):
								__magic_name__		:Union[str, Any]        =      [np.array(i.resize((w, h),  resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
								__magic_name__		:Any        =      np.concatenate(snake_case,  axis=0 )
								__magic_name__		:Optional[Any]        =      np.array(snake_case ).astype(np.floataa ) / 255.0
								__magic_name__		:str        =      image.transpose(0,  3,  1,  2 )
								__magic_name__		:Optional[Any]        =      2.0 * image - 1.0
								__magic_name__		:Dict        =      torch.from_numpy(snake_case )
				elif isinstance(image[0],  torch.Tensor ):
								__magic_name__		:Optional[Any]        =      torch.cat(snake_case,  dim=0 )
				return image

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case=0.9995 ):







				"""simple docstring"""






				if not isinstance(snake_case,  np.ndarray ):
								__magic_name__		:Union[str, Any]        =      True
								__magic_name__		:Any        =      va.device
								__magic_name__		:List[str]        =      va.cpu().numpy()
								__magic_name__		:Tuple        =      va.cpu().numpy()

				__magic_name__		:Optional[int]        =      np.sum(va * va / (np.linalg.norm(snake_case ) * np.linalg.norm(snake_case )) )
				if np.abs(snake_case ) > DOT_THRESHOLD:
								__magic_name__		:Tuple        =      (1 - t) * va + t * va
				else:
								__magic_name__		:Optional[Any]        =      np.arccos(snake_case )
								__magic_name__		:Any        =      np.sin(snake_case )
								__magic_name__		:int        =      theta_a * t
								__magic_name__		:List[str]        =      np.sin(snake_case )
								__magic_name__		:Optional[int]        =      np.sin(theta_a - theta_t ) / sin_theta_a
								__magic_name__		:List[Any]        =      sin_theta_t / sin_theta_a
								__magic_name__		:Optional[int]        =      sa * va + sa * va

				if inputs_are_torch:
								__magic_name__		:Optional[int]        =      torch.from_numpy(snake_case ).to(snake_case )

				return va

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      F.normalize(snake_case,  dim=-1 )
				__magic_name__		:str        =      F.normalize(snake_case,  dim=-1 )
				return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				for param in model.parameters():
								__magic_name__		:Union[str, Any]        =      value

class 						lowerCamelCase_				(      lowerCamelCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					):
								"""simple docstring"""
								super().__init__()
								self.register_modules(
								    vae=__lowerCAmelCase       ,					text_encoder=__lowerCAmelCase       ,					clip_model=__lowerCAmelCase       ,					tokenizer=__lowerCAmelCase       ,					unet=__lowerCAmelCase       ,					scheduler=__lowerCAmelCase       ,					feature_extractor=__lowerCAmelCase       ,					coca_model=__lowerCAmelCase       ,					coca_tokenizer=__lowerCAmelCase       ,					coca_transform=__lowerCAmelCase       ,					)
								__magic_name__		:int        =      (
								    feature_extractor.size
								    if isinstance(feature_extractor.size       ,					__lowerCAmelCase     )
								    else feature_extractor.size['''shortest_edge''']
								)
								__magic_name__		:List[str]        =      transforms.Normalize(mean=feature_extractor.image_mean       ,					std=feature_extractor.image_std     )
								set_requires_grad(self.text_encoder       ,					__lowerCAmelCase     )
								set_requires_grad(self.clip_model       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase = "auto"     ):
								"""simple docstring"""
								if slice_size == "auto":
												# half the attention head size is usually a good trade-off between
												# speed and memory
												__magic_name__		:str        =      self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.enable_attention_slicing(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								set_requires_grad(self.vae       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								set_requires_grad(self.vae       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								set_requires_grad(self.unet       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								set_requires_grad(self.unet       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								# get the original timestep using init_timestep
								__magic_name__		:Optional[Any]        =      min(int(num_inference_steps * strength     )       ,					__lowerCAmelCase     )

								__magic_name__		:int        =      max(num_inference_steps - init_timestep       ,					0     )
								__magic_name__		:Any        =      self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								if not isinstance(__lowerCAmelCase       ,					torch.Tensor     ):
												raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(__lowerCAmelCase     )}'''     )

								__magic_name__		:List[str]        =      image.to(device=__lowerCAmelCase       ,					dtype=__lowerCAmelCase     )

								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:Any        =      [
												    self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(__lowerCAmelCase     )
												]
												__magic_name__		:Optional[int]        =      torch.cat(__lowerCAmelCase       ,					dim=0     )
								else:
												__magic_name__		:List[Any]        =      self.vae.encode(__lowerCAmelCase     ).latent_dist.sample(__lowerCAmelCase     )

								# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								__magic_name__		:Tuple        =      0.18215 * init_latents
								__magic_name__		:str        =      init_latents.repeat_interleave(__lowerCAmelCase       ,					dim=0     )

								__magic_name__		:Optional[int]        =      randn_tensor(init_latents.shape       ,					generator=__lowerCAmelCase       ,					device=__lowerCAmelCase       ,					dtype=__lowerCAmelCase     )

								# get latents
								__magic_name__		:List[str]        =      self.scheduler.add_noise(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      init_latents

								return latents







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.coca_transform(__lowerCAmelCase     ).unsqueeze(0     )
								with torch.no_grad(), torch.cuda.amp.autocast():
												__magic_name__		:Dict        =      self.coca_model.generate(transformed_image.to(device=self.device       ,					dtype=self.coca_model.dtype     )     )
								__magic_name__		:str        =      self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
								return generated.split('''<end_of_text>'''     )[0].replace('''<start_of_text>'''       ,					''''''     ).rstrip(''' .,'''     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.feature_extractor.preprocess(__lowerCAmelCase     )
								__magic_name__		:int        =      torch.from_numpy(clip_image_input['''pixel_values'''][0]     ).unsqueeze(0     ).to(self.device     ).half()
								__magic_name__		:str        =      self.clip_model.get_image_features(__lowerCAmelCase     )
								__magic_name__		:Dict        =      image_embeddings_clip / image_embeddings_clip.norm(p=2       ,					dim=-1       ,					keepdim=__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      image_embeddings_clip.repeat_interleave(__lowerCAmelCase       ,					dim=0     )
								return image_embeddings_clip







				@torch.enable_grad()
				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      latents.detach().requires_grad_()

								__magic_name__		:int        =      self.scheduler.scale_model_input(__lowerCAmelCase       ,					__lowerCAmelCase     )

								# predict the noise residual
								__magic_name__		:str        =      self.unet(__lowerCAmelCase       ,					__lowerCAmelCase       ,					encoder_hidden_states=__lowerCAmelCase     ).sample

								if isinstance(self.scheduler       ,					(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
												__magic_name__		:str        =      self.scheduler.alphas_cumprod[timestep]
												__magic_name__		:Tuple        =      1 - alpha_prod_t
												# compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												__magic_name__		:Optional[Any]        =      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

												__magic_name__		:int        =      torch.sqrt(__lowerCAmelCase     )
												__magic_name__		:Tuple        =      pred_original_sample * (fac) + latents * (1 - fac)
								elif isinstance(self.scheduler       ,					__lowerCAmelCase     ):
												__magic_name__		:int        =      self.scheduler.sigmas[index]
												__magic_name__		:Any        =      latents - sigma * noise_pred
								else:
												raise ValueError(F'''scheduler type {type(self.scheduler     )} not supported'''     )

								# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								__magic_name__		:List[str]        =      1 / 0.18215 * sample
								__magic_name__		:str        =      self.vae.decode(__lowerCAmelCase     ).sample
								__magic_name__		:Union[str, Any]        =      (image / 2 + 0.5).clamp(0       ,					1     )

								__magic_name__		:Any        =      transforms.Resize(self.feature_extractor_size     )(__lowerCAmelCase     )
								__magic_name__		:Any        =      self.normalize(__lowerCAmelCase     ).to(latents.dtype     )

								__magic_name__		:Union[str, Any]        =      self.clip_model.get_image_features(__lowerCAmelCase     )
								__magic_name__		:Tuple        =      image_embeddings_clip / image_embeddings_clip.norm(p=2       ,					dim=-1       ,					keepdim=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      spherical_dist_loss(__lowerCAmelCase       ,					__lowerCAmelCase     ).mean() * clip_guidance_scale

								__magic_name__		:Tuple        =      -torch.autograd.grad(__lowerCAmelCase       ,					__lowerCAmelCase     )[0]

								if isinstance(self.scheduler       ,					__lowerCAmelCase     ):
												__magic_name__		:Union[str, Any]        =      latents.detach() + grads * (sigma**2)
												__magic_name__		:List[Any]        =      noise_pred_original
								else:
												__magic_name__		:Union[str, Any]        =      noise_pred_original - torch.sqrt(__lowerCAmelCase     ) * grads
								return noise_pred, latents







				@torch.no_grad()
				def __call__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = 5_1_2       ,					__lowerCAmelCase = 5_1_2       ,					__lowerCAmelCase = 0.6       ,					__lowerCAmelCase = 5_0       ,					__lowerCAmelCase = 7.5       ,					__lowerCAmelCase = 1       ,					__lowerCAmelCase = 0.0       ,					__lowerCAmelCase = 1_0_0       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "pil"       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = 0.8       ,					__lowerCAmelCase = 0.1       ,					__lowerCAmelCase = 0.1       ,					):
								"""simple docstring"""
								if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and len(__lowerCAmelCase     ) != batch_size:
												raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(__lowerCAmelCase     )} generators.'''     )

								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 isinstance(__lowerCAmelCase       ,					torch.Generator     ) and batch_size > 1:
												__magic_name__		:Tuple        =      [generator] + [None] * (batch_size - 1)

								__magic_name__		:Optional[Any]        =      [
								    ('''model''', self.coca_model is None),
								    ('''tokenizer''', self.coca_tokenizer is None),
								    ('''transform''', self.coca_transform is None),
								]
								__magic_name__		:Any        =      [x[0] for x in coca_is_none if x[1]]
								__magic_name__		:int        =      ''', '''.join(__lowerCAmelCase     )
								# generate prompts with coca model if prompt is None
								if content_prompt is None:
												if len(__lowerCAmelCase     ):
																raise ValueError(
																    F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
																    F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
												__magic_name__		:str        =      self.get_image_description(__lowerCAmelCase     )
								if style_prompt is None:
												if len(__lowerCAmelCase     ):
																raise ValueError(
																    F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
																    F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
												__magic_name__		:Dict        =      self.get_image_description(__lowerCAmelCase     )

								# get prompt text embeddings for content and style
								__magic_name__		:List[Any]        =      self.tokenizer(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=self.tokenizer.model_max_length       ,					truncation=__lowerCAmelCase       ,					return_tensors='''pt'''       ,					)
								__magic_name__		:Dict        =      self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

								__magic_name__		:Any        =      self.tokenizer(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=self.tokenizer.model_max_length       ,					truncation=__lowerCAmelCase       ,					return_tensors='''pt'''       ,					)
								__magic_name__		:Dict        =      self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

								__magic_name__		:Union[str, Any]        =      slerp(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

								# duplicate text embeddings for each generation per prompt
								__magic_name__		:int        =      text_embeddings.repeat_interleave(__lowerCAmelCase       ,					dim=0     )

								# set timesteps
								__magic_name__		:Tuple        =      '''offset''' in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
								__magic_name__		:int        =      {}
								if accepts_offset:
												__magic_name__		:Tuple        =      1

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

								__magic_name__     ,				__magic_name__		:List[str]        =      self.get_timesteps(__lowerCAmelCase       ,					__lowerCAmelCase       ,					self.device     )
								__magic_name__		:Optional[Any]        =      timesteps[:1].repeat(__lowerCAmelCase     )

								# Preprocess image
								__magic_name__		:Dict        =      preprocess(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								__magic_name__		:Optional[Any]        =      self.prepare_latents(
								    __lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					text_embeddings.dtype       ,					self.device       ,					__lowerCAmelCase     )

								__magic_name__		:Any        =      preprocess(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								__magic_name__		:int        =      self.prepare_latents(
								    __lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					text_embeddings.dtype       ,					self.device       ,					__lowerCAmelCase     )

								__magic_name__		:Any        =      slerp(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

								if clip_guidance_scale > 0:
												__magic_name__		:Optional[int]        =      self.get_clip_image_embeddings(__lowerCAmelCase       ,					__lowerCAmelCase     )
												__magic_name__		:Any        =      self.get_clip_image_embeddings(__lowerCAmelCase       ,					__lowerCAmelCase     )
												__magic_name__		:Dict        =      slerp(
												    __lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

								# 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.
								__magic_name__		:Dict        =      guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												__magic_name__		:Tuple        =      content_text_input.input_ids.shape[-1]
												__magic_name__		:List[Any]        =      self.tokenizer(['''''']       ,					padding='''max_length'''       ,					max_length=__lowerCAmelCase       ,					return_tensors='''pt'''     )
												__magic_name__		:Dict        =      self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
												# duplicate unconditional embeddings for each generation per prompt
												__magic_name__		:Dict        =      uncond_embeddings.repeat_interleave(__lowerCAmelCase       ,					dim=0     )

												# 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
												__magic_name__		:Any        =      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`.
								__magic_name__		:Tuple        =      (batch_size, self.unet.config.in_channels, height // 8, width // 8)
								__magic_name__		:Optional[int]        =      text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not work reproducibly on mps
																__magic_name__		:Dict        =      torch.randn(__lowerCAmelCase       ,					generator=__lowerCAmelCase       ,					device='''cpu'''       ,					dtype=__lowerCAmelCase     ).to(
																    self.device     )
												else:
																__magic_name__		:List[Any]        =      torch.randn(__lowerCAmelCase       ,					generator=__lowerCAmelCase       ,					device=self.device       ,					dtype=__lowerCAmelCase     )
								else:
												if latents.shape != latents_shape:
																raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
												__magic_name__		:Union[str, Any]        =      latents.to(self.device     )

								# scale the initial noise by the standard deviation required by the scheduler
								__magic_name__		:Union[str, Any]        =      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]
								__magic_name__		:List[Any]        =      '''eta''' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
								__magic_name__		:Optional[int]        =      {}
								if accepts_eta:
												__magic_name__		:List[Any]        =      eta

								# check if the scheduler accepts generator
								__magic_name__		:Union[str, Any]        =      '''generator''' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
								if accepts_generator:
												__magic_name__		:str        =      generator

								with self.progress_bar(total=__lowerCAmelCase     ):
												for i, t in enumerate(__lowerCAmelCase     ):
																# expand the latents if we are doing classifier free guidance
																__magic_name__		:Any        =      torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
																__magic_name__		:Optional[int]        =      self.scheduler.scale_model_input(__lowerCAmelCase       ,					__lowerCAmelCase     )

																# predict the noise residual
																__magic_name__		:Tuple        =      self.unet(__lowerCAmelCase       ,					__lowerCAmelCase       ,					encoder_hidden_states=__lowerCAmelCase     ).sample

																# perform classifier free guidance
																if do_classifier_free_guidance:
																				__magic_name__     ,				__magic_name__		:Dict        =      noise_pred.chunk(2     )
																				__magic_name__		:List[Any]        =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																# perform clip guidance
																if clip_guidance_scale > 0:
																				__magic_name__		:Dict        =      (
																				    text_embeddings.chunk(2     )[1] if do_classifier_free_guidance else text_embeddings
																				)
																				__magic_name__     ,				__magic_name__		:Optional[int]        =      self.cond_fn(
																				    __lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					)

																# compute the previous noisy sample x_t -> x_t-1
																__magic_name__		:Union[str, Any]        =      self.scheduler.step(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					**__lowerCAmelCase     ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								__magic_name__		:Tuple        =      1 / 0.18215 * latents
								__magic_name__		:str        =      self.vae.decode(__lowerCAmelCase     ).sample

								__magic_name__		:Any        =      (image / 2 + 0.5).clamp(0       ,					1     )
								__magic_name__		:Any        =      image.cpu().permute(0       ,					2       ,					3       ,					1     ).numpy()

								if output_type == "pil":
												__magic_name__		:List[Any]        =      self.numpy_to_pil(__lowerCAmelCase     )

								if not return_dict:
												return (image, None)

								return StableDiffusionPipelineOutput(images=__lowerCAmelCase       ,					nsfw_content_detected=__lowerCAmelCase     )














import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      :			List[str]							=		logging.get_logger(__name__)

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      DPTConfig()

				if "large" in checkpoint_url:
								__magic_name__		:str        =      1_0_2_4
								__magic_name__		:Tuple        =      4_0_9_6
								__magic_name__		:Union[str, Any]        =      2_4
								__magic_name__		:int        =      1_6
								__magic_name__		:Dict        =      [5, 1_1, 1_7, 2_3]
								__magic_name__		:Optional[int]        =      [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
								__magic_name__		:Union[str, Any]        =      (1, 3_8_4, 3_8_4)

				if "ade" in checkpoint_url:
								__magic_name__		:Tuple        =      True

								__magic_name__		:int        =      1_5_0
								__magic_name__		:List[Any]        =      '''huggingface/label-files'''
								__magic_name__		:Dict        =      '''ade20k-id2label.json'''
								__magic_name__		:str        =      json.load(open(cached_download(hf_hub_url(snake_case,  snake_case,  repo_type='''dataset''' ) ),  '''r''' ) )
								__magic_name__		:Optional[int]        =      {int(snake_case ): v for k, v in idalabel.items()}
								__magic_name__		:Any        =      idalabel
								__magic_name__		:List[str]        =      {v: k for k, v in idalabel.items()}
								__magic_name__		:Optional[Any]        =      [1, 1_5_0, 4_8_0, 4_8_0]

				return config, expected_shape

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(snake_case,  snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								__magic_name__		:int        =      name.replace('''pretrained.model''',  '''dpt.encoder''' )
				if "pretrained.model" in name:
								__magic_name__		:str        =      name.replace('''pretrained.model''',  '''dpt.embeddings''' )
				if "patch_embed" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''patch_embed''',  '''patch_embeddings''' )
				if "pos_embed" in name:
								__magic_name__		:List[Any]        =      name.replace('''pos_embed''',  '''position_embeddings''' )
				if "attn.proj" in name:
								__magic_name__		:List[Any]        =      name.replace('''attn.proj''',  '''attention.output.dense''' )
				if "proj" in name and "project" not in name:
								__magic_name__		:Dict        =      name.replace('''proj''',  '''projection''' )
				if "blocks" in name:
								__magic_name__		:str        =      name.replace('''blocks''',  '''layer''' )
				if "mlp.fc1" in name:
								__magic_name__		:Dict        =      name.replace('''mlp.fc1''',  '''intermediate.dense''' )
				if "mlp.fc2" in name:
								__magic_name__		:Tuple        =      name.replace('''mlp.fc2''',  '''output.dense''' )
				if "norm1" in name:
								__magic_name__		:List[Any]        =      name.replace('''norm1''',  '''layernorm_before''' )
				if "norm2" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''norm2''',  '''layernorm_after''' )
				if "scratch.output_conv" in name:
								__magic_name__		:Any        =      name.replace('''scratch.output_conv''',  '''head''' )
				if "scratch" in name:
								__magic_name__		:str        =      name.replace('''scratch''',  '''neck''' )
				if "layer1_rn" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''layer1_rn''',  '''convs.0''' )
				if "layer2_rn" in name:
								__magic_name__		:int        =      name.replace('''layer2_rn''',  '''convs.1''' )
				if "layer3_rn" in name:
								__magic_name__		:str        =      name.replace('''layer3_rn''',  '''convs.2''' )
				if "layer4_rn" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''layer4_rn''',  '''convs.3''' )
				if "refinenet" in name:
								__magic_name__		:int        =      int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								__magic_name__		:List[Any]        =      name.replace(f'''refinenet{layer_idx}''',  f'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
				if "out_conv" in name:
								__magic_name__		:str        =      name.replace('''out_conv''',  '''projection''' )
				if "resConfUnit1" in name:
								__magic_name__		:Dict        =      name.replace('''resConfUnit1''',  '''residual_layer1''' )
				if "resConfUnit2" in name:
								__magic_name__		:int        =      name.replace('''resConfUnit2''',  '''residual_layer2''' )
				if "conv1" in name:
								__magic_name__		:str        =      name.replace('''conv1''',  '''convolution1''' )
				if "conv2" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''conv2''',  '''convolution2''' )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								__magic_name__		:Optional[int]        =      name.replace('''pretrained.act_postprocess1.0.project.0''',  '''neck.reassemble_stage.readout_projects.0.0''' )
				if "pretrained.act_postprocess2.0.project.0" in name:
								__magic_name__		:int        =      name.replace('''pretrained.act_postprocess2.0.project.0''',  '''neck.reassemble_stage.readout_projects.1.0''' )
				if "pretrained.act_postprocess3.0.project.0" in name:
								__magic_name__		:Optional[int]        =      name.replace('''pretrained.act_postprocess3.0.project.0''',  '''neck.reassemble_stage.readout_projects.2.0''' )
				if "pretrained.act_postprocess4.0.project.0" in name:
								__magic_name__		:str        =      name.replace('''pretrained.act_postprocess4.0.project.0''',  '''neck.reassemble_stage.readout_projects.3.0''' )
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								__magic_name__		:Any        =      name.replace('''pretrained.act_postprocess1.3''',  '''neck.reassemble_stage.layers.0.projection''' )
				if "pretrained.act_postprocess1.4" in name:
								__magic_name__		:Any        =      name.replace('''pretrained.act_postprocess1.4''',  '''neck.reassemble_stage.layers.0.resize''' )
				if "pretrained.act_postprocess2.3" in name:
								__magic_name__		:Dict        =      name.replace('''pretrained.act_postprocess2.3''',  '''neck.reassemble_stage.layers.1.projection''' )
				if "pretrained.act_postprocess2.4" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess2.4''',  '''neck.reassemble_stage.layers.1.resize''' )
				if "pretrained.act_postprocess3.3" in name:
								__magic_name__		:List[str]        =      name.replace('''pretrained.act_postprocess3.3''',  '''neck.reassemble_stage.layers.2.projection''' )
				if "pretrained.act_postprocess4.3" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess4.3''',  '''neck.reassemble_stage.layers.3.projection''' )
				if "pretrained.act_postprocess4.4" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''pretrained.act_postprocess4.4''',  '''neck.reassemble_stage.layers.3.resize''' )
				if "pretrained" in name:
								__magic_name__		:Any        =      name.replace('''pretrained''',  '''dpt''' )
				if "bn" in name:
								__magic_name__		:int        =      name.replace('''bn''',  '''batch_norm''' )
				if "head" in name:
								__magic_name__		:str        =      name.replace('''head''',  '''head.head''' )
				if "encoder.norm" in name:
								__magic_name__		:Union[str, Any]        =      name.replace('''encoder.norm''',  '''layernorm''' )
				if "auxlayer" in name:
								__magic_name__		:Dict        =      name.replace('''auxlayer''',  '''auxiliary_head.head''' )

				return name

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				for i in range(config.num_hidden_layers ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								__magic_name__		:int        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
								__magic_name__		:Union[str, Any]        =      state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
								# next, add query, keys and values (in that order) to the state dict
								__magic_name__		:Optional[Any]        =      in_proj_weight[: config.hidden_size, :]
								__magic_name__		:str        =      in_proj_bias[: config.hidden_size]
								__magic_name__		:List[str]        =      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__magic_name__		:Optional[Any]        =      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__magic_name__		:int        =      in_proj_weight[
								    -config.hidden_size :, :
								]
								__magic_name__		:Union[str, Any]        =      in_proj_bias[-config.hidden_size :]

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:Union[str, Any]        =      Image.open(requests.get(snake_case,  stream=snake_case ).raw )
				return im

@torch.no_grad()
def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:Any        =      get_dpt_config(snake_case )
				# load original state_dict from URL
				__magic_name__		:List[str]        =      torch.hub.load_state_dict_from_url(snake_case,  map_location='''cpu''' )
				# remove certain keys
				remove_ignore_keys_(snake_case )
				# rename keys
				for key in state_dict.copy().keys():
								__magic_name__		:int        =      state_dict.pop(snake_case )
								__magic_name__		:Tuple        =      val
				# read in qkv matrices
				read_in_q_k_v(snake_case,  snake_case )

				# load HuggingFace model
				__magic_name__		:int        =      DPTForSemanticSegmentation(snake_case ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(snake_case )
				model.load_state_dict(snake_case )
				model.eval()

				# Check outputs on an image
				__magic_name__		:Union[str, Any]        =      4_8_0 if '''ade''' in checkpoint_url else 3_8_4
				__magic_name__		:int        =      DPTImageProcessor(size=snake_case )

				__magic_name__		:Dict        =      prepare_img()
				__magic_name__		:List[str]        =      image_processor(snake_case,  return_tensors='''pt''' )

				# forward pass
				__magic_name__		:Dict        =      model(**snake_case ).logits if '''ade''' in checkpoint_url else model(**snake_case ).predicted_depth

				# Assert logits
				__magic_name__		:int        =      torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
				if "ade" in checkpoint_url:
								__magic_name__		:Any        =      torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
				assert outputs.shape == torch.Size(snake_case )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3],  snake_case,  atol=1E-4 )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3],  snake_case )
				)

				Path(snake_case ).mkdir(exist_ok=snake_case )
				print(f'''Saving model to {pytorch_dump_folder_path}''' )
				model.save_pretrained(snake_case )
				print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(snake_case )

				if push_to_hub:
								print('''Pushing model to hub...''' )
								model.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add model''',  use_temp_dir=snake_case,  )
								image_processor.push_to_hub(
								    repo_path_or_name=Path(snake_case,  snake_case ),  organization='''nielsr''',  commit_message='''Add image processor''',  use_temp_dir=snake_case,  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
					    type=str,
					    help="""URL of the original DPT checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					)
					parser.add_argument(
					    """--model_name""",
					    default="""dpt-large""",
					    type=str,
					    help="""Name of the model, in case you're pushing to the hub.""",
					)

					SCREAMING_SNAKE_CASE__      :			int							=		parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






