
"""simple docstring"""



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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



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

							if isinstance(image[0]     ,  PIL.Image.Image						):
														_lowerCAmelCase  	=			[np.array(i.resize((w, h)     ,  resample=PIL_INTERPOLATION['lanczos']						)						)[None, :] for i in image]
														_lowerCAmelCase  	=			np.concatenate(SCREAMING_SNAKE_CASE     ,  axis=0						)
														_lowerCAmelCase  	=			np.array(SCREAMING_SNAKE_CASE						).astype(np.floataa						) / 2_55.0
														_lowerCAmelCase  	=			image.transpose(0     ,  3     ,  1     ,  2						)
														_lowerCAmelCase  	=			2.0 * image - 1.0
														_lowerCAmelCase  	=			torch.from_numpy(SCREAMING_SNAKE_CASE						)
							elif isinstance(image[0]     ,  torch.Tensor						):
														_lowerCAmelCase  	=			torch.cat(SCREAMING_SNAKE_CASE     ,  dim=0						)
							return image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]=0.99_95						):






							"""simple docstring"""



							if not isinstance(SCREAMING_SNAKE_CASE     ,  np.ndarray						):
														_lowerCAmelCase  	=			True
														_lowerCAmelCase  	=			va.device
														_lowerCAmelCase  	=			va.cpu().numpy()
														_lowerCAmelCase  	=			va.cpu().numpy()

							_lowerCAmelCase  	=			np.sum(va * va / (np.linalg.norm(SCREAMING_SNAKE_CASE						) * np.linalg.norm(SCREAMING_SNAKE_CASE						))						)
							if np.abs(SCREAMING_SNAKE_CASE						) > DOT_THRESHOLD:
														_lowerCAmelCase  	=			(1 - t) * va + t * va
							else:
														_lowerCAmelCase  	=			np.arccos(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			np.sin(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			theta_a * t
														_lowerCAmelCase  	=			np.sin(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			np.sin(theta_a - theta_t						) / sin_theta_a
														_lowerCAmelCase  	=			sin_theta_t / sin_theta_a
														_lowerCAmelCase  	=			sa * va + sa * va

							if inputs_are_torch:
														_lowerCAmelCase  	=			torch.from_numpy(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)

							return va






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			F.normalize(SCREAMING_SNAKE_CASE     ,  dim=-1						)
							_lowerCAmelCase  	=			F.normalize(SCREAMING_SNAKE_CASE     ,  dim=-1						)
							return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							for param in model.parameters():
														_lowerCAmelCase  	=			value

class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				def __init__(					self	:			Dict				,     UpperCAmelCase_	:			AutoencoderKL				,     UpperCAmelCase_	:			CLIPTextModel				,     UpperCAmelCase_	:			CLIPModel				,     UpperCAmelCase_	:			CLIPTokenizer				,     UpperCAmelCase_	:			UNetaDConditionModel				,     UpperCAmelCase_	:			Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]				,     UpperCAmelCase_	:			CLIPFeatureExtractor				,     UpperCAmelCase_	:			Dict=None				,     UpperCAmelCase_	:			List[str]=None				,     UpperCAmelCase_	:			List[str]=None				,     )		->  Any:







											"""simple docstring"""




											super().__init__()
											self.register_modules(
											    vae=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     clip_model=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     feature_extractor=UpperCAmelCase_				,     coca_model=UpperCAmelCase_				,     coca_tokenizer=UpperCAmelCase_				,     coca_transform=UpperCAmelCase_				,     )
											_lowerCAmelCase  	=			(
											    feature_extractor.size
											    if isinstance(feature_extractor.size				,     UpperCAmelCase_  )
											    else feature_extractor.size['shortest_edge']
											)
											_lowerCAmelCase  	=			transforms.Normalize(mean=feature_extractor.image_mean				,     std=feature_extractor.image_std  )
											set_requires_grad(self.text_encoder				,     UpperCAmelCase_  )
											set_requires_grad(self.clip_model				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			Optional[Union[str, int]] = "auto"  )		->  List[Any]:







											"""simple docstring"""




											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(UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			str  )		->  Union[str, Any]:







											"""simple docstring"""




											self.enable_attention_slicing(UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			int  )		->  Any:







											"""simple docstring"""




											set_requires_grad(self.vae				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											set_requires_grad(self.vae				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  int:







											"""simple docstring"""




											set_requires_grad(self.unet				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Any  )		->  List[str]:







											"""simple docstring"""




											set_requires_grad(self.unet				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Tuple  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			min(int(num_inference_steps * strength  )				,     UpperCAmelCase_  )

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

											return timesteps, num_inference_steps - t_start


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Any=None  )		->  str:







											"""simple docstring"""




											if not isinstance(UpperCAmelCase_				,     torch.Tensor  ):
																		raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(UpperCAmelCase_  )}"""  )

											_lowerCAmelCase  	=			image.to(device=UpperCAmelCase_				,     dtype=UpperCAmelCase_  )

											if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			[
																		    self.vae.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(UpperCAmelCase_  )
																		]
																		_lowerCAmelCase  	=			torch.cat(UpperCAmelCase_				,     dim=0  )
											else:
																		_lowerCAmelCase  	=			self.vae.encode(UpperCAmelCase_  ).latent_dist.sample(UpperCAmelCase_  )

											# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
											_lowerCAmelCase  	=			0.18215 * init_latents
											_lowerCAmelCase  	=			init_latents.repeat_interleave(UpperCAmelCase_				,     dim=0  )

											_lowerCAmelCase  	=			randn_tensor(init_latents.shape				,     generator=UpperCAmelCase_				,     device=UpperCAmelCase_				,     dtype=UpperCAmelCase_  )

											# get latents
											_lowerCAmelCase  	=			self.scheduler.add_noise(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			init_latents

											return latents


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Tuple  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.coca_transform(UpperCAmelCase_  ).unsqueeze(0  )
											with torch.no_grad(), torch.cuda.amp.autocast():
																		_lowerCAmelCase  	=			self.coca_model.generate(transformed_image.to(device=self.device				,     dtype=self.coca_model.dtype  )  )
											_lowerCAmelCase  	=			self.coca_tokenizer.decode(generated[0].cpu().numpy()  )
											return generated.split('<end_of_text>'  )[0].replace('<start_of_text>'				,     ''  ).rstrip(' .,'  )


				def 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			List[Any]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.feature_extractor.preprocess(UpperCAmelCase_  )
											_lowerCAmelCase  	=			torch.from_numpy(clip_image_input['pixel_values'][0]  ).unsqueeze(0  ).to(self.device  ).half()
											_lowerCAmelCase  	=			self.clip_model.get_image_features(UpperCAmelCase_  )
											_lowerCAmelCase  	=			image_embeddings_clip / image_embeddings_clip.norm(p=2				,     dim=-1				,     keepdim=UpperCAmelCase_  )
											_lowerCAmelCase  	=			image_embeddings_clip.repeat_interleave(UpperCAmelCase_				,     dim=0  )
											return image_embeddings_clip


				@torch.enable_grad()
				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			Dict				,     )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			latents.detach().requires_grad_()

											_lowerCAmelCase  	=			self.scheduler.scale_model_input(UpperCAmelCase_				,     UpperCAmelCase_  )

											# predict the noise residual
											_lowerCAmelCase  	=			self.unet(UpperCAmelCase_				,     UpperCAmelCase_				,     encoder_hidden_states=UpperCAmelCase_  ).sample

											if isinstance(self.scheduler				,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)  ):
																		_lowerCAmelCase  	=			self.scheduler.alphas_cumprod[timestep]
																		_lowerCAmelCase  	=			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
																		_lowerCAmelCase  	=			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																		_lowerCAmelCase  	=			torch.sqrt(UpperCAmelCase_  )
																		_lowerCAmelCase  	=			pred_original_sample * (fac) + latents * (1 - fac)
											elif isinstance(self.scheduler				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			self.scheduler.sigmas[index]
																		_lowerCAmelCase  	=			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
											_lowerCAmelCase  	=			1 / 0.18215 * sample
											_lowerCAmelCase  	=			self.vae.decode(UpperCAmelCase_  ).sample
											_lowerCAmelCase  	=			(image / 2 + 0.5).clamp(0				,     1  )

											_lowerCAmelCase  	=			transforms.Resize(self.feature_extractor_size  )(UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.normalize(UpperCAmelCase_  ).to(latents.dtype  )

											_lowerCAmelCase  	=			self.clip_model.get_image_features(UpperCAmelCase_  )
											_lowerCAmelCase  	=			image_embeddings_clip / image_embeddings_clip.norm(p=2				,     dim=-1				,     keepdim=UpperCAmelCase_  )

											_lowerCAmelCase  	=			spherical_dist_loss(UpperCAmelCase_				,     UpperCAmelCase_  ).mean() * clip_guidance_scale

											_lowerCAmelCase  	=			-torch.autograd.grad(UpperCAmelCase_				,     UpperCAmelCase_  )[0]

											if isinstance(self.scheduler				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			latents.detach() + grads * (sigma**2)
																		_lowerCAmelCase  	=			noise_pred_original
											else:
																		_lowerCAmelCase  	=			noise_pred_original - torch.sqrt(UpperCAmelCase_  ) * grads
											return noise_pred, latents







				@torch.no_grad()
				def __call__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Union[torch.FloatTensor, PIL.Image.Image]				,     UpperCAmelCase_	:			Union[torch.FloatTensor, PIL.Image.Image]				,     UpperCAmelCase_	:			Optional[str] = None				,     UpperCAmelCase_	:			Optional[str] = None				,     UpperCAmelCase_	:			Optional[int] = 512				,     UpperCAmelCase_	:			Optional[int] = 512				,     UpperCAmelCase_	:			float = 0.6				,     UpperCAmelCase_	:			Optional[int] = 50				,     UpperCAmelCase_	:			Optional[float] = 7.5				,     UpperCAmelCase_	:			Optional[int] = 1				,     UpperCAmelCase_	:			float = 0.0				,     UpperCAmelCase_	:			Optional[float] = 100				,     UpperCAmelCase_	:			Optional[torch.Generator] = None				,     UpperCAmelCase_	:			Optional[str] = "pil"				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			float = 0.8				,     UpperCAmelCase_	:			float = 0.1				,     UpperCAmelCase_	:			float = 0.1				,     )		->  List[str]:







											"""simple docstring"""




											if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) and len(UpperCAmelCase_  ) != batch_size:
																		raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(UpperCAmelCase_  )} 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(UpperCAmelCase_				,     torch.Generator  ) and batch_size > 1:
																		_lowerCAmelCase  	=			[generator] + [None] * (batch_size - 1)

											_lowerCAmelCase  	=			[
											    ('model', self.coca_model is None),
											    ('tokenizer', self.coca_tokenizer is None),
											    ('transform', self.coca_transform is None),
											]
											_lowerCAmelCase  	=			[x[0] for x in coca_is_none if x[1]]
											_lowerCAmelCase  	=			', '.join(UpperCAmelCase_  )
											# generate prompts with coca model if prompt is None
											if content_prompt is None:
																		if len(UpperCAmelCase_  ):
																									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."""  )
																		_lowerCAmelCase  	=			self.get_image_description(UpperCAmelCase_  )
											if style_prompt is None:
																		if len(UpperCAmelCase_  ):
																									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."""  )
																		_lowerCAmelCase  	=			self.get_image_description(UpperCAmelCase_  )

											# get prompt text embeddings for content and style
											_lowerCAmelCase  	=			self.tokenizer(
											    UpperCAmelCase_				,     padding='max_length'				,     max_length=self.tokenizer.model_max_length				,     truncation=UpperCAmelCase_				,     return_tensors='pt'				,     )
											_lowerCAmelCase  	=			self.text_encoder(content_text_input.input_ids.to(self.device  )  )[0]

											_lowerCAmelCase  	=			self.tokenizer(
											    UpperCAmelCase_				,     padding='max_length'				,     max_length=self.tokenizer.model_max_length				,     truncation=UpperCAmelCase_				,     return_tensors='pt'				,     )
											_lowerCAmelCase  	=			self.text_encoder(style_text_input.input_ids.to(self.device  )  )[0]

											_lowerCAmelCase  	=			slerp(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )

											# duplicate text embeddings for each generation per prompt
											_lowerCAmelCase  	=			text_embeddings.repeat_interleave(UpperCAmelCase_				,     dim=0  )

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

											self.scheduler.set_timesteps(UpperCAmelCase_				,     **UpperCAmelCase_  )
											# 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  )

											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.get_timesteps(UpperCAmelCase_				,     UpperCAmelCase_				,     self.device  )
											_lowerCAmelCase  	=			timesteps[:1].repeat(UpperCAmelCase_  )

											# Preprocess image
											_lowerCAmelCase  	=			preprocess(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.prepare_latents(
											    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     text_embeddings.dtype				,     self.device				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			preprocess(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.prepare_latents(
											    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     text_embeddings.dtype				,     self.device				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			slerp(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )

											if clip_guidance_scale > 0:
																		_lowerCAmelCase  	=			self.get_clip_image_embeddings(UpperCAmelCase_				,     UpperCAmelCase_  )
																		_lowerCAmelCase  	=			self.get_clip_image_embeddings(UpperCAmelCase_				,     UpperCAmelCase_  )
																		_lowerCAmelCase  	=			slerp(
																		    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )

											# 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  	=			content_text_input.input_ids.shape[-1]
																		_lowerCAmelCase  	=			self.tokenizer(['']				,     padding='max_length'				,     max_length=UpperCAmelCase_				,     return_tensors='pt'  )
																		_lowerCAmelCase  	=			self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
																		# duplicate unconditional embeddings for each generation per prompt
																		_lowerCAmelCase  	=			uncond_embeddings.repeat_interleave(UpperCAmelCase_				,     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
																		_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, self.unet.config.in_channels, height // 8, width // 8)
											_lowerCAmelCase  	=			text_embeddings.dtype
											if latents is None:
																		if self.device.type == "mps":
																									# randn does not work reproducibly on mps
																									_lowerCAmelCase  	=			torch.randn(UpperCAmelCase_				,     generator=UpperCAmelCase_				,     device='cpu'				,     dtype=UpperCAmelCase_  ).to(
																									    self.device  )
																		else:
																									_lowerCAmelCase  	=			torch.randn(UpperCAmelCase_				,     generator=UpperCAmelCase_				,     device=self.device				,     dtype=UpperCAmelCase_  )
											else:
																		if latents.shape != latents_shape:
																									raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""  )
																		_lowerCAmelCase  	=			latents.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

											# check if the scheduler accepts generator
											_lowerCAmelCase  	=			'generator' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
											if accepts_generator:
																		_lowerCAmelCase  	=			generator

											with self.progress_bar(total=UpperCAmelCase_  ):
																		for i, t in enumerate(UpperCAmelCase_  ):
																									# 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(UpperCAmelCase_				,     UpperCAmelCase_  )

																									# predict the noise residual
																									_lowerCAmelCase  	=			self.unet(UpperCAmelCase_				,     UpperCAmelCase_				,     encoder_hidden_states=UpperCAmelCase_  ).sample

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

																									# perform clip guidance
																									if clip_guidance_scale > 0:
																																_lowerCAmelCase  	=			(
																																    text_embeddings.chunk(2  )[1] if do_classifier_free_guidance else text_embeddings
																																)
																																_lowerCAmelCase	,		_lowerCAmelCase  	=			self.cond_fn(
																																    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     )

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
											_lowerCAmelCase  	=			1 / 0.18215 * latents
											_lowerCAmelCase  	=			self.vae.decode(UpperCAmelCase_  ).sample

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

											if output_type == "pil":
																		_lowerCAmelCase  	=			self.numpy_to_pil(UpperCAmelCase_  )

											if not return_dict:
																		return (image, None)

											return StableDiffusionPipelineOutput(images=UpperCAmelCase_				,     nsfw_content_detected=UpperCAmelCase_  )








"""simple docstring"""



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_snake_case          =						getLogger(__name__)


_snake_case          =						'''cuda''' if torch.cuda.is_available() else '''cpu'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int = 8     ,  SCREAMING_SNAKE_CASE:					str = DEFAULT_DEVICE     ,  SCREAMING_SNAKE_CASE:					Optional[int]=False     ,  SCREAMING_SNAKE_CASE:					int="summarization"     ,  SCREAMING_SNAKE_CASE:					List[Any]=None     ,  **SCREAMING_SNAKE_CASE:					Tuple     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						).open('w'     ,  encoding='utf-8'						)
							_lowerCAmelCase  	=			str(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
							if fpaa:
														_lowerCAmelCase  	=			model.half()

							_lowerCAmelCase  	=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						)
							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

							_lowerCAmelCase  	=			time.time()
							# update config with task specific params
							use_task_specific_params(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							if prefix is None:
														_lowerCAmelCase  	=			prefix or getattr(model.config     ,  'prefix'     ,  ''						) or ''
							for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)						)						):
														_lowerCAmelCase  	=			[prefix + text for text in examples_chunk]
														_lowerCAmelCase  	=			tokenizer(SCREAMING_SNAKE_CASE     ,  return_tensors='pt'     ,  truncation=SCREAMING_SNAKE_CASE     ,  padding='longest'						).to(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			model.generate(
														    input_ids=batch.input_ids     ,  attention_mask=batch.attention_mask     ,  **SCREAMING_SNAKE_CASE     ,  )
														_lowerCAmelCase  	=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE     ,  skip_special_tokens=SCREAMING_SNAKE_CASE     ,  clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
														for hypothesis in dec:
																					fout.write(hypothesis + '\n'						)
																					fout.flush()
							fout.close()
							_lowerCAmelCase  	=			int(time.time() - start_time						)  # seconds
							_lowerCAmelCase  	=			len(SCREAMING_SNAKE_CASE						)
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,  4						)}






def 	__snake_case  (    ):






							"""simple docstring"""



							return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any=True						):






							"""simple docstring"""



							_lowerCAmelCase  	=			argparse.ArgumentParser()
							parser.add_argument('model_name'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like facebook/bart-large-cnn,t5-base, etc.'						)
							parser.add_argument('input_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.source'						)
							parser.add_argument('save_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='where to save summaries'						)
							parser.add_argument('--reference_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.target'						)
							parser.add_argument('--score_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default='metrics.json'     ,  help='where to save metrics'						)
							parser.add_argument('--device'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='cuda, cuda:1, cpu etc.'						)
							parser.add_argument(
							    '--prefix'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='will be added to the begininng of src examples'						)
							parser.add_argument('--task'     ,  type=SCREAMING_SNAKE_CASE     ,  default='summarization'     ,  help='used for task_specific_params + metrics'						)
							parser.add_argument('--bs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=8     ,  required=SCREAMING_SNAKE_CASE     ,  help='batch size'						)
							parser.add_argument(
							    '--n_obs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=-1     ,  required=SCREAMING_SNAKE_CASE     ,  help='How many observations. Defaults to all.'						)
							parser.add_argument('--fp16'     ,  action='store_true'						)
							parser.add_argument('--dump-args'     ,  action='store_true'     ,  help='print the custom hparams with the results'						)
							parser.add_argument(
							    '--info'     ,  nargs='?'     ,  type=SCREAMING_SNAKE_CASE     ,  const=datetime_now()     ,  help=(
							        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
							        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
							    )     ,  )
							# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
							_lowerCAmelCase	,		_lowerCAmelCase  	=			parser.parse_known_args()
							_lowerCAmelCase  	=			parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE						)
							if parsed_args and verbose:
														print(f"""parsed the following generate kwargs: {parsed_args}"""						)
							_lowerCAmelCase  	=			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
							if args.n_obs > 0:
														_lowerCAmelCase  	=			examples[: args.n_obs]
							Path(args.save_path						).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE						)

							if args.reference_path is None and Path(args.score_path						).exists():
														warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""						)

							if args.device == "cpu" and args.fpaa:
														# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
														raise ValueError('Can\'t mix --fp16 and --device cpu'						)

							_lowerCAmelCase  	=			generate_summaries_or_translations(
							    SCREAMING_SNAKE_CASE     ,  args.save_path     ,  args.model_name     ,  batch_size=args.bs     ,  device=args.device     ,  fpaa=args.fpaa     ,  task=args.task     ,  prefix=args.prefix     ,  **SCREAMING_SNAKE_CASE     ,  )

							if args.reference_path is None:
														return {}

							# Compute scores
							_lowerCAmelCase  	=			calculate_bleu if 'translation' in args.task else calculate_rouge
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.save_path						).readlines()]
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.reference_path						).readlines()][: len(SCREAMING_SNAKE_CASE						)]
							_lowerCAmelCase  	=			score_fn(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							scores.update(SCREAMING_SNAKE_CASE						)

							if args.dump_args:
														scores.update(SCREAMING_SNAKE_CASE						)
							if args.info:
														_lowerCAmelCase  	=			args.info

							if verbose:
														print(SCREAMING_SNAKE_CASE						)

							if args.score_path is not None:
														json.dump(SCREAMING_SNAKE_CASE     ,  open(args.score_path     ,  'w'						)						)

							return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)






