


'''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    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Any   ,     A     :    List[str]  ):




						'''simple docstring'''






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

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


def    lowerCamelCase__   ( A     :    str   ,     A     :    str   ,     A     :    List[Any]   ,     A     :    Optional[int]=0.9_995  ):




						'''simple docstring'''






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

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

						if inputs_are_torch:
												UpperCAmelCase         =			torch.from_numpy(A  ).to(A  )

						return va


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Optional[Any]  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Any  ):




						'''simple docstring'''






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






class  UpperCamelCase__(     lowerCAmelCase ):




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

										"""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   ,					)
										UpperCAmelCase         =			(
										    feature_extractor.size
										    if isinstance(feature_extractor.size   ,					lowerCAmelCase   )
										    else feature_extractor.size['''shortest_edge''']
										)
										UpperCAmelCase         =			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    :				Tuple   ,					lowerCAmelCase    :				Optional[Union[str, int]] = "auto"   )->  Any:

										"""simple docstring"""




										if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																UpperCAmelCase         =			self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCAmelCase   )




				def   a__(	self    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										self.enable_attention_slicing(lowerCAmelCase   )




				def   a__(	self    :				str   )->  str:

										"""simple docstring"""




										set_requires_grad(self.vae   ,					lowerCAmelCase   )




				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										set_requires_grad(self.vae   ,					lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  Dict:

										"""simple docstring"""




										set_requires_grad(self.unet   ,					lowerCAmelCase   )




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

										"""simple docstring"""




										set_requires_grad(self.unet   ,					lowerCAmelCase   )




				def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Tuple   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			min(int(num_inference_steps * strength   )   ,					lowerCAmelCase   )

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

										return timesteps, num_inference_steps - t_start




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				List[str]=None   )->  List[str]:

										"""simple docstring"""




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

										UpperCAmelCase         =			image.to(device=lowerCAmelCase   ,					dtype=lowerCAmelCase   )

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

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

										UpperCAmelCase         =			randn_tensor(init_latents.shape   ,					generator=lowerCAmelCase   ,					device=lowerCAmelCase   ,					dtype=lowerCAmelCase   )

										# get latents
										UpperCAmelCase         =			self.scheduler.add_noise(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			init_latents

										return latents




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Optional[Any]:

										"""simple docstring"""




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




				def   a__(	self    :				int   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Any   )->  List[str]:

										"""simple docstring"""




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




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

										"""simple docstring"""




										UpperCAmelCase         =			latents.detach().requires_grad_()

										UpperCAmelCase         =			self.scheduler.scale_model_input(lowerCAmelCase   ,					lowerCAmelCase   )

										# predict the noise residual
										UpperCAmelCase         =			self.unet(lowerCAmelCase   ,					lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ).sample

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

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

										UpperCAmelCase         =			transforms.Resize(self.feature_extractor_size   )(lowerCAmelCase   )
										UpperCAmelCase         =			self.normalize(lowerCAmelCase   ).to(latents.dtype   )

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

										UpperCAmelCase         =			spherical_dist_loss(lowerCAmelCase   ,					lowerCAmelCase   ).mean() * clip_guidance_scale

										UpperCAmelCase         =			-torch.autograd.grad(lowerCAmelCase   ,					lowerCAmelCase   )[0]

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





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

										"""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:
																UpperCAmelCase         =			[generator] + [None] * (batch_size - 1)

										UpperCAmelCase         =			[
										    ('''model''', self.coca_model is None),
										    ('''tokenizer''', self.coca_tokenizer is None),
										    ('''transform''', self.coca_transform is None),
										]
										UpperCAmelCase         =			[x[0] for x in coca_is_none if x[1]]
										UpperCAmelCase         =			''', '''.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."""   )
																UpperCAmelCase         =			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."""   )
																UpperCAmelCase         =			self.get_image_description(lowerCAmelCase   )

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

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

										UpperCAmelCase         =			slerp(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

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

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

										UpperCAmelCase       ,       UpperCAmelCase         =			self.get_timesteps(lowerCAmelCase   ,					lowerCAmelCase   ,					self.device   )
										UpperCAmelCase         =			timesteps[:1].repeat(lowerCAmelCase   )

										# Preprocess image
										UpperCAmelCase         =			preprocess(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			self.prepare_latents(
										    lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					text_embeddings.dtype   ,					self.device   ,					lowerCAmelCase   )

										UpperCAmelCase         =			preprocess(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			self.prepare_latents(
										    lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					text_embeddings.dtype   ,					self.device   ,					lowerCAmelCase   )

										UpperCAmelCase         =			slerp(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

										if clip_guidance_scale > 0:
																UpperCAmelCase         =			self.get_clip_image_embeddings(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			self.get_clip_image_embeddings(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			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.
										UpperCAmelCase         =			guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																UpperCAmelCase         =			content_text_input.input_ids.shape[-1]
																UpperCAmelCase         =			self.tokenizer(['''''']   ,					padding='''max_length'''   ,					max_length=lowerCAmelCase   ,					return_tensors='''pt'''   )
																UpperCAmelCase         =			self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																# duplicate unconditional embeddings for each generation per prompt
																UpperCAmelCase         =			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
																UpperCAmelCase         =			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`.
										UpperCAmelCase         =			(batch_size, self.unet.config.in_channels, height // 8, width // 8)
										UpperCAmelCase         =			text_embeddings.dtype
										if latents is None:
																if self.device.type == "mps":
																						# randn does not work reproducibly on mps
																						UpperCAmelCase         =			torch.randn(lowerCAmelCase   ,					generator=lowerCAmelCase   ,					device='''cpu'''   ,					dtype=lowerCAmelCase   ).to(
																						    self.device   )
																else:
																						UpperCAmelCase         =			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}"""   )
																UpperCAmelCase         =			latents.to(self.device   )

										# scale the initial noise by the standard deviation required by the scheduler
										UpperCAmelCase         =			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]
										UpperCAmelCase         =			'''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
										UpperCAmelCase         =			{}
										if accepts_eta:
																UpperCAmelCase         =			eta

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

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

																						# predict the noise residual
																						UpperCAmelCase         =			self.unet(lowerCAmelCase   ,					lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ).sample

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

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

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

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

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

										if output_type == "pil":
																UpperCAmelCase         =			self.numpy_to_pil(lowerCAmelCase   )

										if not return_dict:
																return (image, None)

										return StableDiffusionPipelineOutput(images=lowerCAmelCase   ,					nsfw_content_detected=lowerCAmelCase   )









'''simple docstring'''



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_lowercase    :    Tuple			     =  {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def    lowerCamelCase__   ( A     :    Optional[Any]  ):




						'''simple docstring'''






						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts  )
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config  )
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights  )

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def    lowerCamelCase__   ( A     :    Any   ,     A     :    str  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False
						elif args.student_type == "gpt2":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    List[str]  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Training'''  )
						parser.add_argument('''--force'''   ,     action='''store_true'''   ,     help='''Overwrite dump_path if it already exists.'''  )

						parser.add_argument(
						    '''--dump_path'''   ,     type=A   ,     required=A   ,     help='''The output directory (log, checkpoints, parameters, etc.)'''  )
						parser.add_argument(
						    '''--data_file'''   ,     type=A   ,     required=A   ,     help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''   ,     )

						parser.add_argument(
						    '''--student_type'''   ,     type=A   ,     choices=['''distilbert''', '''roberta''', '''gpt2''']   ,     required=A   ,     help='''The student type (DistilBERT, RoBERTa).'''   ,     )
						parser.add_argument('''--student_config'''   ,     type=A   ,     required=A   ,     help='''Path to the student configuration.'''  )
						parser.add_argument(
						    '''--student_pretrained_weights'''   ,     default=A   ,     type=A   ,     help='''Load student initialization checkpoint.'''  )

						parser.add_argument(
						    '''--teacher_type'''   ,     choices=['''bert''', '''roberta''', '''gpt2''']   ,     required=A   ,     help='''Teacher type (BERT, RoBERTa).'''  )
						parser.add_argument('''--teacher_name'''   ,     type=A   ,     required=A   ,     help='''The teacher model.'''  )

						parser.add_argument('''--temperature'''   ,     default=2.0   ,     type=A   ,     help='''Temperature for the softmax temperature.'''  )
						parser.add_argument(
						    '''--alpha_ce'''   ,     default=0.5   ,     type=A   ,     help='''Linear weight for the distillation loss. Must be >=0.'''  )
						parser.add_argument(
						    '''--alpha_mlm'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''   ,     )
						parser.add_argument('''--alpha_clm'''   ,     default=0.5   ,     type=A   ,     help='''Linear weight for the CLM loss. Must be >=0.'''  )
						parser.add_argument('''--alpha_mse'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight of the MSE loss. Must be >=0.'''  )
						parser.add_argument(
						    '''--alpha_cos'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight of the cosine embedding loss. Must be >=0.'''  )

						parser.add_argument(
						    '''--mlm'''   ,     action='''store_true'''   ,     help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''  )
						parser.add_argument(
						    '''--mlm_mask_prop'''   ,     default=0.15   ,     type=A   ,     help='''Proportion of tokens for which we need to make a prediction.'''   ,     )
						parser.add_argument('''--word_mask'''   ,     default=0.8   ,     type=A   ,     help='''Proportion of tokens to mask out.'''  )
						parser.add_argument('''--word_keep'''   ,     default=0.1   ,     type=A   ,     help='''Proportion of tokens to keep.'''  )
						parser.add_argument('''--word_rand'''   ,     default=0.1   ,     type=A   ,     help='''Proportion of tokens to randomly replace.'''  )
						parser.add_argument(
						    '''--mlm_smoothing'''   ,     default=0.7   ,     type=A   ,     help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''   ,     )
						parser.add_argument('''--token_counts'''   ,     type=A   ,     help='''The token counts in the data_file for MLM.'''  )

						parser.add_argument(
						    '''--restrict_ce_to_mask'''   ,     action='''store_true'''   ,     help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''   ,     )
						parser.add_argument(
						    '''--freeze_pos_embs'''   ,     action='''store_true'''   ,     help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''   ,     )
						parser.add_argument(
						    '''--freeze_token_type_embds'''   ,     action='''store_true'''   ,     help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''   ,     )

						parser.add_argument('''--n_epoch'''   ,     type=A   ,     default=3   ,     help='''Number of pass on the whole dataset.'''  )
						parser.add_argument('''--batch_size'''   ,     type=A   ,     default=5   ,     help='''Batch size (for each process).'''  )
						parser.add_argument(
						    '''--group_by_size'''   ,     action='''store_false'''   ,     help='''If true, group sequences that have similar length into the same batch. Default is true.'''   ,     )

						parser.add_argument(
						    '''--gradient_accumulation_steps'''   ,     type=A   ,     default=50   ,     help='''Gradient accumulation for larger training batches.'''   ,     )
						parser.add_argument('''--warmup_prop'''   ,     default=0.05   ,     type=A   ,     help='''Linear warmup proportion.'''  )
						parser.add_argument('''--weight_decay'''   ,     default=0.0   ,     type=A   ,     help='''Weight decay if we apply some.'''  )
						parser.add_argument('''--learning_rate'''   ,     default=5E-4   ,     type=A   ,     help='''The initial learning rate for Adam.'''  )
						parser.add_argument('''--adam_epsilon'''   ,     default=1E-6   ,     type=A   ,     help='''Epsilon for Adam optimizer.'''  )
						parser.add_argument('''--max_grad_norm'''   ,     default=5.0   ,     type=A   ,     help='''Max gradient norm.'''  )
						parser.add_argument('''--initializer_range'''   ,     default=0.02   ,     type=A   ,     help='''Random initialization range.'''  )

						parser.add_argument(
						    '''--fp16'''   ,     action='''store_true'''   ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,     )
						parser.add_argument(
						    '''--fp16_opt_level'''   ,     type=A   ,     default='''O1'''   ,     help=(
						        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
						        '''See details at https://nvidia.github.io/apex/amp.html'''
						    )   ,     )
						parser.add_argument('''--n_gpu'''   ,     type=A   ,     default=1   ,     help='''Number of GPUs in the node.'''  )
						parser.add_argument('''--local_rank'''   ,     type=A   ,     default=-1   ,     help='''Distributed training - Local rank'''  )
						parser.add_argument('''--seed'''   ,     type=A   ,     default=56   ,     help='''Random seed'''  )

						parser.add_argument('''--log_interval'''   ,     type=A   ,     default=5_00   ,     help='''Tensorboard logging interval.'''  )
						parser.add_argument('''--checkpoint_interval'''   ,     type=A   ,     default=40_00   ,     help='''Checkpoint interval.'''  )
						UpperCAmelCase         =			parser.parse_args()
						sanity_checks(A  )

						# ARGS #
						init_gpu_params(A  )
						set_seed(A  )
						if args.is_master:
												if os.path.exists(args.dump_path  ):
																		if not args.force:
																								raise ValueError(
																								    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																								    ''' itUse `--force` if you want to overwrite it'''  )
																		else:
																								shutil.rmtree(args.dump_path  )

												if not os.path.exists(args.dump_path  ):
																		os.makedirs(args.dump_path  )
												logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""  )

												# SAVE PARAMS #
												logger.info(f"""Param: {args}"""  )
												with open(os.path.join(args.dump_path   ,     '''parameters.json'''  )   ,     '''w'''  ) as f:
																		json.dump(vars(A  )   ,     A   ,     indent=4  )
												git_log(args.dump_path  )

						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.student_type]
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						UpperCAmelCase         =			teacher_tokenizer_class.from_pretrained(args.teacher_name  )
						UpperCAmelCase         =			{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												UpperCAmelCase         =			tokenizer.all_special_tokens.index(A  )
												UpperCAmelCase         =			tokenizer.all_special_ids[idx]
						logger.info(f"""Special tokens {special_tok_ids}"""  )
						UpperCAmelCase         =			special_tok_ids
						UpperCAmelCase         =			tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(f"""Loading data from {args.data_file}"""  )
						with open(args.data_file   ,     '''rb'''  ) as fp:
												UpperCAmelCase         =			pickle.load(A  )

						if args.mlm:
												logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""  )
												with open(args.token_counts   ,     '''rb'''  ) as fp:
																		UpperCAmelCase         =			pickle.load(A  )

												UpperCAmelCase         =			np.maximum(A   ,     1  ) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		UpperCAmelCase         =			0.0  # do not predict special tokens
												UpperCAmelCase         =			torch.from_numpy(A  )
						else:
												UpperCAmelCase         =			None

						UpperCAmelCase         =			LmSeqsDataset(params=A   ,     data=A  )
						logger.info('''Data loader created.'''  )

						# STUDENT #
						logger.info(f"""Loading student config from {args.student_config}"""  )
						UpperCAmelCase         =			student_config_class.from_pretrained(args.student_config  )
						UpperCAmelCase         =			True

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
												UpperCAmelCase         =			student_model_class.from_pretrained(args.student_pretrained_weights   ,     config=A  )
						else:
												UpperCAmelCase         =			student_model_class(A  )

						if args.n_gpu > 0:
												student.to(f"""cuda:{args.local_rank}"""  )
						logger.info('''Student loaded.'''  )

						# TEACHER #
						UpperCAmelCase         =			teacher_model_class.from_pretrained(args.teacher_name   ,     output_hidden_states=A  )
						if args.n_gpu > 0:
												teacher.to(f"""cuda:{args.local_rank}"""  )
						logger.info(f"""Teacher loaded from {args.teacher_name}."""  )

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(A   ,     A  )
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(A   ,     A  )

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0  ) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						UpperCAmelCase         =			Distiller(
						    params=A   ,     dataset=A   ,     token_probs=A   ,     student=A   ,     teacher=A  )
						distiller.train()
						logger.info('''Let\'s go get some drinks.'''  )


if __name__ == "__main__":
	main()





