

'''simple docstring'''





import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


__a						:List[str]            = {
    'gpt-j-6b': {'is_causal': True, 'model': 'sgugger/sharded-gpt-j-6B', 'tokenizer': 'EleutherAI/gpt-j-6B'},
    'gpt-neox': {'is_causal': True, 'model': 'EleutherAI/gpt-neox-20b'},
    'opt': {'is_causal': True, 'model': 'facebook/opt-30b'},
    'T0pp': {'is_causal': False, 'model': 'bigscience/T0pp', 'model_revision': 'sharded'},
}

__a						:Any            = [
    'Hello, my name is',
    'Are unicorns real? Unicorns are',
    'For the first time in several years,',
    'My name is Julien and I am',
    'The goal of life is',
    'Whenever I\'m sad, I like to',
]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."						)
      parser.add_argument("model_name"							,type=lowercase__							,default=lowercase__							,help="The name of the model to try."						)
      parser.add_argument(
          "--tokenizer_name"							,type=lowercase__							,default=lowercase__							,help="The name of the tokenizer (if different from the model."						)
      parser.add_argument("--is_causal"							,type=lowercase__							,default=lowercase__							,help="Whether or not the model is causal."						)
      parser.add_argument(
          "--model_revision"							,type=lowercase__							,default=lowercase__							,help="The revision to use for the model checkpoint."						)
      parser.add_argument("--torch_dtype"							,type=lowercase__							,default=lowercase__							,help="The dtype for the model."						)
      parser.add_argument("--disk_offload"							,action="store_true"						)

      A_	     =							parser.parse_args()

      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
            A_	     =							DEFAULT_MODELS[args.model_name]
            A_	     =							defaults["model"]
            if args.tokenizer_name is None:
                  A_	     =							defaults.get("tokenizer"							,args.model_name						)
            if args.is_causal is None:
                  A_	     =							defaults["is_causal"]
            if args.model_revision is None:
                  A_	     =							defaults.get("model_revision"							,"main"						)

      if args.is_causal is None:
            raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."						)
      if args.tokenizer_name is None:
            A_	     =							args.model_name
      if args.model_revision is None:
            A_	     =							"main"

      return args

def 			__snake_case	(   ):



      """simple docstring"""

      transformers.utils.logging.set_verbosity_error()
      A_	     =							parse_args()

      if args.torch_dtype is None:
            A_	     =							AutoConfig.from_pretrained(args.model_name						)
            A_	     =							getattr(lowercase__							,"torch_dtype"							,torch.floataa						)
      else:
            A_	     =							getattr(lowercase__							,args.torch_dtype						)
      A_	     =							AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      A_	     =							{
          "torch_dtype": torch_dtype,
          "revision": args.model_revision,
      }
      if args.disk_offload:
            A_	     =							"tmp_offload"
            A_	     =							True

      A_	     =							start_measure()
      A_	     =							model_cls.from_pretrained(args.model_name							,device_map="auto"							,**lowercase__						)
      A_	     =							end_measure(lowercase__						)
      log_measures(lowercase__							,"Model loading"						)

      A_	     =							compute_module_sizes(lowercase__						)
      A_	     =							{v: 0 for v in model.hf_device_map.values()}
      for module, device in model.hf_device_map.items():
            device_size[device] += module_sizes[module]
      A_	     =							"\n".join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]						)
      print(f'''\nTheoretical use:\n{message}'''						)

      A_	     =							AutoTokenizer.from_pretrained(args.tokenizer_name						)

      A_	     =							start_measure()
      A_	     =							[]
      A_	     =							[]
      A_	     =							[]
      for prompt in PROMPTS:
            A_	     =							tokenizer(lowercase__							,return_tensors="pt"						).to(0						)
            A_	     =							inputs["input_ids"][0].tolist()
            A_	     =							time.time()
            A_	     =							model.generate(inputs["input_ids"]						)
            A_	     =							time.time()
            A_	     =							outputs[0].tolist()
            A_	     =							len(lowercase__						) if outputs[: len(lowercase__						)] != tokens else len(lowercase__						) - len(lowercase__						)
            A_	     =							after_generate - before_generate

            A_	     =							tokenizer.decode(lowercase__							,skip_special_tokens=lowercase__						)
            texts_outs.append(lowercase__						)
            generation_times.append(lowercase__						)
            gen_tokens.append(lowercase__						)
            print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''						)

      A_	     =							end_measure(lowercase__						)
      log_measures(lowercase__							,"Model generation"						)

      A_	     =							[gen / tok for gen, tok in zip(lowercase__							,lowercase__						)]
      A_	     =							sum(lowercase__						) / len(lowercase__						)
      print(f'''Average time of generation per token: {avg_gen:.2f}s'''						)
      print(f'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s'''						)
      A_	     =							sum(generation_times_per_token[1:]						) / (len(lowercase__						) - 1)
      print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''						)


if __name__ == "__main__":
      main()


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

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

class      _a				( nn.Module							):




      """simple docstring"""

      def __init__(      self       :							int	, UpperCAmelCase       :							str	, UpperCAmelCase       :							str=1.0  ):
            super().__init__()

            A_	     =							cut_size
            A_	     =							cut_power







      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[Any]  ):
            A_     ,    A_	     =							pixel_values.shape[2:4]
            A_	     =							min(UpperCAmelCase	, UpperCAmelCase  )
            A_	     =							min(UpperCAmelCase	, UpperCAmelCase	, self.cut_size  )
            A_	     =							[]
            for _ in range(UpperCAmelCase  ):
                  A_	     =							int(torch.rand([]  ) ** self.cut_power * (max_size - min_size) + min_size  )
                  A_	     =							torch.randint(0	, sideX - size + 1	, ()  )
                  A_	     =							torch.randint(0	, sideY - size + 1	, ()  )
                  A_	     =							pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                  cutouts.append(F.adaptive_avg_poolad(UpperCAmelCase	, self.cut_size  )  )
            return torch.cat(UpperCAmelCase  )




def 			__snake_case	(   __UpperCamelCase  :   Dict							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

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

def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

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



class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							int	, UpperCAmelCase       :							AutoencoderKL	, UpperCAmelCase       :							CLIPTextModel	, UpperCAmelCase       :							CLIPModel	, UpperCAmelCase       :							CLIPTokenizer	, UpperCAmelCase       :							UNetaDConditionModel	, UpperCAmelCase       :							Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]	, UpperCAmelCase       :							CLIPImageProcessor	, ):
            super().__init__()
            self.register_modules(
                vae=UpperCAmelCase	, text_encoder=UpperCAmelCase	, clip_model=UpperCAmelCase	, tokenizer=UpperCAmelCase	, unet=UpperCAmelCase	, scheduler=UpperCAmelCase	, feature_extractor=UpperCAmelCase	, )

            A_	     =							transforms.Normalize(mean=feature_extractor.image_mean	, std=feature_extractor.image_std  )
            A_	     =							(
                feature_extractor.size
                if isinstance(feature_extractor.size	, UpperCAmelCase  )
                else feature_extractor.size["shortest_edge"]
            )
            A_	     =							MakeCutouts(self.cut_out_size  )

            set_requires_grad(self.text_encoder	, UpperCAmelCase  )
            set_requires_grad(self.clip_model	, UpperCAmelCase  )





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





      def 		__A  (      self       :							Tuple  ):
            self.enable_attention_slicing(UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            set_requires_grad(self.vae	, UpperCAmelCase  )





      def 		__A  (      self       :							Dict  ):
            set_requires_grad(self.vae	, UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            set_requires_grad(self.unet	, UpperCAmelCase  )





      def 		__A  (      self       :							Tuple  ):
            set_requires_grad(self.unet	, UpperCAmelCase  )





      @torch.enable_grad()
      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Tuple=True	, ):
            A_	     =							latents.detach().requires_grad_()

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

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

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

                  A_	     =							torch.sqrt(UpperCAmelCase  )
                  A_	     =							pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler	, UpperCAmelCase  ):
                  A_	     =							self.scheduler.sigmas[index]
                  A_	     =							latents - sigma * noise_pred
            else:
                  raise ValueError(f'''scheduler type {type(self.scheduler  )} not supported'''  )

            A_	     =							1 / self.vae.config.scaling_factor * sample
            A_	     =							self.vae.decode(UpperCAmelCase  ).sample
            A_	     =							(image / 2 + 0.5).clamp(0	, 1  )

            if use_cutouts:
                  A_	     =							self.make_cutouts(UpperCAmelCase	, UpperCAmelCase  )
            else:
                  A_	     =							transforms.Resize(self.cut_out_size  )(UpperCAmelCase  )
            A_	     =							self.normalize(UpperCAmelCase  ).to(latents.dtype  )

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

            if use_cutouts:
                  A_	     =							spherical_dist_loss(UpperCAmelCase	, UpperCAmelCase  )
                  A_	     =							dists.view([num_cutouts, sample.shape[0], -1]  )
                  A_	     =							dists.sum(2  ).mean(0  ).sum() * clip_guidance_scale
            else:
                  A_	     =							spherical_dist_loss(UpperCAmelCase	, UpperCAmelCase  ).mean() * clip_guidance_scale

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

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







      @torch.no_grad()
      def __call__(      self       :							Optional[Any]	, UpperCAmelCase       :							Union[str, List[str]]	, UpperCAmelCase       :							Optional[int] = 512	, UpperCAmelCase       :							Optional[int] = 512	, UpperCAmelCase       :							Optional[int] = 50	, UpperCAmelCase       :							Optional[float] = 7.5	, UpperCAmelCase       :							Optional[int] = 1	, UpperCAmelCase       :							float = 0.0	, UpperCAmelCase       :							Optional[float] = 100	, UpperCAmelCase       :							Optional[Union[str, List[str]]] = None	, UpperCAmelCase       :							Optional[int] = 4	, UpperCAmelCase       :							Optional[bool] = True	, UpperCAmelCase       :							Optional[torch.Generator] = None	, UpperCAmelCase       :							Optional[torch.FloatTensor] = None	, UpperCAmelCase       :							Optional[str] = "pil"	, UpperCAmelCase       :							bool = True	, ):
            if isinstance(UpperCAmelCase	, UpperCAmelCase  ):
                  A_	     =							1
            elif isinstance(UpperCAmelCase	, UpperCAmelCase  ):
                  A_	     =							len(UpperCAmelCase  )
            else:
                  raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase  )}'''  )

            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}.'''  )

            # get prompt text embeddings
            A_	     =							self.tokenizer(
                UpperCAmelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=UpperCAmelCase	, return_tensors="pt"	, )
            A_	     =							self.text_encoder(text_input.input_ids.to(self.device  )  )[0]
            # duplicate text embeddings for each generation per prompt
            A_	     =							text_embeddings.repeat_interleave(UpperCAmelCase	, dim=0  )

            if clip_guidance_scale > 0:
                  if clip_prompt is not None:
                        A_	     =							self.tokenizer(
                            UpperCAmelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=UpperCAmelCase	, return_tensors="pt"	, ).input_ids.to(self.device  )
                  else:
                        A_	     =							text_input.input_ids.to(self.device  )
                  A_	     =							self.clip_model.get_text_features(UpperCAmelCase  )
                  A_	     =							text_embeddings_clip / text_embeddings_clip.norm(p=2	, dim=-1	, keepdim=UpperCAmelCase  )
                  # duplicate text embeddings clip for each generation per prompt
                  A_	     =							text_embeddings_clip.repeat_interleave(UpperCAmelCase	, dim=0  )

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

            # set timesteps
            A_	     =							"offset" in set(inspect.signature(self.scheduler.set_timesteps  ).parameters.keys()  )
            A_	     =							{}
            if accepts_offset:
                  A_	     =							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
            A_	     =							self.scheduler.timesteps.to(self.device  )

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

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

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

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

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

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

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

            # scale and decode the image latents with vae
            A_	     =							1 / self.vae.config.scaling_factor * latents
            A_	     =							self.vae.decode(UpperCAmelCase  ).sample

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

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

            if not return_dict:
                  return (image, None)

            return StableDiffusionPipelineOutput(images=UpperCAmelCase	, nsfw_content_detected=UpperCAmelCase  )