

"""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	:       Dict        =						[
    '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   __SCREAMING_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   __SCREAMING_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()









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

      self.register_modules(unet=UpperCAmelCase						,	scheduler=UpperCAmelCase			)





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

      self.scheduler.set_timesteps(UpperCAmelCase			)
      for t in self.progress_bar(self.scheduler.timesteps			):
       _lowercase				 =self.unet(UpperCAmelCase						,	UpperCAmelCase			).sample

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

      _lowercase				 =(image / 2 + 0.5).clamp(0						,	1			)
      _lowercase				 =image.cpu().permute(0						,	2						,	3						,	1			).numpy()
      if output_type == "pil":
       _lowercase				 =self.numpy_to_pil(UpperCAmelCase			)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

