
"""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


SCREAMING_SNAKE_CASE_  					= {
    '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'},
}

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

   _UpperCAmelCase 						=       parser.parse_args()

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

   return args
def       __UpperCAmelCase		(		)  ->							str:
   transformers.utils.logging.set_verbosity_error()
   _UpperCAmelCase 						=       parse_args()

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

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

   _UpperCAmelCase 						=       compute_module_sizes(UpperCamelCase_					)
   _UpperCAmelCase 						=       {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]
   _UpperCAmelCase 						=       "\n".join([F'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]					)
   print(F'''\nTheoretical use:\n{message}'''					)

   _UpperCAmelCase 						=       AutoTokenizer.from_pretrained(args.tokenizer_name					)

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

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

   _UpperCAmelCase 						=       end_measure(UpperCamelCase_					)
   log_measures(UpperCamelCase_     ,	"Model generation"					)

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


if __name__ == "__main__":
  main()









"""simple docstring"""




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							lowerCAmelCase			(   UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Any       )							->				Tuple:







  '''simple docstring'''







  _a					      =						{"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
  _a      ,						_a      ,						_a					      =						MODELS[model_size]  # set model hyperparameters
  _a					      =						ImageGPTConfig(n_embd=UpperCamelCase_  ,			n_layer=UpperCamelCase_  ,			n_head=UpperCamelCase_       )
  _a					      =						ImageGPTForCausalLM(UpperCamelCase_       )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )

  # Save pytorch-model
  _a					      =						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  _a					      =						pytorch_dump_folder_path + "/" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
  torch.save(model.state_dict()  ,			UpperCamelCase_       )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''       )
  with open(UpperCamelCase_  ,			"w"  ,			encoding="utf-8"       ) as f:
    f.write(config.to_json_string()       )


if __name__ == "__main__":
     UpperCamelCase											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--imagegpt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the TensorFlow checkpoint path.""",
     )
     parser.add_argument(
         """--model_size""",
         default=None,
         type=str,
         required=True,
         help="""Size of the model (can be either 'small', 'medium' or 'large').""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     UpperCamelCase											=     parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )


