




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


SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       getLogger(__name__)


SCREAMING_SNAKE_CASE__	:				int  =       'cuda' if torch.cuda.is_available() else 'cpu'

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = 8			,_SCREAMING_SNAKE_CASE = DEFAULT_DEVICE			,_SCREAMING_SNAKE_CASE=False			,_SCREAMING_SNAKE_CASE="summarization"			,_SCREAMING_SNAKE_CASE=None			,**_SCREAMING_SNAKE_CASE			,)     ->			Dict:
       lowerCamelCase       :				List[Any]      			= Path(_SCREAMING_SNAKE_CASE   ).open("w"			,encoding="utf-8"   )
       lowerCamelCase       :				Dict      			= str(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[str]      			= AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE   ).to(_SCREAMING_SNAKE_CASE   )
       if fpaa:
              lowerCamelCase       :				Tuple      			= model.half()

       lowerCamelCase       :				List[Any]      			= AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
       logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''   )  # if this is wrong, check config.model_type.

       lowerCamelCase       :				str      			= time.time()
       # update config with task specific params
       use_task_specific_params(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       if prefix is None:
              lowerCamelCase       :				Tuple      			= prefix or getattr(model.config			,"prefix"			,""   ) or ""
       for examples_chunk in tqdm(list(chunks(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )   )   ):
              lowerCamelCase       :				Dict      			= [prefix + text for text in examples_chunk]
              lowerCamelCase       :				int      			= tokenizer(_SCREAMING_SNAKE_CASE			,return_tensors="pt"			,truncation=_SCREAMING_SNAKE_CASE			,padding="longest"   ).to(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				str      			= model.generate(
                  input_ids=batch.input_ids			,attention_mask=batch.attention_mask			,**_SCREAMING_SNAKE_CASE			,)
              lowerCamelCase       :				Tuple      			= 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       :				List[Any]      			= int(time.time() - start_time   )  # seconds
       lowerCamelCase       :				Any      			= len(_SCREAMING_SNAKE_CASE   )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,4   )}

def 					A				(				)     ->			Optional[Any]:
       return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"   )

def 					A				(				_SCREAMING_SNAKE_CASE=True   )     ->			int:
       lowerCamelCase       :				Tuple      			= 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       :				Optional[int]      			= parser.parse_known_args()
       lowerCamelCase       :				str      			= parse_numeric_n_bool_cl_kwargs(_SCREAMING_SNAKE_CASE   )
       if parsed_args and verbose:
              print(f'''parsed the following generate kwargs: {parsed_args}'''   )
       with open(args.input_path   ) as f:
              lowerCamelCase       :				Any      			= [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              lowerCamelCase       :				Optional[int]      			= 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.'''   )
       lowerCamelCase       :				Tuple      			= 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       :				Optional[int]      			= calculate_bleu if "translation" in args.task else calculate_rouge
       lowerCamelCase       :				List[str]      			= [x.rstrip() for x in open(args.save_path   ).readlines()]
       lowerCamelCase       :				List[str]      			= [x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_SCREAMING_SNAKE_CASE   )]
       lowerCamelCase       :				dict      			= 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       :				Tuple      			= 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)












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,start_method="spawn"   )


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
       # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

       # on-demand GPU
       # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
       SCREAMING_SNAKE_CASE__	:				Tuple  =       rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
       gpu.up_if_not()

       # on-prem GPU
       # gpu = rh.cluster(
       #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
       #       )

       # Set up remote function
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       launch_train_gpu(config, train_args, stream_logs=True)

       # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
       # gpu.install_packages(reqs)
       # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






