code: 
  
  
  
  
  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
  
  
  _A							    =	getLogger(__name__)
  
  
  _A							    =	"""cuda""" if torch.cuda.is_available() else """cpu"""
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a = 8	,	__a = DEFAULT_DEVICE	,	__a=False	,	__a="summarization"	,	__a=None	,	**__a	,	)       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =Path(__a ).open('''w'''	,	encoding='''utf-8''' )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =str(__a )
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =AutoModelForSeqaSeqLM.from_pretrained(__a ).to(__a )
  							if fpaa:
  														SCREAMING_SNAKE_CASE			: Any	     =model.half()
  
  							SCREAMING_SNAKE_CASE			: Any	     =AutoTokenizer.from_pretrained(__a )
  							logger.info(f'Inferred tokenizer type: {tokenizer.__class__}' )  # if this is wrong, check config.model_type.
  
  							SCREAMING_SNAKE_CASE			: Any	     =time.time()
  							# update config with task specific params
  							use_task_specific_params(__a	,	__a )
  							if prefix is None:
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =prefix or getattr(model.config	,	'''prefix'''	,	'''''' ) or ''''''
  							for examples_chunk in tqdm(list(chunks(__a	,	__a ) ) ):
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =[prefix + text for text in examples_chunk]
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tokenizer(__a	,	return_tensors='''pt'''	,	truncation=__a	,	padding='''longest''' ).to(__a )
  														SCREAMING_SNAKE_CASE			: Dict	     =model.generate(
  														    input_ids=batch.input_ids	,	attention_mask=batch.attention_mask	,	**__a	,	)
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =tokenizer.batch_decode(__a	,	skip_special_tokens=__a	,	clean_up_tokenization_spaces=__a )
  														for hypothesis in dec:
  																					fout.write(hypothesis + '''\n''' )
  																					fout.flush()
  							fout.close()
  							SCREAMING_SNAKE_CASE			: List[Any]	     =int(time.time() - start_time )  # seconds
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =len(__a )
  							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,	4 )}
  def 						lowerCAmelCase_       (	)       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''' )
  def 						lowerCAmelCase_       (	__a=True )       ->			Tuple:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =argparse.ArgumentParser()
  							parser.add_argument('''model_name'''	,	type=__a	,	help='''like facebook/bart-large-cnn,t5-base, etc.''' )
  							parser.add_argument('''input_path'''	,	type=__a	,	help='''like cnn_dm/test.source''' )
  							parser.add_argument('''save_path'''	,	type=__a	,	help='''where to save summaries''' )
  							parser.add_argument('''--reference_path'''	,	type=__a	,	required=__a	,	help='''like cnn_dm/test.target''' )
  							parser.add_argument('''--score_path'''	,	type=__a	,	required=__a	,	default='''metrics.json'''	,	help='''where to save metrics''' )
  							parser.add_argument('''--device'''	,	type=__a	,	required=__a	,	default=__a	,	help='''cuda, cuda:1, cpu etc.''' )
  							parser.add_argument(
  							    '''--prefix'''	,	type=__a	,	required=__a	,	default=__a	,	help='''will be added to the begininng of src examples''' )
  							parser.add_argument('''--task'''	,	type=__a	,	default='''summarization'''	,	help='''used for task_specific_params + metrics''' )
  							parser.add_argument('''--bs'''	,	type=__a	,	default=8	,	required=__a	,	help='''batch size''' )
  							parser.add_argument(
  							    '''--n_obs'''	,	type=__a	,	default=-1	,	required=__a	,	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=__a	,	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
  							SCREAMING_SNAKE_CASE			: Tuple	     =parser.parse_known_args()
  							SCREAMING_SNAKE_CASE			: Tuple	     =parse_numeric_n_bool_cl_kwargs(__a )
  							if parsed_args and verbose:
  														print(f'parsed the following generate kwargs: {parsed_args}' )
  							with open(args.input_path ) as f:
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
  							if args.n_obs > 0:
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =examples[: args.n_obs]
  							Path(args.save_path ).parent.mkdir(exist_ok=__a )
  							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.' )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =generate_summaries_or_translations(
  							    __a	,	args.save_path	,	args.model_name	,	batch_size=args.bs	,	device=args.device	,	fpaa=args.fpaa	,	task=args.task	,	prefix=args.prefix	,	**__a	,	)
  
  							if args.reference_path is None:
  														return {}
  
  							# Compute scores
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =calculate_bleu if '''translation''' in args.task else calculate_rouge
  							SCREAMING_SNAKE_CASE			: List[Any]	     =[x.rstrip() for x in open(args.save_path ).readlines()]
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[x.rstrip() for x in open(args.reference_path ).readlines()][: len(__a )]
  							SCREAMING_SNAKE_CASE			: dict	     =score_fn(__a	,	__a )
  							scores.update(__a )
  
  							if args.dump_args:
  														scores.update(__a )
  							if args.info:
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =args.info
  
  							if verbose:
  														print(__a )
  
  							if args.score_path is not None:
  														json.dump(__a	,	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)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  def 						lowerCAmelCase_       (	__a = "mytext.txt"	,	__a = False )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							import socket
  
  							SCREAMING_SNAKE_CASE			: int	     =12312  # Reserve a port for your service.
  							SCREAMING_SNAKE_CASE			: Dict	     =socket.socket()  # Create a socket object
  							SCREAMING_SNAKE_CASE			: Any	     =socket.gethostname()  # Get local machine name
  							sock.bind((host, port) )  # Bind to the port
  							sock.listen(5 )  # Now wait for client connection.
  
  							print('''Server listening....''' )
  
  							while True:
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =sock.accept()  # Establish connection with client.
  														print(f'Got connection from {addr}' )
  														SCREAMING_SNAKE_CASE			: List[str]	     =conn.recv(1024 )
  														print(f'Server received: {data = }' )
  
  														with open(__a	,	'''rb''' ) as in_file:
  																					SCREAMING_SNAKE_CASE			: Dict	     =in_file.read(1024 )
  																					while data:
  																												conn.send(__a )
  																												print(f'Sent {data!r}' )
  																												SCREAMING_SNAKE_CASE			: str	     =in_file.read(1024 )
  
  														print('''Done sending''' )
  														conn.close()
  														if testing:  # Allow the test to complete
  																					break
  
  							sock.shutdown(1 )
  							sock.close()
  
  
  if __name__ == "__main__":
  							send_file()
  
  
  
  
  
  
  