






'''simple docstring'''
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


UpperCamelCase__ =			getLogger(__name__)


UpperCamelCase__ =			'cuda' if torch.cuda.is_available() else 'cpu'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 8						,		_UpperCamelCase = DEFAULT_DEVICE						,		_UpperCamelCase=False						,		_UpperCamelCase="summarization"						,		_UpperCamelCase=None						,		**_UpperCamelCase						,		):


   """simple docstring"""




   lowercase_	:    Dict           =     Path(_UpperCamelCase							).open("w"						,		encoding="utf-8"							)
   lowercase_	:    Any           =     str(_UpperCamelCase							)
   lowercase_	:    List[str]           =     AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase							).to(_UpperCamelCase							)
   if fpaa:
      lowercase_	:    List[str]           =     model.half()

   lowercase_	:    Tuple           =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
   logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""							)  # if this is wrong, check config.model_type.

   lowercase_	:    List[Any]           =     time.time()
   # update config with task specific params
   use_task_specific_params(_UpperCamelCase						,		_UpperCamelCase							)
   if prefix is None:
      lowercase_	:    Any           =     prefix or getattr(model.config						,		"prefix"						,		""							) or ""
   for examples_chunk in tqdm(list(chunks(_UpperCamelCase						,		_UpperCamelCase							)							)							):
      lowercase_	:    Optional[int]           =     [prefix + text for text in examples_chunk]
      lowercase_	:    Optional[Any]           =     tokenizer(_UpperCamelCase						,		return_tensors="pt"						,		truncation=_UpperCamelCase						,		padding="longest"							).to(_UpperCamelCase							)
      lowercase_	:    Tuple           =     model.generate(
          input_ids=batch.input_ids						,		attention_mask=batch.attention_mask						,		**_UpperCamelCase						,		)
      lowercase_	:    int           =     tokenizer.batch_decode(_UpperCamelCase						,		skip_special_tokens=_UpperCamelCase						,		clean_up_tokenization_spaces=_UpperCamelCase							)
      for hypothesis in dec:
         fout.write(hypothesis + "\n"							)
         fout.flush()
   fout.close()
   lowercase_	:    List[str]           =     int(time.time() - start_time							)  # seconds
   lowercase_	:    List[Any]           =     len(_UpperCamelCase							)
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,		4							)}




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase=True							):


   """simple docstring"""




   lowercase_	:    Tuple           =     argparse.ArgumentParser()
   parser.add_argument("model_name"						,		type=_UpperCamelCase						,		help="like facebook/bart-large-cnn,t5-base, etc."							)
   parser.add_argument("input_path"						,		type=_UpperCamelCase						,		help="like cnn_dm/test.source"							)
   parser.add_argument("save_path"						,		type=_UpperCamelCase						,		help="where to save summaries"							)
   parser.add_argument("--reference_path"						,		type=_UpperCamelCase						,		required=_UpperCamelCase						,		help="like cnn_dm/test.target"							)
   parser.add_argument("--score_path"						,		type=_UpperCamelCase						,		required=_UpperCamelCase						,		default="metrics.json"						,		help="where to save metrics"							)
   parser.add_argument("--device"						,		type=_UpperCamelCase						,		required=_UpperCamelCase						,		default=_UpperCamelCase						,		help="cuda, cuda:1, cpu etc."							)
   parser.add_argument(
       "--prefix"						,		type=_UpperCamelCase						,		required=_UpperCamelCase						,		default=_UpperCamelCase						,		help="will be added to the begininng of src examples"							)
   parser.add_argument("--task"						,		type=_UpperCamelCase						,		default="summarization"						,		help="used for task_specific_params + metrics"							)
   parser.add_argument("--bs"						,		type=_UpperCamelCase						,		default=8						,		required=_UpperCamelCase						,		help="batch size"							)
   parser.add_argument(
       "--n_obs"						,		type=_UpperCamelCase						,		default=-1						,		required=_UpperCamelCase						,		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=_UpperCamelCase						,		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
   lowercase_    ,		lowercase_	:    Dict           =     parser.parse_known_args()
   lowercase_	:    str           =     parse_numeric_n_bool_cl_kwargs(_UpperCamelCase							)
   if parsed_args and verbose:
      print(F"""parsed the following generate kwargs: {parsed_args}"""							)
   lowercase_	:    Optional[Any]           =     [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path							).readlines()]
   if args.n_obs > 0:
      lowercase_	:    str           =     examples[: args.n_obs]
   Path(args.save_path							).parent.mkdir(exist_ok=_UpperCamelCase							)

   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."""							)

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError("Can't mix --fp16 and --device cpu"							)

   lowercase_	:    Union[str, Any]           =     generate_summaries_or_translations(
       _UpperCamelCase						,		args.save_path						,		args.model_name						,		batch_size=args.bs						,		device=args.device						,		fpaa=args.fpaa						,		task=args.task						,		prefix=args.prefix						,		**_UpperCamelCase						,		)

   if args.reference_path is None:
      return {}

   # Compute scores
   lowercase_	:    Tuple           =     calculate_bleu if "translation" in args.task else calculate_rouge
   lowercase_	:    List[str]           =     [x.rstrip() for x in open(args.save_path							).readlines()]
   lowercase_	:    Any           =     [x.rstrip() for x in open(args.reference_path							).readlines()][: len(_UpperCamelCase							)]
   lowercase_	:    dict           =     score_fn(_UpperCamelCase						,		_UpperCamelCase							)
   scores.update(_UpperCamelCase							)

   if args.dump_args:
      scores.update(_UpperCamelCase							)
   if args.info:
      lowercase_	:    List[str]           =     args.info

   if verbose:
      print(_UpperCamelCase							)

   if args.score_path is not None:
      json.dump(_UpperCamelCase						,		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)












'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     str(id_			)
       lowercase_	:    List[str]           =     None
       lowercase_	:    Tuple           =     None
       lowercase_	:    Dict           =     []
       lowercase_	:    Union[str, Any]           =     {}  # {vertex:distance}




    def __lt__(			self  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       return self.key < other.key




    def __repr__(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return self.id




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[int]			):






       '''simple docstring'''






       self.neighbors.append(a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     weight




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   graph[a - 1].add_neighbor(graph[b - 1]							)
   graph[b - 1].add_neighbor(graph[a - 1]							)
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]						,		_UpperCamelCase							)
   graph[b - 1].add_edge(graph[a - 1]						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   for u in graph:
      lowercase_	:    List[Any]           =     math.inf
      lowercase_	:    str           =     None
   lowercase_	:    Tuple           =     0
   lowercase_	:    Tuple           =     graph[:]
   while q:
      lowercase_	:    List[Any]           =     min(_UpperCamelCase							)
      q.remove(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            lowercase_	:    Optional[int]           =     u
            lowercase_	:    Union[str, Any]           =     u.edges[v.id]
   for i in range(1						,		len(_UpperCamelCase							)							):
      a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
   return a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for u in graph:
      lowercase_	:    str           =     math.inf
      lowercase_	:    int           =     None
   lowercase_	:    List[Any]           =     0

   lowercase_	:    str           =     list(_UpperCamelCase							)
   hq.heapify(_UpperCamelCase							)

   while h:
      lowercase_	:    List[Any]           =     hq.heappop(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            lowercase_	:    str           =     u
            lowercase_	:    Optional[int]           =     u.edges[v.id]
            hq.heapify(_UpperCamelCase							)

   for i in range(1						,		len(_UpperCamelCase							)							):
      yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""






if __name__ == "__main__":
 import doctest

 doctest.testmod()




