






"""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						:    Dict		=						getLogger(__name__)


_UpperCamelCase						:    int		=						'cuda' if torch.cuda.is_available() else 'cpu'
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    str						,   __snake_case     :    str						,   __snake_case     :    int = 8						,   __snake_case     :    str = DEFAULT_DEVICE						,   __snake_case     :    Tuple=False						,   __snake_case     :    Tuple="summarization"						,   __snake_case     :    List[str]=None						,   **__snake_case     :    Dict						,   ):





  '''simple docstring'''





  lowercase				       =			Path(__snake_case       ).open('w'						,   encoding='utf-8'       )
  lowercase				       =			str(__snake_case       )
  lowercase				       =			AutoModelForSeqaSeqLM.from_pretrained(__snake_case       ).to(__snake_case       )
  if fpaa:
    lowercase				       =			model.half()

  lowercase				       =			AutoTokenizer.from_pretrained(__snake_case       )
  logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'       )  # if this is wrong, check config.model_type.

  lowercase				       =			time.time()
  # update config with task specific params
  use_task_specific_params(__snake_case						,   __snake_case       )
  if prefix is None:
    lowercase				       =			prefix or getattr(model.config						,   'prefix'						,   ''       ) or ''
  for examples_chunk in tqdm(list(chunks(__snake_case						,   __snake_case       )       )       ):
    lowercase				       =			[prefix + text for text in examples_chunk]
    lowercase				       =			tokenizer(__snake_case						,   return_tensors='pt'						,   truncation=__snake_case						,   padding='longest'       ).to(__snake_case       )
    lowercase				       =			model.generate(
        input_ids=batch.input_ids						,   attention_mask=batch.attention_mask						,   **__snake_case						,   )
    lowercase				       =			tokenizer.batch_decode(__snake_case						,   skip_special_tokens=__snake_case						,   clean_up_tokenization_spaces=__snake_case       )
    for hypothesis in dec:
      fout.write(hypothesis + '\n'       )
      fout.flush()
  fout.close()
  lowercase				       =			int(time.time() - start_time       )  # seconds
  lowercase				       =			len(__snake_case       )
  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					(       __snake_case     :    Any=True       ):





  '''simple docstring'''





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

  if args.reference_path is None:
    return {}

  # Compute scores
  lowercase				       =			calculate_bleu if 'translation' in args.task else calculate_rouge
  lowercase				       =			[x.rstrip() for x in open(args.save_path       ).readlines()]
  lowercase				       =			[x.rstrip() for x in open(args.reference_path       ).readlines()][: len(__snake_case       )]
  lowercase				       =			score_fn(__snake_case						,   __snake_case       )
  scores.update(__snake_case       )

  if args.dump_args:
    scores.update(__snake_case       )
  if args.info:
    lowercase				       =			args.info

  if verbose:
    print(__snake_case       )

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










"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase						:    Optional[int]		=						TypeVar('T', bound=bool)

class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			value
      lowercase				       =			None
      lowercase				       =			None







    @property
    def      UpperCamelCase_ (     self			):
      return self._value







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase				,						_lowerCamelCase			):
      if not roota:
        return roota

      if not roota:
        return roota

      if roota.value > roota.value:
        lowercase       ,	lowercase				       =			roota, roota

      if random.choice([True, False]			):
        lowercase       ,	lowercase				       =			roota.right, roota.left

      lowercase				       =			RandomizedHeapNode.merge(roota.left				,						_lowerCamelCase			)

      return roota


class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase = ()			):
      lowercase				       =			None

      if data:
        for item in data:
          self.insert(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			RandomizedHeapNode.merge(self._root				,						RandomizedHeapNode(_lowerCamelCase			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.top()

      if self._root is None:
        return None

      lowercase				       =			RandomizedHeapNode.merge(self._root.left				,						self._root.right			)

      return result







    def      UpperCamelCase_ (     self			):
      if not self._root:
        raise IndexError('Can\'t get top element for the empty heap.'			)
      return self._root.value







    def      UpperCamelCase_ (     self			):
      lowercase				       =			None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[]
      while self:
        result.append(self.pop()			)

      return result







    def __bool__(     self			):
      return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()


