





"""simple docstring"""




import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCamelCase_					:						List[str]      = logging.getLogger(__name__)





def 						UpperCAmelCase__		(					):






  """simple docstring"""





  A_  :	int    						=  argparse.ArgumentParser(description='PyTorch Transformer Language Model' )
  parser.add_argument('--model_name'			, type=_UpperCAmelCase			, default='transfo-xl-wt103'			, help='pretrained model name' )
  parser.add_argument(
      '--split'			, type=_UpperCAmelCase			, default='test'			, choices=['all', 'valid', 'test']			, help='which split to evaluate' )
  parser.add_argument('--batch_size'			, type=_UpperCAmelCase			, default=10			, help='batch size' )
  parser.add_argument('--tgt_len'			, type=_UpperCAmelCase			, default=128			, help='number of tokens to predict' )
  parser.add_argument('--ext_len'			, type=_UpperCAmelCase			, default=0			, help='length of the extended context' )
  parser.add_argument('--mem_len'			, type=_UpperCAmelCase			, default=1600			, help='length of the retained previous heads' )
  parser.add_argument('--clamp_len'			, type=_UpperCAmelCase			, default=1000			, help='max positional embedding index' )
  parser.add_argument('--no_cuda'			, action='store_true'			, help='Do not use CUDA even though CUA is available' )
  parser.add_argument('--work_dir'			, type=_UpperCAmelCase			, required=_UpperCAmelCase			, help='path to the work_dir' )
  parser.add_argument('--no_log'			, action='store_true'			, help='do not log the eval result' )
  parser.add_argument('--same_length'			, action='store_true'			, help='set same length attention with masking' )
  parser.add_argument('--server_ip'			, type=_UpperCAmelCase			, default=''			, help='Can be used for distant debugging.' )
  parser.add_argument('--server_port'			, type=_UpperCAmelCase			, default=''			, help='Can be used for distant debugging.' )
  A_  :	Dict    						=  parser.parse_args()
  assert args.ext_len >= 0, "extended context length must be non-negative"

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print('Waiting for debugger attach' )
    ptvsd.enable_attach(address=(args.server_ip, args.server_port)			, redirect_output=_UpperCAmelCase )
    ptvsd.wait_for_attach()

  A_  :	Dict    						=  torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
  logger.info('device: {}'.format(_UpperCAmelCase ) )

  # Load a pre-processed dataset
  # You can also build the corpus yourself using TransfoXLCorpus methods
  # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
  # and tokenizing the dataset
  # The pre-processed corpus is a convertion (using the conversion script )
  A_  :	Dict    						=  TransfoXLCorpus.from_pretrained(args.model_name )

  A_  :	Any    						=  corpus.get_iterator('valid'			, args.batch_size			, args.tgt_len			, device=_UpperCAmelCase			, ext_len=args.ext_len )
  A_  :	Optional[int]    						=  corpus.get_iterator('test'			, args.batch_size			, args.tgt_len			, device=_UpperCAmelCase			, ext_len=args.ext_len )

  # Load a pre-trained model
  A_  :	str    						=  TransfoXLLMHeadModel.from_pretrained(args.model_name )
  model.to(_UpperCAmelCase )

  logger.info(
      'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
          args.batch_size			, args.tgt_len			, args.ext_len			, args.mem_len			, args.clamp_len ) )

  model.reset_memory_length(args.mem_len )
  if args.clamp_len > 0:
    A_  :	Dict    						=  args.clamp_len
  if args.same_length:
    A_  :	Union[str, Any]    						=  True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(_UpperCAmelCase ):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    A_						,	A_  :	Optional[int]    						=  0, 0.0
    A_  :	str    						=  time.time()
    with torch.no_grad():
      A_  :	int    						=  None
      for idx, (data, target, seq_len) in enumerate(_UpperCAmelCase ):
        A_  :	List[Any]    						=  model(_UpperCAmelCase			, lm_labels=_UpperCAmelCase			, mems=_UpperCAmelCase )
        A_						,	A_						,	A_  :	Dict    						=  ret
        A_  :	List[str]    						=  loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      A_  :	Dict    						=  time.time() - start_time
    logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_UpperCAmelCase			, 1000 * total_time / (idx + 1) ) )
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    A_  :	Tuple    						=  evaluate(_UpperCAmelCase )
    A_  :	List[Any]    						=  evaluate(_UpperCAmelCase )
  elif args.split == "valid":
    A_  :	Optional[Any]    						=  evaluate(_UpperCAmelCase )
    A_  :	Dict    						=  None
  elif args.split == "test":
    A_  :	List[Any]    						=  evaluate(_UpperCAmelCase )
    A_  :	Optional[int]    						=  None

  def format_log(_UpperCAmelCase			, _UpperCAmelCase ):
    A_  :	List[str]    						=  '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_UpperCAmelCase			, _UpperCAmelCase			, math.exp(_UpperCAmelCase ) )
    return log_str

  A_  :	Optional[int]    						=  ''
  if valid_loss is not None:
    log_str += format_log(_UpperCAmelCase			, 'valid' )
  if test_loss is not None:
    log_str += format_log(_UpperCAmelCase			, 'test' )

  logger.info('=' * 100 )
  logger.info(_UpperCAmelCase )
  logger.info('=' * 100 )


if __name__ == "__main__":
   main()






"""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


lowerCamelCase_					:						List[str]      = getLogger(__name__)


lowerCamelCase_					:						Optional[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 8			, _UpperCAmelCase = DEFAULT_DEVICE			, _UpperCAmelCase=False			, _UpperCAmelCase="summarization"			, _UpperCAmelCase=None			, **_UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

  A_  :	int    						=  AutoTokenizer.from_pretrained(_UpperCAmelCase )
  logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

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





def 						UpperCAmelCase__		(					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	int    						=  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
  A_						,	A_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase )
  if parsed_args and verbose:
    print(f"""parsed the following generate kwargs: {parsed_args}""" )
  with open(args.input_path ) as f:
    A_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	int    						=  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.""" )
  A_  :	Optional[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
  A_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

  if args.dump_args:
    scores.update(_UpperCAmelCase )
  if args.info:
    A_  :	Any    						=  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)