






"""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)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							SCREAMING_SNAKE_CASE__       			=				None


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__       			=				{
    """facebook/xglm-564M""": 2_0_4_8,
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__          =				["input_ids", "attention_mask"]
							lowerCAmelCase__          =				XGLMTokenizer




							def __init__(  self						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="<unk>"						,       UpperCAmelCase="<pad>"						,       **UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 7
								lowercase_			 = [F'<madeupword{i}>' for i in range(self.num_madeup_words  )]

								lowercase_			 = kwargs.get("additional_special_tokens"						,       []  )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    UpperCAmelCase						,       tokenizer_file=UpperCAmelCase						,       bos_token=UpperCAmelCase						,       eos_token=UpperCAmelCase						,       sep_token=UpperCAmelCase						,       cls_token=UpperCAmelCase						,       unk_token=UpperCAmelCase						,       pad_token=UpperCAmelCase						,       **UpperCAmelCase						,       )

								lowercase_			 = vocab_file
								lowercase_			 = False if not self.vocab_file else True




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								lowercase_			 = [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								lowercase_			 = [self.sep_token_id]

								if token_ids_a is None:
									return len(sep + token_ids_a  ) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

								if not os.path.isdir(UpperCAmelCase  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'  )
									return
								lowercase_			 = os.path.join(
								    UpperCAmelCase						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
									copyfile(self.vocab_file						,       UpperCAmelCase  )

								return (out_vocab_file,)

