






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__UpperCamelCase   :							Union[str, Any]         =   {
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase   :							List[str]         =   [
     """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """LongT5EncoderModel""",
     """LongT5ForConditionalGeneration""",
     """LongT5Model""",
     """LongT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase   :							int         =   [
     """FlaxLongT5ForConditionalGeneration""",
     """FlaxLongT5Model""",
     """FlaxLongT5PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 __UpperCamelCase   :							Dict         =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''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   :							int         =   getLogger(__name__)


__UpperCamelCase   :							int         =   """cuda""" if torch.cuda.is_available() else """cpu"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int = 8,       SCREAMING_SNAKE_CASE__:						str = DEFAULT_DEVICE,       SCREAMING_SNAKE_CASE__:						Any=False,       SCREAMING_SNAKE_CASE__:						Tuple="summarization",       SCREAMING_SNAKE_CASE__:						List[Any]=None,       **SCREAMING_SNAKE_CASE__:						int,       )	->      Dict:

   """simple docstring"""
   __a					  =     Path(SCREAMING_SNAKE_CASE__     ).open('w',       encoding='utf-8'     )
   __a					  =     str(SCREAMING_SNAKE_CASE__     )
   __a					  =     AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
   if fpaa:
      __a					  =     model.half()

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

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



def  __UpperCAmelCase						(					)	->      List[str]:

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]=True     )	->      List[Any]:

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

   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'     )

   __a					  =     generate_summaries_or_translations(
       SCREAMING_SNAKE_CASE__,       args.save_path,       args.model_name,       batch_size=args.bs,       device=args.device,       fpaa=args.fpaa,       task=args.task,       prefix=args.prefix,       **SCREAMING_SNAKE_CASE__,       )

   if args.reference_path is None:
      return {}

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

   if args.dump_args:
      scores.update(SCREAMING_SNAKE_CASE__     )
   if args.info:
      __a					  =     args.info

   if verbose:
      print(SCREAMING_SNAKE_CASE__     )

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