
'''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_							: Tuple =							getLogger(__name__)


UpperCAmelCase_							: Dict =							'cuda' if torch.cuda.is_available() else 'cpu'







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     int = 8					,						_lowerCAmelCase :     str = DEFAULT_DEVICE					,						_lowerCAmelCase :     Dict=False					,						_lowerCAmelCase :     str="summarization"					,						_lowerCAmelCase :     Dict=None					,						**_lowerCAmelCase :     Tuple					,						):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = Path(_lowerCAmelCase				).open("w"					,						encoding="utf-8"				)
      _lowerCamelCase					: str		    = str(_lowerCAmelCase				)
      _lowerCamelCase					: Dict		    = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase				).to(_lowerCAmelCase				)
      if fpaa:
            _lowerCamelCase					: int		    = model.half()

      _lowerCamelCase					: Any		    = AutoTokenizer.from_pretrained(_lowerCAmelCase				)
      logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'				)  # if this is wrong, check config.model_type.

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







def 		A_							(						):



      """simple docstring"""




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







def 		A_							(						_lowerCAmelCase :     Dict=True				):



      """simple docstring"""




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

      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"				)

      _lowerCamelCase					: int		    = generate_summaries_or_translations(
          _lowerCAmelCase					,						args.save_path					,						args.model_name					,						batch_size=args.bs					,						device=args.device					,						fpaa=args.fpaa					,						task=args.task					,						prefix=args.prefix					,						**_lowerCAmelCase					,						)

      if args.reference_path is None:
            return {}

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

      if args.dump_args:
            scores.update(_lowerCAmelCase				)
      if args.info:
            _lowerCamelCase					: Union[str, Any]		    = args.info

      if verbose:
            print(_lowerCAmelCase				)

      if args.score_path is not None:
            json.dump(_lowerCAmelCase					,						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 typing import TYPE_CHECKING

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


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






