





import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


_snake_case    :       str		       =    logging.get_logger(__name__)

_snake_case    :       Tuple		       =    list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_snake_case    :       Union[str, Any]		       =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 							_UpperCAmelCase       :

  """simple docstring"""


  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """Model type selected in the list: """ + """, """.join(_UpperCamelCase       )}       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""}       )
  a_              =					field(
      default=1_28						,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }						,       )
  a_              =					field(
      default=1_28						,       metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""}						,       )
  a_              =					field(
      default=64						,       metadata={
          """help""": (
              """The maximum number of tokens for the question. Questions longer than this will """
              """be truncated to this length."""
          )
      }						,       )
  a_              =					field(
      default=30						,       metadata={
          """help""": (
              """The maximum length of an answer that can be generated. This is needed because the start """
              """and end predictions are not conditioned on one another."""
          )
      }						,       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )
  a_              =					field(
      default=_UpperCamelCase						,       metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""}       )
  a_              =					field(
      default=0.0						,       metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}       )
  a_              =					field(
      default=20						,       metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}       )
  a_              =					field(
      default=0						,       metadata={
          """help""": (
              """language id of input for language-specific xlm models (see"""
              """ tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
          )
      }						,       )
  a_              =					field(default=1						,       metadata={"""help""": """multiple threads for converting example to features"""}       )





class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					"""train"""
  a_              =					"""dev"""





class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					42
  a_              =					42
  a_              =					42
  a_              =					42






  def __init__(							self					:						int   ,    lowerCAmelCase_					:						SquadDataTrainingArguments   ,    lowerCAmelCase_					:						PreTrainedTokenizer   ,    lowerCAmelCase_					:						Optional[int] = None   ,    lowerCAmelCase_					:						Union[str, Split] = Split.train   ,    lowerCAmelCase_					:						Optional[bool] = False   ,    lowerCAmelCase_					:						Optional[str] = None   ,    lowerCAmelCase_					:						Optional[str] = "pt"   ,    )				->		Tuple:
     __lowerCAmelCase				      =      args
     __lowerCAmelCase				      =      is_language_sensitive
     __lowerCAmelCase				      =      SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        try:
           __lowerCAmelCase				      =      Split[mode]
        except KeyError:
           raise KeyError('mode is not a valid split name'	)
     __lowerCAmelCase				      =      mode
     # Load data features from cache or dataset file
     __lowerCAmelCase				      =      'v2' if args.version_2_with_negative else 'v1'
     __lowerCAmelCase				      =      os.path.join(
         cache_dir if cache_dir is not None else args.data_dir   ,    f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""   ,    )

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     __lowerCAmelCase				      =      cached_features_file + '.lock'
     with FileLock(lowerCAmelCase_	):
        if os.path.exists(lowerCAmelCase_	) and not args.overwrite_cache:
           __lowerCAmelCase				      =      time.time()
           __lowerCAmelCase				      =      torch.load(lowerCAmelCase_	)

           # Legacy cache files have only features, while new cache files
           # will have dataset and examples also.
           __lowerCAmelCase				      =      self.old_features['features']
           __lowerCAmelCase				      =      self.old_features.get('dataset'   ,    lowerCAmelCase_	)
           __lowerCAmelCase				      =      self.old_features.get('examples'   ,    lowerCAmelCase_	)
           logger.info(
               f"""Loading features from cached file {cached_features_file} [took %.3f s]"""   ,    time.time() - start	)

           if self.dataset is None or self.examples is None:
              logger.warning(
                  f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
                  ' future run'	)
        else:
           if mode == Split.dev:
              __lowerCAmelCase				      =      self.processor.get_dev_examples(args.data_dir	)
           else:
              __lowerCAmelCase				      =      self.processor.get_train_examples(args.data_dir	)

           __lowerCAmelCase      ,      __lowerCAmelCase				      =      squad_convert_examples_to_features(
               examples=self.examples   ,    tokenizer=lowerCAmelCase_   ,    max_seq_length=args.max_seq_length   ,    doc_stride=args.doc_stride   ,    max_query_length=args.max_query_length   ,    is_training=mode == Split.train   ,    threads=args.threads   ,    return_dataset=lowerCAmelCase_   ,    )

           __lowerCAmelCase				      =      time.time()
           torch.save(
               {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}   ,    lowerCAmelCase_   ,    )
           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
           logger.info(
               f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""	)






  def __len__(							self					:						Tuple	)				->		List[str]:
     return len(self.features	)






  def __getitem__(							self					:						Dict   ,    lowerCAmelCase_					:						Optional[int]	)				->		Dict[str, torch.Tensor]:
     # Convert to Tensors and build dataset
     __lowerCAmelCase				      =      self.features[i]

     __lowerCAmelCase				      =      torch.tensor(feature.input_ids   ,    dtype=torch.long	)
     __lowerCAmelCase				      =      torch.tensor(feature.attention_mask   ,    dtype=torch.long	)
     __lowerCAmelCase				      =      torch.tensor(feature.token_type_ids   ,    dtype=torch.long	)
     __lowerCAmelCase				      =      torch.tensor(feature.cls_index   ,    dtype=torch.long	)
     __lowerCAmelCase				      =      torch.tensor(feature.p_mask   ,    dtype=torch.float	)
     __lowerCAmelCase				      =      torch.tensor(feature.is_impossible   ,    dtype=torch.float	)

     __lowerCAmelCase				      =      {
         'input_ids': input_ids,
         'attention_mask': attention_mask,
         'token_type_ids': token_type_ids,
     }

     if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
        del inputs["token_type_ids"]

     if self.args.model_type in ["xlnet", "xlm"]:
        inputs.update({'cls_index': cls_index, 'p_mask': p_mask}	)
        if self.args.version_2_with_negative:
           inputs.update({'is_impossible': is_impossible}	)
        if self.is_language_sensitive:
           inputs.update({'langs': (torch.ones(input_ids.shape   ,    dtype=torch.intaa	) * self.args.lang_id)}	)

     if self.mode == Split.train:
        __lowerCAmelCase				      =      torch.tensor(feature.start_position   ,    dtype=torch.long	)
        __lowerCAmelCase				      =      torch.tensor(feature.end_position   ,    dtype=torch.long	)
        inputs.update({'start_positions': start_positions, 'end_positions': end_positions}	)

     return inputs





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


SCREAMING_SNAKE_CASE							    =							getLogger(__name__)


SCREAMING_SNAKE_CASE							    =							'cuda' if torch.cuda.is_available() else 'cpu'




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = 8		,				lowerCAmelCase__ = DEFAULT_DEVICE		,				lowerCAmelCase__=False		,				lowerCAmelCase__="summarization"		,				lowerCAmelCase__=None		,				**lowerCAmelCase__		,				):
   __a					=  Path(lowerCAmelCase__   ).open("""w"""		,				encoding="""utf-8"""   )
   __a					=  str(lowerCAmelCase__   )
   __a					=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__   ).to(lowerCAmelCase__   )
   if fpaa:
      __a					=  model.half()

   __a					=  AutoTokenizer.from_pretrained(lowerCAmelCase__   )
   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(lowerCAmelCase__		,				lowerCAmelCase__   )
   if prefix is None:
      __a					=  prefix or getattr(model.config		,				"""prefix"""		,				""""""   ) or """"""
   for examples_chunk in tqdm(list(chunks(lowerCAmelCase__		,				lowerCAmelCase__   )   )   ):
      __a					=  [prefix + text for text in examples_chunk]
      __a					=  tokenizer(lowerCAmelCase__		,				return_tensors="""pt"""		,				truncation=lowerCAmelCase__		,				padding="""longest"""   ).to(lowerCAmelCase__   )
      __a					=  model.generate(
          input_ids=batch.input_ids		,				attention_mask=batch.attention_mask		,				**lowerCAmelCase__		,				)
      __a					=  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()
   __a					=  int(time.time() - start_time   )  # seconds
   __a					=  len(lowerCAmelCase__   )
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,				4   )}




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




def  a   (lowerCAmelCase__=True   ):
   __a					=  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
   __a	,    __a					=  parser.parse_known_args()
   __a					=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__   )
   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=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"""   )

   __a					=  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
   __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(lowerCAmelCase__   )]
   __a					=  score_fn(lowerCAmelCase__		,				lowerCAmelCase__   )
   scores.update(lowerCAmelCase__   )

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



