


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


_snake_case					=					getLogger(__name__)


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






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_ = 8				,						SCREAMING_SNAKE_CASE_ = DEFAULT_DEVICE				,						SCREAMING_SNAKE_CASE_=False				,						SCREAMING_SNAKE_CASE_="summarization"				,						SCREAMING_SNAKE_CASE_=None				,						**SCREAMING_SNAKE_CASE_				,						):


  '''simple docstring'''

  lowerCamelCase			: List[Any]   =    Path(SCREAMING_SNAKE_CASE_     ).open("w"				,						encoding="utf-8"     )
  lowerCamelCase			: Any   =    str(SCREAMING_SNAKE_CASE_     )
  lowerCamelCase			: Tuple   =    AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     )
  if fpaa:
    lowerCamelCase			: Any   =    model.half()

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

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






def 		lowercase_(  ):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_=True     ):


  '''simple docstring'''

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







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Union[str, Any]							=		"MarkupLMFeatureExtractor"
   __A			:				List[str]							=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   __A			:				str							=		True






   def __call__(		self						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A = True						,    __A = False						,    __A = None						,    __A = None						,    __A = 0						,    __A = None						,    __A = None						,    __A = None						,    __A = False						,    __A = False						,    __A = False						,    __A = False						,    __A = True						,    __A = None						,    **__A						,    ):






     """simple docstring"""





     if self.parse_html:
       if html_strings is None:
         raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"			)

       if nodes is not None or xpaths is not None or node_labels is not None:
         raise ValueError(
             "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"			)

       lowerCamelCase			: Any   =    self.feature_extractor(__A			)
       lowerCamelCase			: Optional[int]   =    features["nodes"]
       lowerCamelCase			: Any   =    features["xpaths"]
     else:
       if html_strings is not None:
         raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."			)
       if nodes is None or xpaths is None:
         raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"			)

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
       if isinstance(__A						,    __A			):
         lowerCamelCase			: Tuple   =    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     lowerCamelCase			: Tuple   =    self.tokenizer(
         text=questions if questions is not None else nodes						,    text_pair=nodes if questions is not None else None						,    xpaths=__A						,    node_labels=__A						,    add_special_tokens=__A						,    padding=__A						,    truncation=__A						,    max_length=__A						,    stride=__A						,    pad_to_multiple_of=__A						,    return_token_type_ids=__A						,    return_attention_mask=__A						,    return_overflowing_tokens=__A						,    return_special_tokens_mask=__A						,    return_offsets_mapping=__A						,    return_length=__A						,    verbose=__A						,    return_tensors=__A						,    **__A						,    )

     return encoded_inputs






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,    **__A			)






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,    **__A			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.tokenizer.model_input_names
     return tokenizer_input_names



