"""simple docstring"""


import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


__A       			=      logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


__A       			=      namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])

def 						UpperCamelCase				(	_lowerCAmelCase   :							int						):
    __a			      =						BertTokenizer.from_pretrained("""bert-base-uncased"""				, do_lower_case=_lowerCAmelCase						)
    __a			      =						BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""						)
    model.to(args.device						)
    model.eval()

    __a			      =						{
        """BOS""": tokenizer.vocab["""[unused0]"""],
        """EOS""": tokenizer.vocab["""[unused1]"""],
        """PAD""": tokenizer.vocab["""[PAD]"""],
    }

    if args.compute_rouge:
        __a			      =						[]
        __a			      =						[]

        import nltk
        import rouge

        nltk.download("""punkt"""						)
        __a			      =						rouge.Rouge(
            metrics=["""rouge-n""", """rouge-l"""]				, max_n=2				, limit_length=_lowerCAmelCase				, length_limit=args.beam_size				, length_limit_type="""words"""				, apply_avg=_lowerCAmelCase				, apply_best=_lowerCAmelCase				, alpha=0.5				, weight_factor=1.2				, stemming=_lowerCAmelCase				, )

    # these (unused) arguments are defined to keep the compatibility
    # with the legacy code and will be deleted in a next iteration.
    __a			      =						""""""
    __a			      =						""""""

    __a			      =						build_data_iterator(_lowerCAmelCase				, _lowerCAmelCase						)
    __a			      =						build_predictor(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)

    logger.info("""***** Running evaluation *****"""						)
    logger.info("""  Number examples = %d"""				, len(data_iterator.dataset						)						)
    logger.info("""  Batch size = %d"""				, args.batch_size						)
    logger.info(""""""						)
    logger.info("""***** Beam Search parameters *****"""						)
    logger.info("""  Beam size = %d"""				, args.beam_size						)
    logger.info("""  Minimum length = %d"""				, args.min_length						)
    logger.info("""  Maximum length = %d"""				, args.max_length						)
    logger.info("""  Alpha (length penalty) = %.2f"""				, args.alpha						)
    logger.info("""  Trigrams %s be blocked"""				, ("""will""" if args.block_trigram else """will NOT""")						)

    for batch in tqdm(_lowerCAmelCase						):
        __a			      =						predictor.translate_batch(_lowerCAmelCase						)
        __a			      =						predictor.from_batch(_lowerCAmelCase						)
        __a			      =						[format_summary(_lowerCAmelCase						) for t in translations]
        save_summaries(_lowerCAmelCase				, args.summaries_output_dir				, batch.document_names						)

        if args.compute_rouge:
            reference_summaries += batch.tgt_str
            generated_summaries += summaries

    if args.compute_rouge:
        __a			      =						rouge_evaluator.get_scores(_lowerCAmelCase				, _lowerCAmelCase						)
        __a			      =						format_rouge_scores(_lowerCAmelCase						)
        save_rouge_scores(_lowerCAmelCase						)
        print(_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							str						):
    for summary, document_name in zip(_lowerCAmelCase				, _lowerCAmelCase						):
        # Prepare the summary file's name
        if "." in document_name:
            __a			      =						""".""".join(document_name.split("""."""						)[:-1]						)
            __a			      =						document_name.split("""."""						)[-1]
            __a			      =						bare_document_name + """_summary.""" + extension
        else:
            __a			      =						document_name + """_summary"""

        __a			      =						os.path.join(_lowerCAmelCase				, _lowerCAmelCase						)
        with open(_lowerCAmelCase				, """w"""						) as output:
            output.write(_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]						):
    __a      ,  __a      ,  __a			      =						translation
    __a			      =						(
        raw_summary.replace("""[unused0]"""				, """"""						)
        .replace("""[unused3]"""				, """"""						)
        .replace("""[PAD]"""				, """"""						)
        .replace("""[unused1]"""				, """"""						)
        .replace(r""" +"""				, """ """						)
        .replace(""" [unused2] """				, """. """						)
        .replace("""[unused2]"""				, """"""						)
        .strip()
    )

    return summary

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]						):
    return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
        scores["""rouge-1"""]["""f"""]				, scores["""rouge-1"""]["""p"""]				, scores["""rouge-1"""]["""r"""]				, scores["""rouge-2"""]["""f"""]				, scores["""rouge-2"""]["""p"""]				, scores["""rouge-2"""]["""r"""]				, scores["""rouge-l"""]["""f"""]				, scores["""rouge-l"""]["""p"""]				, scores["""rouge-l"""]["""r"""]				, )

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[str, Any]						):
    with open("""rouge_scores.txt"""				, """w"""						) as output:
        output.write(_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							str				, _lowerCAmelCase   :							List[str]						):
    __a			      =						load_and_cache_examples(_lowerCAmelCase				, _lowerCAmelCase						)
    __a			      =						SequentialSampler(_lowerCAmelCase						)

    def collate_fn(_lowerCAmelCase   :							Optional[int]						):
        return collate(_lowerCAmelCase				, _lowerCAmelCase				, block_size=512				, device=args.device						)

    __a			      =						DataLoader(
        _lowerCAmelCase				, sampler=_lowerCAmelCase				, batch_size=args.batch_size				, collate_fn=_lowerCAmelCase				, )

    return iterator

def 						UpperCamelCase				(	_lowerCAmelCase   :							Any				, _lowerCAmelCase   :							List[str]						):
    __a			      =						CNNDMDataset(args.documents_dir						)
    return dataset

def 						UpperCamelCase				(	_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							Optional[Any]				, _lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[Any]						):
    __a			      =						[x for x in data if not len(x[1]						) == 0]  # remove empty_files
    __a			      =						[name for name, _, _ in data]
    __a			      =						[""" """.join(_lowerCAmelCase						) for _, _, summary_list in data]

    __a			      =						[encode_for_summarization(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						) for _, story, summary in data]
    __a			      =						torch.tensor(
        [truncate_or_pad(_lowerCAmelCase				, _lowerCAmelCase				, tokenizer.pad_token_id						) for story, _ in encoded_text]						)
    __a			      =						compute_token_type_ids(_lowerCAmelCase				, tokenizer.cls_token_id						)
    __a			      =						build_mask(_lowerCAmelCase				, tokenizer.pad_token_id						)

    __a			      =						Batch(
        document_names=_lowerCAmelCase				, batch_size=len(_lowerCAmelCase						)				, src=encoded_stories.to(_lowerCAmelCase						)				, segs=encoder_token_type_ids.to(_lowerCAmelCase						)				, mask_src=encoder_mask.to(_lowerCAmelCase						)				, tgt_str=_lowerCAmelCase				, )

    return batch

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Any						):
    __a			      =						summary_tokens.to("""cpu"""						).numpy()
    __a			      =						tokenizer.decode(_lowerCAmelCase						)
    __a			      =						summary.split("""."""						)
    __a			      =						[s + """.""" for s in sentences]
    return sentences

def 						UpperCamelCase				(	):
    __a			      =						argparse.ArgumentParser()
    parser.add_argument(
        """--documents_dir"""				, default=_lowerCAmelCase				, type=_lowerCAmelCase				, required=_lowerCAmelCase				, help="""The folder where the documents to summarize are located."""				, )
    parser.add_argument(
        """--summaries_output_dir"""				, default=_lowerCAmelCase				, type=_lowerCAmelCase				, required=_lowerCAmelCase				, help="""The folder in wich the summaries should be written. Defaults to the folder where the documents are"""				, )
    parser.add_argument(
        """--compute_rouge"""				, default=_lowerCAmelCase				, type=_lowerCAmelCase				, required=_lowerCAmelCase				, help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."""				, )
    # EVALUATION options
    parser.add_argument(
        """--no_cuda"""				, default=_lowerCAmelCase				, type=_lowerCAmelCase				, help="""Whether to force the execution on CPU."""				, )
    parser.add_argument(
        """--batch_size"""				, default=4				, type=_lowerCAmelCase				, help="""Batch size per GPU/CPU for training."""				, )
    # BEAM SEARCH arguments
    parser.add_argument(
        """--min_length"""				, default=50				, type=_lowerCAmelCase				, help="""Minimum number of tokens for the summaries."""				, )
    parser.add_argument(
        """--max_length"""				, default=200				, type=_lowerCAmelCase				, help="""Maixmum number of tokens for the summaries."""				, )
    parser.add_argument(
        """--beam_size"""				, default=5				, type=_lowerCAmelCase				, help="""The number of beams to start with for each example."""				, )
    parser.add_argument(
        """--alpha"""				, default=0.95				, type=_lowerCAmelCase				, help="""The value of alpha for the length penalty in the beam search."""				, )
    parser.add_argument(
        """--block_trigram"""				, default=_lowerCAmelCase				, type=_lowerCAmelCase				, help="""Whether to block the existence of repeating trigrams in the text generated by beam search."""				, )
    __a			      =						parser.parse_args()

    # Select device (distibuted not available)
    __a			      =						torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""						)

    # Check the existence of directories
    if not args.summaries_output_dir:
        __a			      =						args.documents_dir

    if not documents_dir_is_valid(args.documents_dir						):
        raise FileNotFoundError(
            """We could not find the directory you specified for the documents to summarize, or it was empty. Please"""
            """ specify a valid path."""						)
    os.makedirs(args.summaries_output_dir				, exist_ok=_lowerCAmelCase						)

    evaluate(_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]						):
    if not os.path.exists(_lowerCAmelCase						):
        return False

    __a			      =						os.listdir(_lowerCAmelCase						)
    if len(_lowerCAmelCase						) == 0:
        return False

    return True


if __name__ == "__main__":
 main()

"""simple docstring"""


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    """cross_attention""",
    """0.20.0""",
    """Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.""",
    standard_warn=False,
)


__A       			=      AttentionProcessor






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Union[str, Any]						,  **lowerCamelCase_   :    str     )      -> int:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    Union[str, Any]     )      -> List[str]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    List[str]     )      -> Optional[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    str						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Any     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Union[str, Any]						,  *lowerCamelCase_   :    Optional[Any]						,  **lowerCamelCase_   :    Tuple     )      -> Union[str, Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[str]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    List[Any]     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    List[str]						,  **lowerCamelCase_   :    Dict     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[Any]						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Optional[Any]     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )
