


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,
)


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


__lowerCamelCase   	=  namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])



def 			_a      (    __UpperCamelCase			):
     a_     :  str				      =     BertTokenizer.from_pretrained("""bert-base-uncased"""	,	do_lower_case=__UpperCamelCase			)
     a_     :  Union[str, Any]				      =     BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""			)
     model.to(args.device			)
     model.eval()

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

     if args.compute_rouge:
          a_     :  Union[str, Any]				      =     []
          a_     :  Tuple				      =     []

          import nltk
          import rouge

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

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

     a_     :  Union[str, Any]				      =     build_data_iterator(__UpperCamelCase	,	__UpperCamelCase			)
     a_     :  int				      =     build_predictor(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)

     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(__UpperCamelCase			):
          a_     :  Dict				      =     predictor.translate_batch(__UpperCamelCase			)
          a_     :  int				      =     predictor.from_batch(__UpperCamelCase			)
          a_     :  Any				      =     [format_summary(__UpperCamelCase			) for t in translations]
          save_summaries(__UpperCamelCase	,	args.summaries_output_dir	,	batch.document_names			)

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

     if args.compute_rouge:
          a_     :  Union[str, Any]				      =     rouge_evaluator.get_scores(__UpperCamelCase	,	__UpperCamelCase			)
          a_     :  Any				      =     format_rouge_scores(__UpperCamelCase			)
          save_rouge_scores(__UpperCamelCase			)
          print(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     for summary, document_name in zip(__UpperCamelCase	,	__UpperCamelCase			):
          # Prepare the summary file's name
          if "." in document_name:
               a_     :  Dict				      =     """.""".join(document_name.split("""."""			)[:-1]			)
               a_     :  Optional[Any]				      =     document_name.split("""."""			)[-1]
               a_     :  Tuple				      =     bare_document_name + """_summary.""" + extension
          else:
               a_     :  str				      =     document_name + """_summary"""

          a_     :  Dict				      =     os.path.join(__UpperCamelCase	,	__UpperCamelCase			)
          with open(__UpperCamelCase	,	"""w"""			) as output:
               output.write(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase			):
     a_      ,      a_      ,      a_     :  List[Any]				      =     translation
     a_     :  str				      =     (
         raw_summary.replace("""[unused0]"""	,	""""""			)
         .replace("""[unused3]"""	,	""""""			)
         .replace("""[PAD]"""	,	""""""			)
         .replace("""[unused1]"""	,	""""""			)
         .replace(r""" +"""	,	""" """			)
         .replace(""" [unused2] """	,	""". """			)
         .replace("""[unused2]"""	,	""""""			)
         .strip()
     )

     return summary



def 			_a      (    __UpperCamelCase			):
     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 			_a      (    __UpperCamelCase			):
     with open("""rouge_scores.txt"""	,	"""w"""			) as output:
          output.write(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Tuple				      =     load_and_cache_examples(__UpperCamelCase	,	__UpperCamelCase			)
     a_     :  Optional[Any]				      =     SequentialSampler(__UpperCamelCase			)

     def collate_fn(__UpperCamelCase			):
          return collate(__UpperCamelCase	,	__UpperCamelCase	,	block_size=5_1_2	,	device=args.device			)

     a_     :  List[str]				      =     DataLoader(
         __UpperCamelCase	,	sampler=__UpperCamelCase	,	batch_size=args.batch_size	,	collate_fn=__UpperCamelCase	,	)

     return iterator



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     CNNDMDataset(args.documents_dir			)
     return dataset



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     [x for x in data if not len(x[1]			) == 0]  # remove empty_files
     a_     :  Optional[Any]				      =     [name for name, _, _ in data]
     a_     :  Any				      =     [""" """.join(__UpperCamelCase			) for _, _, summary_list in data]

     a_     :  Union[str, Any]				      =     [encode_for_summarization(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			) for _, story, summary in data]
     a_     :  List[str]				      =     torch.tensor(
         [truncate_or_pad(__UpperCamelCase	,	__UpperCamelCase	,	tokenizer.pad_token_id			) for story, _ in encoded_text]			)
     a_     :  Tuple				      =     compute_token_type_ids(__UpperCamelCase	,	tokenizer.cls_token_id			)
     a_     :  Any				      =     build_mask(__UpperCamelCase	,	tokenizer.pad_token_id			)

     a_     :  List[Any]				      =     Batch(
         document_names=__UpperCamelCase	,	batch_size=len(__UpperCamelCase			)	,	src=encoded_stories.to(__UpperCamelCase			)	,	segs=encoder_token_type_ids.to(__UpperCamelCase			)	,	mask_src=encoder_mask.to(__UpperCamelCase			)	,	tgt_str=__UpperCamelCase	,	)

     return batch



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  str				      =     summary_tokens.to("""cpu"""			).numpy()
     a_     :  List[Any]				      =     tokenizer.decode(__UpperCamelCase			)
     a_     :  List[Any]				      =     summary.split("""."""			)
     a_     :  int				      =     [s + """.""" for s in sentences]
     return sentences



def 			_a      (    ):
     a_     :  Any				      =     argparse.ArgumentParser()
     parser.add_argument(
         """--documents_dir"""	,	default=__UpperCamelCase	,	type=__UpperCamelCase	,	required=__UpperCamelCase	,	help="""The folder where the documents to summarize are located."""	,	)
     parser.add_argument(
         """--summaries_output_dir"""	,	default=__UpperCamelCase	,	type=__UpperCamelCase	,	required=__UpperCamelCase	,	help="""The folder in wich the summaries should be written. Defaults to the folder where the documents are"""	,	)
     parser.add_argument(
         """--compute_rouge"""	,	default=__UpperCamelCase	,	type=__UpperCamelCase	,	required=__UpperCamelCase	,	help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."""	,	)
     # EVALUATION options
     parser.add_argument(
         """--no_cuda"""	,	default=__UpperCamelCase	,	type=__UpperCamelCase	,	help="""Whether to force the execution on CPU."""	,	)
     parser.add_argument(
         """--batch_size"""	,	default=4	,	type=__UpperCamelCase	,	help="""Batch size per GPU/CPU for training."""	,	)
     # BEAM SEARCH arguments
     parser.add_argument(
         """--min_length"""	,	default=5_0	,	type=__UpperCamelCase	,	help="""Minimum number of tokens for the summaries."""	,	)
     parser.add_argument(
         """--max_length"""	,	default=2_0_0	,	type=__UpperCamelCase	,	help="""Maixmum number of tokens for the summaries."""	,	)
     parser.add_argument(
         """--beam_size"""	,	default=5	,	type=__UpperCamelCase	,	help="""The number of beams to start with for each example."""	,	)
     parser.add_argument(
         """--alpha"""	,	default=0.95	,	type=__UpperCamelCase	,	help="""The value of alpha for the length penalty in the beam search."""	,	)
     parser.add_argument(
         """--block_trigram"""	,	default=__UpperCamelCase	,	type=__UpperCamelCase	,	help="""Whether to block the existence of repeating trigrams in the text generated by beam search."""	,	)
     a_     :  str				      =     parser.parse_args()

     # Select device (distibuted not available)
     a_     :  Any				      =     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_     :  Tuple				      =     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=__UpperCamelCase			)

     evaluate(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase			):
     if not os.path.exists(__UpperCamelCase			):
          return False

     a_     :  Dict				      =     os.listdir(__UpperCamelCase			)
     if len(__UpperCamelCase			) == 0:
          return False

     return True


if __name__ == "__main__":
       main()








import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case=None     )  ->  Any:






      '''simple docstring'''







      UpperCAmelCase_	:						Dict					   =		None
      # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
      if str(weight.dtype     ) == "torch.bfloat16":
            # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
            UpperCAmelCase_	:						Optional[Any]					   =		weight.view(torch.intaa     )
            UpperCAmelCase_	:						Dict					   =		"bfloat16"
      UpperCAmelCase_	:						Optional[int]					   =		weight.cpu().numpy()
      UpperCAmelCase_	:						Tuple					   =		os.path.join(__snake_case       ,	F"""{weight_name}.dat"""     )
      if index is not None:
            if dtype is None:
                  UpperCAmelCase_	:						int					   =		str(array.dtype     )
            UpperCAmelCase_	:						Any					   =		{"dtype": dtype, "shape": list(array.shape     )}
      if array.ndim == 0:
            UpperCAmelCase_	:						List[str]					   =		array[None]
      UpperCAmelCase_	:						List[Any]					   =		np.memmap(__snake_case       ,	dtype=array.dtype       ,	mode="w+"       ,	shape=array.shape     )
      UpperCAmelCase_	:						List[str]					   =		array[:]
      file_array.flush()
      return index







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Any:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		tuple(weight_info["shape"]     )
      if shape == ():
            # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
            UpperCAmelCase_	:						Dict					   =		(1,)

      UpperCAmelCase_	:						Tuple					   =		weight_info["dtype"]
      if dtype == "bfloat16":
            # NumPy does not support bfloat16 so this was saved as a int16
            UpperCAmelCase_	:						Tuple					   =		"int16"

      UpperCAmelCase_	:						Union[str, Any]					   =		np.memmap(__snake_case       ,	dtype=__snake_case       ,	shape=__snake_case       ,	mode="r"     )

      if len(weight_info["shape"]     ) == 0:
            UpperCAmelCase_	:						Optional[Any]					   =		weight[0]
      UpperCAmelCase_	:						List[str]					   =		torch.tensor(__snake_case     )
      if weight_info["dtype"] == "bfloat16":
            UpperCAmelCase_	:						Union[str, Any]					   =		weight.view(torch.bfloataa     )

      return weight







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  List[Any]:






      '''simple docstring'''







      if index is None or len(__snake_case     ) == 0:
            # Nothing to save
            return

      UpperCAmelCase_	:						str					   =		os.path.join(__snake_case       ,	"index.json"     )
      if os.path.isfile(__snake_case     ):
            with open(__snake_case       ,	"r"       ,	encoding="utf-8"     ) as f:
                  UpperCAmelCase_	:						str					   =		json.load(__snake_case     )
      else:
            UpperCAmelCase_	:						Dict					   =		{}
      current_index.update(__snake_case     )

      with open(__snake_case       ,	"w"       ,	encoding="utf-8"     ) as f:
            json.dump(__snake_case       ,	__snake_case       ,	indent=2     )







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Union[str, Any]:






      '''simple docstring'''







      os.makedirs(__snake_case       ,	exist_ok=__snake_case     )
      UpperCAmelCase_	:						Any					   =		{}
      for name, parameter in state_dict.items():
            UpperCAmelCase_	:						Tuple					   =		offload_weight(__snake_case       ,	__snake_case       ,	__snake_case       ,	index=__snake_case     )

      # Update index
      save_offload_index(__snake_case       ,	__snake_case     )


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[Any]					   =		dataset
             UpperCAmelCase_	:						Any					   =		prefix



       def __getitem__(  self  ,lowercase):

             """simple docstring"""





             return self.dataset[F"""{self.prefix}{key}"""]



       def __iter__(  self):

             """simple docstring"""





             return iter([key for key in self.dataset if key.startswith(self.prefix)])



       def __len__(  self):

             """simple docstring"""





             return len(self.dataset)


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       def __init__(  self  ,lowercase = None  ,lowercase = None  ,lowercase = None  ,lowercase=None  ,):

             """simple docstring"""





             if state_dict is None and save_folder is None:
                   raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights.")

             UpperCAmelCase_	:						Tuple					   =		{} if state_dict is None else state_dict
             UpperCAmelCase_	:						List[Any]					   =		save_folder
             if index is None and save_folder is not None:
                   with open(os.path.join(lowercase  ,"index.json")) as f:
                         UpperCAmelCase_	:						Dict					   =		json.load(lowercase)
             UpperCAmelCase_	:						List[str]					   =		{} if index is None else index
             UpperCAmelCase_	:						int					   =		list(self.state_dict.keys())
             self.all_keys.extend([key for key in self.index if key not in self.all_keys])
             UpperCAmelCase_	:						Optional[Any]					   =		device



       def __getitem__(  self  ,lowercase):

             """simple docstring"""





             if key in self.state_dict:
                   return self.state_dict[key]
             UpperCAmelCase_	:						str					   =		self.index[key]
             if weight_info.get("safetensors_file") is not None:
                   if not is_safetensors_available():
                         raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`.")

                   from safetensors import safe_open

                   UpperCAmelCase_	:						Any					   =		"cpu" if self.device is None else self.device
                   with safe_open(weight_info["safetensors_file"]  ,framework="pt"  ,device=lowercase) as f:
                         UpperCAmelCase_	:						int					   =		f.get_tensor(weight_info.get("weight_name"  ,lowercase))

                   if "dtype" in weight_info:
                         return tensor.to(getattr(lowercase  ,weight_info["dtype"]))
                   else:
                         return tensor

             UpperCAmelCase_	:						Optional[int]					   =		os.path.join(self.save_folder  ,F"""{key}.dat""")
             return load_offloaded_weight(lowercase  ,lowercase)



       def __iter__(  self):

             """simple docstring"""





             return iter(self.all_keys)



       def __len__(  self):

             """simple docstring"""





             return len(self.all_keys)







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						str					   =		{}
      for module_name in submodule_names:
            # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
            # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
            result.update(
                {
                    key: param
                    for key, param in state_dict.items()
                    if key == module_name or key.startswith(module_name + "."     )
                }     )
      return result
