
'''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 	snake_case_							(				_a  :				str      ):
      snake_case_    							=				BertTokenizer.from_pretrained("bert-base-uncased"			,       do_lower_case=_a      )
      snake_case_    							=				BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"      )
      model.to(args.device      )
      model.eval()

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

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

            import nltk
            import rouge

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

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

      snake_case_    							=				build_data_iterator(_a			,       _a      )
      snake_case_    							=				build_predictor(_a			,       _a			,       _a			,       _a      )

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

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

      if args.compute_rouge:
            snake_case_    							=				rouge_evaluator.get_scores(_a			,       _a      )
            snake_case_    							=				format_rouge_scores(_a      )
            save_rouge_scores(_a      )
            print(_a      )
def 	snake_case_							(				_a  :				List[Any]			,       _a  :				List[str]			,       _a  :				Any      ):
      for summary, document_name in zip(_a			,       _a      ):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case_    							=				".".join(document_name.split("."      )[:-1]      )
                  snake_case_    							=				document_name.split("."      )[-1]
                  snake_case_    							=				bare_document_name + "_summary." + extension
            else:
                  snake_case_    							=				document_name + "_summary"

            snake_case_    							=				os.path.join(_a			,       _a      )
            with open(_a			,       "w"      ) as output:
                  output.write(_a      )
def 	snake_case_							(				_a  :				Optional[int]      ):
      snake_case_							,						snake_case_							,						snake_case_    							=				translation
      snake_case_    							=				(
          raw_summary.replace("[unused0]"			,       ""      )
          .replace("[unused3]"			,       ""      )
          .replace("[PAD]"			,       ""      )
          .replace("[unused1]"			,       ""      )
          .replace(R" +"			,       " "      )
          .replace(" [unused2] "			,       ". "      )
          .replace("[unused2]"			,       ""      )
          .strip()
      )

      return summary
def 	snake_case_							(				_a  :				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 	snake_case_							(				_a  :				str      ):
      with open("rouge_scores.txt"			,       "w"      ) as output:
            output.write(_a      )
def 	snake_case_							(				_a  :				Tuple			,       _a  :				Union[str, Any]      ):
      snake_case_    							=				load_and_cache_examples(_a			,       _a      )
      snake_case_    							=				SequentialSampler(_a      )

      def collate_fn(_a  :				Tuple      ):
            return collate(_a			,       _a			,       block_size=512			,       device=args.device      )

      snake_case_    							=				DataLoader(
          _a			,       sampler=_a			,       batch_size=args.batch_size			,       collate_fn=_a			,       )

      return iterator
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
      snake_case_    							=				CNNDMDataset(args.documents_dir      )
      return dataset
def 	snake_case_							(				_a  :				int			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				[x for x in data if not len(x[1]      ) == 0]  # remove empty_files
      snake_case_    							=				[name for name, _, _ in data]
      snake_case_    							=				[" ".join(_a      ) for _, _, summary_list in data]

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

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

      return batch
def 	snake_case_							(				_a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				summary_tokens.to("cpu"      ).numpy()
      snake_case_    							=				tokenizer.decode(_a      )
      snake_case_    							=				summary.split("."      )
      snake_case_    							=				[s + "." for s in sentences]
      return sentences
def 	snake_case_							(				):
      snake_case_    							=				argparse.ArgumentParser()
      parser.add_argument(
          "--documents_dir"			,       default=_a			,       type=_a			,       required=_a			,       help="The folder where the documents to summarize are located."			,       )
      parser.add_argument(
          "--summaries_output_dir"			,       default=_a			,       type=_a			,       required=_a			,       help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"			,       )
      parser.add_argument(
          "--compute_rouge"			,       default=_a			,       type=_a			,       required=_a			,       help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."			,       )
      # EVALUATION options
      parser.add_argument(
          "--no_cuda"			,       default=_a			,       type=_a			,       help="Whether to force the execution on CPU."			,       )
      parser.add_argument(
          "--batch_size"			,       default=4			,       type=_a			,       help="Batch size per GPU/CPU for training."			,       )
      # BEAM SEARCH arguments
      parser.add_argument(
          "--min_length"			,       default=50			,       type=_a			,       help="Minimum number of tokens for the summaries."			,       )
      parser.add_argument(
          "--max_length"			,       default=200			,       type=_a			,       help="Maixmum number of tokens for the summaries."			,       )
      parser.add_argument(
          "--beam_size"			,       default=5			,       type=_a			,       help="The number of beams to start with for each example."			,       )
      parser.add_argument(
          "--alpha"			,       default=0.9_5			,       type=_a			,       help="The value of alpha for the length penalty in the beam search."			,       )
      parser.add_argument(
          "--block_trigram"			,       default=_a			,       type=_a			,       help="Whether to block the existence of repeating trigrams in the text generated by beam search."			,       )
      snake_case_    							=				parser.parse_args()

      # Select device (distibuted not available)
      snake_case_    							=				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:
            snake_case_    							=				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=_a      )

      evaluate(_a      )
def 	snake_case_							(				_a  :				Optional[int]      ):
      if not os.path.exists(_a      ):
            return False

      snake_case_    							=				os.listdir(_a      )
      if len(_a      ) == 0:
            return False

      return True


if __name__ == "__main__":
     main()



"""simple docstring"""


import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__A					=			logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__A					=			direct_transformers_import(Path(__file__).parent)


__A					=			{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						['''feature_extractor''', '''tokenizer''']
			# Names need to be attr_class for attr in attributes
			lowerCamelCase       =						None
			lowerCamelCase       =						None
			lowerCamelCase       =						None



			def __init__(				self	,	*__UpperCAmelCase	,	**__UpperCAmelCase    )					->	Dict:
				# Sanitize args and kwargs
				for key in kwargs:
					if key not in self.attributes:
						raise TypeError(f'''Unexpected keyword argument {key}.'''    )
				for arg, attribute_name in zip(__UpperCAmelCase	,	self.attributes    ):
					if attribute_name in kwargs:
						raise TypeError(f'''Got multiple values for argument {attribute_name}.'''    )
					else:
						_lowerCAmelCase								=arg

				if len(__UpperCAmelCase    ) != len(self.attributes    ):
					raise ValueError(
					    f'''This processor requires {len(self.attributes    )} arguments: {', '.join(self.attributes    )}. Got '''
					    f'''{len(__UpperCAmelCase    )} arguments instead.'''    )

				# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
				for attribute_name, arg in kwargs.items():
					_lowerCAmelCase								=getattr(self	,	f'''{attribute_name}_class'''    )
					# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
					_lowerCAmelCase								=AUTO_TO_BASE_CLASS_MAPPING.get(__UpperCAmelCase	,	__UpperCAmelCase    )
					if isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ):
						_lowerCAmelCase								=tuple(getattr(__UpperCAmelCase	,	__UpperCAmelCase    ) for n in class_name if n is not None    )
					else:
						_lowerCAmelCase								=getattr(__UpperCAmelCase	,	__UpperCAmelCase    )

					if not isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ):
						raise ValueError(
						    f'''Received a {type(__UpperCAmelCase    ).__name__} for argument {attribute_name}, but a {class_name} was expected.'''    )

					setattr(self	,	__UpperCAmelCase	,	__UpperCAmelCase    )



			def __repr__(				self    )					->	Optional[int]:
				_lowerCAmelCase								=[f'''- {name}: {repr(getattr(self	,	__UpperCAmelCase    )    )}''' for name in self.attributes]
				_lowerCAmelCase								="""\n""".join(__UpperCAmelCase    )
				return f'''{self.__class__.__name__}:\n{attributes_repr}'''



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase = False	,	**__UpperCAmelCase    )					->	Union[str, Any]:
				os.makedirs(__UpperCAmelCase	,	exist_ok=__UpperCAmelCase    )

				if push_to_hub:
					_lowerCAmelCase								=kwargs.pop("""commit_message"""	,	__UpperCAmelCase    )
					_lowerCAmelCase								=kwargs.pop("""repo_id"""	,	save_directory.split(os.path.sep    )[-1]    )
					_lowerCAmelCase								=self._create_repo(__UpperCAmelCase	,	**__UpperCAmelCase    )
					_lowerCAmelCase								=self._get_files_timestamps(__UpperCAmelCase    )
				# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
				# loaded from the Hub.
				if self._auto_class is not None:
					_lowerCAmelCase								=[getattr(self	,	__UpperCAmelCase    ) for attribute_name in self.attributes]
					_lowerCAmelCase								=[(a.init_kwargs if isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ) else a) for a in attrs]
					custom_object_save(self	,	__UpperCAmelCase	,	config=__UpperCAmelCase    )

				for attribute_name in self.attributes:
					_lowerCAmelCase								=getattr(self	,	__UpperCAmelCase    )
					# Include the processor class in the attribute config so this processor can then be reloaded with the
					# `AutoProcessor` API.
					if hasattr(__UpperCAmelCase	,	"""_set_processor_class"""    ):
						attribute._set_processor_class(self.__class__.__name__    )
					attribute.save_pretrained(__UpperCAmelCase    )

				if self._auto_class is not None:
					# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
					for attribute_name in self.attributes:
						_lowerCAmelCase								=getattr(self	,	__UpperCAmelCase    )
						if isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ):
							del attribute.init_kwargs["auto_map"]

				if push_to_hub:
					self._upload_modified_files(
					    __UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	commit_message=__UpperCAmelCase	,	token=kwargs.get("""use_auth_token"""    )	,	)



			@classmethod
			def    _lowerCAmelCase						(				cls	,	__UpperCAmelCase	,	__UpperCAmelCase = None	,	__UpperCAmelCase = False	,	__UpperCAmelCase = False	,	__UpperCAmelCase = None	,	__UpperCAmelCase = "main"	,	**__UpperCAmelCase	,	)					->	Any:
				_lowerCAmelCase								=cache_dir
				_lowerCAmelCase								=force_download
				_lowerCAmelCase								=local_files_only
				_lowerCAmelCase								=revision

				_lowerCAmelCase								=kwargs.pop("""use_auth_token"""	,	__UpperCAmelCase    )
				if use_auth_token is not None:
					warnings.warn(
					    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""	,	__UpperCAmelCase    )
					if token is not None:
						raise ValueError(
						    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""    )
					_lowerCAmelCase								=use_auth_token

				if token is not None:
					# change to `token` in a follow-up PR
					_lowerCAmelCase								=token

				_lowerCAmelCase								=cls._get_arguments_from_pretrained(__UpperCAmelCase	,	**__UpperCAmelCase    )
				return cls(*__UpperCAmelCase    )



			@classmethod
			def    _lowerCAmelCase						(				cls	,	__UpperCAmelCase="AutoProcessor"    )					->	Optional[int]:
				if not isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ):
					_lowerCAmelCase								=auto_class.__name__

				import transformers.models.auto as auto_module

				if not hasattr(__UpperCAmelCase	,	__UpperCAmelCase    ):
					raise ValueError(f'''{auto_class} is not a valid auto class.'''    )

				_lowerCAmelCase								=auto_class



			@classmethod
			def    _lowerCAmelCase						(				cls	,	__UpperCAmelCase	,	**__UpperCAmelCase    )					->	Optional[Any]:
				_lowerCAmelCase								=[]
				for attribute_name in cls.attributes:
					_lowerCAmelCase								=getattr(cls	,	f'''{attribute_name}_class'''    )
					if isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ):
						_lowerCAmelCase								=tuple(getattr(__UpperCAmelCase	,	__UpperCAmelCase    ) if n is not None else None for n in class_name    )
						_lowerCAmelCase								=kwargs.get("""use_fast"""	,	__UpperCAmelCase    )
						if use_fast and classes[1] is not None:
							_lowerCAmelCase								=classes[1]
						else:
							_lowerCAmelCase								=classes[0]
					else:
						_lowerCAmelCase								=getattr(__UpperCAmelCase	,	__UpperCAmelCase    )

					args.append(attribute_class.from_pretrained(__UpperCAmelCase	,	**__UpperCAmelCase    )    )
				return args



			@property
			def    _lowerCAmelCase						(				self    )					->	Optional[int]:
				_lowerCAmelCase								=getattr(self	,	self.attributes[0]    )
				return getattr(__UpperCAmelCase	,	"""model_input_names"""	,	__UpperCAmelCase    )







__A					=			copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
			__A					=			ProcessorMixin.push_to_hub.__doc__.format(
			    object='processor', object_class='AutoProcessor', object_files='processor files'
			)



