
'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


A							     = logging.get_logger(__name__)



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			['pixel_values']

  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Dict[str, int] = None						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Dict[str, int] = None						,			lowerCAmelCase_	:			PILImageResampling = PILImageResampling.BICUBIC						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Union[int, float] = 1 / 2_55						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			**lowerCAmelCase_	:			Optional[Any]						,			) ->							None:
        super().__init__(**lowerCAmelCase_     )
        snake_case_    							=				crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )
        snake_case_    							=				size if size is not None else {"height": 2_24, "width": 2_24}
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )

        snake_case_    							=				do_center_crop
        snake_case_    							=				crop_size
        snake_case_    							=				do_resize
        snake_case_    							=				size
        snake_case_    							=				resample
        snake_case_    							=				do_rescale
        snake_case_    							=				rescale_factor
        snake_case_    							=				do_normalize
        snake_case_    							=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        snake_case_    							=				image_std if image_std is not None else IMAGENET_DEFAULT_STD

  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Dict[str, int]						,			lowerCAmelCase_	:			Optional[int] = None						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			List[Any]						,			) ->							np.ndarray:
        snake_case_    							=				self.size if size is None else size
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )

        snake_case_							,						snake_case_    							=				get_image_size(lowerCAmelCase_     )
        snake_case_    							=				min(lowerCAmelCase_						,			lowerCAmelCase_     )
        snake_case_    							=				(size["height"] / crop_size["height"]) * min_dim
        snake_case_    							=				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowerCAmelCase_						,			size=(cropped_height, cropped_width)						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Dict[str, int]						,			lowerCAmelCase_	:			PILImageResampling = PIL.Image.BICUBIC						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Any						,			) ->							np.ndarray:
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''     )
        return resize(
            lowerCAmelCase_						,			size=(size["height"], size["width"])						,			resample=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Union[int, float]						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							Optional[int]:
        return rescale(lowerCAmelCase_						,			scale=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Union[float, List[float]]						,			lowerCAmelCase_	:			Union[float, List[float]]						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							np.ndarray:
        return normalize(lowerCAmelCase_						,			mean=lowerCAmelCase_						,			std=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			ImageInput						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Dict[str, int]] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Dict[str, int]] = None						,			lowerCAmelCase_	:			PILImageResampling = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[float] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[str, TensorType]] = None						,			lowerCAmelCase_	:			ChannelDimension = ChannelDimension.FIRST						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							PIL.Image.Image:
        snake_case_    							=				do_center_crop if do_center_crop is not None else self.do_center_crop
        snake_case_    							=				crop_size if crop_size is not None else self.crop_size
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )
        snake_case_    							=				do_resize if do_resize is not None else self.do_resize
        snake_case_    							=				size if size is not None else self.size
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        snake_case_    							=				resample if resample is not None else self.resample
        snake_case_    							=				do_rescale if do_rescale is not None else self.do_rescale
        snake_case_    							=				rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case_    							=				do_normalize if do_normalize is not None else self.do_normalize
        snake_case_    							=				image_mean if image_mean is not None else self.image_mean
        snake_case_    							=				image_std if image_std is not None else self.image_std

        snake_case_    							=				make_list_of_images(lowerCAmelCase_     )

        if not valid_images(lowerCAmelCase_     ):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."     )

        if do_center_crop and crop_size is None:
              raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."     )

        if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."     )

        if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."     )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."     )

        # All transformations expect numpy arrays.
        snake_case_    							=				[to_numpy_array(lowerCAmelCase_     ) for image in images]

        if do_center_crop:
              snake_case_    							=				[self.center_crop(lowerCAmelCase_						,			lowerCAmelCase_						,			size=lowerCAmelCase_     ) for image in images]

        if do_resize:
              snake_case_    							=				[self.resize(image=lowerCAmelCase_						,			size=lowerCAmelCase_						,			resample=lowerCAmelCase_     ) for image in images]

        if do_rescale:
              snake_case_    							=				[self.rescale(image=lowerCAmelCase_						,			scale=lowerCAmelCase_     ) for image in images]

        if do_normalize:
              snake_case_    							=				[self.normalize(image=lowerCAmelCase_						,			mean=lowerCAmelCase_						,			std=lowerCAmelCase_     ) for image in images]

        snake_case_    							=				[to_channel_dimension_format(lowerCAmelCase_						,			lowerCAmelCase_     ) for image in images]

        snake_case_    							=				{"pixel_values": images}
        return BatchFeature(data=lowerCAmelCase_						,			tensor_type=lowerCAmelCase_     )



'''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()

