





'''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,
)


snake_case_  :							Any            =	logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


snake_case_  :							Dict            =	namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple )			->						Dict:
						UpperCAmelCase_      :   Any      =	BertTokenizer.from_pretrained('''bert-base-uncased''',      do_lower_case=SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''' )
						model.to(args.device )
						model.eval()

						UpperCAmelCase_      :   Optional[int]      =	{
						    '''BOS''': tokenizer.vocab['''[unused0]'''],
						    '''EOS''': tokenizer.vocab['''[unused1]'''],
						    '''PAD''': tokenizer.vocab['''[PAD]'''],
						}

						if args.compute_rouge:
												UpperCAmelCase_      :   Union[str, Any]      =	[]
												UpperCAmelCase_      :   Optional[int]      =	[]

												import nltk
												import rouge

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

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

						UpperCAmelCase_      :   Any      =	build_data_iterator(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Any      =	build_predictor(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						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(SCREAMING_SNAKE_CASE__ ):
												UpperCAmelCase_      :   List[Any]      =	predictor.translate_batch(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Union[str, Any]      =	predictor.from_batch(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Tuple      =	[format_summary(SCREAMING_SNAKE_CASE__ ) for t in translations]
												save_summaries(SCREAMING_SNAKE_CASE__,      args.summaries_output_dir,      batch.document_names )

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

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





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

												UpperCAmelCase_      :   Any      =	os.path.join(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
												with open(SCREAMING_SNAKE_CASE__,      '''w''' ) as output:
																		output.write(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Optional[int]:
						UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   int      =	translation
						UpperCAmelCase_      :   Any      =	(
						    raw_summary.replace('''[unused0]''',      '''''' )
						    .replace('''[unused3]''',      '''''' )
						    .replace('''[PAD]''',      '''''' )
						    .replace('''[unused1]''',      '''''' )
						    .replace(R''' +''',      ''' ''' )
						    .replace(''' [unused2] ''',      '''. ''' )
						    .replace('''[unused2]''',      '''''' )
						    .strip()
						)

						return summary





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str )			->						Any:
						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 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any )			->						Tuple:
						with open('''rouge_scores.txt''',      '''w''' ) as output:
												output.write(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Optional[int]:
						UpperCAmelCase_      :   Dict      =	load_and_cache_examples(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Any      =	SequentialSampler(SCREAMING_SNAKE_CASE__ )

						def collate_fn(SCREAMING_SNAKE_CASE__   :      str ):
												return collate(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      block_size=512,      device=args.device )

						UpperCAmelCase_      :   Any      =	DataLoader(
						    SCREAMING_SNAKE_CASE__,      sampler=SCREAMING_SNAKE_CASE__,      batch_size=args.batch_size,      collate_fn=SCREAMING_SNAKE_CASE__,      )

						return iterator





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						str:
						UpperCAmelCase_      :   List[Any]      =	CNNDMDataset(args.documents_dir )
						return dataset





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      int )			->						Optional[int]:
						UpperCAmelCase_      :   Optional[int]      =	[x for x in data if not len(x[1] ) == 0]  # remove empty_files
						UpperCAmelCase_      :   Optional[int]      =	[name for name, _, _ in data]
						UpperCAmelCase_      :   List[Any]      =	[''' '''.join(SCREAMING_SNAKE_CASE__ ) for _, _, summary_list in data]

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

						UpperCAmelCase_      :   Optional[int]      =	Batch(
						    document_names=SCREAMING_SNAKE_CASE__,      batch_size=len(SCREAMING_SNAKE_CASE__ ),      src=encoded_stories.to(SCREAMING_SNAKE_CASE__ ),      segs=encoder_token_type_ids.to(SCREAMING_SNAKE_CASE__ ),      mask_src=encoder_mask.to(SCREAMING_SNAKE_CASE__ ),      tgt_str=SCREAMING_SNAKE_CASE__,      )

						return batch





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Union[str, Any]:
						UpperCAmelCase_      :   Dict      =	summary_tokens.to('''cpu''' ).numpy()
						UpperCAmelCase_      :   Optional[int]      =	tokenizer.decode(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	summary.split('''.''' )
						UpperCAmelCase_      :   Dict      =	[s + '''.''' for s in sentences]
						return sentences





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

						# Select device (distibuted not available)
						UpperCAmelCase_      :   int      =	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:
												UpperCAmelCase_      :   Optional[Any]      =	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=SCREAMING_SNAKE_CASE__ )

						evaluate(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Optional[Any]:
						if not os.path.exists(SCREAMING_SNAKE_CASE__ ):
												return False

						UpperCAmelCase_      :   str      =	os.listdir(SCREAMING_SNAKE_CASE__ )
						if len(SCREAMING_SNAKE_CASE__ ) == 0:
												return False

						return True


if __name__ == "__main__":
					main()












'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case_  :							Any            =	logging.get_logger(__name__)


if is_vision_available():
					import PIL


class        __a   (lowerCamelCase  ):
							__a       : Any          =							["pixel_values"]

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  bool = True							,  __magic_name__				:  Union[int, float] = 1 / 2_55							,  __magic_name__				:  bool = True							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = True							,  **__magic_name__				:  Union[str, Any]							,  )       ->							None:







													"""simple docstring"""
													super().__init__(**__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	size if size is not None else {'''shortest_edge''': 2_24}
													UpperCAmelCase_      :   List[str]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   int      =	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__ )

													UpperCAmelCase_      :   str      =	do_resize
													UpperCAmelCase_      :   Optional[Any]      =	size
													UpperCAmelCase_      :   int      =	resample
													UpperCAmelCase_      :   List[str]      =	do_center_crop
													UpperCAmelCase_      :   Optional[Any]      =	crop_size
													UpperCAmelCase_      :   List[str]      =	do_rescale
													UpperCAmelCase_      :   Union[str, Any]      =	rescale_factor
													UpperCAmelCase_      :   int      =	do_normalize
													UpperCAmelCase_      :   Any      =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													UpperCAmelCase_      :   Optional[Any]      =	image_std if image_std is not None else OPENAI_CLIP_STD
													UpperCAmelCase_      :   int      =	do_convert_rgb

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   Any      =	get_resize_output_image_size(
													    __magic_name__							,  size=(size['''height'''], size['''width'''])							,  default_to_square=__magic_name__ )
													return resize(__magic_name__							,  size=__magic_name__							,  resample=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	get_size_dict(__magic_name__ )
													return center_crop(__magic_name__							,  size=(size['''height'''], size['''width'''])							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[int, float]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							Union[str, Any]:







													"""simple docstring"""
													return rescale(__magic_name__							,  scale=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													return normalize(__magic_name__							,  mean=__magic_name__							,  std=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )



							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  ImageInput							,  __magic_name__				:  bool = None							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  int = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  float = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[str, TensorType]] = None							,  __magic_name__				:  Optional[ChannelDimension] = ChannelDimension.FIRST							,  **__magic_name__				:  Union[str, Any]							,  )       ->							PIL.Image.Image:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_      :   Dict      =	size if size is not None else self.size
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   str      =	resample if resample is not None else self.resample
													UpperCAmelCase_      :   Tuple      =	do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_      :   List[str]      =	crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__ )
													UpperCAmelCase_      :   str      =	do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_      :   Tuple      =	rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_      :   List[Any]      =	do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_      :   Tuple      =	image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_      :   int      =	image_std if image_std is not None else self.image_std
													UpperCAmelCase_      :   Optional[Any]      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													UpperCAmelCase_      :   Any      =	make_list_of_images(__magic_name__ )

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

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

													if do_center_crop and crop_size is None:
																			raise ValueError('''Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.''' )

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			UpperCAmelCase_      :   Optional[int]      =	[convert_to_rgb(__magic_name__ ) for image in images]

													# All transformations expect numpy arrays.
													UpperCAmelCase_      :   Optional[Any]      =	[to_numpy_array(__magic_name__ ) for image in images]

													if do_resize:
																			UpperCAmelCase_      :   Dict      =	[self.resize(image=__magic_name__							,  size=__magic_name__							,  resample=__magic_name__ ) for image in images]

													if do_center_crop:
																			UpperCAmelCase_      :   Union[str, Any]      =	[self.center_crop(image=__magic_name__							,  size=__magic_name__ ) for image in images]

													if do_rescale:
																			UpperCAmelCase_      :   Dict      =	[self.rescale(image=__magic_name__							,  scale=__magic_name__ ) for image in images]

													if do_normalize:
																			UpperCAmelCase_      :   Optional[int]      =	[self.normalize(image=__magic_name__							,  mean=__magic_name__							,  std=__magic_name__ ) for image in images]

													UpperCAmelCase_      :   Optional[Any]      =	[to_channel_dimension_format(__magic_name__							,  __magic_name__ ) for image in images]

													UpperCAmelCase_      :   int      =	{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__							,  tensor_type=__magic_name__ )





