





'''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()








from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Union[str, Any]												=	"prophetnet"
	_lowercase	: Any												=	["past_key_values"]
	_lowercase	: int												=	{
	    "num_attention_heads": "num_encoder_attention_heads",
	}







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[Union[str, Callable]] = "gelu"	,			__UpperCamelCase:			Optional[int] = 3_05_22	,			__UpperCamelCase:			Optional[int] = 10_24	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[int] = 5_12	,			__UpperCamelCase:			Optional[float] = 0.02	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 2	,			__UpperCamelCase:			Optional[int] = 32	,			__UpperCamelCase:			Optional[int] = 1_28	,			__UpperCamelCase:			Optional[bool] = False	,			__UpperCamelCase:			Optional[float] = 0.0	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 1	,			__UpperCamelCase:			Optional[int] = 2	,			**__UpperCamelCase:			Optional[int]	,			):

						'''simple docstring'''





						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     encoder_ffn_dim
						__magic_name__				=     num_encoder_layers
						__magic_name__				=     num_encoder_attention_heads
						__magic_name__				=     decoder_ffn_dim
						__magic_name__				=     num_decoder_layers
						__magic_name__				=     num_decoder_attention_heads
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     init_std  # Normal(0, this parameter)
						__magic_name__				=     activation_function

						# parameters for prophetnet
						__magic_name__				=     ngram
						__magic_name__				=     num_buckets
						__magic_name__				=     relative_max_distance
						__magic_name__				=     disable_ngram_loss
						__magic_name__				=     eps

						# 3 Types of Dropout
						__magic_name__				=     attention_dropout
						__magic_name__				=     activation_dropout
						__magic_name__				=     dropout

						__magic_name__				=     use_cache

						super().__init__(
						    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			add_cross_attention=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			**__UpperCamelCase	,			)







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						return self.num_encoder_layers + self.num_decoder_layers







	@num_hidden_layers.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





						raise NotImplementedError(
						    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
						    ' `num_decoder_layers`.'   )




