



'''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,
)


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


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


def   lowercase_	(   lowerCAmelCase__	:  str ):

			"""simple docstring"""





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

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

			if args.compute_rouge:
						__UpperCAmelCase   :      Dict										=     []
						__UpperCAmelCase   :      List[Any]										=     []

						import nltk
						import rouge

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

			# these (unused) arguments are defined to keep the compatibility
			# with the legacy code and will be deleted in a next iteration.
			__UpperCAmelCase   :      Any										=     """"""
			__UpperCAmelCase   :      Optional[int]										=     """"""

			__UpperCAmelCase   :      Optional[int]										=     build_data_iterator(lowerCAmelCase__			,							lowerCAmelCase__ )
			__UpperCAmelCase   :      List[str]										=     build_predictor(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )

			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(lowerCAmelCase__ ):
						__UpperCAmelCase   :      Dict										=     predictor.translate_batch(lowerCAmelCase__ )
						__UpperCAmelCase   :      List[str]										=     predictor.from_batch(lowerCAmelCase__ )
						__UpperCAmelCase   :      Dict										=     [format_summary(lowerCAmelCase__ ) for t in translations]
						save_summaries(lowerCAmelCase__			,							args.summaries_output_dir			,							batch.document_names )

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

			if args.compute_rouge:
						__UpperCAmelCase   :      List[str]										=     rouge_evaluator.get_scores(lowerCAmelCase__			,							lowerCAmelCase__ )
						__UpperCAmelCase   :      Optional[int]										=     format_rouge_scores(lowerCAmelCase__ )
						save_rouge_scores(lowerCAmelCase__ )
						print(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  Union[str, Any]			,							lowerCAmelCase__	:  List[Any] ):

			"""simple docstring"""





			for summary, document_name in zip(lowerCAmelCase__			,							lowerCAmelCase__ ):
						# Prepare the summary file's name
						if "." in document_name:
									__UpperCAmelCase   :      Tuple										=     """.""".join(document_name.split(""".""" )[:-1] )
									__UpperCAmelCase   :      Optional[int]										=     document_name.split(""".""" )[-1]
									__UpperCAmelCase   :      List[str]										=     bare_document_name + """_summary.""" + extension
						else:
									__UpperCAmelCase   :      List[Any]										=     document_name + """_summary"""

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


def   lowercase_	(   lowerCAmelCase__	:  str ):

			"""simple docstring"""





			__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Optional[int]										=     translation
			__UpperCAmelCase   :      Optional[Any]										=     (
			    raw_summary.replace("""[unused0]"""			,							"""""" )
			    .replace("""[unused3]"""			,							"""""" )
			    .replace("""[PAD]"""			,							"""""" )
			    .replace("""[unused1]"""			,							"""""" )
			    .replace(r""" +"""			,							""" """ )
			    .replace(""" [unused2] """			,							""". """ )
			    .replace("""[unused2]"""			,							"""""" )
			    .strip()
			)

			return summary


def   lowercase_	(   lowerCAmelCase__	:  Optional[int] ):

			"""simple docstring"""





			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   lowercase_	(   lowerCAmelCase__	:  List[str] ):

			"""simple docstring"""





			with open("""rouge_scores.txt"""			,							"""w""" ) as output:
						output.write(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  Optional[Any]			,							lowerCAmelCase__	:  Dict ):

			"""simple docstring"""





			__UpperCAmelCase   :      Dict										=     load_and_cache_examples(lowerCAmelCase__			,							lowerCAmelCase__ )
			__UpperCAmelCase   :      str										=     SequentialSampler(lowerCAmelCase__ )

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

			__UpperCAmelCase   :      Any										=     DataLoader(
			    lowerCAmelCase__			,							sampler=lowerCAmelCase__			,							batch_size=args.batch_size			,							collate_fn=lowerCAmelCase__			,							)

			return iterator


def   lowercase_	(   lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  Optional[Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Union[str, Any]										=     CNNDMDataset(args.documents_dir )
			return dataset


def   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  List[str] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Optional[Any]										=     [x for x in data if not len(x[1] ) == 0]  # remove empty_files
			__UpperCAmelCase   :      Optional[Any]										=     [name for name, _, _ in data]
			__UpperCAmelCase   :      List[Any]										=     [""" """.join(lowerCAmelCase__ ) for _, _, summary_list in data]

			__UpperCAmelCase   :      str										=     [encode_for_summarization(lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ ) for _, story, summary in data]
			__UpperCAmelCase   :      Any										=     torch.tensor(
			    [truncate_or_pad(lowerCAmelCase__			,							lowerCAmelCase__			,							tokenizer.pad_token_id ) for story, _ in encoded_text] )
			__UpperCAmelCase   :      Optional[Any]										=     compute_token_type_ids(lowerCAmelCase__			,							tokenizer.cls_token_id )
			__UpperCAmelCase   :      Union[str, Any]										=     build_mask(lowerCAmelCase__			,							tokenizer.pad_token_id )

			__UpperCAmelCase   :      List[str]										=     Batch(
			    document_names=lowerCAmelCase__			,							batch_size=len(lowerCAmelCase__ )			,							src=encoded_stories.to(lowerCAmelCase__ )			,							segs=encoder_token_type_ids.to(lowerCAmelCase__ )			,							mask_src=encoder_mask.to(lowerCAmelCase__ )			,							tgt_str=lowerCAmelCase__			,							)

			return batch


def   lowercase_	(   lowerCAmelCase__	:  str			,							lowerCAmelCase__	:  List[Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      int										=     summary_tokens.to("""cpu""" ).numpy()
			__UpperCAmelCase   :      Dict										=     tokenizer.decode(lowerCAmelCase__ )
			__UpperCAmelCase   :      Optional[int]										=     summary.split(""".""" )
			__UpperCAmelCase   :      Optional[int]										=     [s + """.""" for s in sentences]
			return sentences


def   lowercase_	(   ):

			"""simple docstring"""





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

			# Select device (distibuted not available)
			__UpperCAmelCase   :      Optional[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:
						__UpperCAmelCase   :      int										=     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=lowerCAmelCase__ )

			evaluate(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  List[str] ):

			"""simple docstring"""





			if not os.path.exists(lowerCAmelCase__ ):
						return False

			__UpperCAmelCase   :      Any										=     os.listdir(lowerCAmelCase__ )
			if len(lowerCAmelCase__ ) == 0:
						return False

			return True


if __name__ == "__main__":
					main()









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     vocab_size
					__UpperCAmelCase   :      Optional[int]										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     hidden_act
					__UpperCAmelCase   :      List[Any]										=     intermediate_size
					__UpperCAmelCase   :      Optional[Any]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Dict										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      int										=     max_position_embeddings
					__UpperCAmelCase   :      str										=     type_vocab_size
					__UpperCAmelCase   :      int										=     task_type_vocab_size
					__UpperCAmelCase   :      List[str]										=     use_task_id
					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      List[str]										=     position_embedding_type
					__UpperCAmelCase   :      Union[str, Any]										=     use_cache
					__UpperCAmelCase   :      Optional[int]										=     classifier_dropout



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase   :      Union[str, Any]										=     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					        ("""task_type_ids""", dynamic_axis),
					    ]				)




