
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__				:      Union[str, Any]    		=     logging.get_logger(__name__)

a__				:      str    		=     {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}






class     a_       (			a__							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  'bigbird_pegasus'
       __SCREAMING_SNAKE_CASE       :					Optional[Any]						=  ['past_key_values']
       __SCREAMING_SNAKE_CASE       :					List[str]						=  {
           'num_attention_heads': 'encoder_attention_heads',
           'hidden_size': 'd_model',
           'attention_probs_dropout_prob': 'attention_dropout',
       }






       def __init__(		self				,       _lowerCamelCase=9_6103				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=16				,       _lowerCamelCase=4096				,       _lowerCamelCase=16				,       _lowerCamelCase=0.0				,       _lowerCamelCase=0.0				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase="gelu_new"				,       _lowerCamelCase=1024				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.0				,       _lowerCamelCase=0.0				,       _lowerCamelCase=0.0_2				,       _lowerCamelCase=2				,       _lowerCamelCase=0.0				,       _lowerCamelCase=True				,       _lowerCamelCase=0				,       _lowerCamelCase=2				,       _lowerCamelCase=1				,       _lowerCamelCase="block_sparse"				,       _lowerCamelCase=64				,       _lowerCamelCase=3				,       _lowerCamelCase=False				,       **_lowerCamelCase				,       )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				Any				      = vocab_size
           SCREAMING_SNAKE_CASE				:				Any				      = max_position_embeddings
           SCREAMING_SNAKE_CASE				:				str				      = d_model
           SCREAMING_SNAKE_CASE				:				str				      = encoder_ffn_dim
           SCREAMING_SNAKE_CASE				:				List[Any]				      = encoder_layers
           SCREAMING_SNAKE_CASE				:				Dict				      = encoder_attention_heads
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = decoder_ffn_dim
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = decoder_layers
           SCREAMING_SNAKE_CASE				:				int				      = decoder_attention_heads
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = dropout
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = attention_dropout
           SCREAMING_SNAKE_CASE				:				Dict				      = activation_dropout
           SCREAMING_SNAKE_CASE				:				str				      = activation_function
           SCREAMING_SNAKE_CASE				:				str				      = init_std
           SCREAMING_SNAKE_CASE				:				int				      = encoder_layerdrop
           SCREAMING_SNAKE_CASE				:				Any				      = decoder_layerdrop
           SCREAMING_SNAKE_CASE				:				Dict				      = classifier_dropout
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = use_cache
           SCREAMING_SNAKE_CASE				:				List[str]				      = encoder_layers
           SCREAMING_SNAKE_CASE				:				Any				      = scale_embedding  # scale factor will be sqrt(d_model) if True

           # extra config
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = attention_type
           SCREAMING_SNAKE_CASE				:				str				      = block_size
           SCREAMING_SNAKE_CASE				:				List[Any]				      = num_random_blocks
           SCREAMING_SNAKE_CASE				:				Dict				      = use_bias

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






class     a_       (			a__							):



       """simple docstring"""






       @property
       def 	__lowerCAmelCase     (		self   )					->Mapping[str, Mapping[int, str]]:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				str				      = OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ]   )

               if self.use_past:
                   SCREAMING_SNAKE_CASE				:				Any				      = {0: '''batch'''}
                   SCREAMING_SNAKE_CASE				:				str				      = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                   SCREAMING_SNAKE_CASE				:				Any				      = {0: '''batch''', 1: '''decoder_sequence'''}
                   SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {0: '''batch''', 1: '''decoder_sequence'''}

               if self.use_past:
                   self.fill_with_past_key_values_(_lowerCamelCase				,       direction='''inputs'''   )
           elif self.task == "causal-lm":
               # TODO: figure this case out.
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ]   )
               if self.use_past:
                   SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = self.num_layers
                   for i in range(_lowerCamelCase   ):
                       SCREAMING_SNAKE_CASE				:				Optional[int]				      = {0: '''batch''', 2: '''past_sequence + sequence'''}
                       SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
               SCREAMING_SNAKE_CASE				:				Tuple				      = OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                       ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ]   )

           return common_inputs






       @property
       def 	__lowerCAmelCase     (		self   )					->Mapping[str, Mapping[int, str]]:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = super().outputs
           else:
               SCREAMING_SNAKE_CASE				:				Dict				      = super(_lowerCamelCase				,       self   ).outputs
               if self.use_past:
                   SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.num_layers
                   for i in range(_lowerCamelCase   ):
                       SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {0: '''batch''', 2: '''past_sequence + sequence'''}
                       SCREAMING_SNAKE_CASE				:				Optional[int]				      = {0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Mapping[str, Any]:
           SCREAMING_SNAKE_CASE				:				int				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )

           # Generate decoder inputs
           SCREAMING_SNAKE_CASE				:				List[Any]				      = seq_length if not self.use_past else 1
           SCREAMING_SNAKE_CASE				:				str				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = dict(**_lowerCamelCase				,       **_lowerCamelCase   )

           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
               else:
                   import torch
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = common_inputs['''input_ids'''].shape
               SCREAMING_SNAKE_CASE				:				List[Any]				      = common_inputs['''decoder_input_ids'''].shape[1]
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				int				      = self.num_attention_heads
               SCREAMING_SNAKE_CASE				:				Dict				      = (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               SCREAMING_SNAKE_CASE				:				int				      = decoder_seq_length + 3
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               SCREAMING_SNAKE_CASE				:				Tuple				      = torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase				,       _lowerCamelCase   )]				,       dim=1   )

               SCREAMING_SNAKE_CASE				:				List[Any]				      = []
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Tuple				      = self.num_layers
               SCREAMING_SNAKE_CASE				:				int				      = min(_lowerCamelCase				,       _lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = max(_lowerCamelCase				,       _lowerCamelCase   ) - min_num_layers
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

               for _ in range(_lowerCamelCase   ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_lowerCamelCase   ),
                           torch.zeros(_lowerCamelCase   ),
                           torch.zeros(_lowerCamelCase   ),
                           torch.zeros(_lowerCamelCase   ),
                       )   )
               # TODO: test this.
               SCREAMING_SNAKE_CASE				:				List[str]				      = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(_lowerCamelCase				,       _lowerCamelCase   ):
                   common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase   ), torch.zeros(_lowerCamelCase   ))   )
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Mapping[str, Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )

           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
               else:
                   import torch
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               SCREAMING_SNAKE_CASE				:				Dict				      = seqlen + 2
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = self.num_layers
               SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = self.num_attention_heads
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               SCREAMING_SNAKE_CASE				:				List[Any]				      = common_inputs['''attention_mask'''].dtype
               SCREAMING_SNAKE_CASE				:				List[str]				      = torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase				,       _lowerCamelCase				,       dtype=_lowerCamelCase   )]				,       dim=1   )
               SCREAMING_SNAKE_CASE				:				List[str]				      = [
                   (torch.zeros(_lowerCamelCase   ), torch.zeros(_lowerCamelCase   )) for _ in range(_lowerCamelCase   )
               ]
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Mapping[str, Any]:
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           SCREAMING_SNAKE_CASE				:				Dict				      = compute_effective_axis_dimension(
               _lowerCamelCase				,       fixed_dimension=OnnxConfig.default_fixed_batch				,       num_token_to_add=0   )

           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           SCREAMING_SNAKE_CASE				:				List[Any]				      = tokenizer.num_special_tokens_to_add(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = compute_effective_axis_dimension(
               _lowerCamelCase				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=_lowerCamelCase   )

           # Generate dummy inputs according to compute batch and sequence
           SCREAMING_SNAKE_CASE				:				str				      = [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
           SCREAMING_SNAKE_CASE				:				List[str]				      = dict(tokenizer(_lowerCamelCase				,       return_tensors=_lowerCamelCase   )   )
           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase = -1				,       _lowerCamelCase = -1				,       _lowerCamelCase = False				,       _lowerCamelCase = None				,       )					->Mapping[str, Any]:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				List[Any]				      = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   _lowerCamelCase				,       batch_size=_lowerCamelCase				,       seq_length=_lowerCamelCase				,       is_pair=_lowerCamelCase				,       framework=_lowerCamelCase   )

           elif self.task == "causal-lm":
               SCREAMING_SNAKE_CASE				:				int				      = self._generate_dummy_inputs_for_causal_lm(
                   _lowerCamelCase				,       batch_size=_lowerCamelCase				,       seq_length=_lowerCamelCase				,       is_pair=_lowerCamelCase				,       framework=_lowerCamelCase   )
           else:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   _lowerCamelCase				,       batch_size=_lowerCamelCase				,       seq_length=_lowerCamelCase				,       is_pair=_lowerCamelCase				,       framework=_lowerCamelCase   )

           return common_inputs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[Any]:
           if self.task in ["default", "seq2seq-lm"]:
               SCREAMING_SNAKE_CASE				:				Any				      = super()._flatten_past_key_values_(_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )
           else:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = super(_lowerCamelCase				,       self   )._flatten_past_key_values_(
                   _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )


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


a__				:      Optional[int]    		=     namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = BertTokenizer.from_pretrained('''bert-base-uncased''' ,						do_lower_case=a__   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''   )
    model.to(args.device   )
    model.eval()

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

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
        SCREAMING_SNAKE_CASE				:				Dict				      = []

        import nltk
        import rouge

        nltk.download('''punkt'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = 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.
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ''''''
    SCREAMING_SNAKE_CASE				:				Tuple				      = ''''''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = build_data_iterator(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = 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__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = predictor.translate_batch(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = predictor.from_batch(a__   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = [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:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = rouge_evaluator.get_scores(a__ ,						a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = format_rouge_scores(a__   )
        save_rouge_scores(a__   )
        print(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



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

        SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(a__ ,						a__   )
        with open(a__ ,						'''w'''   ) as output:
            output.write(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    return summary







def 				UpperCAmelCase_(    a__   ):



    """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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    with open('''rouge_scores.txt''' ,						'''w'''   ) as output:
        output.write(a__   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = load_and_cache_examples(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = SequentialSampler(a__   )

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

    SCREAMING_SNAKE_CASE				:				Dict				      = DataLoader(
        a__ ,						sampler=a__ ,						batch_size=args.batch_size ,						collate_fn=a__ ,						)

    return iterator







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CNNDMDataset(args.documents_dir   )
    return dataset







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
    SCREAMING_SNAKE_CASE				:				int				      = [name for name, _, _ in data]
    SCREAMING_SNAKE_CASE				:				List[str]				      = [''' '''.join(a__   ) for _, _, summary_list in data]

    SCREAMING_SNAKE_CASE				:				Dict				      = [encode_for_summarization(a__ ,						a__ ,						a__   ) for _, story, summary in data]
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(
        [truncate_or_pad(a__ ,						a__ ,						tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = compute_token_type_ids(a__ ,						tokenizer.cls_token_id   )
    SCREAMING_SNAKE_CASE				:				int				      = build_mask(a__ ,						tokenizer.pad_token_id   )

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = 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 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summary_tokens.to('''cpu'''   ).numpy()
    SCREAMING_SNAKE_CASE				:				str				      = tokenizer.decode(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = summary.split('''.'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = [s + '''.''' for s in sentences]
    return sentences







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = 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=0.95 ,						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.''' ,						)
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE				:				List[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:
        SCREAMING_SNAKE_CASE				:				Tuple				      = 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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    SCREAMING_SNAKE_CASE				:				List[str]				      = os.listdir(a__   )
    if len(a__   ) == 0:
        return False

    return True


if __name__ == "__main__":
      main()
