






import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__UpperCamelCase   :						Any							=  1000


class 					__SCREAMING_SNAKE_CASE:
							def __init__(       self: List[str]    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: List[Any]=1.2    ,		UpperCamelCase: Optional[Any]="g"    ,		UpperCamelCase: Optional[Any]=0.5    ,		UpperCamelCase: int="-"    ,		UpperCamelCase: List[Any]="test_out.jpg"    ,		UpperCamelCase: Optional[int]=True    ,		UpperCamelCase: Union[str, Any]=False    ,		UpperCamelCase: Optional[Any]=None    ,		UpperCamelCase: Optional[int]=None    ,		UpperCamelCase: Dict=0.7    ,		)						->						Union[str, Any]:
											if isinstance(UpperCamelCase    ,		torch.Tensor  ):
															snake_case__													=       img.numpy().astype('np.uint8'  )
											if isinstance(UpperCamelCase    ,		UpperCamelCase  ):
															snake_case__													=       img_tensorize(UpperCamelCase  )
											assert isinstance(UpperCamelCase    ,		np.ndarray  )

											snake_case__							,  snake_case__													=       img.shape[1], img.shape[0]
											snake_case__													=       mplfigure.Figure(frameon=UpperCamelCase  )
											snake_case__													=       fig.get_dpi()
											snake_case__													=       (width * scale + 1e-2) / dpi
											snake_case__													=       (height * scale + 1e-2) / dpi
											fig.set_size_inches(UpperCamelCase    ,		UpperCamelCase  )
											snake_case__													=       fig.add_axes([0.0, 0.0, 1.0, 1.0]  )
											ax.axis('off'  )
											ax.set_xlim(0.0    ,		UpperCamelCase  )
											ax.set_ylim(UpperCamelCase  )

											snake_case__													=       saveas
											snake_case__													=       rgb
											snake_case__													=       pynb
											snake_case__													=       img
											snake_case__													=       edgecolor
											snake_case__													=       0.5
											snake_case__													=       linestyle
											snake_case__													=       int(np.sqrt(min(UpperCamelCase    ,		UpperCamelCase  )  ) * scale // 3  )
											snake_case__													=       width
											snake_case__													=       height
											snake_case__													=       scale
											snake_case__													=       fig
											snake_case__													=       ax
											snake_case__													=       pad
											snake_case__													=       idaobj
											snake_case__													=       idaattr
											snake_case__													=       FigureCanvasAgg(UpperCamelCase  )
							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: int=None  )						->						Dict:
											if color is None:
															snake_case__													=       self.edgecolor
											((snake_case__)							,  (snake_case__)							,  (snake_case__)							,  (snake_case__))													=       box
											snake_case__													=       xa - xa
											snake_case__													=       ya - ya
											self.ax.add_patch(
											    mpl.patches.Rectangle(
											        (xa, ya)    ,		UpperCamelCase    ,		UpperCamelCase    ,		fill=UpperCamelCase    ,		edgecolor=UpperCamelCase    ,		linewidth=self.font_size // 3    ,		alpha=self.alpha    ,		linestyle=self.linestyle    ,		)  )
							def lowerCAmelCase_	(       self: List[Any]    ,		UpperCamelCase: str    ,		UpperCamelCase: List[Any]=None    ,		UpperCamelCase: List[str]=None    ,		UpperCamelCase: Union[str, Any]=None    ,		UpperCamelCase: Union[str, Any]=None  )						->						Tuple:
											if len(boxes.shape  ) > 2:
															snake_case__													=       boxes[0]
											if len(obj_ids.shape  ) > 1:
															snake_case__													=       obj_ids[0]
											if len(obj_scores.shape  ) > 1:
															snake_case__													=       obj_scores[0]
											if len(attr_ids.shape  ) > 1:
															snake_case__													=       attr_ids[0]
											if len(attr_scores.shape  ) > 1:
															snake_case__													=       attr_scores[0]
											if isinstance(UpperCamelCase    ,		torch.Tensor  ):
															snake_case__													=       boxes.numpy()
											if isinstance(UpperCamelCase    ,		UpperCamelCase  ):
															snake_case__													=       np.array(UpperCamelCase  )
											assert isinstance(UpperCamelCase    ,		np.ndarray  )
											snake_case__													=       np.prod(boxes[:, 2:] - boxes[:, :2]    ,		axis=1  )
											snake_case__													=       np.argsort(-areas  ).tolist()
											snake_case__													=       boxes[sorted_idxs] if boxes is not None else None
											snake_case__													=       obj_ids[sorted_idxs] if obj_ids is not None else None
											snake_case__													=       obj_scores[sorted_idxs] if obj_scores is not None else None
											snake_case__													=       attr_ids[sorted_idxs] if attr_ids is not None else None
											snake_case__													=       attr_scores[sorted_idxs] if attr_scores is not None else None

											snake_case__													=       [self._random_color(maximum=1  ) for _ in range(len(UpperCamelCase  )  )]
											snake_case__													=       [assigned_colors[idx] for idx in sorted_idxs]
											if obj_ids is not None:
															snake_case__													=       self._create_text_labels_attr(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase  )
															for i in range(len(UpperCamelCase  )  ):
																			snake_case__													=       assigned_colors[i]
																			self.add_box(boxes[i]    ,		UpperCamelCase  )
																			self.draw_labels(labels[i]    ,		boxes[i]    ,		UpperCamelCase  )
							def lowerCAmelCase_	(       self: int    ,		UpperCamelCase: Optional[int]    ,		UpperCamelCase: str    ,		UpperCamelCase: int  )						->						int:
											snake_case__							,  snake_case__							,  snake_case__							,  snake_case__													=       box
											snake_case__													=       (xa, ya)
											snake_case__													=       (ya - ya) * (xa - xa)
											snake_case__													=       _SMALL_OBJ * self.scale
											if instance_area < small or ya - ya < 40 * self.scale:
															if ya >= self.height - 5:
																			snake_case__													=       (xa, ya)
															else:
																			snake_case__													=       (xa, ya)

											snake_case__													=       (ya - ya) / np.sqrt(self.height * self.width  )
											snake_case__													=       self._change_color_brightness(UpperCamelCase    ,		brightness_factor=0.7  )
											snake_case__													=       np.clip((height_ratio - 0.02) / 0.08 + 1    ,		1.2    ,		2  )
											font_size *= 0.75 * self.font_size

											self.draw_text(
											    text=UpperCamelCase    ,		position=UpperCamelCase    ,		color=UpperCamelCase    ,		)
							def lowerCAmelCase_	(       self: Any    ,		UpperCamelCase: Optional[int]    ,		UpperCamelCase: Dict    ,		UpperCamelCase: str="g"    ,		UpperCamelCase: int="left"    ,		)						->						Any:
											snake_case__													=       0
											snake_case__													=       self.font_size
											snake_case__													=       np.maximum(list(mplc.to_rgb(UpperCamelCase  )  )    ,		0.2  )
											snake_case__													=       max(0.8    ,		np.max(UpperCamelCase  )  )
											snake_case__													=       {
											    'facecolor': 'black',
											    'alpha': self.alpha,
											    'pad': self.pad,
											    'edgecolor': 'none',
											}
											snake_case__							,  snake_case__													=       position
											self.ax.text(
											    UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		size=font_size * self.scale    ,		family='sans-serif'    ,		bbox=UpperCamelCase    ,		verticalalignment='top'    ,		horizontalalignment=UpperCamelCase    ,		color=UpperCamelCase    ,		zorder=10    ,		rotation=UpperCamelCase    ,		)
							def lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: int=None  )						->						List[str]:
											if saveas is None:
															snake_case__													=       self.saveas
											if saveas.lower().endswith('.jpg'  ) or saveas.lower().endswith('.png'  ):
															cva.imwrite(
															    UpperCamelCase    ,		self._get_buffer()[:, :, ::-1]    ,		)
											else:
															self.fig.savefig(UpperCamelCase  )
							def lowerCAmelCase_	(       self: Any    ,		UpperCamelCase: List[Any]    ,		UpperCamelCase: Any    ,		UpperCamelCase: Dict    ,		UpperCamelCase: Union[str, Any]  )						->						int:
											snake_case__													=       [self.idaobj[i] for i in classes]
											snake_case__													=       [self.idaattr[i] for i in attr_classes]
											snake_case__													=       [
											    F'''{label} {score:.2f} {attr} {attr_score:.2f}'''
											    for label, score, attr, attr_score in zip(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase  )
											]
											return labels
							def lowerCAmelCase_	(       self: Dict    ,		UpperCamelCase: str    ,		UpperCamelCase: Optional[int]  )						->						Dict:
											snake_case__													=       [self.idaobj[i] for i in classes]
											if scores is not None:
															if labels is None:
																			snake_case__													=       ['{:.0f}%'.format(s * 1_00  ) for s in scores]
															else:
																			snake_case__													=       ['{} {:.0f}%'.format(UpperCamelCase    ,		s * 1_00  ) for li, s in zip(UpperCamelCase    ,		UpperCamelCase  )]
											return labels
							def lowerCAmelCase_	(       self: Dict    ,		UpperCamelCase: str=2_55  )						->						List[str]:
											snake_case__													=       np.random.randint(0    ,		len(_COLORS  )  )
											snake_case__													=       _COLORS[idx] * maximum
											if not self.rgb:
															snake_case__													=       ret[::-1]
											return ret
							def lowerCAmelCase_	(       self: Optional[Any]  )						->						int:
											if not self.pynb:
															snake_case__							,  snake_case__													=       self.canvas.print_to_buffer()
															if (width, height) != (self.width, self.height):
																			snake_case__													=       cva.resize(self.img    ,		(width, height)  )
															else:
																			snake_case__													=       self.img
											else:
															snake_case__													=       io.BytesIO()  # works for cairo backend
															self.canvas.print_rgba(UpperCamelCase  )
															snake_case__							,  snake_case__													=       self.width, self.height
															snake_case__													=       buf.getvalue()
															snake_case__													=       self.img

											snake_case__													=       np.frombuffer(UpperCamelCase    ,		dtype='uint8'  )
											snake_case__													=       buffer.reshape(UpperCamelCase    ,		UpperCamelCase    ,		4  )
											snake_case__							,  snake_case__													=       np.split(UpperCamelCase    ,		[3]    ,		axis=2  )

											try:
															import numexpr as ne  # fuse them with numexpr

															snake_case__													=       ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'  )
											except ImportError:
															snake_case__													=       alpha.astype('float32'  ) / 255.0
															snake_case__													=       img * (1 - alpha) + rgb * alpha

											return visualized_image.astype('uint8'  )


							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: List[Any]    ,		UpperCamelCase: str  )						->						List[str]:
											assert brightness_factor >= -1.0 and brightness_factor <= 1.0
											snake_case__													=       mplc.to_rgb(UpperCamelCase  )
											snake_case__													=       colorsys.rgb_to_hls(*mplc.to_rgb(UpperCamelCase  )  )
											snake_case__													=       polygon_color[1] + (brightness_factor * polygon_color[1])
											snake_case__													=       0.0 if modified_lightness < 0.0 else modified_lightness
											snake_case__													=       1.0 if modified_lightness > 1.0 else modified_lightness
											snake_case__													=       colorsys.hls_to_rgb(polygon_color[0]    ,		UpperCamelCase    ,		polygon_color[2]  )
											return modified_color


__UpperCamelCase   :						Any							=  (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)












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   :						List[str]							=  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


__UpperCamelCase   :						Tuple							=  namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])
def        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				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        a_	( _A ,				_A ,				_A					)     ->     Optional[Any]:







				"""simple docstring"""
				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        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				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        a_	( _A					)     ->     Any:







				"""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        a_	( _A					)     ->     int:







				"""simple docstring"""
				with open('rouge_scores.txt' ,				'w'					) as output:
								output.write(_A					)
def        a_	( _A ,				_A					)     ->     Optional[Any]:







				"""simple docstring"""
				snake_case__													=       load_and_cache_examples(_A ,				_A					)
				snake_case__													=       SequentialSampler(_A					)

				def collate_fn(_A					):
								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        a_	( _A ,				_A					)     ->     Any:







				"""simple docstring"""
				snake_case__													=       CNNDMDataset(args.documents_dir					)
				return dataset
def        a_	( _A ,				_A ,				_A ,				_A					)     ->     str:







				"""simple docstring"""
				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        a_	( _A ,				_A					)     ->     Optional[Any]:







				"""simple docstring"""
				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        a_	( )     ->     int:







				"""simple docstring"""
				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=0.95 ,				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.' ,				)
				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        a_	( _A					)     ->     str:







				"""simple docstring"""
				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()




