






'''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,
)


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


__lowerCAmelCase							=namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])

def 							a	(   _UpperCAmelCase )				->				List[str]:






					"""simple docstring"""







					a_									=   BertTokenizer.from_pretrained('bert-base-uncased'					,  do_lower_case=_UpperCAmelCase )
					a_									=   BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization' )
					model.to(args.device )
					model.eval()

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

					if args.compute_rouge:
										a_									=   []
										a_									=   []

										import nltk
										import rouge

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

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

					a_									=   build_data_iterator(_UpperCAmelCase					,  _UpperCAmelCase )
					a_									=   build_predictor(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )

					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(_UpperCAmelCase ):
										a_									=   predictor.translate_batch(_UpperCAmelCase )
										a_									=   predictor.from_batch(_UpperCAmelCase )
										a_									=   [format_summary(_UpperCAmelCase ) for t in translations]
										save_summaries(_UpperCAmelCase					,  args.summaries_output_dir					,  batch.document_names )

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

					if args.compute_rouge:
										a_									=   rouge_evaluator.get_scores(_UpperCAmelCase					,  _UpperCAmelCase )
										a_									=   format_rouge_scores(_UpperCAmelCase )
										save_rouge_scores(_UpperCAmelCase )
										print(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Dict:






					"""simple docstring"""







					for summary, document_name in zip(_UpperCAmelCase					,  _UpperCAmelCase ):
										# Prepare the summary file's name
										if "." in document_name:
															a_									=   '.'.join(document_name.split('.' )[:-1] )
															a_									=   document_name.split('.' )[-1]
															a_									=   bare_document_name + '_summary.' + extension
										else:
															a_									=   document_name + '_summary'

										a_									=   os.path.join(_UpperCAmelCase					,  _UpperCAmelCase )
										with open(_UpperCAmelCase					,  'w' ) as output:
															output.write(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase )				->				Union[str, Any]:






					"""simple docstring"""







					a_							,					a_							,					a_									=   translation
					a_									=   (
					    raw_summary.replace('[unused0]'					,  '' )
					    .replace('[unused3]'					,  '' )
					    .replace('[PAD]'					,  '' )
					    .replace('[unused1]'					,  '' )
					    .replace(R' +'					,  ' ' )
					    .replace(' [unused2] '					,  '. ' )
					    .replace('[unused2]'					,  '' )
					    .strip()
					)

					return summary

def 							a	(   _UpperCAmelCase )				->				str:






					"""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	(   _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







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

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				List[str]:






					"""simple docstring"""







					a_									=   load_and_cache_examples(_UpperCAmelCase					,  _UpperCAmelCase )
					a_									=   SequentialSampler(_UpperCAmelCase )

					def collate_fn(_UpperCAmelCase ):
										return collate(_UpperCAmelCase					,  _UpperCAmelCase					,  block_size=5_1_2					,  device=args.device )

					a_									=   DataLoader(
					    _UpperCAmelCase					,  sampler=_UpperCAmelCase					,  batch_size=args.batch_size					,  collate_fn=_UpperCAmelCase					,  )

					return iterator

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				List[Any]:






					"""simple docstring"""







					a_									=   CNNDMDataset(args.documents_dir )
					return dataset

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[Any]:






					"""simple docstring"""







					a_									=   [x for x in data if not len(x[1] ) == 0]  # remove empty_files
					a_									=   [name for name, _, _ in data]
					a_									=   [' '.join(_UpperCAmelCase ) for _, _, summary_list in data]

					a_									=   [encode_for_summarization(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase ) for _, story, summary in data]
					a_									=   torch.tensor(
					    [truncate_or_pad(_UpperCAmelCase					,  _UpperCAmelCase					,  tokenizer.pad_token_id ) for story, _ in encoded_text] )
					a_									=   compute_token_type_ids(_UpperCAmelCase					,  tokenizer.cls_token_id )
					a_									=   build_mask(_UpperCAmelCase					,  tokenizer.pad_token_id )

					a_									=   Batch(
					    document_names=_UpperCAmelCase					,  batch_size=len(_UpperCAmelCase )					,  src=encoded_stories.to(_UpperCAmelCase )					,  segs=encoder_token_type_ids.to(_UpperCAmelCase )					,  mask_src=encoder_mask.to(_UpperCAmelCase )					,  tgt_str=_UpperCAmelCase					,  )

					return batch

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Dict:






					"""simple docstring"""







					a_									=   summary_tokens.to('cpu' ).numpy()
					a_									=   tokenizer.decode(_UpperCAmelCase )
					a_									=   summary.split('.' )
					a_									=   [s + '.' for s in sentences]
					return sentences

def 							a	(   )				->				Optional[Any]:






					"""simple docstring"""







					a_									=   argparse.ArgumentParser()
					parser.add_argument(
					    '--documents_dir'					,  default=_UpperCAmelCase					,  type=_UpperCAmelCase					,  required=_UpperCAmelCase					,  help='The folder where the documents to summarize are located.'					,  )
					parser.add_argument(
					    '--summaries_output_dir'					,  default=_UpperCAmelCase					,  type=_UpperCAmelCase					,  required=_UpperCAmelCase					,  help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'					,  )
					parser.add_argument(
					    '--compute_rouge'					,  default=_UpperCAmelCase					,  type=_UpperCAmelCase					,  required=_UpperCAmelCase					,  help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'					,  )
					# EVALUATION options
					parser.add_argument(
					    '--no_cuda'					,  default=_UpperCAmelCase					,  type=_UpperCAmelCase					,  help='Whether to force the execution on CPU.'					,  )
					parser.add_argument(
					    '--batch_size'					,  default=4					,  type=_UpperCAmelCase					,  help='Batch size per GPU/CPU for training.'					,  )
					# BEAM SEARCH arguments
					parser.add_argument(
					    '--min_length'					,  default=5_0					,  type=_UpperCAmelCase					,  help='Minimum number of tokens for the summaries.'					,  )
					parser.add_argument(
					    '--max_length'					,  default=2_0_0					,  type=_UpperCAmelCase					,  help='Maixmum number of tokens for the summaries.'					,  )
					parser.add_argument(
					    '--beam_size'					,  default=5					,  type=_UpperCAmelCase					,  help='The number of beams to start with for each example.'					,  )
					parser.add_argument(
					    '--alpha'					,  default=0.9_5					,  type=_UpperCAmelCase					,  help='The value of alpha for the length penalty in the beam search.'					,  )
					parser.add_argument(
					    '--block_trigram'					,  default=_UpperCAmelCase					,  type=_UpperCAmelCase					,  help='Whether to block the existence of repeating trigrams in the text generated by beam search.'					,  )
					a_									=   parser.parse_args()

					# Select device (distibuted not available)
					a_									=   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:
										a_									=   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=_UpperCAmelCase )

					evaluate(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







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

					a_									=   os.listdir(_UpperCAmelCase )
					if len(_UpperCAmelCase ) == 0:
										return False

					return True


if __name__ == "__main__":
						main()









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

