



"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


A     =      logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   UpperCAmelCase__		(   a__  ):
		lowerCAmelCase_							:						torch.FloatTensor
		lowerCAmelCase_							:						Optional[torch.FloatTensor]							=  None



def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=0.9_99					,   lowerCamelCase__="cosine"					,   )			->				str:



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.exp(t * -12.0      )

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

							A    =       []
							for i in range(a__      ):
														A    =       i / num_diffusion_timesteps
														A    =       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(a__      ) / alpha_bar_fn(a__      )					,   a__      )      )
							return torch.tensor(a__					,   dtype=torch.floataa      )






class   UpperCAmelCase__		(   a__ ,a__  ):
		lowerCAmelCase_							:						List[str]							=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_							:						int							=  1



		@register_to_config
		def __init__(			self	:     Optional[int]   ,				snake_case	:     Optional[Any] = 1_000   ,				snake_case	:     List[str] = 0.0001   ,				snake_case	:     Optional[Any] = 0.02   ,				snake_case	:     Tuple = "linear"   ,				snake_case	:     List[str] = None   ,				snake_case	:     Any = "epsilon"   ,				snake_case	:     Dict = "linspace"   ,				snake_case	:     List[str] = 0   ,				)      ->    Tuple:






									'''simple docstring'''

									if trained_betas is not None:
																A    =       torch.tensor(_lowerCamelCase   ,				dtype=torch.floataa					)
									elif beta_schedule == "linear":
																A    =       torch.linspace(_lowerCamelCase   ,				_lowerCamelCase   ,				_lowerCamelCase   ,				dtype=torch.floataa					)
									elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																A    =       (
																    torch.linspace(beta_start**0.5   ,				beta_end**0.5   ,				_lowerCamelCase   ,				dtype=torch.floataa					) ** 2
																)
									elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																A    =       betas_for_alpha_bar(_lowerCamelCase					)
									else:
																raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

									A    =       1.0 - self.betas
									A    =       torch.cumprod(self.alphas   ,				dim=0					)

									A    =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
									A    =       np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
									A    =       torch.from_numpy(_lowerCamelCase					)

									# setable values
									A    =       None
									A    =       np.linspace(0   ,				num_train_timesteps - 1   ,				_lowerCamelCase   ,				dtype=_lowerCamelCase					)[::-1].copy()
									A    =       torch.from_numpy(_lowerCamelCase					)
									A    =       False



		@property
		def        A_		(			self	:     Optional[int]					)      ->    List[str]:






									'''simple docstring'''

									if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     str   ,				snake_case	:     Tuple					)      ->    torch.FloatTensor:






									'''simple docstring'''

									if isinstance(_lowerCamelCase   ,				torch.Tensor					):
																A    =       timestep.to(self.timesteps.device					)
									A    =       (self.timesteps == timestep).nonzero().item()
									A    =       self.sigmas[step_index]
									A    =       sample / ((sigma**2 + 1) ** 0.5)
									A    =       True
									return sample



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     Optional[Any]   ,				snake_case	:     Dict = None					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       num_inference_steps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
																A    =       np.linspace(0   ,				self.config.num_train_timesteps - 1   ,				_lowerCamelCase   ,				dtype=_lowerCamelCase					)[
																    ::-1
																].copy()
									elif self.config.timestep_spacing == "leading":
																A    =       self.config.num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A    =       (np.arange(0   ,				_lowerCamelCase					) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase					)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																A    =       self.config.num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A    =       (np.arange(self.config.num_train_timesteps   ,				0   ,				-step_ratio					)).round().copy().astype(_lowerCamelCase					)
																timesteps -= 1
									else:
																raise ValueError(
																    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""					)

									A    =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
									A    =       np.interp(_lowerCamelCase   ,				np.arange(0   ,				len(_lowerCamelCase					)					)   ,				_lowerCamelCase					)
									A    =       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
									A    =       torch.from_numpy(_lowerCamelCase					).to(device=_lowerCamelCase					)
									if str(_lowerCamelCase					).startswith('mps'					):
																# mps does not support float64
																A    =       torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase   ,				dtype=torch.floataa					)
									else:
																A    =       torch.from_numpy(_lowerCamelCase					).to(device=_lowerCamelCase					)



		def        A_		(			self	:     Any   ,				snake_case	:     List[str]   ,				snake_case	:     str   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[int] = None   ,				snake_case	:     Optional[int] = True   ,				)      ->    Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:






									'''simple docstring'''

									if (
									    isinstance(_lowerCamelCase   ,				_lowerCamelCase					)
									    or isinstance(_lowerCamelCase   ,				torch.IntTensor					)
									    or isinstance(_lowerCamelCase   ,				torch.LongTensor					)
									):
																raise ValueError(
																    (
																        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
																        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
																        ' one of the `scheduler.timesteps` as a timestep.'
																    )   ,				)

									if not self.is_scale_input_called:
																logger.warning(
																    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
																    'See `StableDiffusionPipeline` for a usage example.'					)

									if isinstance(_lowerCamelCase   ,				torch.Tensor					):
																A    =       timestep.to(self.timesteps.device					)

									A    =       (self.timesteps == timestep).nonzero().item()
									A    =       self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																A    =       sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																A    =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
																raise NotImplementedError('prediction_type not implemented yet: sample'					)
									else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""					)

									A    =       self.sigmas[step_index]
									A    =       self.sigmas[step_index + 1]
									A    =       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
									A    =       (sigma_to**2 - sigma_up**2) ** 0.5

									# 2. Convert to an ODE derivative
									A    =       (sample - pred_original_sample) / sigma

									A    =       sigma_down - sigma

									A    =       sample + derivative * dt

									A    =       model_output.device
									A    =       randn_tensor(model_output.shape   ,				dtype=model_output.dtype   ,				device=_lowerCamelCase   ,				generator=_lowerCamelCase					)

									A    =       prev_sample + noise * sigma_up

									if not return_dict:
																return (prev_sample,)

									return EulerAncestralDiscreteSchedulerOutput(
									    prev_sample=_lowerCamelCase   ,				pred_original_sample=_lowerCamelCase					)



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     str   ,				snake_case	:     Optional[int]   ,				snake_case	:     str   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.sigmas.to(device=original_samples.device   ,				dtype=original_samples.dtype					)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase					):
																# mps does not support float64
																A    =       self.timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
																A    =       timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
									else:
																A    =       self.timesteps.to(original_samples.device					)
																A    =       timesteps.to(original_samples.device					)

									A    =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

									A    =       sigmas[step_indices].flatten()
									while len(sigma.shape					) < len(original_samples.shape					):
																A    =       sigma.unsqueeze(-1					)

									A    =       original_samples + noise * sigma
									return noisy_samples



		def __len__(			self	:     Dict					)      ->    Optional[int]:






									'''simple docstring'''

									return self.config.num_train_timesteps





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()
