
"""simple docstring"""





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

import numpy as np
import torch
from scipy import integrate

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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		torch.FloatTensor
  SCREAMING_SNAKE_CASE__     :		Optional[torch.FloatTensor]    	=						None







def       __a	(						__lowerCamelCase,						__lowerCamelCase=0.999,						__lowerCamelCase="cosine",						):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__lowerCamelCase    ):
         return math.cos((t + 0.008) / 1.008 * 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}"""    )

   UpperCAmelCase_   :   Dict     	=       []
   for i in range(__lowerCamelCase    ):
      UpperCAmelCase_   :   Optional[Any]     	=       i / num_diffusion_timesteps
      UpperCAmelCase_   :   List[str]     	=       (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__lowerCamelCase    ) / alpha_bar_fn(__lowerCamelCase    ),						__lowerCamelCase    )    )
   return torch.tensor(__lowerCamelCase,						dtype=torch.floataa    )


class A_      (lowercase__							,lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						[e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE__     :		int    	=						1







  @register_to_config
  def __init__(						self							,  lowercase_ = 1000							,  lowercase_ = 0.00_01							,  lowercase_ = 0.02							,  lowercase_ = "linear"							,  lowercase_ = None							,  lowercase_ = False							,  lowercase_ = "epsilon"							,  lowercase_ = "linspace"							,  lowercase_ = 0							,  ):

     """simple docstring"""
     if trained_betas is not None:
        UpperCAmelCase_   :   int     	=       torch.tensor(lowercase_							,  dtype=torch.floataa    )
     elif beta_schedule == "linear":
        UpperCAmelCase_   :   List[str]     	=       torch.linspace(lowercase_							,  lowercase_							,  lowercase_							,  dtype=torch.floataa    )
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        UpperCAmelCase_   :   Optional[int]     	=       (
            torch.linspace(beta_start**0.5							,  beta_end**0.5							,  lowercase_							,  dtype=torch.floataa    ) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        UpperCAmelCase_   :   Any     	=       betas_for_alpha_bar(lowercase_    )
     else:
        raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

     UpperCAmelCase_   :   Dict     	=       1.0 - self.betas
     UpperCAmelCase_   :   Tuple     	=       torch.cumprod(self.alphas							,  dim=0    )

     UpperCAmelCase_   :   List[Any]     	=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
     UpperCAmelCase_   :   str     	=       np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
     UpperCAmelCase_   :   str     	=       torch.from_numpy(lowercase_    )

     # setable values
     UpperCAmelCase_   :   List[str]     	=       None
     UpperCAmelCase_   :   int     	=       use_karras_sigmas
     self.set_timesteps(lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   int     	=       []
     UpperCAmelCase_   :   Tuple     	=       False







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # standard deviation of the initial noise distribution
     if self.config.timestep_spacing in ["linspace", "trailing"]:
        return self.sigmas.max()

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     if isinstance(lowercase_							,  torch.Tensor    ):
        UpperCAmelCase_   :   Optional[int]     	=       timestep.to(self.timesteps.device    )
     UpperCAmelCase_   :   Tuple     	=       (self.timesteps == timestep).nonzero().item()
     UpperCAmelCase_   :   int     	=       self.sigmas[step_index]
     UpperCAmelCase_   :   Union[str, Any]     	=       sample / ((sigma**2 + 1) ** 0.5)
     UpperCAmelCase_   :   int     	=       True
     return sample







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""

     def lms_derivative(lowercase_    ):
        UpperCAmelCase_   :   Dict     	=       1.0
        for k in range(lowercase_    ):
           if current_order == k:
              continue
           prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
        return prod

     UpperCAmelCase_   :   List[str]     	=       integrate.quad(lowercase_							,  self.sigmas[t]							,  self.sigmas[t + 1]							,  epsrel=1E-4    )[0]

     return integrated_coeff







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       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":
        UpperCAmelCase_   :   Dict     	=       np.linspace(0							,  self.config.num_train_timesteps - 1							,  lowercase_							,  dtype=lowercase_    )[
            ::-1
        ].copy()
     elif self.config.timestep_spacing == "leading":
        UpperCAmelCase_   :   Optional[int]     	=       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
        UpperCAmelCase_   :   Tuple     	=       (np.arange(0							,  lowercase_    ) * step_ratio).round()[::-1].copy().astype(lowercase_    )
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        UpperCAmelCase_   :   Optional[Any]     	=       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
        UpperCAmelCase_   :   Optional[Any]     	=       (np.arange(self.config.num_train_timesteps							,  0							,  -step_ratio    )).round().copy().astype(lowercase_    )
        timesteps -= 1
     else:
        raise ValueError(
            F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

     UpperCAmelCase_   :   int     	=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
     UpperCAmelCase_   :   Union[str, Any]     	=       np.log(lowercase_    )
     UpperCAmelCase_   :   str     	=       np.interp(lowercase_							,  np.arange(0							,  len(lowercase_    )    )							,  lowercase_    )

     if self.use_karras_sigmas:
        UpperCAmelCase_   :   List[Any]     	=       self._convert_to_karras(in_sigmas=lowercase_    )
        UpperCAmelCase_   :   Any     	=       np.array([self._sigma_to_t(lowercase_							,  lowercase_    ) for sigma in sigmas]    )

     UpperCAmelCase_   :   Union[str, Any]     	=       np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )

     UpperCAmelCase_   :   Dict     	=       torch.from_numpy(lowercase_    ).to(device=lowercase_    )
     if str(lowercase_    ).startswith("mps"    ):
        # mps does not support float64
        UpperCAmelCase_   :   List[str]     	=       torch.from_numpy(lowercase_    ).to(lowercase_							,  dtype=torch.floataa    )
     else:
        UpperCAmelCase_   :   str     	=       torch.from_numpy(lowercase_    ).to(device=lowercase_    )

     UpperCAmelCase_   :   int     	=       []







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     # get log sigma
     UpperCAmelCase_   :   List[str]     	=       np.log(lowercase_    )

     # get distribution
     UpperCAmelCase_   :   str     	=       log_sigma - log_sigmas[:, np.newaxis]

     # get sigmas range
     UpperCAmelCase_   :   Dict     	=       np.cumsum((dists >= 0)							,  axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
     UpperCAmelCase_   :   Tuple     	=       low_idx + 1

     UpperCAmelCase_   :   Optional[Any]     	=       log_sigmas[low_idx]
     UpperCAmelCase_   :   Dict     	=       log_sigmas[high_idx]

     # interpolate sigmas
     UpperCAmelCase_   :   List[Any]     	=       (low - log_sigma) / (low - high)
     UpperCAmelCase_   :   Optional[Any]     	=       np.clip(lowercase_							,  0							,  1    )

     # transform interpolation to time range
     UpperCAmelCase_   :   Tuple     	=       (1 - w) * low_idx + w * high_idx
     UpperCAmelCase_   :   Tuple     	=       t.reshape(sigma.shape    )
     return t







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   float     	=       in_sigmas[-1].item()
     UpperCAmelCase_   :   float     	=       in_sigmas[0].item()

     UpperCAmelCase_   :   List[str]     	=       7.0  # 7.0 is the value used in the paper
     UpperCAmelCase_   :   List[str]     	=       np.linspace(0							,  1							,  self.num_inference_steps    )
     UpperCAmelCase_   :   int     	=       sigma_min ** (1 / rho)
     UpperCAmelCase_   :   List[str]     	=       sigma_max ** (1 / rho)
     UpperCAmelCase_   :   int     	=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_ = 4							,  lowercase_ = True							,  ):

     """simple docstring"""
     if not self.is_scale_input_called:
        warnings.warn(
            "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
            "See `StableDiffusionPipeline` for a usage example."    )

     if isinstance(lowercase_							,  torch.Tensor    ):
        UpperCAmelCase_   :   List[Any]     	=       timestep.to(self.timesteps.device    )
     UpperCAmelCase_   :   Union[str, Any]     	=       (self.timesteps == timestep).nonzero().item()
     UpperCAmelCase_   :   str     	=       self.sigmas[step_index]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        UpperCAmelCase_   :   Union[str, Any]     	=       sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        UpperCAmelCase_   :   Optional[int]     	=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
        UpperCAmelCase_   :   int     	=       model_output
     else:
        raise ValueError(
            F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

     # 2. Convert to an ODE derivative
     UpperCAmelCase_   :   Dict     	=       (sample - pred_original_sample) / sigma
     self.derivatives.append(lowercase_    )
     if len(self.derivatives    ) > order:
        self.derivatives.pop(0    )

     # 3. Compute linear multistep coefficients
     UpperCAmelCase_   :   Optional[int]     	=       min(step_index + 1							,  lowercase_    )
     UpperCAmelCase_   :   str     	=       [self.get_lms_coefficient(lowercase_							,  lowercase_							,  lowercase_    ) for curr_order in range(lowercase_    )]

     # 4. Compute previous sample based on the derivatives path
     UpperCAmelCase_   :   Optional[int]     	=       sample + sum(
         coeff * derivative for coeff, derivative in zip(lowercase_							,  reversed(self.derivatives    )    )    )

     if not return_dict:
        return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=lowercase_							,  pred_original_sample=lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     UpperCAmelCase_   :   Any     	=       self.sigmas.to(device=original_samples.device							,  dtype=original_samples.dtype    )
     if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_    ):
        # mps does not support float64
        UpperCAmelCase_   :   int     	=       self.timesteps.to(original_samples.device							,  dtype=torch.floataa    )
        UpperCAmelCase_   :   Tuple     	=       timesteps.to(original_samples.device							,  dtype=torch.floataa    )
     else:
        UpperCAmelCase_   :   List[str]     	=       self.timesteps.to(original_samples.device    )
        UpperCAmelCase_   :   Optional[int]     	=       timesteps.to(original_samples.device    )

     UpperCAmelCase_   :   str     	=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

     UpperCAmelCase_   :   str     	=       original_samples + noise * sigma
     return noisy_samples







  def __len__(						self    ):

     """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()
