

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	List[str]=0.999				,						UpperCAmelCase_		:	Dict="cosine"				,						):




      """simple docstring"""





      if alpha_transform_type == "cosine":

            def alpha_bar_fn(UpperCAmelCase_		:	Union[str, Any]		):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

      elif alpha_transform_type == "exp":

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

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

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







class 	_snake_case						(   _snake_case		,					_snake_case							):
 SCREAMING_SNAKE_CASE__   = [e.name for e in KarrasDiffusionSchedulers]
 SCREAMING_SNAKE_CASE__   = 2




 @register_to_config
 def __init__(	self      ,						_lowerCamelCase = 1000      ,						_lowerCamelCase = 0.0_0085      ,						_lowerCamelCase = 0.012      ,						_lowerCamelCase = "linear"      ,						_lowerCamelCase = None      ,						_lowerCamelCase = "epsilon"      ,						_lowerCamelCase = "linspace"      ,						_lowerCamelCase = 0      ,						):
       if trained_betas is not None:
             a		:Tuple											= torch.tensor(_lowerCamelCase      ,						dtype=torch.floataa					)
       elif beta_schedule == "linear":
             a		:Any											= torch.linspace(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						dtype=torch.floataa					)
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             a		:Dict											= (
                 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		:Any											= betas_for_alpha_bar(_lowerCamelCase					)
       else:
             raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

       a		:List[Any]											= 1.0 - self.betas
       a		:Dict											= torch.cumprod(self.alphas      ,						dim=0					)

       #  set all values
       self.set_timesteps(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase=None					):
       if schedule_timesteps is None:
             a		:List[Any]											= self.timesteps

       a		:int											= (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter					) == 0:
             a		:Any											= 1 if len(_lowerCamelCase					) > 1 else 0
       else:
             a		:Tuple											= timestep.cpu().item() if torch.is_tensor(_lowerCamelCase					) else timestep
             a		:List[Any]											= self._index_counter[timestep_int]

       return indices[pos].item()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       # 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  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Union[str, Any]											= self.index_for_timestep(_lowerCamelCase					)

       if self.state_in_first_order:
             a		:int											= self.sigmas[step_index]
       else:
             a		:Any											= self.sigmas_interpol[step_index - 1]

       a		:Union[str, Any]											= sample / ((sigma**2 + 1) ** 0.5)
       return sample




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						):
       a		:Optional[Any]											= num_inference_steps

       a		:int											= num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
             a		:Union[str, Any]											= np.linspace(0      ,						num_train_timesteps - 1      ,						_lowerCamelCase      ,						dtype=_lowerCamelCase					)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
             a		:Optional[Any]											= 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		:Dict											= (np.arange(0      ,						_lowerCamelCase					) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase					)
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             a		:Optional[Any]											= 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		:Any											= (np.arange(_lowerCamelCase      ,						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		:Optional[int]											= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
       a		:Optional[int]											= torch.from_numpy(np.log(_lowerCamelCase					)					).to(_lowerCamelCase					)

       a		:List[Any]											= np.interp(_lowerCamelCase      ,						np.arange(0      ,						len(_lowerCamelCase					)					)      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
       a		:List[Any]											= torch.from_numpy(_lowerCamelCase					).to(device=_lowerCamelCase					)

       # compute up and down sigmas
       a		:Union[str, Any]											= sigmas.roll(-1					)
       a		:Tuple											= 0.0
       a		:Union[str, Any]											= (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       a		:Tuple											= (sigmas_next**2 - sigmas_up**2) ** 0.5
       a		:Dict											= 0.0

       # compute interpolated sigmas
       a		:List[Any]											= sigmas.log().lerp(sigmas_down.log()      ,						0.5					).exp()
       a		:Tuple											= 0.0

       # set sigmas
       a		:Union[str, Any]											= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
       a		:Optional[Any]											= torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
       a		:int											= torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
       a		:str											= torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

       if str(_lowerCamelCase					).startswith('''mps'''					):
             # mps does not support float64
             a		:Any											= torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase      ,						dtype=torch.floataa					)
       else:
             a		:Tuple											= torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase					)

       a		:Tuple											= self.sigma_to_t(_lowerCamelCase					).to(_lowerCamelCase      ,						dtype=timesteps.dtype					)
       a		:Any											= torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,						dim=-1					).flatten()

       a		:Any											= torch.cat([timesteps[:1], interleaved_timesteps]					)

       a		:List[str]											= None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       a		:int											= defaultdict(_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       # get log sigma
       a		:str											= sigma.log()

       # get distribution
       a		:Optional[int]											= log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       a		:Union[str, Any]											= dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
       a		:Optional[Any]											= low_idx + 1

       a		:int											= self.log_sigmas[low_idx]
       a		:Tuple											= self.log_sigmas[high_idx]

       # interpolate sigmas
       a		:Any											= (low - log_sigma) / (low - high)
       a		:str											= w.clamp(0      ,						1					)

       # transform interpolation to time range
       a		:List[Any]											= (1 - w) * low_idx + w * high_idx
       a		:Dict											= t.view(sigma.shape					)
       return t




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return self.sample is None




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = True      ,						):
       a		:Union[str, Any]											= self.index_for_timestep(_lowerCamelCase					)

       # advance index counter by 1
       a		:Union[str, Any]											= timestep.cpu().item() if torch.is_tensor(_lowerCamelCase					) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
             a		:Optional[int]											= self.sigmas[step_index]
             a		:Any											= self.sigmas_interpol[step_index]
             a		:int											= self.sigmas_up[step_index]
             a		:int											= self.sigmas_down[step_index - 1]
       else:
             # 2nd order / KPDM2's method
             a		:str											= self.sigmas[step_index - 1]
             a		:Optional[Any]											= self.sigmas_interpol[step_index - 1]
             a		:Optional[Any]											= self.sigmas_up[step_index - 1]
             a		:Optional[int]											= self.sigmas_down[step_index - 1]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       a		:List[str]											= 0
       a		:Any											= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       a		:List[str]											= model_output.device
       a		:List[Any]											= randn_tensor(model_output.shape      ,						dtype=model_output.dtype      ,						device=_lowerCamelCase      ,						generator=_lowerCamelCase					)

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             a		:Dict											= sigma_hat if self.state_in_first_order else sigma_interpol
             a		:int											= sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
             a		:Any											= sigma_hat if self.state_in_first_order else sigma_interpol
             a		:Union[str, Any]											= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**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`'''					)

       if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             a		:List[Any]											= (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             a		:List[Any]											= sigma_interpol - sigma_hat

             # store for 2nd order step
             a		:Optional[Any]											= sample
             a		:int											= dt
             a		:List[str]											= sample + derivative * dt
       else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             a		:int											= (sample - pred_original_sample) / sigma_interpol
             # 3. delta timestep
             a		:Optional[int]											= sigma_down - sigma_hat

             a		:Dict											= self.sample
             a		:List[str]											= None

             a		:Tuple											= sample + derivative * dt
             a		:Dict											= prev_sample + noise * sigma_up

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       a		:Optional[int]											= 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		:Tuple											= self.timesteps.to(original_samples.device      ,						dtype=torch.floataa					)
             a		:List[str]											= timesteps.to(original_samples.device      ,						dtype=torch.floataa					)
       else:
             a		:str											= self.timesteps.to(original_samples.device					)
             a		:Union[str, Any]											= timesteps.to(original_samples.device					)

       a		:List[Any]											= [self.index_for_timestep(_lowerCamelCase      ,						_lowerCamelCase					) for t in timesteps]

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

       a		:Tuple											= original_samples + noise * sigma
       return noisy_samples




 def __len__(	self					):
       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,
)


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


snake_case       :					int        =  namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      a		:Optional[Any]											= BertTokenizer.from_pretrained('''bert-base-uncased'''				,						do_lower_case=UpperCAmelCase_		)
      a		:int											= BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''		)
      model.to(args.device		)
      model.eval()

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

      if args.compute_rouge:
            a		:Dict											= []
            a		:List[Any]											= []

            import nltk
            import rouge

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

      a		:Optional[int]											= build_data_iterator(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Optional[Any]											= 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		:Any											= predictor.translate_batch(UpperCAmelCase_		)
            a		:List[Any]											= predictor.from_batch(UpperCAmelCase_		)
            a		:Tuple											= [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		:Union[str, Any]											= rouge_evaluator.get_scores(UpperCAmelCase_				,						UpperCAmelCase_		)
            a		:Dict											= format_rouge_scores(UpperCAmelCase_		)
            save_rouge_scores(UpperCAmelCase_		)
            print(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Dict		):




      """simple docstring"""





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

            a		:Dict											= os.path.join(UpperCAmelCase_				,						UpperCAmelCase_		)
            with open(UpperCAmelCase_				,						'''w'''		) as output:
                  output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a							,       a							,       a		:List[str]											= translation
      a		:int											= (
          raw_summary.replace('''[unused0]'''				,						''''''		)
          .replace('''[unused3]'''				,						''''''		)
          .replace('''[PAD]'''				,						''''''		)
          .replace('''[unused1]'''				,						''''''		)
          .replace(R''' +'''				,						''' '''		)
          .replace(''' [unused2] '''				,						'''. '''		)
          .replace('''[unused2]'''				,						''''''		)
          .strip()
      )

      return summary
def        __lowerCamelCase		(				UpperCAmelCase_		:	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        __lowerCamelCase		(				UpperCAmelCase_		:	int		):




      """simple docstring"""





      with open('''rouge_scores.txt'''				,						'''w'''		) as output:
            output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Union[str, Any]		):




      """simple docstring"""





      a		:Any											= load_and_cache_examples(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Union[str, Any]											= SequentialSampler(UpperCAmelCase_		)

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

      a		:List[Any]											= DataLoader(
          UpperCAmelCase_				,						sampler=UpperCAmelCase_				,						batch_size=args.batch_size				,						collate_fn=UpperCAmelCase_				,						)

      return iterator
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a		:Union[str, Any]											= CNNDMDataset(args.documents_dir		)
      return dataset
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:Optional[int]											= [x for x in data if not len(x[1]		) == 0]  # remove empty_files
      a		:Tuple											= [name for name, _, _ in data]
      a		:str											= [''' '''.join(UpperCAmelCase_		) for _, _, summary_list in data]

      a		:int											= [encode_for_summarization(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		) for _, story, summary in data]
      a		:Optional[int]											= torch.tensor(
          [truncate_or_pad(UpperCAmelCase_				,						UpperCAmelCase_				,						tokenizer.pad_token_id		) for story, _ in encoded_text]		)
      a		:int											= compute_token_type_ids(UpperCAmelCase_				,						tokenizer.cls_token_id		)
      a		:str											= build_mask(UpperCAmelCase_				,						tokenizer.pad_token_id		)

      a		:List[Any]											= 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        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:str											= summary_tokens.to('''cpu'''		).numpy()
      a		:Union[str, Any]											= tokenizer.decode(UpperCAmelCase_		)
      a		:Optional[Any]											= summary.split('''.'''		)
      a		:Any											= [s + '''.''' for s in sentences]
      return sentences
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:Dict											= 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=50				,						type=UpperCAmelCase_				,						help='''Minimum number of tokens for the summaries.'''				,						)
      parser.add_argument(
          '''--max_length'''				,						default=200				,						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=0.95				,						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		:List[str]											= parser.parse_args()

      # Select device (distibuted not available)
      a		:Optional[int]											= 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		:Union[str, Any]											= 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        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]		):




      """simple docstring"""





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

      a		:Union[str, Any]											= os.listdir(UpperCAmelCase_		)
      if len(UpperCAmelCase_		) == 0:
            return False

      return True


if __name__ == "__main__":
     main()





