


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


_lowerCAmelCase	:		Any		  =						namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])

def       __UpperCamelCase     (     _A     :					int					)     ->   Any:




	"""simple docstring"""





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

	lowerCAmelCase       :     Union[str, Any]   				=			{
	    'BOS': tokenizer.vocab['[unused0]'],
	    'EOS': tokenizer.vocab['[unused1]'],
	    'PAD': tokenizer.vocab['[PAD]'],
	}

	if args.compute_rouge:
		lowerCAmelCase       :     Optional[Any]   				=			[]
		lowerCAmelCase       :     Optional[Any]   				=			[]

		import nltk
		import rouge

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

	# these (unused) arguments are defined to keep the compatibility
	# with the legacy code and will be deleted in a next iteration.
	lowerCAmelCase       :     List[Any]   				=			''
	lowerCAmelCase       :     Optional[int]   				=			''

	lowerCAmelCase       :     Dict   				=			build_data_iterator(_lowerCAmelCase       , _lowerCAmelCase					)
	lowerCAmelCase       :     str   				=			build_predictor(_lowerCAmelCase       , _lowerCAmelCase       , _lowerCAmelCase       , _lowerCAmelCase					)

	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(_lowerCAmelCase					):
		lowerCAmelCase       :     List[Any]   				=			predictor.translate_batch(_lowerCAmelCase					)
		lowerCAmelCase       :     str   				=			predictor.from_batch(_lowerCAmelCase					)
		lowerCAmelCase       :     Union[str, Any]   				=			[format_summary(_lowerCAmelCase					) for t in translations]
		save_summaries(_lowerCAmelCase       , args.summaries_output_dir       , batch.document_names					)

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

	if args.compute_rouge:
		lowerCAmelCase       :     List[Any]   				=			rouge_evaluator.get_scores(_lowerCAmelCase       , _lowerCAmelCase					)
		lowerCAmelCase       :     List[str]   				=			format_rouge_scores(_lowerCAmelCase					)
		save_rouge_scores(_lowerCAmelCase					)
		print(_lowerCAmelCase					)

def       __UpperCamelCase     (     _A     :					Union[str, Any]       , _A     :					Optional[int]       , _A     :					str					)     ->   Tuple:




	"""simple docstring"""





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

		lowerCAmelCase       :     Union[str, Any]   				=			os.path.join(_lowerCAmelCase       , _lowerCAmelCase					)
		with open(_lowerCAmelCase       , 'w'					) as output:
			output.write(_lowerCAmelCase					)

def       __UpperCamelCase     (     _A     :					List[str]					)     ->   int:




	"""simple docstring"""





	lowerCAmelCase				, lowerCAmelCase				, lowerCAmelCase       :     str   				=			translation
	lowerCAmelCase       :     Optional[int]   				=			(
	    raw_summary.replace('[unused0]'       , ''					)
	    .replace('[unused3]'       , ''					)
	    .replace('[PAD]'       , ''					)
	    .replace('[unused1]'       , ''					)
	    .replace(r' +'       , ' '					)
	    .replace(' [unused2] '       , '. '					)
	    .replace('[unused2]'       , ''					)
	    .strip()
	)

	return summary

def       __UpperCamelCase     (     _A     :					List[str]					)     ->   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       __UpperCamelCase     (     _A     :					Union[str, Any]					)     ->   List[str]:




	"""simple docstring"""





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

def       __UpperCamelCase     (     _A     :					str       , _A     :					List[str]					)     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Optional[int]   				=			load_and_cache_examples(_lowerCAmelCase       , _lowerCAmelCase					)
	lowerCAmelCase       :     Union[str, Any]   				=			SequentialSampler(_lowerCAmelCase					)

	def collate_fn(_A     :					Optional[int]					):
		return collate(_lowerCAmelCase       , _lowerCAmelCase       , block_size=5_12       , device=args.device					)

	lowerCAmelCase       :     List[str]   				=			DataLoader(
	    _lowerCAmelCase       , sampler=_lowerCAmelCase       , batch_size=args.batch_size       , collate_fn=_lowerCAmelCase       , )

	return iterator

def       __UpperCamelCase     (     _A     :					Any       , _A     :					List[str]					)     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     List[str]   				=			CNNDMDataset(args.documents_dir					)
	return dataset

def       __UpperCamelCase     (     _A     :					Tuple       , _A     :					Optional[Any]       , _A     :					Tuple       , _A     :					List[Any]					)     ->   Optional[int]:




	"""simple docstring"""





	lowerCAmelCase       :     Dict   				=			[x for x in data if not len(x[1]					) == 0]  # remove empty_files
	lowerCAmelCase       :     str   				=			[name for name, _, _ in data]
	lowerCAmelCase       :     Tuple   				=			[' '.join(_lowerCAmelCase					) for _, _, summary_list in data]

	lowerCAmelCase       :     List[Any]   				=			[encode_for_summarization(_lowerCAmelCase       , _lowerCAmelCase       , _lowerCAmelCase					) for _, story, summary in data]
	lowerCAmelCase       :     str   				=			torch.tensor(
	    [truncate_or_pad(_lowerCAmelCase       , _lowerCAmelCase       , tokenizer.pad_token_id					) for story, _ in encoded_text]					)
	lowerCAmelCase       :     Union[str, Any]   				=			compute_token_type_ids(_lowerCAmelCase       , tokenizer.cls_token_id					)
	lowerCAmelCase       :     int   				=			build_mask(_lowerCAmelCase       , tokenizer.pad_token_id					)

	lowerCAmelCase       :     Dict   				=			Batch(
	    document_names=_lowerCAmelCase       , batch_size=len(_lowerCAmelCase					)       , src=encoded_stories.to(_lowerCAmelCase					)       , segs=encoder_token_type_ids.to(_lowerCAmelCase					)       , mask_src=encoder_mask.to(_lowerCAmelCase					)       , tgt_str=_lowerCAmelCase       , )

	return batch

def       __UpperCamelCase     (     _A     :					Optional[int]       , _A     :					Any					)     ->   List[Any]:




	"""simple docstring"""





	lowerCAmelCase       :     Any   				=			summary_tokens.to('cpu'					).numpy()
	lowerCAmelCase       :     List[Any]   				=			tokenizer.decode(_lowerCAmelCase					)
	lowerCAmelCase       :     str   				=			summary.split('.'					)
	lowerCAmelCase       :     Dict   				=			[s + '.' for s in sentences]
	return sentences

def       __UpperCamelCase     (     )     ->   Dict:




	"""simple docstring"""





	lowerCAmelCase       :     Any   				=			argparse.ArgumentParser()
	parser.add_argument(
	    '--documents_dir'       , default=_lowerCAmelCase       , type=_lowerCAmelCase       , required=_lowerCAmelCase       , help='The folder where the documents to summarize are located.'       , )
	parser.add_argument(
	    '--summaries_output_dir'       , default=_lowerCAmelCase       , type=_lowerCAmelCase       , required=_lowerCAmelCase       , help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'       , )
	parser.add_argument(
	    '--compute_rouge'       , default=_lowerCAmelCase       , type=_lowerCAmelCase       , required=_lowerCAmelCase       , help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'       , )
	# EVALUATION options
	parser.add_argument(
	    '--no_cuda'       , default=_lowerCAmelCase       , type=_lowerCAmelCase       , help='Whether to force the execution on CPU.'       , )
	parser.add_argument(
	    '--batch_size'       , default=4       , type=_lowerCAmelCase       , help='Batch size per GPU/CPU for training.'       , )
	# BEAM SEARCH arguments
	parser.add_argument(
	    '--min_length'       , default=50       , type=_lowerCAmelCase       , help='Minimum number of tokens for the summaries.'       , )
	parser.add_argument(
	    '--max_length'       , default=2_00       , type=_lowerCAmelCase       , help='Maixmum number of tokens for the summaries.'       , )
	parser.add_argument(
	    '--beam_size'       , default=5       , type=_lowerCAmelCase       , help='The number of beams to start with for each example.'       , )
	parser.add_argument(
	    '--alpha'       , default=0.95       , type=_lowerCAmelCase       , help='The value of alpha for the length penalty in the beam search.'       , )
	parser.add_argument(
	    '--block_trigram'       , default=_lowerCAmelCase       , type=_lowerCAmelCase       , help='Whether to block the existence of repeating trigrams in the text generated by beam search.'       , )
	lowerCAmelCase       :     Tuple   				=			parser.parse_args()

	# Select device (distibuted not available)
	lowerCAmelCase       :     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:
		lowerCAmelCase       :     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=_lowerCAmelCase					)

	evaluate(_lowerCAmelCase					)

def       __UpperCamelCase     (     _A     :					List[str]					)     ->   Union[str, Any]:




	"""simple docstring"""





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

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

	return True


if __name__ == "__main__":
				main()

"""simple docstring"""


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 					a					:
  A_ :				CommonSchedulerState
  A_ :				jnp.ndarray

  # setable values
  A_ :				jnp.ndarray
  A_ :				jnp.ndarray
  A_ :				Optional[int] 			=							None



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[int]						,  lowerCamelCase_   :    CommonSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> Optional[Any]:
      return cls(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )






@dataclass
class 					a					(				A_							):
  A_ :				DDIMSchedulerState






class 					a					(				A_   ,		A_							):
  A_ :				Optional[int] 			=							[e.name for e in FlaxKarrasDiffusionSchedulers]

  A_ :				jnp.dtype



  @property
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      return True



  @register_to_config
  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    int = 10_00						,  lowerCamelCase_   :    float = 0.00_01						,  lowerCamelCase_   :    float = 0.02						,  lowerCamelCase_   :    str = "linear"						,  lowerCamelCase_   :    Optional[jnp.ndarray] = None						,  lowerCamelCase_   :    bool = True						,  lowerCamelCase_   :    int = 0						,  lowerCamelCase_   :    str = "epsilon"						,  lowerCamelCase_   :    jnp.dtype = jnp.floataa						,  )      -> Any:
      __a			      =						dtype



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    Optional[CommonSchedulerState] = None     )      -> DDIMSchedulerState:
      if common is None:
          __a			      =						CommonSchedulerState.create(self     )

      # At every step in ddim, we are looking into the previous alphas_cumprod
      # For the final step, there is no previous alphas_cumprod because we are already at 0
      # `set_alpha_to_one` decides whether we set this parameter simply to one or
      # whether we use the final alpha of the "non-previous" one.
      __a			      =						(
          jnp.array(1.0						,  dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
      )

      # standard deviation of the initial noise distribution
      __a			      =						jnp.array(1.0						,  dtype=self.dtype     )

      __a			      =						jnp.arange(0						,  self.config.num_train_timesteps     ).round()[::-1]

      return DDIMSchedulerState.create(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    Optional[int] = None     )      -> jnp.ndarray:
      return sample



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Tuple = ()     )      -> DDIMSchedulerState:
      __a			      =						self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      __a			      =						(jnp.arange(0						,  lowerCamelCase_     ) * step_ratio).round()[::-1] + self.config.steps_offset

      return state.replace(
          num_inference_steps=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __a			      =						state.common.alphas_cumprod[timestep]
      __a			      =						jnp.where(
          prev_timestep >= 0						,  state.common.alphas_cumprod[prev_timestep]						,  state.final_alpha_cumprod     )
      __a			      =						1 - alpha_prod_t
      __a			      =						1 - alpha_prod_t_prev

      __a			      =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    float = 0.0						,  lowerCamelCase_   :    bool = True						,  )      -> Union[FlaxDDIMSchedulerOutput, Tuple]:
      if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""     )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      __a			      =						timestep - self.config.num_train_timesteps // state.num_inference_steps

      __a			      =						state.common.alphas_cumprod
      __a			      =						state.final_alpha_cumprod

      # 2. compute alphas, betas
      __a			      =						alphas_cumprod[timestep]
      __a			      =						jnp.where(prev_timestep >= 0						,  alphas_cumprod[prev_timestep]						,  lowerCamelCase_     )

      __a			      =						1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
          __a			      =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          __a			      =						model_output
      elif self.config.prediction_type == "sample":
          __a			      =						model_output
          __a			      =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
          __a			      =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          __a			      =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
              """ `v_prediction`"""     )

      # 4. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      __a			      =						self._get_variance(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )
      __a			      =						eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      __a			      =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
          return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase_						,  state=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return add_noise_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return get_velocity_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def __len__(		self   :    Tuple     )      -> Union[str, Any]:
      return self.config.num_train_timesteps
