




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


__A    :				List[str]    						=	namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       BertTokenizer.from_pretrained("""bert-base-uncased"""       ,						do_lower_case=__UpperCamelCase )
			SCREAMING_SNAKE_CASE     =       BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization""" )
			model.to(args.device )
			model.eval()

			SCREAMING_SNAKE_CASE     =       {
			    """BOS""": tokenizer.vocab["""[unused0]"""],
			    """EOS""": tokenizer.vocab["""[unused1]"""],
			    """PAD""": tokenizer.vocab["""[PAD]"""],
			}

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

						import nltk
						import rouge

						nltk.download("""punkt""" )
						SCREAMING_SNAKE_CASE     =       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.
			SCREAMING_SNAKE_CASE     =       """"""
			SCREAMING_SNAKE_CASE     =       """"""

			SCREAMING_SNAKE_CASE     =       build_data_iterator(__UpperCamelCase       ,						__UpperCamelCase )
			SCREAMING_SNAKE_CASE     =       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 ):
						SCREAMING_SNAKE_CASE     =       predictor.translate_batch(__UpperCamelCase )
						SCREAMING_SNAKE_CASE     =       predictor.from_batch(__UpperCamelCase )
						SCREAMING_SNAKE_CASE     =       [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:
						SCREAMING_SNAKE_CASE     =       rouge_evaluator.get_scores(__UpperCamelCase       ,						__UpperCamelCase )
						SCREAMING_SNAKE_CASE     =       format_rouge_scores(__UpperCamelCase )
						save_rouge_scores(__UpperCamelCase )
						print(__UpperCamelCase )
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for summary, document_name in zip(__UpperCamelCase       ,						__UpperCamelCase ):
						# Prepare the summary file's name
						if "." in document_name:
									SCREAMING_SNAKE_CASE     =       """.""".join(document_name.split(""".""" )[:-1] )
									SCREAMING_SNAKE_CASE     =       document_name.split(""".""" )[-1]
									SCREAMING_SNAKE_CASE     =       bare_document_name + """_summary.""" + extension
						else:
									SCREAMING_SNAKE_CASE     =       document_name + """_summary"""

						SCREAMING_SNAKE_CASE     =       os.path.join(__UpperCamelCase       ,						__UpperCamelCase )
						with open(__UpperCamelCase       ,						"""w""" ) as output:
									output.write(__UpperCamelCase )
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       translation
			SCREAMING_SNAKE_CASE     =       (
			    raw_summary.replace("""[unused0]"""       ,						"""""" )
			    .replace("""[unused3]"""       ,						"""""" )
			    .replace("""[PAD]"""       ,						"""""" )
			    .replace("""[unused1]"""       ,						"""""" )
			    .replace(r""" +"""       ,						""" """ )
			    .replace(""" [unused2] """       ,						""". """ )
			    .replace("""[unused2]"""       ,						"""""" )
			    .strip()
			)

			return summary
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''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_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			with open("""rouge_scores.txt"""       ,						"""w""" ) as output:
						output.write(__UpperCamelCase )
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       load_and_cache_examples(__UpperCamelCase       ,						__UpperCamelCase )
			SCREAMING_SNAKE_CASE     =       SequentialSampler(__UpperCamelCase )

			def collate_fn(SCREAMING_SNAKE_CASE ):
						return collate(__UpperCamelCase       ,						__UpperCamelCase       ,						block_size=5_12       ,						device=args.device )

			SCREAMING_SNAKE_CASE     =       DataLoader(
			    __UpperCamelCase       ,						sampler=__UpperCamelCase       ,						batch_size=args.batch_size       ,						collate_fn=__UpperCamelCase       ,						)

			return iterator
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       CNNDMDataset(args.documents_dir )
			return dataset
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       [x for x in data if not len(x[1] ) == 0]  # remove empty_files
			SCREAMING_SNAKE_CASE     =       [name for name, _, _ in data]
			SCREAMING_SNAKE_CASE     =       [""" """.join(__UpperCamelCase ) for _, _, summary_list in data]

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

			SCREAMING_SNAKE_CASE     =       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_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       summary_tokens.to("""cpu""" ).numpy()
			SCREAMING_SNAKE_CASE     =       tokenizer.decode(__UpperCamelCase )
			SCREAMING_SNAKE_CASE     =       summary.split(""".""" )
			SCREAMING_SNAKE_CASE     =       [s + """.""" for s in sentences]
			return sentences
def        lowerCamelCase_     (     ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       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=2_00       ,						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."""       ,						)
			SCREAMING_SNAKE_CASE     =       parser.parse_args()

			# Select device (distibuted not available)
			SCREAMING_SNAKE_CASE     =       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     =       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_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			if not os.path.exists(__UpperCamelCase ):
						return False

			SCREAMING_SNAKE_CASE     =       os.listdir(__UpperCamelCase )
			if len(__UpperCamelCase ) == 0:
						return False

			return True


if __name__ == "__main__":
		main()









"""simple docstring"""






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 SchedulerMixin


__lowerCAmelCase				      =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    torch.FloatTensor




class 	_lowerCAmelCase    (      __snake_case       ,       __snake_case       ):
							__lowerCAmelCase					:    int     =  1

							@register_to_config
							def __init__( self       :    Union[str, Any] ,		a       :    int = 40 ,		a       :    float = 0.002 ,		a       :    float = 80.0 ,		a       :    float = 0.5 ,		a       :    float = 1.0 ,		a       :    float = 7.0 ,		a       :    bool = True ,		)   ->    List[Any]:

									"""simple docstring"""
									# standard deviation of the initial noise distribution
									lowercase										=							sigma_max

									lowercase										=							np.linspace(0 ,		1 ,		a    )
									lowercase										=							self._convert_to_karras(a    )
									lowercase										=							self.sigma_to_t(a    )

									# setable values
									lowercase										=							None
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							torch.from_numpy(a    )
									lowercase										=							False
									lowercase										=							False

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    Optional[int] ,		a       :    str=None    )   ->    Any:

									"""simple docstring"""
									if schedule_timesteps is None:
											lowercase										=							self.timesteps

									lowercase										=							(schedule_timesteps == timestep).nonzero()
									return indices.item()

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    torch.FloatTensor ,		a       :    Union[float, torch.FloatTensor]    )   ->    torch.FloatTensor:

									"""simple docstring"""
									# Get sigma corresponding to timestep
									if isinstance(a ,		torch.Tensor    ):
											lowercase										=							timestep.to(self.timesteps.device    )
									lowercase										=							self.index_for_timestep(a    )
									lowercase										=							self.sigmas[step_idx]

									lowercase										=							sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									lowercase										=							True
									return sample

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Union[float, np.ndarray]    )   ->    str:

									"""simple docstring"""
									if not isinstance(a ,		np.ndarray    ):
											lowercase										=							np.array(a ,		dtype=np.floataa    )

									lowercase										=							1000 * 0.25 * np.log(sigmas + 1E-44    )

									return timesteps

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Optional[int] = None ,		a       :    Union[str, torch.device] = None ,		a       :    Optional[List[int]] = None ,		)   ->    Dict:

									"""simple docstring"""
									if num_inference_steps is None and timesteps is None:
											raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''    )

									if num_inference_steps is not None and timesteps is not None:
											raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''    )

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
											for i in range(1 ,		len(a    )    ):
													if timesteps[i] >= timesteps[i - 1]:
															raise ValueError('''`timesteps` must be in descending order.'''    )

											if timesteps[0] >= self.config.num_train_timesteps:
													raise ValueError(
													    f"""`timesteps` must start before `self.config.train_timesteps`:"""
													    f""" {self.config.num_train_timesteps}."""    )

											lowercase										=							np.array(a ,		dtype=np.intaa    )
											lowercase										=							True
									else:
											if num_inference_steps > self.config.num_train_timesteps:
													raise ValueError(
													    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
													    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
													    f""" maximal {self.config.num_train_timesteps} timesteps."""    )

											lowercase										=							num_inference_steps

											lowercase										=							self.config.num_train_timesteps // self.num_inference_steps
											lowercase										=							(np.arange(0 ,		a    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
											lowercase										=							False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									lowercase										=							self.config.num_train_timesteps
									lowercase										=							timesteps[::-1].copy()
									lowercase										=							ramp / (num_train_timesteps - 1)
									lowercase										=							self._convert_to_karras(a    )
									lowercase										=							self.sigma_to_t(a    )

									lowercase										=							np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
									lowercase										=							torch.from_numpy(a    ).to(device=a    )

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

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    int    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

									lowercase										=							self.config.rho
									lowercase										=							sigma_min ** (1 / rho)
									lowercase										=							sigma_max ** (1 / rho)
									lowercase										=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    str    )   ->    int:

									"""simple docstring"""
									lowercase										=							self.config.sigma_data

									lowercase										=							sigma_data**2 / (sigma**2 + sigma_data**2)
									lowercase										=							sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Any    )   ->    Optional[int]:

									"""simple docstring"""
									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_data

									lowercase										=							sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									lowercase										=							(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    torch.FloatTensor ,		a       :    Union[float, torch.FloatTensor] ,		a       :    torch.FloatTensor ,		a       :    Optional[torch.Generator] = None ,		a       :    bool = True ,		)   ->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:

									"""simple docstring"""

									if (
									    isinstance(a ,		a    )
									    or isinstance(a ,		torch.IntTensor    )
									    or isinstance(a ,		torch.LongTensor    )
									):
											raise ValueError(
											    (
											        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
											        f""" `{self.__class__}.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(a ,		torch.Tensor    ):
											lowercase										=							timestep.to(self.timesteps.device    )

									lowercase										=							self.config.sigma_min
									lowercase										=							self.config.sigma_max

									lowercase										=							self.index_for_timestep(a    )

									# sigma_next corresponds to next_t in original implementation
									lowercase										=							self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
											lowercase										=							self.sigmas[step_index + 1]
									else:
											# Set sigma_next to sigma_min
											lowercase										=							self.sigmas[-1]

									# Get scalings for boundary conditions
									lowercase					,  lowercase										=							self.get_scalings_for_boundary_condition(a    )

									# 1. Denoise model output using boundary conditions
									lowercase										=							c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
											lowercase										=							denoised.clamp(-1 ,		1    )

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps    ) > 1:
											lowercase										=							randn_tensor(
											    model_output.shape ,		dtype=model_output.dtype ,		device=model_output.device ,		generator=a    )
									else:
											lowercase										=							torch.zeros_like(a    )
									lowercase										=							noise * self.config.s_noise

									lowercase										=							sigma_next.clamp(min=a ,		max=a    )

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									lowercase										=							denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
											return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=a    )

							def 					_lowerCAmelCase  ( self       :    Any ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		a       :    torch.FloatTensor ,		)   ->    torch.FloatTensor:

									"""simple docstring"""
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									lowercase										=							self.sigmas.to(device=original_samples.device ,		dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(a    ):
											# mps does not support float64
											lowercase										=							self.timesteps.to(original_samples.device ,		dtype=torch.floataa    )
											lowercase										=							timesteps.to(original_samples.device ,		dtype=torch.floataa    )
									else:
											lowercase										=							self.timesteps.to(original_samples.device    )
											lowercase										=							timesteps.to(original_samples.device    )

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

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

									lowercase										=							original_samples + noise * sigma
									return noisy_samples

							def __len__( self       :    Union[str, Any]    )   ->    Any:

									"""simple docstring"""
									return self.config.num_train_timesteps