





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_        =  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


A_        =  namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])





def  __UpperCamelCase   (     a)			->Tuple:
					lowerCamelCase__      =							BertTokenizer.from_pretrained("bert-base-uncased",  do_lower_case=a)
					lowerCamelCase__      =							BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
					model.to(args.device)
					model.eval()

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

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

										import nltk
										import rouge

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

					lowerCamelCase__      =							build_data_iterator(a,  a)
					lowerCamelCase__      =							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):
										lowerCamelCase__      =							predictor.translate_batch(a)
										lowerCamelCase__      =							predictor.from_batch(a)
										lowerCamelCase__      =							[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:
										lowerCamelCase__      =							rouge_evaluator.get_scores(a,  a)
										lowerCamelCase__      =							format_rouge_scores(a)
										save_rouge_scores(a)
										print(a)





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

										lowerCamelCase__      =							os.path.join(a,  a)
										with open(a,  "w") as output:
															output.write(a)





def  __UpperCamelCase   (     a)			->Any:
					lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__      =							translation
					lowerCamelCase__      =							(
					    raw_summary.replace("[unused0]",  "")
					    .replace("[unused3]",  "")
					    .replace("[PAD]",  "")
					    .replace("[unused1]",  "")
					    .replace(R" +",  " ")
					    .replace(" [unused2] ",  ". ")
					    .replace("[unused2]",  "")
					    .strip()
					)

					return summary





def  __UpperCamelCase   (     a)			->List[Any]:
					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)			->Dict:
					with open("rouge_scores.txt",  "w") as output:
										output.write(a)





def  __UpperCamelCase   (     a,  a)			->Union[str, Any]:
					lowerCamelCase__      =							load_and_cache_examples(a,  a)
					lowerCamelCase__      =							SequentialSampler(a)

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

					lowerCamelCase__      =							DataLoader(
					    a,  sampler=a,  batch_size=args.batch_size,  collate_fn=a,  )

					return iterator





def  __UpperCamelCase   (     a,  a)			->List[Any]:
					lowerCamelCase__      =							CNNDMDataset(args.documents_dir)
					return dataset





def  __UpperCamelCase   (     a,  a,  a,  a)			->Dict:
					lowerCamelCase__      =							[x for x in data if not len(x[1]) == 0]  # remove empty_files
					lowerCamelCase__      =							[name for name, _, _ in data]
					lowerCamelCase__      =							[" ".join(a) for _, _, summary_list in data]

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

					lowerCamelCase__      =							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)			->Optional[Any]:
					lowerCamelCase__      =							summary_tokens.to("cpu").numpy()
					lowerCamelCase__      =							tokenizer.decode(a)
					lowerCamelCase__      =							summary.split(".")
					lowerCamelCase__      =							[s + "." for s in sentences]
					return sentences





def  __UpperCamelCase   (     )			->List[Any]:
					lowerCamelCase__      =							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=0.9_5,  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.",  )
					lowerCamelCase__      =							parser.parse_args()

					# Select device (distibuted not available)
					lowerCamelCase__      =							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__      =							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)			->List[Any]:
					if not os.path.exists(a):
										return False

					lowerCamelCase__      =							os.listdir(a)
					if len(a) == 0:
										return False

					return True


if __name__ == "__main__":
						main()








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

import flax
import jax.numpy as jnp
from scipy import integrate

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

@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					A__								=     42

					# setable values
					A__								=     42
					A__								=     42
					A__								=     42
					A__								=     None

					# running values
					A__								=     None





					@classmethod
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										return cls(common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						)

@dataclass
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					A__								=     42





					@property
					def        __magic_name__			(			self						):
										return True





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = jnp.floataa						,	):
										lowerCamelCase__      =							dtype





					def        __magic_name__			(			self						,	_lowerCAmelCase = None						):
										if common is None:
															lowerCamelCase__      =							CommonSchedulerState.create(self						)

										lowerCamelCase__      =							jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]
										lowerCamelCase__      =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										lowerCamelCase__      =							sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										((lowerCamelCase__)     ,		)      =							jnp.where(state.timesteps == timestep						,	size=1						)
										lowerCamelCase__      =							step_index[0]

										lowerCamelCase__      =							state.sigmas[step_index]
										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def lms_derivative(_lowerCAmelCase						):
															lowerCamelCase__      =							1.0
															for k in range(_lowerCAmelCase						):
																				if current_order == k:
																									continue
																				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

										lowerCamelCase__      =							integrate.quad(_lowerCAmelCase						,	state.sigmas[t]						,	state.sigmas[t + 1]						,	epsrel=1E-4						)[0]

										return integrated_coeff





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = ()						):
										lowerCamelCase__      =							jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	_lowerCAmelCase						,	dtype=self.dtype						)

										lowerCamelCase__      =							jnp.floor(_lowerCAmelCase						).astype(jnp.intaa						)
										lowerCamelCase__      =							jnp.ceil(_lowerCAmelCase						).astype(jnp.intaa						)

										lowerCamelCase__      =							jnp.mod(_lowerCAmelCase						,	1.0						)

										lowerCamelCase__      =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCamelCase__      =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										lowerCamelCase__      =							jnp.concatenate([sigmas, jnp.array([0.0]						,	dtype=self.dtype						)]						)

										lowerCamelCase__      =							timesteps.astype(jnp.intaa						)

										# initial running values
										lowerCamelCase__      =							jnp.zeros((0,) + shape						,	dtype=self.dtype						)

										return state.replace(
										    timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	num_inference_steps=_lowerCAmelCase						,	derivatives=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 4						,	_lowerCAmelCase = True						,	):
										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"						)

										lowerCamelCase__      =							state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										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
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma
										lowerCamelCase__      =							state.replace(derivatives=jnp.append(state.derivatives						,	_lowerCAmelCase						)						)
										if len(state.derivatives						) > order:
															lowerCamelCase__      =							state.replace(derivatives=jnp.delete(state.derivatives						,	0						)						)

										# 3. Compute linear multistep coefficients
										lowerCamelCase__      =							min(timestep + 1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[self.get_lms_coefficient(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

										# 4. Compute previous sample based on the derivatives path
										lowerCamelCase__      =							sample + sum(
										    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase						,	reversed(state.derivatives						)						)						)

										if not return_dict:
															return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase						,	state=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							state.sigmas[timesteps].flatten()
										lowerCamelCase__      =							broadcast_to_shape_from_left(_lowerCAmelCase						,	noise.shape						)

										lowerCamelCase__      =							original_samples + noise * sigma

										return noisy_samples






					def __len__(			self						):
										return self.config.num_train_timesteps

