# 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 			__snake_case  :
					__lowerCamelCase	 =       42
					__lowerCamelCase	 =       42

					# setable values
					__lowerCamelCase	 =       42
					__lowerCamelCase	 =       42
					__lowerCamelCase	 =       None




					@classmethod
					def       __a      (       cls   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	)					->  List[Any]:



									'''simple docstring'''







									return cls(
									    common=__UpperCamelCase   ,	final_alpha_cumprod=__UpperCamelCase   ,	init_noise_sigma=__UpperCamelCase   ,	timesteps=__UpperCamelCase   ,	)





@dataclass
class 			__snake_case  (      _lowerCamelCase     ):
					__lowerCamelCase	 =       42





class 			__snake_case  (      _lowerCamelCase   ,_lowerCamelCase     ):
					__lowerCamelCase	 =       [e.name for e in FlaxKarrasDiffusionSchedulers]

					__lowerCamelCase	 =       42




					@property
					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									return True




					@register_to_config
					def __init__(       self   ,	__UpperCamelCase = 1000   ,	__UpperCamelCase = 0.0_0_0_1   ,	__UpperCamelCase = 0.0_2   ,	__UpperCamelCase = "linear"   ,	__UpperCamelCase = None   ,	__UpperCamelCase = True   ,	__UpperCamelCase = 0   ,	__UpperCamelCase = "epsilon"   ,	__UpperCamelCase = jnp.floataa   ,	)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     int 							=				dtype




					def       __a      (       self   ,	__UpperCamelCase = None		)					->  DDIMSchedulerState:



									'''simple docstring'''







									if common is None:
													snake_case__ :     Any 							=				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.
									snake_case__ :     List[Any] 							=				(
									    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
									snake_case__ :     Union[str, Any] 							=				jnp.array(1.0   ,	dtype=self.dtype		)

									snake_case__ :     Union[str, Any] 							=				jnp.arange(0   ,	self.config.num_train_timesteps		).round()[::-1]

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




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase = None		)					->  jnp.ndarray:



									'''simple docstring'''







									return sample




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase = ()		)					->  DDIMSchedulerState:



									'''simple docstring'''







									snake_case__ :     Dict 							=				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
									snake_case__ :     Optional[int] 							=				(jnp.arange(0   ,	__UpperCamelCase		) * step_ratio).round()[::-1] + self.config.steps_offset

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




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				state.common.alphas_cumprod[timestep]
									snake_case__ :     Tuple 							=				jnp.where(
									    prev_timestep >= 0   ,	state.common.alphas_cumprod[prev_timestep]   ,	state.final_alpha_cumprod		)
									snake_case__ :     List[str] 							=				1 - alpha_prod_t
									snake_case__ :     int 							=				1 - alpha_prod_t_prev

									snake_case__ :     List[str] 							=				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

									return variance




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase = 0.0   ,	__UpperCamelCase = True   ,	)					->  Union[FlaxDDIMSchedulerOutput, Tuple]:



									'''simple docstring'''







									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)
									snake_case__ :     Dict 							=				timestep - self.config.num_train_timesteps // state.num_inference_steps

									snake_case__ :     Optional[Any] 							=				state.common.alphas_cumprod
									snake_case__ :     List[Any] 							=				state.final_alpha_cumprod

									# 2. compute alphas, betas
									snake_case__ :     Union[str, Any] 							=				alphas_cumprod[timestep]
									snake_case__ :     List[str] 							=				jnp.where(prev_timestep >= 0   ,	alphas_cumprod[prev_timestep]   ,	__UpperCamelCase		)

									snake_case__ :     List[str] 							=				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":
													snake_case__ :     Any 							=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													snake_case__ :     Dict 							=				model_output
									elif self.config.prediction_type == "sample":
													snake_case__ :     Dict 							=				model_output
													snake_case__ :     str 							=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
									elif self.config.prediction_type == "v_prediction":
													snake_case__ :     int 							=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													snake_case__ :     List[str] 							=				(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)
									snake_case__ :     Any 							=				self._get_variance(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)
									snake_case__ :     Tuple 							=				eta * variance ** 0.5

									# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									snake_case__ :     List[Any] 							=				(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
									snake_case__ :     Dict 							=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

									if not return_dict:
													return (prev_sample, state)

									return FlaxDDIMSchedulerOutput(prev_sample=__UpperCamelCase   ,	state=__UpperCamelCase		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	)					->  jnp.ndarray:



									'''simple docstring'''







									return add_noise_common(state.common   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	)					->  jnp.ndarray:



									'''simple docstring'''







									return get_velocity_common(state.common   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)




					def __len__(       self		)					->  Tuple:



									'''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,
)


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


lowerCAmelCase__					:       Union[str, Any]		=       namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def 			UpperCamelCase__						(    A__   )       -> Dict:
				snake_case__ :     List[str] 							=				BertTokenizer.from_pretrained('bert-base-uncased'      ,			do_lower_case=A__   )
				snake_case__ :     Optional[int] 							=				BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'   )
				model.to(args.device   )
				model.eval()

				snake_case__ :     Optional[int] 							=				{
				    'BOS': tokenizer.vocab['[unused0]'],
				    'EOS': tokenizer.vocab['[unused1]'],
				    'PAD': tokenizer.vocab['[PAD]'],
				}

				if args.compute_rouge:
								snake_case__ :     Tuple 							=				[]
								snake_case__ :     Union[str, Any] 							=				[]

								import nltk
								import rouge

								nltk.download('punkt'   )
								snake_case__ :     Optional[Any] 							=				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.
				snake_case__ :     int 							=				''
				snake_case__ :     Any 							=				''

				snake_case__ :     Dict 							=				build_data_iterator(A__      ,			A__   )
				snake_case__ :     List[Any] 							=				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__   ):
								snake_case__ :     Dict 							=				predictor.translate_batch(A__   )
								snake_case__ :     List[str] 							=				predictor.from_batch(A__   )
								snake_case__ :     List[Any] 							=				[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:
								snake_case__ :     str 							=				rouge_evaluator.get_scores(A__      ,			A__   )
								snake_case__ :     Tuple 							=				format_rouge_scores(A__   )
								save_rouge_scores(A__   )
								print(A__   )
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__   )       -> Optional[int]:
				for summary, document_name in zip(A__      ,			A__   ):
								# Prepare the summary file's name
								if "." in document_name:
												snake_case__ :     Optional[int] 							=				'.'.join(document_name.split('.'   )[:-1]   )
												snake_case__ :     List[str] 							=				document_name.split('.'   )[-1]
												snake_case__ :     Optional[Any] 							=				bare_document_name + '_summary.' + extension
								else:
												snake_case__ :     Optional[Any] 							=				document_name + '_summary'

								snake_case__ :     Optional[int] 							=				os.path.join(A__      ,			A__   )
								with open(A__      ,			'w'   ) as output:
												output.write(A__   )
def 			UpperCamelCase__						(    A__   )       -> Dict:
				snake_case__							,  snake_case__							,  snake_case__ :     str 							=				translation
				snake_case__ :     str 							=				(
				    raw_summary.replace('[unused0]'      ,			''   )
				    .replace('[unused3]'      ,			''   )
				    .replace('[PAD]'      ,			''   )
				    .replace('[unused1]'      ,			''   )
				    .replace(r' +'      ,			' '   )
				    .replace(' [unused2] '      ,			'. '   )
				    .replace('[unused2]'      ,			''   )
				    .strip()
				)

				return summary
def 			UpperCamelCase__						(    A__   )       -> Dict:
				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__   )       -> str:
				with open('rouge_scores.txt'      ,			'w'   ) as output:
								output.write(A__   )
def 			UpperCamelCase__						(    A__      ,			A__   )       -> str:
				snake_case__ :     Optional[int] 							=				load_and_cache_examples(A__      ,			A__   )
				snake_case__ :     str 							=				SequentialSampler(A__   )

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

				snake_case__ :     List[str] 							=				DataLoader(
				    A__      ,			sampler=A__      ,			batch_size=args.batch_size      ,			collate_fn=A__      ,			)

				return iterator
def 			UpperCamelCase__						(    A__      ,			A__   )       -> Optional[int]:
				snake_case__ :     List[Any] 							=				CNNDMDataset(args.documents_dir   )
				return dataset
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__   )       -> List[Any]:
				snake_case__ :     List[str] 							=				[x for x in data if not len(x[1]   ) == 0]  # remove empty_files
				snake_case__ :     Tuple 							=				[name for name, _, _ in data]
				snake_case__ :     Any 							=				[' '.join(A__   ) for _, _, summary_list in data]

				snake_case__ :     int 							=				[encode_for_summarization(A__      ,			A__      ,			A__   ) for _, story, summary in data]
				snake_case__ :     List[Any] 							=				torch.tensor(
				    [truncate_or_pad(A__      ,			A__      ,			tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
				snake_case__ :     List[Any] 							=				compute_token_type_ids(A__      ,			tokenizer.cls_token_id   )
				snake_case__ :     Optional[Any] 							=				build_mask(A__      ,			tokenizer.pad_token_id   )

				snake_case__ :     Optional[Any] 							=				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__   )       -> List[str]:
				snake_case__ :     Optional[int] 							=				summary_tokens.to('cpu'   ).numpy()
				snake_case__ :     Dict 							=				tokenizer.decode(A__   )
				snake_case__ :     Tuple 							=				summary.split('.'   )
				snake_case__ :     int 							=				[s + '.' for s in sentences]
				return sentences
def 			UpperCamelCase__						(    )       -> Union[str, Any]:
				snake_case__ :     List[Any] 							=				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.'      ,			)
				snake_case__ :     int 							=				parser.parse_args()

				# Select device (distibuted not available)
				snake_case__ :     Dict 							=				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:
								snake_case__ :     List[str] 							=				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__   )       -> Dict:
				if not os.path.exists(A__   ):
								return False

				snake_case__ :     List[str] 							=				os.listdir(A__   )
				if len(A__   ) == 0:
								return False

				return True


if __name__ == "__main__":
				main()


