




import math
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 KarrasDiffusionSchedulers, SchedulerMixin


a_         =      logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          =42
			lowerCamelCase__          =None

def 	lowerCamelCase__	(   _a							,     _a=0.999							,     _a="cosine"							,     ):
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_a):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

	elif alpha_transform_type == "exp":

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

	else:
		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")

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






class _UpperCamelCase							(							__A							,		__A				):
			'''simple docstring'''







			lowerCamelCase__          =[e.name for e in KarrasDiffusionSchedulers]
			lowerCamelCase__          =1
			@register_to_config
			def __init__(     self							:	int , a							:	int = 1000 , a							:	float = 0.0001 , a							:	float = 0.02 , a							:	str = "linear" , a							:	Optional[Union[np.ndarray, List[float]]] = None , a							:	str = "epsilon" , a							:	str = "linspace" , a							:	int = 0 , )							->    Optional[Any]:

				"""simple docstring"""

				if trained_betas is not None:
					SCREAMING_SNAKE_CASE				: List[str]			       =    torch.tensor(a , dtype=torch.floataa		)
				elif beta_schedule == "linear":
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.linspace(a , a , a , dtype=torch.floataa		)
				elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					SCREAMING_SNAKE_CASE				: Any			       =    (
					    torch.linspace(beta_start**0.5 , beta_end**0.5 , a , dtype=torch.floataa		) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					SCREAMING_SNAKE_CASE				: List[str]			       =    betas_for_alpha_bar(a		)
				else:
					raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"		)

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    1.0 - self.betas
				SCREAMING_SNAKE_CASE				: Dict			       =    torch.cumprod(self.alphas , dim=0		)

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    np.concatenate([sigmas[::-1], [0.0]]		).astype(np.floataa		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    torch.from_numpy(a		)

				# setable values
				SCREAMING_SNAKE_CASE				: str			       =    None
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    np.linspace(0 , num_train_timesteps - 1 , a , dtype=a		)[::-1].copy()
				SCREAMING_SNAKE_CASE				: int			       =    torch.from_numpy(a		)
				SCREAMING_SNAKE_CASE				: str			       =    False
			@property
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    int:

				"""simple docstring"""

				if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5
			def 							__UpperCamelCase	(     self							:	Dict , a							:	torch.FloatTensor , a							:	Union[float, torch.FloatTensor]		)							->    torch.FloatTensor:

				"""simple docstring"""

				if isinstance(a , torch.Tensor		):
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    timestep.to(self.timesteps.device		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    (self.timesteps == timestep).nonzero().item()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.sigmas[step_index]
				SCREAMING_SNAKE_CASE				: str			       =    sample / ((sigma**2 + 1) ** 0.5)
				SCREAMING_SNAKE_CASE				: Dict			       =    True
				return sample
			def 							__UpperCamelCase	(     self							:	int , a							:	int , a							:	Union[str, torch.device] = None		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    num_inference_steps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
					SCREAMING_SNAKE_CASE				: List[str]			       =    np.linspace(0 , self.config.num_train_timesteps - 1 , a , dtype=a		)[
					    ::-1
					].copy()
				elif self.config.timestep_spacing == "leading":
					SCREAMING_SNAKE_CASE				: Dict			       =    self.config.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
					SCREAMING_SNAKE_CASE				: Tuple			       =    (np.arange(0 , a		) * step_ratio).round()[::-1].copy().astype(a		)
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					SCREAMING_SNAKE_CASE				: Tuple			       =    self.config.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
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    (np.arange(self.config.num_train_timesteps , 0 , -step_ratio		)).round().copy().astype(a		)
					timesteps -= 1
				else:
					raise ValueError(
					    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
				SCREAMING_SNAKE_CASE				: str			       =    np.interp(a , np.arange(0 , len(a		)		) , a		)
				SCREAMING_SNAKE_CASE				: str			       =    np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.from_numpy(a		).to(device=a		)
				if str(a		).startswith("mps"		):
					# mps does not support float64
					SCREAMING_SNAKE_CASE				: Tuple			       =    torch.from_numpy(a		).to(a , dtype=torch.floataa		)
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.from_numpy(a		).to(device=a		)
			def 							__UpperCamelCase	(     self							:	str , a							:	torch.FloatTensor , a							:	Union[float, torch.FloatTensor] , a							:	torch.FloatTensor , a							:	Optional[torch.Generator] = None , a							:	bool = True , )							->    Union[EulerAncestralDiscreteSchedulerOutput, 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"
					        " `EulerDiscreteScheduler.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		):
					SCREAMING_SNAKE_CASE				: Any			       =    timestep.to(self.timesteps.device		)

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    (self.timesteps == timestep).nonzero().item()
				SCREAMING_SNAKE_CASE				: int			       =    self.sigmas[step_index]

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					SCREAMING_SNAKE_CASE				: Any			       =    sample - sigma * model_output
				elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					SCREAMING_SNAKE_CASE				: int			       =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"		)

				SCREAMING_SNAKE_CASE				: int			       =    self.sigmas[step_index]
				SCREAMING_SNAKE_CASE				: Tuple			       =    self.sigmas[step_index + 1]
				SCREAMING_SNAKE_CASE				: List[Any]			       =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    (sigma_to**2 - sigma_up**2) ** 0.5

				# 2. Convert to an ODE derivative
				SCREAMING_SNAKE_CASE				: List[Any]			       =    (sample - pred_original_sample) / sigma

				SCREAMING_SNAKE_CASE				: Any			       =    sigma_down - sigma

				SCREAMING_SNAKE_CASE				: Any			       =    sample + derivative * dt

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

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    prev_sample + noise * sigma_up

				if not return_dict:
					return (prev_sample,)

				return EulerAncestralDiscreteSchedulerOutput(
				    prev_sample=a , pred_original_sample=a		)
			def 							__UpperCamelCase	(     self							:	str , a							:	torch.FloatTensor , a							:	torch.FloatTensor , a							:	torch.FloatTensor , )							->    torch.FloatTensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    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
					SCREAMING_SNAKE_CASE				: List[str]			       =    self.timesteps.to(original_samples.device , dtype=torch.floataa		)
					SCREAMING_SNAKE_CASE				: Dict			       =    timesteps.to(original_samples.device , dtype=torch.floataa		)
				else:
					SCREAMING_SNAKE_CASE				: Tuple			       =    self.timesteps.to(original_samples.device		)
					SCREAMING_SNAKE_CASE				: Any			       =    timesteps.to(original_samples.device		)

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    sigmas[step_indices].flatten()
				while len(sigma.shape		) < len(original_samples.shape		):
					SCREAMING_SNAKE_CASE				: Tuple			       =    sigma.unsqueeze(-1		)

				SCREAMING_SNAKE_CASE				: Tuple			       =    original_samples + noise * sigma
				return noisy_samples






			def __len__(     self							:	List[Any]		)							->    Dict:

				"""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,
)


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

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

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

		import nltk
		import rouge

		nltk.download("punkt")
		SCREAMING_SNAKE_CASE				: List[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.
	SCREAMING_SNAKE_CASE				: int			       =    ""
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    ""

	SCREAMING_SNAKE_CASE				: Tuple			       =    build_data_iterator(_a							,     _a)
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    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):
		SCREAMING_SNAKE_CASE				: Tuple			       =    predictor.translate_batch(_a)
		SCREAMING_SNAKE_CASE				: List[Any]			       =    predictor.from_batch(_a)
		SCREAMING_SNAKE_CASE				: List[str]			       =    [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:
		SCREAMING_SNAKE_CASE				: List[Any]			       =    rouge_evaluator.get_scores(_a							,     _a)
		SCREAMING_SNAKE_CASE				: Tuple			       =    format_rouge_scores(_a)
		save_rouge_scores(_a)
		print(_a)

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

		SCREAMING_SNAKE_CASE				: Dict			       =    os.path.join(_a							,     _a)
		with open(_a							,     "w") as output:
			output.write(_a)

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Optional[Any]			       =    translation
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    (
	    raw_summary.replace("[unused0]"							,     "")
	    .replace("[unused3]"							,     "")
	    .replace("[PAD]"							,     "")
	    .replace("[unused1]"							,     "")
	    .replace(r" +"							,     " ")
	    .replace(" [unused2] "							,     ". ")
	    .replace("[unused2]"							,     "")
	    .strip()
	)

	return summary

def 	lowerCamelCase__	(   _a):
	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__	(   _a):
	with open("rouge_scores.txt"							,     "w") as output:
		output.write(_a)

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Any			       =    load_and_cache_examples(_a							,     _a)
	SCREAMING_SNAKE_CASE				: int			       =    SequentialSampler(_a)

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

	SCREAMING_SNAKE_CASE				: Tuple			       =    DataLoader(
	    _a							,     sampler=_a							,     batch_size=args.batch_size							,     collate_fn=_a							,     )

	return iterator

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Tuple			       =    CNNDMDataset(args.documents_dir)
	return dataset

def 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: str			       =    [x for x in data if not len(x[1]) == 0]  # remove empty_files
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    [name for name, _, _ in data]
	SCREAMING_SNAKE_CASE				: Dict			       =    [" ".join(_a) for _, _, summary_list in data]

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

	SCREAMING_SNAKE_CASE				: Optional[int]			       =    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 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: int			       =    summary_tokens.to("cpu").numpy()
	SCREAMING_SNAKE_CASE				: Tuple			       =    tokenizer.decode(_a)
	SCREAMING_SNAKE_CASE				: List[str]			       =    summary.split(".")
	SCREAMING_SNAKE_CASE				: int			       =    [s + "." for s in sentences]
	return sentences

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: str			       =    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=0.95							,     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."							,     )
	SCREAMING_SNAKE_CASE				: Tuple			       =    parser.parse_args()

	# Select device (distibuted not available)
	SCREAMING_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:
		SCREAMING_SNAKE_CASE				: Dict			       =    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 	lowerCamelCase__	(   _a):
	if not os.path.exists(_a):
		return False

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

	return True


if __name__ == "__main__":
			main()