




"""simple docstring"""






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase__     						=						"""
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowercase__     						=						"""\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


lowercase__     						=						"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""







def      _snake_case			(				lowercase__		):

				def remove_articles(lowercase__		):
								_lowerCamelCase     :			str    =    re.compile(r'\b(a|an|the)\b'							,    re.UNICODE		)
								return re.sub(lowercase__							,    ' '							,    lowercase__		)

				def white_space_fix(lowercase__		):
								return " ".join(text.split()		)

				def remove_punc(lowercase__		):
								_lowerCamelCase     :			Tuple    =    set(string.punctuation		)
								return "".join(ch for ch in text if ch not in exclude		)

				def lower(lowercase__		):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowercase__		)		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):
				return int(normalize_answer(lowercase__		) == normalize_answer(lowercase__		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    [any(compute_exact(lowercase__							,    lowercase__		) for ref in refs		) for pred, refs in zip(lowercase__							,    lowercase__		)]
				return (sum(lowercase__		) / len(lowercase__		)) * 100







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    [rgram for rgrams in rgramslist for rgram in rgrams]
				_lowerCamelCase     :			str    =    Counter(lowercase__		)

				_lowerCamelCase     :			Tuple    =    Counter(lowercase__		)
				_lowerCamelCase     :			Any    =    Counter()
				for sgram, scount in sgramcounter.items():
								_lowerCamelCase     :			Optional[Any]    =    scount * numref

				_lowerCamelCase     :			Tuple    =    Counter(lowercase__		)
				_lowerCamelCase     :			List[Any]    =    Counter()
				for cgram, ccount in cgramcounter.items():
								_lowerCamelCase     :			Tuple    =    ccount * numref

				# KEEP
				_lowerCamelCase     :			List[Any]    =    sgramcounter_rep & cgramcounter_rep
				_lowerCamelCase     :			int    =    keepgramcounter_rep & rgramcounter
				_lowerCamelCase     :			str    =    sgramcounter_rep & rgramcounter

				_lowerCamelCase     :			List[Any]    =    0
				_lowerCamelCase     :			Tuple    =    0
				for keepgram in keepgramcountergood_rep:
								keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
								# Fix an alleged bug [2] in the keep score computation.
								# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
								keeptmpscorea += keepgramcountergood_rep[keepgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				#      a target exactly.
				_lowerCamelCase     :			int    =    1
				_lowerCamelCase     :			int    =    1
				if len(lowercase__		) > 0:
								_lowerCamelCase     :			Dict    =    keeptmpscorea / len(lowercase__		)
				if len(lowercase__		) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								_lowerCamelCase     :			List[Any]    =    keeptmpscorea / sum(keepgramcounterall_rep.values()		)
				_lowerCamelCase     :			str    =    0
				if keepscore_precision > 0 or keepscore_recall > 0:
								_lowerCamelCase     :			Dict    =    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				_lowerCamelCase     :			Optional[Any]    =    sgramcounter_rep - cgramcounter_rep
				_lowerCamelCase     :			List[Any]    =    delgramcounter_rep - rgramcounter
				_lowerCamelCase     :			List[str]    =    sgramcounter_rep - rgramcounter
				_lowerCamelCase     :			str    =    0
				_lowerCamelCase     :			Dict    =    0
				for delgram in delgramcountergood_rep:
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				_lowerCamelCase     :			str    =    1
				if len(lowercase__		) > 0:
								_lowerCamelCase     :			Dict    =    deltmpscorea / len(lowercase__		)

				# ADDITION
				_lowerCamelCase     :			Union[str, Any]    =    set(lowercase__		) - set(lowercase__		)
				_lowerCamelCase     :			Tuple    =    set(lowercase__		) & set(lowercase__		)
				_lowerCamelCase     :			str    =    set(lowercase__		) - set(lowercase__		)

				_lowerCamelCase     :			Union[str, Any]    =    0
				for addgram in addgramcountergood:
								addtmpscore += 1

				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				_lowerCamelCase     :			int    =    1
				_lowerCamelCase     :			Union[str, Any]    =    1
				if len(lowercase__		) > 0:
								_lowerCamelCase     :			Any    =    addtmpscore / len(lowercase__		)
				if len(lowercase__		) > 0:
								_lowerCamelCase     :			Any    =    addtmpscore / len(lowercase__		)
				_lowerCamelCase     :			Union[str, Any]    =    0
				if addscore_precision > 0 or addscore_recall > 0:
								_lowerCamelCase     :			Union[str, Any]    =    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    len(lowercase__		)

				_lowerCamelCase     :			str    =    ssent.split(' '		)
				_lowerCamelCase     :			List[Any]    =    csent.split(' '		)
				_lowerCamelCase     :			Optional[Any]    =    []
				_lowerCamelCase     :			Optional[int]    =    []
				_lowerCamelCase     :			Dict    =    []
				_lowerCamelCase     :			Optional[int]    =    []
				_lowerCamelCase     :			List[Any]    =    []
				_lowerCamelCase     :			Dict    =    []

				_lowerCamelCase     :			Union[str, Any]    =    []
				_lowerCamelCase     :			Dict    =    []
				_lowerCamelCase     :			int    =    []
				_lowerCamelCase     :			List[Any]    =    []
				for rsent in rsents:
								_lowerCamelCase     :			Tuple    =    rsent.split(' '		)
								_lowerCamelCase     :			Any    =    []
								_lowerCamelCase     :			int    =    []
								_lowerCamelCase     :			Tuple    =    []
								ragramslist.append(lowercase__		)
								for i in range(0							,    len(lowercase__		) - 1		):
												if i < len(lowercase__		) - 1:
																_lowerCamelCase     :			str    =    ragrams[i] + ' ' + ragrams[i + 1]
																ragrams.append(lowercase__		)
												if i < len(lowercase__		) - 2:
																_lowerCamelCase     :			List[str]    =    ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																ragrams.append(lowercase__		)
												if i < len(lowercase__		) - 3:
																_lowerCamelCase     :			List[str]    =    ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
																ragrams.append(lowercase__		)
								ragramslist.append(lowercase__		)
								ragramslist.append(lowercase__		)
								ragramslist.append(lowercase__		)

				for i in range(0							,    len(lowercase__		) - 1		):
								if i < len(lowercase__		) - 1:
												_lowerCamelCase     :			List[str]    =    sagrams[i] + ' ' + sagrams[i + 1]
												sagrams.append(lowercase__		)
								if i < len(lowercase__		) - 2:
												_lowerCamelCase     :			List[Any]    =    sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
												sagrams.append(lowercase__		)
								if i < len(lowercase__		) - 3:
												_lowerCamelCase     :			List[Any]    =    sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
												sagrams.append(lowercase__		)

				for i in range(0							,    len(lowercase__		) - 1		):
								if i < len(lowercase__		) - 1:
												_lowerCamelCase     :			Optional[int]    =    cagrams[i] + ' ' + cagrams[i + 1]
												cagrams.append(lowercase__		)
								if i < len(lowercase__		) - 2:
												_lowerCamelCase     :			Optional[Any]    =    cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
												cagrams.append(lowercase__		)
								if i < len(lowercase__		) - 3:
												_lowerCamelCase     :			Optional[int]    =    cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
												cagrams.append(lowercase__		)

				((_lowerCamelCase), (_lowerCamelCase), (_lowerCamelCase))     :			Optional[Any]    =    SARIngram(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)
				((_lowerCamelCase), (_lowerCamelCase), (_lowerCamelCase))     :			Tuple    =    SARIngram(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)
				((_lowerCamelCase), (_lowerCamelCase), (_lowerCamelCase))     :			List[str]    =    SARIngram(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)
				((_lowerCamelCase), (_lowerCamelCase), (_lowerCamelCase))     :			Optional[int]    =    SARIngram(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)
				_lowerCamelCase     :			List[Any]    =    sum([keepascore, keepascore, keepascore, keepascore]		) / 4
				_lowerCamelCase     :			int    =    sum([delascore, delascore, delascore, delascore]		) / 4
				_lowerCamelCase     :			Union[str, Any]    =    sum([addascore, addascore, addascore, addascore]		) / 4
				_lowerCamelCase     :			List[Any]    =    (avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore







def      _snake_case			(				lowercase__							,    lowercase__ = True							,    lowercase__ = "13a"							,    lowercase__ = True		):
				# Normalization is requried for the ASSET dataset (one of the primary
				# datasets in sentence simplification) to allow using space
				# to split the sentence. Even though Wiki-Auto and TURK datasets,
				# do not require normalization, we do it for consistency.
				# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
				# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

				if lowercase:
								_lowerCamelCase     :			Union[str, Any]    =    sentence.lower()

				if tokenizer in ["13a", "intl"]:
								if version.parse(sacrebleu.__version__		).major >= 2:
												_lowerCamelCase     :			Union[str, Any]    =    sacrebleu.metrics.bleu._get_tokenizer(lowercase__		)()(lowercase__		)
								else:
												_lowerCamelCase     :			Optional[int]    =    sacrebleu.TOKENIZERS[tokenizer]()(lowercase__		)
				elif tokenizer == "moses":
								_lowerCamelCase     :			str    =    sacremoses.MosesTokenizer().tokenize(lowercase__							,    return_str=lowercase__							,    escape=lowercase__		)
				elif tokenizer == "penn":
								_lowerCamelCase     :			List[str]    =    sacremoses.MosesTokenizer().penn_tokenize(lowercase__							,    return_str=lowercase__		)
				else:
								_lowerCamelCase     :			str    =    sentence

				if not return_str:
								_lowerCamelCase     :			List[Any]    =    normalized_sent.split()

				return normalized_sent







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if not (len(lowercase__		) == len(lowercase__		) == len(lowercase__		)):
								raise ValueError('Sources length must match predictions and references lengths.'		)
				_lowerCamelCase     :			List[str]    =    0
				for src, pred, refs in zip(lowercase__							,    lowercase__							,    lowercase__		):
								sari_score += SARIsent(normalize(lowercase__		)							,    normalize(lowercase__		)							,    [normalize(lowercase__		) for sent in refs]		)
				_lowerCamelCase     :			List[str]    =    sari_score / len(lowercase__		)
				return 100 * sari_score







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__="exp"							,    lowercase__=None							,    lowercase__=False							,    lowercase__=False							,    lowercase__=False							,    ):
				_lowerCamelCase     :			Any    =    len(references[0]		)
				if any(len(lowercase__		) != references_per_prediction for refs in references		):
								raise ValueError('Sacrebleu requires the same number of references for each prediction'		)
				_lowerCamelCase     :			Optional[Any]    =    [[refs[i] for refs in references] for i in range(lowercase__		)]
				_lowerCamelCase     :			Optional[Any]    =    sacrebleu.corpus_bleu(
				    lowercase__							,    lowercase__							,    smooth_method=lowercase__							,    smooth_value=lowercase__							,    force=lowercase__							,    lowercase=lowercase__							,    use_effective_order=lowercase__							,    )
				return output.score






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,	_KWARGS_DESCRIPTION						)
class lowerCAmelCase__	(     datasets.Metric						):






			'''simple docstring'''



			def 					A_    (  self  ):
							return datasets.MetricInfo(
							    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
							        {
							            'predictions': datasets.Value('string'  ,      id='sequence'  ),
							            'references': datasets.Sequence(datasets.Value('string'  ,      id='sequence'  )  ,      id='references'  ),
							        }  )  ,      codebase_urls=[
							        'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
							        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
							        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
							        'https://github.com/mjpost/sacreBLEU',
							    ]  ,      reference_urls=[
							        'https://www.aclweb.org/anthology/Q16-1029.pdf',
							        'https://github.com/mjpost/sacreBLEU',
							        'https://en.wikipedia.org/wiki/BLEU',
							        'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
							    ]  ,      )






			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    {}
							result.update({'sari': compute_sari(sources=lowercase  ,      predictions=lowercase  ,      references=lowercase  )}  )
							result.update({'sacrebleu': compute_sacrebleu(predictions=lowercase  ,      references=lowercase  )}  )
							result.update({'exact': compute_em(predictions=lowercase  ,      references=lowercase  )}  )
							return result






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]=0.999		,   __UpperCAmelCase:			Tuple="cosine"		,   )      ->	Optional[Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.exp(t * -12.0      )

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

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






class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[Any]   		=		[e.name for e in KarrasDiffusionSchedulers]
   a						:							Union[str, Any]   		=		2




   @register_to_config
   def __init__(    self,		__magic_name__ = 1000,		__magic_name__ = 0.0_0085,		__magic_name__ = 0.012,		__magic_name__ = "linear",		__magic_name__ = None,		__magic_name__ = "epsilon",		__magic_name__ = "linspace",		__magic_name__ = 0,		) ->      Tuple:






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

       UpperCamelCase__							:				Optional[int]          =      1.0 - self.betas
       UpperCamelCase__							:				List[Any]          =      torch.cumprod(self.alphas,		dim=0							)

       #  set all values
       self.set_timesteps(__magic_name__,		__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None							) ->      str:






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

       UpperCamelCase__							:				Tuple          =      (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter							) == 0:
           UpperCamelCase__							:				List[str]          =      1 if len(__magic_name__							) > 1 else 0
       else:
           UpperCamelCase__							:				List[Any]          =      timestep.cpu().item() if torch.is_tensor(__magic_name__							) else timestep
           UpperCamelCase__							:				int          =      self._index_counter[timestep_int]

       return indices[pos].item()




   @property
   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.index_for_timestep(__magic_name__							)

       if self.state_in_first_order:
           UpperCamelCase__							:				str          =      self.sigmas[step_index]
       else:
           UpperCamelCase__							:				Optional[int]          =      self.sigmas_interpol[step_index]

       UpperCamelCase__							:				Optional[int]          =      sample / ((sigma**2 + 1) ** 0.5)
       return sample




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      num_inference_steps

       UpperCamelCase__							:				Tuple          =      num_train_timesteps or self.config.num_train_timesteps

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

       UpperCamelCase__							:				int          =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
       UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(np.log(__magic_name__							)							).to(__magic_name__							)

       UpperCamelCase__							:				Any          =      np.interp(__magic_name__,		np.arange(0,		len(__magic_name__							)							),		__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
       UpperCamelCase__							:				Any          =      torch.from_numpy(__magic_name__							).to(device=__magic_name__							)

       # interpolate sigmas
       UpperCamelCase__							:				int          =      sigmas.log().lerp(sigmas.roll(1							).log(),		0.5							).exp()

       UpperCamelCase__							:				List[str]          =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
       UpperCamelCase__							:				str          =      torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

       if str(__magic_name__							).startswith('''mps'''							):
           # mps does not support float64
           UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				List[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__							)

       # interpolate timesteps
       UpperCamelCase__							:				str          =      self.sigma_to_t(__magic_name__							).to(__magic_name__,		dtype=timesteps.dtype							)
       UpperCamelCase__							:				Dict          =      torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),		dim=-1							).flatten()

       UpperCamelCase__							:				Optional[int]          =      torch.cat([timesteps[:1], interleaved_timesteps]							)

       UpperCamelCase__							:				List[str]          =      None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       UpperCamelCase__							:				Dict          =      defaultdict(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       # get log sigma
       UpperCamelCase__							:				Any          =      sigma.log()

       # get distribution
       UpperCamelCase__							:				List[str]          =      log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       UpperCamelCase__							:				int          =      dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
       UpperCamelCase__							:				Optional[Any]          =      low_idx + 1

       UpperCamelCase__							:				str          =      self.log_sigmas[low_idx]
       UpperCamelCase__							:				int          =      self.log_sigmas[high_idx]

       # interpolate sigmas
       UpperCamelCase__							:				List[Any]          =      (low - log_sigma) / (low - high)
       UpperCamelCase__							:				str          =      w.clamp(0,		1							)

       # transform interpolation to time range
       UpperCamelCase__							:				Tuple          =      (1 - w) * low_idx + w * high_idx
       UpperCamelCase__							:				int          =      t.view(sigma.shape							)
       return t




   @property
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       return self.sample is None




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = True,		) ->      Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.index_for_timestep(__magic_name__							)

       # advance index counter by 1
       UpperCamelCase__							:				int          =      timestep.cpu().item() if torch.is_tensor(__magic_name__							) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
           UpperCamelCase__							:				Optional[Any]          =      self.sigmas[step_index]
           UpperCamelCase__							:				Union[str, Any]          =      self.sigmas_interpol[step_index + 1]
           UpperCamelCase__							:				List[Any]          =      self.sigmas[step_index + 1]
       else:
           # 2nd order / KDPM2's method
           UpperCamelCase__							:				Tuple          =      self.sigmas[step_index - 1]
           UpperCamelCase__							:				Tuple          =      self.sigmas_interpol[step_index]
           UpperCamelCase__							:				Dict          =      self.sigmas[step_index]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       UpperCamelCase__							:				Optional[int]          =      0
       UpperCamelCase__							:				List[Any]          =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           UpperCamelCase__							:				Union[str, Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				List[str]          =      sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__							:				List[Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				Optional[Any]          =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**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`"							)

       if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           UpperCamelCase__							:				List[Any]          =      (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           UpperCamelCase__							:				List[str]          =      sigma_interpol - sigma_hat

           # store for 2nd order step
           UpperCamelCase__							:				Dict          =      sample
       else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           UpperCamelCase__							:				List[str]          =      (sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           UpperCamelCase__							:				Union[str, Any]          =      sigma_next - sigma_hat

           UpperCamelCase__							:				Union[str, Any]          =      self.sample
           UpperCamelCase__							:				Dict          =      None

       UpperCamelCase__							:				Optional[int]          =      sample + derivative * dt

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:				List[str]          =      self.sigmas.to(device=original_samples.device,		dtype=original_samples.dtype							)
       if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__							):
           # mps does not support float64
           UpperCamelCase__							:				Union[str, Any]          =      self.timesteps.to(original_samples.device,		dtype=torch.floataa							)
           UpperCamelCase__							:				Tuple          =      timesteps.to(original_samples.device,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				str          =      self.timesteps.to(original_samples.device							)
           UpperCamelCase__							:				int          =      timesteps.to(original_samples.device							)

       UpperCamelCase__							:				Any          =      [self.index_for_timestep(__magic_name__,		__magic_name__							) for t in timesteps]

       UpperCamelCase__							:				List[str]          =      sigmas[step_indices].flatten()
       while len(sigma.shape							) < len(original_samples.shape							):
           UpperCamelCase__							:				int          =      sigma.unsqueeze(-1							)

       UpperCamelCase__							:				List[str]          =      original_samples + noise * sigma
       return noisy_samples




   def __len__(    self							) ->      Any:






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

