"""simple docstring"""




import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


lowerCAmelCase__							=				datasets.logging.get_logger(__name__)


lowerCAmelCase__							=				'\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n    author = "Moosavi, Nafise Sadat  and\n      Strube, Michael",\n    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = aug,\n    year = "2016",\n    address = "Berlin, Germany",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P16-1060",\n    doi = "10.18653/v1/P16-1060",\n    pages = "632--642",\n}\n\n'

lowerCAmelCase__							=				'\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'

lowerCAmelCase__							=				'\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting \'keep_singletons=False\', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    \'mentions\': mentions\n    \'muc\': MUC metric [Vilain et al, 1995]\n    \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n    \'ceafe\': CEAFe [Luo et al., 2005]\n    \'lea\': LEA [Moosavi and Strube, 2016]\n    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric(\'coval\')\n    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',\n    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'



def        _lowerCamelCase ( __a,							__a,							__a=False,							__a=False,							__a=True,							__a=False,							__a="dummy_doc"    ):
		SCREAMING_SNAKE_CASE_						 = {doc: key_lines}
		SCREAMING_SNAKE_CASE_						 = {doc: sys_lines}

		SCREAMING_SNAKE_CASE_						 = {}

		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = 0

		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = reader.get_doc_mentions(__a,							key_doc_lines[doc],							__a    )
		key_singletons_num += singletons_num

		if NP_only or min_span:
				SCREAMING_SNAKE_CASE_						 = reader.set_annotated_parse_trees(__a,							key_doc_lines[doc],							__a,							__a    )

		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = reader.get_doc_mentions(__a,							sys_doc_lines[doc],							__a    )
		sys_singletons_num += singletons_num

		if NP_only or min_span:
				SCREAMING_SNAKE_CASE_						 = reader.set_annotated_parse_trees(__a,							key_doc_lines[doc],							__a,							__a    )

		if remove_nested:
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = reader.remove_nested_coref_mentions(__a,							__a    )
				key_nested_coref_num += nested_mentions
				key_removed_nested_clusters += removed_clusters

				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = reader.remove_nested_coref_mentions(__a,							__a    )
				sys_nested_coref_num += nested_mentions
				sys_removed_nested_clusters += removed_clusters

		SCREAMING_SNAKE_CASE_						 = reader.get_mention_assignments(__a,							__a    )
		SCREAMING_SNAKE_CASE_						 = reader.get_mention_assignments(__a,							__a    )

		SCREAMING_SNAKE_CASE_						 = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

		if remove_nested:
				logger.info(
				    '''Number of removed nested coreferring mentions in the key '''
				    F'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}'    )
				logger.info(
				    '''Number of resulting singleton clusters in the key '''
				    F'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}'    )

		if not keep_singletons:
				logger.info(
				    F'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
				    '''files, respectively'''    )

		return doc_coref_infos



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = get_coref_infos(__a,							__a,							__a,							__a,							__a,							__a    )

		SCREAMING_SNAKE_CASE_						 = {}
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = 0

		for name, metric in metrics:
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = evaluator.evaluate_documents(__a,							__a,							beta=1    )
				if name in ["muc", "bcub", "ceafe"]:
						conll += fa
						conll_subparts_num += 1
				output_scores.update({F'{name}/recall': recall, F'{name}/precision': precision, F'{name}/f1': fa}    )

				logger.info(
				    name.ljust(10    ),							F'Recall: {recall * 100:.2f}',							F' Precision: {precision * 100:.2f}',							F' F1: {fa * 100:.2f}',							)

		if conll_subparts_num == 3:
				SCREAMING_SNAKE_CASE_						 = (conll / 3) * 100
				logger.info(F'CoNLL score: {conll:.2f}'    )
				output_scores.update({'''conll_score''': conll}    )

		return output_scores



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = False
		for line in key_lines:
				if not line.startswith('''#'''    ):
						if len(line.split()    ) > 6:
								SCREAMING_SNAKE_CASE_						 = line.split()[5]
								if not parse_col == "-":
										SCREAMING_SNAKE_CASE_						 = True
										break
								else:
										break
		return has_gold_parse





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

							def _lowercase					(self    ):

									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,       citation=_CITATION       ,       inputs_description=_KWARGS_DESCRIPTION       ,       features=datasets.Features(
									        {
									            '''predictions''': datasets.Sequence(datasets.Value('''string'''    )    ),
									            '''references''': datasets.Sequence(datasets.Value('''string'''    )    ),
									        }    )       ,       codebase_urls=['''https://github.com/ns-moosavi/coval''']       ,       reference_urls=[
									        '''https://github.com/ns-moosavi/coval''',
									        '''https://www.aclweb.org/anthology/P16-1060''',
									        '''http://www.conll.cemantix.org/2012/data.html''',
									    ]       ,       )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = [
									    ('''mentions''', evaluator.mentions),
									    ('''muc''', evaluator.muc),
									    ('''bcub''', evaluator.b_cubed),
									    ('''ceafe''', evaluator.ceafe),
									    ('''lea''', evaluator.lea),
									]

									if min_span:
											SCREAMING_SNAKE_CASE_						 = util.check_gold_parse_annotation(SCREAMING_SNAKE_CASE_    )
											if not has_gold_parse:
													raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.'''    )
													# util.parse_key_file(key_file)
													# key_file = key_file + ".parsed"

									SCREAMING_SNAKE_CASE_						 = evaluate(
									    key_lines=SCREAMING_SNAKE_CASE_       ,       sys_lines=SCREAMING_SNAKE_CASE_       ,       metrics=SCREAMING_SNAKE_CASE_       ,       NP_only=SCREAMING_SNAKE_CASE_       ,       remove_nested=SCREAMING_SNAKE_CASE_       ,       keep_singletons=SCREAMING_SNAKE_CASE_       ,       min_span=SCREAMING_SNAKE_CASE_       ,       )

									return score

"""simple docstring"""




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 ( __a,							__a=0.9_9_9,							__a="cosine",							):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__a    ):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(__a    ):
						return math.exp(t * -1_2.0    )

		else:
				raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

		SCREAMING_SNAKE_CASE_						 = []
		for i in range(__a    ):
				SCREAMING_SNAKE_CASE_						 = i / num_diffusion_timesteps
				SCREAMING_SNAKE_CASE_						 = (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  snake_case     (							__lowercase       ,			__lowercase   ):
							UpperCAmelCase__						     =      [e.name for e in KarrasDiffusionSchedulers]
							UpperCAmelCase__						     =      2

							@register_to_config
							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 10_00       ,       SCREAMING_SNAKE_CASE_ = 0.0_00_85       ,       SCREAMING_SNAKE_CASE_ = 0.0_12       ,       SCREAMING_SNAKE_CASE_ = "linear"       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "epsilon"       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = "linspace"       ,       SCREAMING_SNAKE_CASE_ = 0       ,       ):

									"""simple docstring"""







									if trained_betas is not None:
											SCREAMING_SNAKE_CASE_						 = torch.tensor(SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "linear":
											SCREAMING_SNAKE_CASE_						 = torch.linspace(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    )
									elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											SCREAMING_SNAKE_CASE_						 = (
											    torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       SCREAMING_SNAKE_CASE_       ,       dtype=torch.floataa    ) ** 2
											)
									elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											SCREAMING_SNAKE_CASE_						 = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       ,       alpha_transform_type='''cosine'''    )
									elif beta_schedule == "exp":
											SCREAMING_SNAKE_CASE_						 = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       ,       alpha_transform_type='''exp'''    )
									else:
											raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'    )

									SCREAMING_SNAKE_CASE_						 = 1.0 - self.betas
									SCREAMING_SNAKE_CASE_						 = torch.cumprod(self.alphas       ,       dim=0    )

									#  set all values
									self.set_timesteps(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = use_karras_sigmas

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None    ):

									"""simple docstring"""







									if schedule_timesteps is None:
											SCREAMING_SNAKE_CASE_						 = self.timesteps

									SCREAMING_SNAKE_CASE_						 = (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:
											SCREAMING_SNAKE_CASE_						 = 1 if len(SCREAMING_SNAKE_CASE_    ) > 1 else 0
									else:
											SCREAMING_SNAKE_CASE_						 = timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_    ) else timestep
											SCREAMING_SNAKE_CASE_						 = self._index_counter[timestep_int]

									return indices[pos].item()

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = num_inference_steps

									SCREAMING_SNAKE_CASE_						 = 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":
											SCREAMING_SNAKE_CASE_						 = np.linspace(0       ,       num_train_timesteps - 1       ,       SCREAMING_SNAKE_CASE_       ,       dtype=SCREAMING_SNAKE_CASE_    )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
											SCREAMING_SNAKE_CASE_						 = 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_						 = (np.arange(0       ,       SCREAMING_SNAKE_CASE_    ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_    )
											timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
											SCREAMING_SNAKE_CASE_						 = 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_						 = (np.arange(SCREAMING_SNAKE_CASE_       ,       0       ,       -step_ratio    )).round().copy().astype(SCREAMING_SNAKE_CASE_    )
											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_						 = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
									SCREAMING_SNAKE_CASE_						 = np.log(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.interp(SCREAMING_SNAKE_CASE_       ,       np.arange(0       ,       len(SCREAMING_SNAKE_CASE_    )    )       ,       SCREAMING_SNAKE_CASE_    )

									if self.config.use_karras_sigmas:
											SCREAMING_SNAKE_CASE_						 = self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=self.num_inference_steps    )
											SCREAMING_SNAKE_CASE_						 = np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) for sigma in sigmas]    )

									SCREAMING_SNAKE_CASE_						 = np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    ).to(device=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

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

									# empty dt and derivative
									SCREAMING_SNAKE_CASE_						 = None
									SCREAMING_SNAKE_CASE_						 = None

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = np.log(SCREAMING_SNAKE_CASE_    )

									# get distribution
									SCREAMING_SNAKE_CASE_						 = log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									SCREAMING_SNAKE_CASE_						 = np.cumsum((dists >= 0)       ,       axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
									SCREAMING_SNAKE_CASE_						 = low_idx + 1

									SCREAMING_SNAKE_CASE_						 = log_sigmas[low_idx]
									SCREAMING_SNAKE_CASE_						 = log_sigmas[high_idx]

									# interpolate sigmas
									SCREAMING_SNAKE_CASE_						 = (low - log_sigma) / (low - high)
									SCREAMING_SNAKE_CASE_						 = np.clip(SCREAMING_SNAKE_CASE_       ,       0       ,       1    )

									# transform interpolation to time range
									SCREAMING_SNAKE_CASE_						 = (1 - w) * low_idx + w * high_idx
									SCREAMING_SNAKE_CASE_						 = t.reshape(sigma.shape    )
									return t

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = in_sigmas[-1].item()
									SCREAMING_SNAKE_CASE_						 = in_sigmas[0].item()

									SCREAMING_SNAKE_CASE_						 = 7.0  # 7.0 is the value used in the paper
									SCREAMING_SNAKE_CASE_						 = np.linspace(0       ,       1       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = sigma_min ** (1 / rho)
									SCREAMING_SNAKE_CASE_						 = sigma_max ** (1 / rho)
									SCREAMING_SNAKE_CASE_						 = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.dt is None

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.index_for_timestep(SCREAMING_SNAKE_CASE_    )

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

									if self.state_in_first_order:
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index]
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index + 1]
									else:
											# 2nd order / Heun's method
											SCREAMING_SNAKE_CASE_						 = self.sigmas[step_index - 1]
											SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = 0
									SCREAMING_SNAKE_CASE_						 = 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":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_next
											SCREAMING_SNAKE_CASE_						 = sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
											SCREAMING_SNAKE_CASE_						 = sigma_hat if self.state_in_first_order else sigma_next
											SCREAMING_SNAKE_CASE_						 = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
									elif self.config.prediction_type == "sample":
											SCREAMING_SNAKE_CASE_						 = model_output
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'    )

									if self.config.clip_sample:
											SCREAMING_SNAKE_CASE_						 = pred_original_sample.clamp(
											    -self.config.clip_sample_range       ,       self.config.clip_sample_range    )

									if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											SCREAMING_SNAKE_CASE_						 = sigma_next - sigma_hat

											# store for 2nd order step
											SCREAMING_SNAKE_CASE_						 = derivative
											SCREAMING_SNAKE_CASE_						 = dt
											SCREAMING_SNAKE_CASE_						 = sample
									else:
											# 2. 2nd order / Heun's method
											SCREAMING_SNAKE_CASE_						 = (sample - pred_original_sample) / sigma_next
											SCREAMING_SNAKE_CASE_						 = (self.prev_derivative + derivative) / 2

											# 3. take prev timestep & sample
											SCREAMING_SNAKE_CASE_						 = self.dt
											SCREAMING_SNAKE_CASE_						 = self.sample

											# free dt and derivative
											# Note, this puts the scheduler in "first order mode"
											SCREAMING_SNAKE_CASE_						 = None
											SCREAMING_SNAKE_CASE_						 = None
											SCREAMING_SNAKE_CASE_						 = None

									SCREAMING_SNAKE_CASE_						 = sample + derivative * dt

									if not return_dict:
											return (prev_sample,)

									return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.sigmas.to(device=original_samples.device       ,       dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_    ):
											# mps does not support float64
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device       ,       dtype=torch.floataa    )
									else:
											SCREAMING_SNAKE_CASE_						 = self.timesteps.to(original_samples.device    )
											SCREAMING_SNAKE_CASE_						 = timesteps.to(original_samples.device    )

									SCREAMING_SNAKE_CASE_						 = [self.index_for_timestep(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) for t in timesteps]

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

									SCREAMING_SNAKE_CASE_						 = original_samples + noise * sigma
									return noisy_samples







							def __len__(self    ):

									"""simple docstring"""







									return self.config.num_train_timesteps