o yiC @s>ddlZddlmZmZmZddlmZmZmZm Z m Z dZ dZ e dZeGdddeeZGd d d eZGd d d Zd e eeeeefde eeefde eeeeeffddZdeedeede fddZde edfde edffddZde edfde ee e fee ee ffddZdS)N)EnumIntEnumunique)DictListSequenceTupleUnioni'g7yACc@s$eZdZdZdZdZdZdZdZdS)_EDIT_OPERATIONSz1Enumerations for the Levenhstein edit operations.insertdelete substitutenothing undefinedN) __name__ __module__ __qualname____doc__ OP_INSERT OP_DELETE OP_SUBSTITUTE OP_NOTHING OP_UNDEFINEDrrW/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/text/helper.pyr +sr c@s$eZdZdZdZdZdZdZeZ dS)_EDIT_OPERATIONS_COSTz6Enumerations for the Levenhstein edit operation costs.rN) rrrrrrrr _INT_INFINITYrrrrrr6src@sfeZdZdZdeeddfddZdeedeeee dfffd d Z deed ed eeeee fdeeeeeee fee dfffd dZ dedeeeee fdee dffddZ deedeeeee fddfddZ deedeeeeeee fffddZededeeee ffddZededeeee ffddZdS)_LevenshteinEditDistancea`A convenience class for calculating the Levenshtein edit distance, which also caches some intermediate values to hasten the calculation. The implementation follows the implemenation from https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/metrics/lib_ter.py, where the most of this implementation is adapted and copied from. reference_tokensreturnNcCs ||_t||_i|_d|_dS)zInitialize _LevenshteinEditDistance object. Args: reference_tokens: A tokenized reference sentence. rN)r len reference_lencache cache_size)selfr rrr__init__Is  z!_LevenshteinEditDistance.__init__prediction_tokens.cCs6||\}}||||\}}}|||||fS)a5Calculate edit distance between self._words_ref and the hypothesis. Uses cache to skip some of the computation. Args: prediction_tokens: A tokenized predicted sentence. Return: A tuple of a calculated edit distance and a trace of executed operations. ) _find_cache_levenshtein_edit_distance _add_cache)r&r(start_positioncached_edit_distanceedit_distance_int edit_distancetracerrr__call__Vs   z!_LevenshteinEditDistance.__call__prediction_startr$cst|}fddt||D}||}|rj|nd}t|dkr-t|dtnt}t|d|dD]} t| |} td| |} | |krQjdn tjd| |} t| | D]}} | dkr{|| d| dt j t j f|| | <q`|| dj | dkrt j }t j }nt j}t j}|| d| dd||f|| d| dt j t j f|| | ddt jt jff}|D]\}}|| | d|kr||f|| | <qq`q8||}|ddd|t|d|fS) aA dynamic programming algorithm to compute the Levenhstein edit distance. Args: prediction_tokens: A tokenized predicted sentence. prediction_start: An index where a predicted sentence to be considered from. cache: A cached Levenshtein edit distance. Returns: Edit distance between the predicted sentence and the reference sentence csg|] }tjqSr)list_get_empty_rowr#.0_r&rr ~szG_LevenshteinEditDistance._levenshtein_edit_distance..g?rrN)r"ranger# _BEAM_WIDTHmathceilfloormaxminrrr r rrr _get_trace)r&r(r2r$prediction_len empty_rowsr/ length_ratio beam_widthi pseudo_diagmin_jmax_jjcost_substituteoperation_substitute operationsoperation_costoperation_namer0rr8rr*lsD  "$  "z3_LevenshteinEditDistance._levenshtein_edit_distancerDr/cCsd}|}|j}|dks|dkrP|||d}|f|}|tjtjfvr-|d8}|d8}n|tjkr7|d8}n|tjkrA|d8}ntd||dks|dks|S)a?Get a trace of executed operations from the edit distance matrix. Args: prediction_len: A length of a tokenized predicted sentence. edit_distance: A matrix of the Levenshtedin edit distance. The element part of the matrix is a tuple of an edit operation cost and an edit operation itself. Return: A trace of executed operations returned as a tuple of `_EDIT_OPERATIONS` enumerates. Raises: ValueError: If an unknown operation has been applied. rrrUnknown operation )r#r rrrr ValueError)r&rDr/r0rHrL operationrrrrCs        z#_LevenshteinEditDistance._get_tracec Cs|jtkrdS|j}t|t|}t|D] }|||d}qt||d|D]\}}||vrAit|f||<|jd7_||}|d}q*dS)a*Add newly computed rows to cache. Since edit distance is only calculated on the hypothesis suffix that was not in cache, the number of rows in `edit_distance` matrx may be shorter than hypothesis length. In that case we skip over these initial words. Args: prediction_tokens: A tokenized predicted sentence. edit_distance: A matrix of the Levenshtedin edit distance. The element part of the matrix is a tuple of an edit operation cost and an edit operation itself. Nrr)r%_MAX_CACHE_SIZEr$r"r<ziptuple) r&r(r/nodeskip_numrHwordrowvaluerrrr+s   z#_LevenshteinEditDistance._add_cachecCsZ|j}d}||jg}|D]}||vr$|d7}||\}}||q||fS||fS)a*Find the already calculated rows of the Levenshtein edit distance matric. Args: prediction_tokens: A tokenized predicted sentence. Return: A tuple of a start hypothesis position and `edit_distance` matrix. prediction_start: An index where a predicted sentence to be considered from. edit_distance: A matrix of the cached Levenshtedin edit distance. The element part of the matrix is a tuple of an edit operation cost and an edit operation itself. rr)r$_get_initial_rowr#append)r&r(rXr,r/rZr[rrrr)s  z$_LevenshteinEditDistance._find_cachelengthcCsttjtjfg|d}|S)aPrecomputed empty matrix row for Levenhstein edit distance. Args: length: A length of a tokenized sentence. Return: A list of tuples containing infinite edit operation costs and yet undefined edit operations. r)intrrr )r_ empty_rowrrrr4s z'_LevenshteinEditDistance._get_empty_rowcCsddt|dD}|S)a7First row corresponds to insertion operations of the reference, so we do 1 edit operation per reference word. Args: length: A length of a tokenized sentence. Return: A list of tuples containing edit operation costs of insert and insert edit operations. cSsg|] }|tjtjfqSr)rrr )r6rHrrrr9&sz=_LevenshteinEditDistance._get_initial_row..r)r<)r_ initial_rowrrrr]s z)_LevenshteinEditDistance._get_initial_row)rrrrrstrr'rr`r r1r*rCr+r) staticmethodr4r]rrrrr@s4& $ C  *%.  $rreference_corpushypothesis_corpusr!cCst|tr|g}tdd|Dr"t|dkr|g}ndd|D}|rCtdd|DrCt|t|krCtdt|dt|||fS) a Check and update (if needed) the format of reference and hypothesis corpora for various text evaluation metrics. Args: reference_corpus: An iterable of iterables of reference corpus. hypothesis_corpus: An iterable of hypothesis corpus. Return: reference_corpus: An iterable of iterables of reference corpus. hypothesis_corpus: An iterable of hypothesis corpus. Raises: ValueError: If length of `reference_corpus` and `hypothesis_corpus` differs. css|]}t|tVqdSN) isinstancercr6refrrr Az#_validate_inputs..rcSsg|]}|gqSrrrirrrr9Esz$_validate_inputs..css|]}|VqdSrgrrirrrrkGszCorpus has different size z != )rhrcallr"rS)rerfrrr_validate_inputs*s  &rnr(r csfddtt|dD}tt|dD]}|||d<qttdD]}||d|<q(tdt|dD]I}tdtdD]=}||d|dkrb||d|d|||<qEt||d||||d||d|dd|||<qEq:|ddS)a$Standard dynamic programming algorithm to compute the edit distance. Args: prediction_tokens: A tokenized predicted sentence reference_tokens: A tokenized reference sentence Returns: Edit distance between the predicted sentence and the reference sentence csg|] }dgtdqS)rr)r"r5r rrr9Vsz"_edit_distance..rrr;)r<r"rB)r(r dprHrLrror_edit_distanceMs B rqr0.csNtjtjtjtjidtdtttfdtfddtfdd|D}|S)aFlip the trace of edit operations. Instead of rewriting a->b, get a recipe for rewriting b->a. Simply flips insertions and deletions. Args: trace: A tuple of edit operations. Return: inverted_trace: A tuple of inverted edit operations. rT_flip_operationsr!cSs||vr ||S|Srg)get)rTrrrrr_replace_operation_or_retaints z1_flip_trace.._replace_operation_or_retainc3s|]}|VqdSrgr)r6rTrrrtrrrk{rlz_flip_trace..)r rrrrW)r0inverted_tracerrur _flip_traceds   rwcCsd}}g}g}i}|D]c}|tjkr*|d7}|d7}|||<|d|dq |tjkrF|d7}|d7}|||<|d|dq |tjkrU|d7}|dq |tjkrh|d7}|||<|dq td|d|||fS)aTransform trace of edit operations into an alignment of the sequences. Args: trace: A trace of edit operations as a tuple of `_EDIT_OPERATIONS` enumerates. Return: alignments: A dictionary mapping aligned positions between a reference and a hypothesis. reference_errors: A list of error positions in a reference. hypothesis_errors: A list of error positions in a hypothesis. Raises: ValueError: If an unknown operation is r;rrrR.)r rr^rrrrS)r0reference_positionhypothesis_positionreference_errorshypothesis_errors alignmentsrTrrr_trace_to_alignments4           r~)r>enumrrrtypingrrrrr r=rUr`rrcr rrrnrqrwr~rrrrs*  k #"8