|j9 dZddlZddlZddlZddlZddlmZdZdZdZ dZ dZ ej dj Zej d j Z d!d ed ed eededeeeff dZd edeeeefeeefffdZd ededeedeeefdzfdZd ededeedeeefdzfdZdededefdZdedefdZdedeeeeeefefffdZdeeeeeefeffdefdZdeeeeeefeffdedeeeffdZdeededefd Z dS)"aOwner of the model-artifacts file format, write side and read side. The model artifacts are the digest-locked file set the trainer produces as one unit: ``models.bin`` (``CMD2``: per-model names and L2 norms, then zlib-compressed dense bigram tables), ``rowmax.bin`` (``CRM1``: per-model row maxima for upper-bound prescreening, digest-locked to the ``models.bin`` they were computed from), ``idf.bin`` (a 65536-byte quantized IDF table), and ``langnorm.bin`` (``CLN1``: per-model norms under the IDF-weighted metric the language tiers score in, digest-locked like ``rowmax.bin``). Keeping both directions in one module means a format change cannot land on one side only: ``scripts/train.py`` calls :func:`write_model_artifacts` and :func:`read_models`, while :mod:`chardet.models` calls :func:`parse_models_bin` and :func:`parse_rowmax_bin` behind its cached accessors. Note: ``from __future__ import annotations`` is intentionally omitted because this module is compiled with mypyc, which does not support PEP 563 string annotations. N)PathsCMD2sCRM1$sCLN1z>Iz>ddataoffsetnames chunk_sizereturnct|}|dz}tj}i}t}d} |} t|} d} t||krdt|z } |jr||j| }nY| | kr6|| | |z}| t|z } ||| }n| sd} |}|snnn| t|z } ||z }t|dkr:t|||t|<|nt|dkrnt||kt||krd}|jr||jd}|sQ|j sJ| | krD|| | |z}| t|z } ||d}|s |j s| | kD|s|j s| s|}| t|z } | |kst||krd| d|}t||S) uDecompress the model tables from ``data[offset:]``, one per name. Each model is stored as its own bytes object rather than a memoryview slice of one big blob: mypyc compiles bytes indexing in the scoring hot loop to a native C array access, while memoryview indexing goes through a boxed generic call. Decompression is incremental, one 64 KB table at a time — materializing the whole multi-megabyte blob and slicing it would transiently double the allocation and strand the freed pages in process RSS. Trailing compressed bytes are ignored, as with whole-blob ``zlib.decompress``; the decompressed size is validated instead. :raises ValueError: If the decompressed size is not exactly ``len(names) * 65536``. rFTz&corrupt models.bin: decompressed size z != expected ) lenzlib decompressobj bytearrayunconsumed_tail decompressflushbytescleareof ValueError)rrrr num_models expected_sizedecompmodelstableproducedposendflushedneedpiecechunkextramsgs /Users/tedpeters/Documents/Projects/Claude Projects/Misc Tools/notename-sheets/.venv/lib/python3.11/site-packages/chardet/models/_format.py_decompress_tablesr),s"UJ&M   ! !F!F KKEH C d))CG f++ " "s5zz!  ! %%f&ys#% 8| # #:CS// !&tV44 !    NJNNNCS// !"$z""  A(v66KX aKF#~~LhLLL oo%(!223::7CCD h F%dF33GT aKF LL   E$KK#D&%88 :%%%(Q((oo1$ L, -%%%(Q((oo1$% 5=s$DD E!D..E!EE! models_digest model_keyscddtkrYdt|krFttt|dzzkrfdt|DSdS)uParse rowmax.bin into per-model row-maxima tables. :param data: Raw bytes of rowmax.bin. :param models_digest: SHA-256 digest of the current models.bin bytes. :param model_keys: Model keys in models.bin header order. :returns: Mapping of model key to its 256-byte row-maxima table, or ``None`` when the magic, digest, or size does not match — a stale or mismatched file would silently under-estimate row maxima and break the upper bound that prescreening depends on. Nr+r,c^i|])\}}|t|dzzt|dzdzz*S)r,r)ROWMAX_HEADER_SIZE.0ikeyrs r( z$parse_rowmax_bin..sR   3 (1s725G1q5TW-5WWX   r ) ROWMAX_MAGICrCr enumeraterr?r@s` r(parse_rowmax_binrLs RaRL  %% &- 7 7 II+c*oo.CC C C    #J//     4r cddtkrYdt|krFttt|dzzkrfdt|DSdS)aParse langnorm.bin into per-model language-scoring norms. :param data: Raw bytes of langnorm.bin. :param models_digest: SHA-256 digest of the current models.bin bytes. :param model_keys: Model keys in models.bin header order. :returns: Mapping of model key to its IDF-weighted norm, or ``None`` when the magic, digest, or size does not match -- a stale file would normalize every language score against the wrong length and silently tilt the comparison between languages. Nr+r.c Zi|]'\}}|tt|dzzd(S)r.r)r3LANGNORM_HEADER_SIZErDs r(rHz&parse_langnorm_bin..sH   3 ';a!e'CDDQG   r )LANGNORM_MAGICrOrrJrKs` r(parse_langnorm_binrQs RaRN"" '' (M 9 9 II-J!0CC C C    #J//     4r ridfcd}tdD]}||}|r|||z||zz }tj|S)aDerive one model's norm under the language-scoring metric. The language tiers compare an input profile weighted by IDF against models weighted by the square root of IDF, so a model's length in that space is the root of the sum of ``weight**2 * idf`` over its bigrams. Scoring folds the model-side factor into the profile and divides by this norm, so the table itself is never rescaled. rr )r1mathsqrt)rrRtotalrFvs r(langnorm_from_tablerXsV E 5\\$$ !H  $ QUSV^ #E 9U  r cXtfdtdddDS)zDerive the 256-byte row-maxima table of one dense model table. Entry ``b1`` holds the maximum weight in the model's row for lead byte ``b1``; a row with no bigrams yields 0. c3LK|]}t||dzVdS)r,N)max)rEstartrs r( z$rowmax_from_table..s9SSUU553;./00SSSSSSr rr r,)rr1)rs`r(rowmax_from_tabler^s4 SSSSeAuc>R>RSSS S SSr models_pathc&|siS|}|siSt|\}}i}|D]9\}}i}t dD]}||} | dkr | ||dz |dzf<|||<:|S)aRead models.bin into the sparse per-model bigram dicts training uses. The inverse of :func:`write_model_artifacts`'s models.bin output: only non-zero weights appear in the dicts. A missing or empty file reads as no models, so a first-ever training run and a full retrain look the same to the caller. :returns: Mapping of model key to ``{(b1, b2): weight}``. :raises ValueError: If the file exists but is corrupt. r rr.)is_file read_bytesr>itemsr1) r_rtables_normsrr;rbigramsidxweights r( read_modelsrjs      ! ! # #D  %d++NFF46F||~~ e.0<< 9 9C3ZFzz283:./t Mr rct|}dgdz}|D] }|D]\}}||dz|zxxdz cc<!|dkrtj|nd}|dkrd|z nd}t d}t dD]S} || } | dkr>tj|| z } t dt| |zdz|| <Nd|| <Tt|S)aACompute the 65536-byte quantized IDF table over all models. For each bigram index the byte holds a scaled inverse document frequency: bigrams present in every model score 1 (minimal signal), bigrams in exactly one model score 255 (maximum signal), and bigrams in no model score 1 (unknown, neutral). rr r.rg?go@g) rvaluesrTlogrr1r[roundr) rrdoc_freqrgb1b2max_idfscale idf_tablerhdfidf_vals r( _idf_tablerws'VJsU{H==??** * *FB bAg^ $ $ $ ) $ $ $ $ *&01nndhz"""#G&{{EGOOE%  IU|| c] 66hzB//G E'E/$:$:Q$>??IcNNIcNN   r c|jddt|}t|}t t }|t jdt|z }t }g}g}|D] }t d} d} || D]\\} } } | | | dz| z<| | | zz } | d}|t jdt||zz }|t jdtj | z }t| }||z }|t||t!|| t|t#jt|d z}||t)j|}|d |t t0}||z }|D]}||z }|d |t3||}|d |t|t|t|t|d S)aEWrite the model artifacts as one digest-locked set. ``models.bin`` is written at *models_path* (whatever its basename); ``rowmax.bin`` and ``idf.bin`` are written beside it under their fixed names. ``rowmax.bin`` embeds the SHA-256 of the exact ``models.bin`` bytes written here and is deliberately written **last**: it is the commit marker for the whole set, so an interrupted run leaves a digest mismatch that :func:`parse_rowmax_bin` rejects at load time instead of a silently stale sibling (``idf.bin`` is validated by size only). :param models: Mapping of model key to sparse ``{(b1, b2): weight}``. :param models_path: Destination path for models.bin. :returns: Sizes in bytes, keyed by canonical artifact name (``models.bin``, ``rowmax.bin``, ``idf.bin``, ``langnorm.bin``). T)parentsexist_okz!Ir rr.r-!d idf.bin rowmax.bin langnorm.bin)z models.binr~r}r)parentmkdirsortedrwrr/r6packrrdencoderTrUrr4r^rXrcompress write_byteshashlibsha256digest with_namerIlangnorm_blob_for)rr_ sorted_namesrtheaderre rowmax_rows langnormsr;rsq_sumrprqri name_bytesfrozen models_blobr? rowmax_blobrow langnorm_blobs r(write_model_artifactsr,s&TD999&>>L6""I | $ $F fk$L 1 1222F [[F!KIAA%   &t 2 2 4 4 & & HRf$*E27b. ! fvo %FF[[)) &+dC OO44zAA&+dDIf$5$5666u&,V44555,VY??@@@@--$-f q"A"AAKK(((N;//6688M)$$00;;;L))K= Ks ,''33K@@@%i??M.))55mDDD+&&+&&y>>M**   r rctt}||z }|D]}|tjd|z }t |S)zrLrQrXr^rjrwrrr r(rs(    t$$0&-%%1CIJJ JJ%)#YJ