U 5-e@sUddlZddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl m Z m Z ddl mZddlmZddlmZddlmZddlmZdd lmZmZmZmZmZmZmZmZm Z m!Z!dd l"m#Z#ddl$Z$ddl%Z%ddl&Z&ddl'm(Z(d Z)e*d j+Z,e*d j+Z-e*dj+Z.dZ/dZ0dZ1e e2ej3ee!e4fZ5e#e6d<ee eej7e2gej7fej8e2ee2e2ffZ9e#e6d<e eej:j;ejGd%dde?Z@e d&dZAgZBGd'd"d"eZCdaDeeCe6d(<eeCd)d*d#ZEd+d$ZFeGd)d,d-ZHeIee=gee2fee=e2gee=fd.d/dZJdd1dZKd2d3ZLd4d5ZMd6d7ZNd8d9ZOd:d;ZPdd?ZRd@dZSdAdBZTdCdZUdDdEZVdFdGZWdHdIZXdJdKZYdLdMZZeJdNeLeReJdOeMeTeJdPeOeWeJdQePeXeJdReQeZeJdSeNeVe eej:j;ejs c@seZdZdZdZdZdS)r'rrN)r*r+r,NATIVELITTLEBIGr-r-r-r.r'Is_default_load_endianreturncCstS)a Get fallback byte order for loading files If byteorder mark is not present in saved checkpoint, this byte order is used as fallback. By default, it's "native" byte order. Returns: default_load_endian: Optional[LoadEndianness] )r9r-r-r-r.r(Ps cCs"t|ts|dk rtd|adS)z Set fallback byte order for loading files If byteorder mark is not present in saved checkpoint, this byte order is used as fallback. By default, it's "native" byte order. Args: endianness: the new fallback byte order Nz=Invalid argument type in function set_default_load_endianness) isinstancer' TypeErrorr9)Z endiannessr-r-r.r)]s cCs`g}|}|d}|dkrB||t|dkr6qB|d}q||ddddg}||kS)NrPK)tellreadappendlenseek)f read_bytesstartbyteZlocal_header_magic_numberr-r-r. _is_zipfilems       rM)prioritytagger deserializercCs |||f}t|tdS)a Registers callables for tagging and deserializing storage objects with an associated priority. Tagging associates a device with a storage object at save time while deserializing moves a storage object to an appropriate device at load time. :attr:`tagger` and :attr:`deserializer` are run in the order given by their :attr:`priority` until a tagger/deserializer returns a value that is not `None`. To override the deserialization behavior for a device in the global registry, one can register a tagger with a higher priority than the existing tagger. This function can also be used to register a tagger and deserializer for new devices. Args: priority: Indicates the priority associated with the tagger and deserializer, where a lower value indicates higher priority. tagger: Callable that takes in a storage object and returns its tagged device as a string or None. deserializer: Callable that takes in storage object and a device string and returns a storage object on the appropriate device or None. Returns: `None` Example: >>> def ipu_tag(obj): >>> if obj.device.type == 'ipu': >>> return 'ipu' >>> def ipu_deserialize(obj, location): >>> if location.startswith('ipu'): >>> ipu = getattr(torch, "ipu", None) >>> assert ipu is not None, "IPU device module is not loaded" >>> assert torch.ipu.is_available(), "ipu is not available" >>> return obj.ipu(location) >>> torch.serialization.register_package(11, ipu_tag, ipu_deserialize) N)_package_registryrFsort)rNrOrPZ queue_elemr-r-r.rs(  Tc sz2|jdtfddt|D}||k}Wnbtk r}zDd|jd|jdt|}|rrt||nt |dd}W5d }~XYnX|S) a Check if a module's version satisfies requirements Usually, a module's version string will be like 'x.y.z', which would be represented as a tuple (x, y, z), but sometimes it could be an unexpected format. If the version string does not match the given tuple's format up to the length of the tuple, then error and exit or emit a warning. Args: module: the module to check the version of req_version_tuple: tuple (usually of ints) representing the required version error_if_malformed: whether we should exit if module version string is malformed Returns: requirement_is_met: bool .c3s"|]\}}t||VqdSr/)type).0idxZ req_fieldZ version_strsr-r. sz8check_module_version_greater_or_equal..'z&' module version string is malformed 'z$' and cannot be compared with tuple z1, but continuing assuming that requirement is metTN) __version__splittuple enumerate Exceptionr*str RuntimeErrorwarningswarn)moduleZreq_version_tupleZerror_if_malformedZmodule_versionZrequirement_is_metemessager-rWr.rs    cCs|jjdkrdSdSNcpudevicerTobjr-r-r._cpu_tags rlcCs |jjdkrdt|jjSdS)Ncudazcuda:rirTr_indexrjr-r-r. _cuda_tags rpcCs |jjdkrdt|jjSdS)Nhpuzhpu:rnrjr-r-r._hpu_tags rrcCs|jjdkrdSdSNmpsrhrjr-r-r._mps_tags rucCs|jjdkrdSdS)Nmetarhrjr-r-r. _meta_tags rwcCs>tj}|jj|kr:|jjdkr&|S|dt|jjSdS)N:)torch_C_get_privateuse1_backend_namerirTror_)rk backend_namer-r-r._privateuse1_tags    r}cCs|dkr |SdSrfr-rklocationr-r-r._cpu_deserializesrcCsNtjj|d}tjs"tdtj}||krJtd|d|d|S)NTzAttempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.z0Attempting to deserialize object on CUDA device z" but torch.cuda.device_count() is U. Please use torch.load with map_location to map your storages to an existing device.)ryrm_utils_get_device_index is_availabler` device_count)rrirr-r-r.rs  c Csf|drbt|}t|ddrXtj|&tj|t|dW5QRSQRXn ||SdS)Nrm_torch_load_uninitializedFri) startswithrgetattrryrmriUntypedStoragenbytes)rkrrir-r-r._cuda_deserializes   ,rcCsfttdd}|dk std|jj|dd}|sz" has no callable attribute 'write')callablerr_rTstriprAttributeErrorr=rrrryrzr)rrmsgrr-r.rs z$_open_zipfile_writer_buffer.__init__cGs|j|jdSr/)rrrrrr-r-r.rs z$_open_zipfile_writer_buffer.__exit__rr-r-rr.rs rcCst|rt}nt}||Sr/)rrr)r containerr-r-r._open_zipfile_writersrcCs.dg}z |j|kWStk r(YdSXdS)NgzipF)r+r)rIZcompress_modulesr-r-r._is_compressed_files  rcCsLt|r dSz|dkWStjk r2YdStk rFYdSXdS)z Checks if f is a file that should be read directly. It should be read directly if it is backed by a real file (has a fileno) and is not a a compressed file (e.g. gzip) FrN)rfilenorUnsupportedOperationrrIr-r-r._should_read_directlysrc CsXdd}z||WdStjtfk rR}z|ddg|W5d}~XYnXdS)NcSs>|D]0}|t|krt|ddd}t||q|dS)Nz7. You can only torch.load from a file that is seekable.z; Please pre-load the data into a buffer like io.BytesIO andz try to load from it instead.)r_rT)patternsrdprr-r-r. raise_err_msg s  z&_check_seekable..raise_err_msgTrHrDF)rHrDrrr)rIrrdr-r-r.rs  rcCsH|dk rD|jdkrDd}t||dsDtdddd|D|jdS) zChecks if using dill as the pickle module, and if so, checks if it is the correct version. If dill version is lower than 0.3.1, a ValueError is raised. Args: pickle_module: module used for pickling metadata and objects NZdill)rr5rFz\'torch' supports dill >= {}, but you have dill {}. Please upgrade dill or switch to 'pickle'rScSsg|] }t|qSr-)r_)rUnumr-r-r. )sz'_check_dill_version..)r*r ValueErrorformatjoinrZ) pickle_moduleZrequired_dill_versionr-r-r._check_dill_versions rcCs&t|ttjfs"t|ds"tddS)NrzOexpected 'f' to be string, path, or a file-like object with a 'write' attribute)r<r_osPathLikerrrr-r-r._check_save_filelike.srF)rkrIrpickle_protocol_use_new_zipfile_serialization_disable_byteorder_recordr;c Csxtjdt|t||rPt| }t|||||W5QRdSQRXn$t|d}t||||W5QRXdS)aPsave(obj, f, pickle_module=pickle, pickle_protocol=DEFAULT_PROTOCOL, _use_new_zipfile_serialization=True) Saves an object to a disk file. See also: :ref:`saving-loading-tensors` Args: obj: saved object f: a file-like object (has to implement write and flush) or a string or os.PathLike object containing a file name pickle_module: module used for pickling metadata and objects pickle_protocol: can be specified to override the default protocol .. note:: A common PyTorch convention is to save tensors using .pt file extension. .. note:: PyTorch preserves storage sharing across serialization. See :ref:`preserve-storage-sharing` for more details. .. note:: The 1.6 release of PyTorch switched ``torch.save`` to use a new zipfile-based file format. ``torch.load`` still retains the ability to load files in the old format. If for any reason you want ``torch.save`` to use the old format, pass the kwarg ``_use_new_zipfile_serialization=False``. Example: >>> # xdoctest: +SKIP("makes cwd dirty") >>> # Save to file >>> x = torch.tensor([0, 1, 2, 3, 4]) >>> torch.save(x, 'tensor.pt') >>> # Save to io.BytesIO buffer >>> buffer = io.BytesIO() >>> torch.save(x, buffer) z torch.saveNwb) ryrz_log_api_usage_oncerrr_saver _legacy_save)rkrIrrrropened_zipfile opened_filer-r-r.r$5s0   c sddlmiiitttdfdd }tttjdktt t t dd}|j t ||d|j t||d|j |||d|j||d}||_| |t}|j |||d||D],}|\} } | |t|d tj| qdS) Nr)rkr;cst|trt|jr|kr"dSd|<d}}zt|\}}}d|}Wn(tk rvtd|j dYnXd|||fSt|t j j st |rt|t j j r|j}|j}|}tt |}|j} |} nFt|t jr|}t j}tt|}t j} |} ntdt||dkrd|krX||krdtdn ||<d} t|j} t|} | kr|| f| <|j|jk}|rt|j| |f}nd}d || | | |f}|SdS) NT4Couldn't retrieve source code for container of type 3. It won't be checked for correctness upon loading.rcztype not recognized: rSCannot save multiple tensors or storages that view the same data as different typesr)r<rT issubclassModulerrr^rarbr*ryr TypedStorage is_storage_untyped_storagedtype_pickle_storage_typer_sizeruint8r"rr=data_ptrr`r__cdatar )rk source_filesourceZ source_linesrr storage_dtypestorage_type_strrr storage_numeloffset storage_keyrZis_view view_metadataresnnZserialized_container_typesserialized_storagesstorage_dtypesr-r. persistent_id}sn           z#_legacy_save..persistent_idlittle)shortintlong)protocol_versionZ little_endianZ type_sizesprotocolT)Ztorch.nnrr rrdictPROTOCOL_VERSIONsys byteorder SHORT_SIZEINT_SIZE LONG_SIZEdump MAGIC_NUMBERPicklerrsortedkeysrZ _write_filerryr _element_size) rkrIrrrZsys_infopicklerZserialized_storage_keyskeyrrr-rr.rrs4 l    rc siiifdd}t}|j||d}||_|||}|d|t||stj dkrxt dtj |dtj ttj t  D]D} d| } | } | j jd kr| } | } || | | qdS) Ncst|tjjst|rt|tjjrN|j}|j}|}tt|}| }n|}tj }t t |}| }|dkr|kr||krtdn ||<|jtt}t|}||<d||||fSdS)Nrrr)r<ryrrrrrrrrrr"rTrrr` setdefaultrr_rGr )rkrr r rr r rZid_maprrr-r.r s8      z_save..persistent_idrdata.pkl)rbigUnknown endianness type: rdata/rg)rBytesIOr%rr#getvalueZ write_recordrGrrrr&r'rirTrgrr) rkzip_filerrrrZdata_bufr)Z data_valuer*rr num_bytesr-r,r.rs*0    r) weights_onlymmap)rI map_locationrr5r6pickle_load_argsr;c KsZtjdd}tdddkr(d}|r>|dk rJtdn |dkrJt}|dkrVd }t|d | krrd |d <t |d }t |r| }d} t | } t| rtd t||tjj||dW5QRW5QRS|rt|tstdtj|} tj|d | } |rz2t| |tfd| i|WW5QRW5QRStk r} zt|t| dW5d} ~ XYnXt| ||fd| i|W5QRW5QRSQRX|rtdd|r4zt||tf|WW5QRStk r2} zt|t| dW5d} ~ XYnXt|||f|W5QRSQRXdS)aload(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) Loads an object saved with :func:`torch.save` from a file. :func:`torch.load` uses Python's unpickling facilities but treats storages, which underlie tensors, specially. They are first deserialized on the CPU and are then moved to the device they were saved from. If this fails (e.g. because the run time system doesn't have certain devices), an exception is raised. However, storages can be dynamically remapped to an alternative set of devices using the :attr:`map_location` argument. If :attr:`map_location` is a callable, it will be called once for each serialized storage with two arguments: storage and location. The storage argument will be the initial deserialization of the storage, residing on the CPU. Each serialized storage has a location tag associated with it which identifies the device it was saved from, and this tag is the second argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. :attr:`map_location` should return either ``None`` or a storage. If :attr:`map_location` returns a storage, it will be used as the final deserialized object, already moved to the right device. Otherwise, :func:`torch.load` will fall back to the default behavior, as if :attr:`map_location` wasn't specified. If :attr:`map_location` is a :class:`torch.device` object or a string containing a device tag, it indicates the location where all tensors should be loaded. Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags appearing in the file (keys), to ones that specify where to put the storages (values). User extensions can register their own location tags and tagging and deserialization methods using :func:`torch.serialization.register_package`. Args: f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), or a string or os.PathLike object containing a file name map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage locations pickle_module: module used for unpickling metadata and objects (has to match the :attr:`pickle_module` used to serialize file) weights_only: Indicates whether unpickler should be restricted to loading only tensors, primitive types and dictionaries mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they are moved to the location that they were tagged with when saving, or specified by ``map_location``. This second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. pickle_load_args: (Python 3 only) optional keyword arguments passed over to :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., :attr:`errors=...`. .. warning:: :func:`torch.load()` unless `weights_only` parameter is set to `True`, uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Never load data that could have come from an untrusted source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. .. note:: When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. .. note:: By default, we decode byte strings as ``utf-8``. This is to avoid a common error case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` when loading files saved by Python 2 in Python 3. If this default is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them as byte arrays which can be decoded later with ``byte_array.decode(...)``. Example: >>> # xdoctest: +SKIP("undefined filepaths") >>> torch.load('tensors.pt') # Load all tensors onto the CPU >>> torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1)) # Map tensors from GPU 1 to GPU 0 >>> torch.load('tensors.pt', map_location={'cuda:1': 'cuda:0'}) # Load tensor from io.BytesIO object >>> with open('tensor.pt', 'rb') as f: ... buffer = io.BytesIO(f.read()) >>> torch.load(buffer) # Load a module with 'ascii' encoding for unpickling >>> torch.load('module.pt', encoding='ascii') z torch.loadzWeights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution.Do it only if you get the file from a trusted source. WeightsUnpickler error: ZTORCH_FORCE_WEIGHTS_ONLY_LOAD0)1yyestrueTNzDCan not safely load weights when explicit pickle_module is specifiedFencodingzutf-8rbz'torch.load' received a zip file that looks like a TorchScript archive dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to silence this warning)r7z9f must be a string filename in order to use mmap argumentoverall_storagez,mmap can only be used with files saved with zz`torch.save(_use_new_zipfile_serialization=True), please torch.save your checkpoint with this option in order to use mmap.) ryrzrrgetenvlowerr`picklerr'rrMrDr_is_torchscript_ziprarb UserWarningrHZjitr%r<r_rr4getsizer from_file_load_weights_only_unpicklerUnpicklingError _legacy_load) rIr7rr5r6r8ZUNSAFE_MESSAGErZ orig_positionrArsizerdr-r-r.r%Xszh      (   &$&cCs:tj}|s2tjD]}t|tjr||t|<q||S)z@Get layout extension object from its string representation. ) _get_layoutcachery__dict__valuesr<layoutr_)rrOvr-r-r.rN s  rNcCstt|ffSr/)rNr_rjr-r-r.r>rTc sit|Gdddjddfdd}ifdd}t|t|}|r|d krz ||WStjk rt|rt|j d d| d YnXt |d sd t j krd krnntdt|dj|f}|tkr tdj|f}|tkr2td|j|f} |f} || _| } j|f} |rv|nd} | D]J}|kst|}|j|| |tj|j| dk r~|} q~tj| S)NcseZdZfddZZS)z&_legacy_load..UnpicklerWrappercsBt|tkr4d|kr4z t|WStk r2YnXt||SNr)rTr_r&KeyErrorr find_classrmod_namerrr-r.rWs  z1_legacy_load..UnpicklerWrapper.find_classr*r+r,rWrr-r-rr.UnpicklerWrappersr[c SsLzdt|d}Wn*tk r@td|jdYdSX||krH|jr"|jd}tj| d| d||dd}d|}ztt |dL}| dd }| d|dkr| |n|t |ks||krtW5QRXd |d |d } Wn"tk rd |d} YnXnd} dt|d| } t| tdS)Nrrrrz.patch )Zlinetermza+rzSaved a reverse patch to z. Run `patch -p0 < z` to revert your changes.z;Tried to save a patch, but couldn't create a writable file zD. Make sure it doesn't exist and your working directory is writable.zyou can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.zsource code of class 'z' has changed. )rrr^rarbr*Z dump_patchesdifflibZ unified_diffr[rrHrrGrEOSErrorryrr) container_typerZoriginal_sourceZcurrent_source file_namedifflinesrI file_sizerr-r-r._check_container_source'sR          z-_legacy_load.._check_container_sourcec sifdd}ttj|dtjdf}tR}|jd|dttj|ddd}j |f}t |D]^}j |f}|\}}} | j } t t tj|tj| } | |} tjj| | d d |<qrj |f} | D]T\} }}}|}tj|j}||}tjj|j|||||jd d | <qW5QRX|jd |dttj|d dd}j |f}t |D]}j |f}|\}}}|}td |d \}|d td|d|d|}td|d|d|}td|d\}tjg|jd|j|||}||<qW5QRX|d}|f}||_| }|W5QRW5QRSQRXW5QRXdS)Ncs6t|tr*t|ddr"||dSt|S)Nrr)r<r\allr)saved_id)rddeserialized_objectsr-r.persistent_loadRs  z:_legacy_load..legacy_load..persistent_loadzr:)fileobjrrZstoragesr3r?rTZ wrap_storager _internalZtensorsz.legacy_loadcsxt|tstt|d}|dd}|dkrNt|ddrF||dS|dkrb|\}}}}}t|}|j}|tj|} |krt t t | } d| _ tj j| ||dd} | |<n*|} | dkrtj j| jj|dd} |dk rZ|\} } }| tj|}|tj|}| krPtj j| j||||dd| <| }n| }|Std|ddS) NrrrcrTrj)rirrkzUnknown saved id type: )r<r\r_maybe_decode_asciirerryrr(r rrrrr _data_ptrrrir`)rfrdatarZroot_keyrr{rrrrk typed_storageZview_keyr Z view_sizer}Zview_size_bytesr)rdrgrr-r.rhsT          z%_legacy_load..persistent_loadrz9 is a zip archive (did you mean to use torch.jit.load()?)readinto)r5rnr)r5rnrztorch.load does not work with file-like objects that do not implement readinto on Python 3.8.0 and 3.8.1. Received object of type "zH". Please update to Python 3.8.2 or newer to restore this functionality.z#Invalid magic number; corrupt file?zInvalid protocol version: )_get_restore_location UnpicklerrrrDrpTarErrorrMr`rrHrr version_inforTr%r$rrhrrZ_set_from_fileryrr(r_validate_loaded_sparse_tensors)rIr7rr8rrhZf_should_read_directlyZ magic_numberrZ _sys_inforrZdeserialized_storage_keysr r*rr-)r[rdrgr8rrr.rLsb (A6  $       rL) bytes_strr;cCst|tr|dS|S)Nr)r<bytesdecode)rr-r-r.rs  rcsldkrt}nZttr&fdd}nBtttfrBfdd}n&ttjr\fdd}n fdd}|S)Ncs||}t||Sr/)getr!rrr@r-r.r s z/_get_restore_location..restore_locationcs t|Sr/r!rr@r-r.rscst|tSr/)r!r_rr@r-r.rscs ||}|dkrt||}|Sr/r)rrrr@r-r.rs  )r!r<rr_rryri)r7rr-r@r.rs   rc@seZdZddZddZdS)r&cCst||_dSr/)r rrr-r-r.rszStorageType.__init__cCsd|jdS)NzStorageType(dtype=rrorr-r-r.__str__!szStorageType.__str__N)r*r+r,rrr-r-r-r.r&sr-c  s>t|id}d|rB|dkrtdnDttjksXtdkr^dn(ttjkrpdnttj kr~ntd|stdkrt j dkrt dtfd d fd d }d diGfddd|j}t|} || f|} || _| } tjtjddi| S)Nr)littlebigr/rrzInvalid load endianness typer.aThe default load endianness for checkpoints without a byteorder mark on big endian machines was changed from 'native' to 'little' endian, to avoid this behavior please use torch.serialization.set_default_load_endianness to set the desired default load endiannesscsd|}dk r.|}|||}n||tjj}dk rdtjkrd| |tj j |||dd}| dkr||<|S)Nr0Trjr) Zget_record_offsetZget_storage_from_recordryrZ_typed_storagerrrrbyteswaprrr)rr{r*rrrrr) byteorderdataloaded_storagesrArr3r-r. load_tensorGs     z_load..load_tensorc st|tstt|d}|dd}|dks>td|d|\}}}}|tjkr\tj}n|j}|krt|}n"|tj |} || |t|}|S)NrrrzBUnknown typename for persistent_load, expected 'storage' but got 'rY) r<r\rrryrrrrr() rfrrrr*rr{rrr)rrr-r.rh_s       z_load..persistent_loadz torch.tensorz torch._tensorcseZdZfddZZS)z_load..UnpicklerWrappercsNt|tkr4d|kr4z t|WStk r2YnX||}t||SrU)rTr_r&rVrrrWrX)rload_module_mappingr-r.rWs  z*_load..UnpicklerWrapper.find_classrZr-)rrr.r[|sr[ztorch.load.metadataserialization_id)rZ has_recordZ get_recordrrr(r'r7r8r6rrrarbrFrrr1rhr%ryrrrzZ_log_api_usage_metadatar) r3r7rrrAr8Z byteordernamerhr[Z data_filerrr-)rrrrrArr3r.rI%sR         rIcCs d|kS)Nz constants.pkl)Zget_all_records)r3r-r-r.rEsrE)T)NN)r-N)|r]rrr1rtrryrpr0ra contextlibrrenumrrrZtorch._sourcesrZ torch.typesrZ torch.storager typingr r r r rrrrrrZtyping_extensionsrcopyregrDrZtorch._weights_only_unpicklerrJDEFAULT_PROTOCOLStructrMr"r!r r$rZSTORAGE_KEY_SEPARATORr_rrr__annotations__rrirrrrr__all__WarningrrrQr'r9r(r)boolrMrrrrlrprrrurwr}rrrrrrrrrr r!r"r#rrrrrrrrrrrrrrrobjectr$rrr%rNrOrRrLrrr&rIrEr-r-r-r.s      0     6   - '                =X 2 d  r