DŽg* ddlZddlZddlmZmZmZmZddlZddlmZmZm Z m Z ddl m Z e ddddejdeed efd Zd Zd Ze d eje dededede j*diZdZeZdej2d efdZdej6ej8d ej6ejfdZy)N)ListOptionalSequenceUnion)devicedtypeTensortypes) exposed_inz torch.library)op_nameprototype_functionr returnc d}tj|fddtffd }g}t}d}t j j D]R\}\} } t| sd| jtjjk(r|s|jdd}| jtjjur d | d | j} t| tk(r|| } | tj!vr| j"t$ur[t'| } d } | tj!vrd | d } d | d| jd| dtj!d n.d | d| jdtj!dt| }t|tk(r6||k7r t)d|j+drJd|d|t-dd}n5| |vr1|j+ds d | dd|d|t-dd}|j/| | j0tjjur|j|d| 7d}| j0%t3| j0t4t6t8frt| j0}nt3| j0tt:j<frd| j0d}nt3| j0t:j>r9t| j0}d}|j+|sJ|t-|d}n"d | dt| j0d|j|d| d|U||k7r't||z }t-|dkDr |d j@}t|tk(r||}tC|}||d!d"jE|d#|Sd!d"jE|d#|S)$aJParses the schema of a given function with type hints. The schema is inferred from the function's type hints, and can be used to define a new operator. We make the following assumptions: * None of the outputs alias any of the inputs or each other. * | String type annotations "device, dtype, Tensor, types" without library specification are | assumed to be torch.*. Similarly, string type annotations "Optional, List, Sequence, Union" | without library specification are assumed to be typing.*. * | Only the args listed in ``mutates_args`` are being mutated. If ``mutates_args`` is "unknown", | it assumes that all inputs to the operator are being mutates. Callers (e.g. the custom ops API) are responsible for checking these assumptions. Args: prototype_function: The function from which to infer a schema for from its type annotations. op_name (Optional[str]): The name of the operator in the schema. If ``name`` is None, then the name is not included in the inferred schema. Note that the input schema to ``torch.library.Library.define`` requires a operator name. mutates_args ("unknown" | Iterable[str]): The arguments that are mutated in the function. Returns: The inferred schema. Example: >>> def foo_impl(x: torch.Tensor) -> torch.Tensor: >>> return x.sin() >>> >>> infer_schema(foo_impl, op_name="foo", mutates_args={}) foo(Tensor x) -> Tensor >>> >>> infer_schema(foo_impl, mutates_args={}) (Tensor x) -> Tensor unknownc(td|dd)Nzinfer_schema(func): z Got func with signature )) ValueError)whatsigs c/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/torch/_library/infer_schema.pyerror_fnzinfer_schema..error_fn9s#"4&(EcU! L  annotation_typec` t|S#t$r}d|dYd}~yd}~wwxYw)NzUnsupported type annotation z. It is not a type.)eval Exception)rers rconvert_type_stringz)infer_schema..convert_type_string>s> ( (  ..??RS   s  - (-Fz>We do not support positional-only args, varargs, or varkwargs.*Tz Parameter z must have a type annotation.z z For example, z. z has unsupported type z]. We do not support Tuple inputs in schema. As a workaround, please try to use List instead. zThe valid types are: .. The valid types are: zomutates_args must either be a sequence of the names of the arguments that are mutated or the string 'unknown'. r zTensor(az!)NzM is in mutable_args but only Tensors or collections of Tensors can be mutated "ztorch.z' has an unsupported default value type z;. Please file an issue on GitHub so we can prioritize this.=rz in mutates_args were not found in the custom op's signature. mutates_args should contain the names of all args that the custom op mutates, or just the string 'unknown' if you don't know.(, z) -> )#inspect signaturestrset enumerate parametersitemssupported_paramkind Parameter KEYWORD_ONLYappend annotationemptytypeSUPPORTED_PARAM_TYPESkeys __origin__tuple tuple_to_listr startswithlenadddefault isinstanceintfloatbooltorchrrreturn_annotation parse_returnjoin)r mutates_argsr UNKNOWN_MUTATESrparams seen_argssaw_kwarg_only_argidxnameparamr list_typeexample_type_str schema_type default_repr dtype_repr torch_dotmutates_args_not_seenrDretrrs @@r infer_schemarW sfT O   . /C SFI'(<(<(>?HB]dEu% U V ::**77 7% c"%)"   w0066 6 z$'DE F **  C '1/BO "7"<"<"> >))U2)/: #)  5 : : <<)6yk'G$ &?!"5==/3 EMM5;;7 / $ !,,Y777)#i.*:;  &MdSXS`S`NaMbcPQ MM[M4&,@ AQHBR& #L 1I = $ % ) ()*UV  -- #%/0AB (( 3C!DIIf-.eC599 tyy !se ,,rcf||ftj||dfg}d}|r#||D]}|j||df|r4|tj|D]}|j||df|r4||D])}|jtj||df+|S)N?cNtj|tj|gSN)typingrr)typs rderived_seq_typesz(derived_types..derived_seq_typess$ OOC KK   rz[]z?[]z[]?)r\rr2) base_typecpp_type list_baseoptional_base_listoptional_list_baseresultr^seq_typs r derived_typesrfs H  #z^4F  (3 6G MM7xjO4 5 6()CD 7G MM7xj$45 6 7(3 HG MM6??73z5EF G H Mrc tddddftddddftddddftddddftddddft j ddddftd dddftd dddfg}g}|D]}|jt|t|S) Nr TFSymIntrArBr)Scalar ScalarTypeDevice) r r@rArBr)r Numberrrextendrfdict)datardlines rget_supported_param_typesrqs 4u- heT* ud+ vtUD) eUE5) xue4  eUE2 5%/ DF, mT*+, <rr zTensor[]rhrArBric|y|tjjur|dtj|}|t ur2|t jvr|d|dt dt |Stj|}|D]}|t vs |d|dt d ddj|Dcgc] }t | c}zdzScc}w) Nz()z7No return type annotation was provided. Please add one.zReturn has unsupported type r!r r%r&r) r'r0r4r\ get_originr9SUPPORTED_RETURN_TYPESr7get_argsrF)r3roriginargsargs rrErEsW&&,,,JK   z *F U 388: : .zl;((>'?qB &j11 ??: &D , , .zl;((>'?qB  4HC237HI IC OOHs?C rNc|jtjjtjjfvSr[)r/r'r0POSITIONAL_OR_KEYWORDr1)rNs rr.r.s6 :://&& r tuple_typecnt|dd}|tjus|dk(s|tjSt |dk(rtj|dSt |dk(r!|dt urtj|dStjtj t|S)z} Convert `tuple_type` into a list type with the same type arguments. Assumes that `tuple_type` is typing.Tuple type. __args__Nr)getattrr\Tuplerr<Ellipsisrr9)r{ type_argss rr:r:s J5IV\\!Y"_ 8I{{ Y1 {{9Q<(( Y1 1!9{{9Q<(({{6<<i(89::r)r'r\rrrrrCrrr r torch.utils._exposed_inr Callabler)rWrfrqr@rArBrlrtrEr6r0r.Typerr:r~rrrs 22 ... O " T-T- c] T-  T-T-n4& H KK 7& LL( P6237,,;fkk&,,7;FKK