;i UddlZddlZddlmZddlmZddlmZmZmZm Z ddl m Z ddl m Z ddlmZmZmZmZmZmZddlmZdd lmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)dd l*m+Z+dd l,m-Z-m.Z.dd l/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>ddl?m@Z@mAZAddlBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZOmPZPmQZQmRZRmSZSmTZTmUZUddlVmWZWmXZXmYZYmZZZm[Z[m\Z\m]Z]m^Z^m_Z_ddl`maZambZbmcZcer ddldmeZeddlfmgZgdZhdZie(e&de&e fZje$ejZkdedde!e<deelefddfdZmddddeidde#e(e&de&d fd!end"e!eld#e!eld$e!eld%eldeeleffd&Zoddeifd'e(e&de&d fd!end$e!eld%eldeeleff d(Zpddeeud?fd@eudAffZye%e%ele(ewe%ewd6ffelfd6fexdB<dezschema..s 999Ie$$999rirkrlrjmodel_name_maprmrn definitions)get_flat_models_from_modelsget_model_name_mapmodel_process_schemaupdate)rorjrkrlrmrn clean_models flat_modelsrwrx output_schemartm_schema m_definitionsm_nested_models model_names rgschemarms8:9&999L-l;;K' 44NK$&M '!& g3'2 m$ + +3G )!% 4 4 4 0- =)))#E* "* J3'2 m$ rirtct|}t|}t|}||}t|||||\}}} || vr|||<t |||d}|r|d|i|S)aa Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level JSON key. :param model: a Pydantic model (a class that inherits from BaseModel) :param by_alias: generate the schemas using the aliases defined, if any :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the top-level key ``definitions``, so you can extract them from there. But all the references will have the set prefix. :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``. :return: dict with the JSON Schema for the passed ``model`` rvFrx)rTget_flat_models_from_modelrzr{get_schema_refr|) rtrjrmrnr~rwrrr nested_modelss rg model_schemars, e  E,U33K' 44N&J-A Jeq...*Hm]]""$, j!!*j,NN8 6777 OriFschema_overridesci}|jjst|jts<|jjp,|jdd|d<|jjrd}|jjr|jj|d<d}|js4|j -t|j st|j |d<d}||fS)N_ rkTrldefault) field_inforkrUtype_r aliasreplacerlrequiredrrN outer_type_encode_default)rZrschema_s rgget_field_info_schemars!G [%7 T%J%J[ +1ZU[5F5F5H5H5P5PQTVY5Z5Z   # !&!1!=  > em7@PQVQb@c@c7+EM::  $ $$ri)rjrmrn known_modelsrwrc  t|\}}t|}|r||d}t|||||||p t \} } } d| vr| | | fS|| || | fS)a Process a Pydantic field and return a tuple with a JSON Schema for it as the first item. Also return a dictionary of definitions with models as keys and their schemas as values. If the passed field is a model and has sub-models, and those sub-models don't have overrides (as ``title``, ``default``, etc), they will be included in the definitions and referenced in the schema instead of included recursively. :param field: a Pydantic ``ModelField`` :param by_alias: use the defined alias (if any) in the returned schema :param model_name_map: used to generate the JSON Schema references to other models included in the definitions :param ref_prefix: the JSON Pointer prefix to use for references to other schemas, if None, the default of #/definitions/ will be used :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``. :param known_models: used to solve circular references :return: tuple of the schema for this field and additional definitions Trjrwrrmrnr$ref)rget_field_schema_validationsr|field_type_schemarb) rZrjrwrmrnrsrvalidation_schemaf_schema f_definitionsf_nested_modelss rgr[r[s4066A4U;;  "###/@ %)!!*SUU000,Hm_77 -00ri max_length maxLength min_length minLengthregexpattern._str_types_attrsgtexclusiveMinimumltexclusiveMaximumgeminimumlemaximum multiple_of multipleOf_numeric_types_attrsci}t|jtr-|jjr||jj|St|jt tfr9tD]1\}}}t|j|d}t||r|||<2t|jtrSt|jts9tD]1\}}}t|j|d}t||r|||<2|j|jjr |j|d<|jjr||jjt|jdd}|rt%||||S)z Get the JSON Schema validation keywords for a ``field`` with an annotation of a Pydantic ``FieldInfo`` with validation arguments. Nconst__modify_schema__)rUrr rextrar|strbytesrgetattr isinstance numeric_types issubclassboolrrrrrh)rZr attr_nametkeywordattrrYs rgrr!s "H%+t,,   ! 4 OOE,2 3 3 3%+U|44)%5 ) ) !Iq'5+Y==D$"" )$(!%+}55)jVZ>[>[)%9 ) ) !Iq'5+Y==D$"" )$(! #(8(>#!M 0(.///E-/BDIIM=]E8<<< Ori unique_modelscri}t}|D]}t|j}||vrt|}|||</||vrO||||}||t|<||t|<|||<d|DS)a Process a set of models and generate unique names for them to be used as keys in the JSON Schema definitions. By default the names are the same as the class name. But if two models in different Python modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be based on the Python module path for those conflicting models to prevent name collisions. :param unique_models: a Python set of models :return: dict mapping models to names ci|]\}}|| Srrrrrskvs rg z&get_model_name_map..Zs 4 4 4TQAq 4 4 4ri)rbnormalize_name__name__get_long_model_nameaddpopitems)rname_model_mapconflicting_namesrtrconflicting_models rgrzrzBsN"%%% / /#EN33 * * *,U33J).N: & & > ) )  ! !* - - - . 2 2: > > EVN./@AA B9>N.u55 6 6).N: & & 4 4^1133 4 4 44ric |p t}t}||||z}ttt|j}|t||z}|S)a? Take a single ``model`` and generate a set with itself and all the sub-models in the tree. I.e. if you pass model ``Foo`` (subclass of Pydantic ``BaseModel``) as ``model``, and it has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``. :param model: a Pydantic ``BaseModel`` subclass :param known_models: used to solve circular references :return: a set with the initial model and all its sub-models r)rbrr#rr3 __fields__valuesget_flat_models_from_fields)rtrr~fieldss rgrr]sx (355L #KOOEKL (:&(8(?(?(A(A B BF.vLQQQQK ricddlm}t}|j}t t |dd|r|j}|jr*t ||s|t|j|z}nSt ||r||vr|t||z}n*t |tr| ||S)a Take a single Pydantic ``ModelField`` (from a model) that could have been declared as a subclass of BaseModel (so, it could be a submodel), and generate a set with its model and all the sub-models in the tree. I.e. if you pass a field that was declared to be of type ``Foo`` (subclass of BaseModel) as ``field``, and that model ``Foo`` has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``. :param field: a Pydantic ``ModelField`` :param known_models: used to solve circular references :return: a set with the model used in the declaration for this field, if any, and all its sub-models rrW__pydantic_model__Nr) pydantic.v1.mainrXrbrrUrr sub_fieldsrrr r)rZrrXr~ field_types rgget_flat_models_from_fieldrqs+***** #KJ'*.BDII9UU32  $ 2:y I I$253CR^____ J 2 2$z7U7U1*<XXXX J - -$ ### rircTt}|D]}|t||z}|S)a Take a list of Pydantic ``ModelField``s (from a model) that could have been declared as subclasses of ``BaseModel`` (so, any of them could be a submodel), and generate a set with their models and all the sub-models in the tree. I.e. if you pass a the fields of a model ``Foo`` (subclass of ``BaseModel``) as ``fields``, and on of them has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``. :param fields: a list of Pydantic ``ModelField``s :param known_models: used to solve circular references :return: a set with any model declared in the fields, and all their sub-models r)rbr)rrr~rZs rgrrs>!$KTT1%lSSSS ricPt}|D]}|t|z}|S)a_ Take a list of ``models`` and generate a set with them and all their sub-models in their trees. I.e. if you pass a list of two models, ``Foo`` and ``Bar``, both subclasses of Pydantic ``BaseModel`` as models, and ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``. )rbr)ror~rts rgryrys7 !$K991%888 ricL|jd|jddS)N__.) __module__ __qualname__rrts rgrrs, 6 6%"4 6 6 > >sD I IIri)rrmc 4ddlm}i}t} |jtt t tttthvrdt||||||\} } } | | | | d| d} |jtthvrd| d<n|jtvrdd i} tt|j}t#|jd d }t||||||\} } } | | | | |rt'j|| i| d <| r| | d <nk|jt*ks%|jt,krt/|j|sg}tt0t|j}|D]Z}t5||||||\}}}| || |||[t9|}|jt,kr|dkr|dnd|d}d|gi} nzd||d} |dkr|| d<nh|jt:t,hvsJ|jt|||||||\} } } | | | | |j|jkrC|jt,kr|j}n|j}t#|dd }|rt?||| | || fS)a  Used by ``field_schema()``, you probably should be using that function. Take a single ``field`` and generate the schema for its type only, not including additional information as title, etc. Also return additional schema definitions, from sub-models. rrWrjrwrmrnrarraytyperT uniqueItemsrobjectrNpatternPropertiesadditionalPropertiesallOf)rminItemsmaxItemsrrr) rrXrbshaper,r1r-r.r)r+r(field_singleton_schemar|r'r#r3 key_fieldrrr= _get_patternr0r*rrrrappendlenr/rrh)rZrjrwrnrrmrrXrxr items_schemarrrrr sub_schemarsf sf_schemasf_definitionssf_nested_modelssub_fields_lenall_of_schemasrrYs rgrrs +*****K!eeM {8N )!%% 8 8 8 4 m_ =)))_---#l;; ;9o6 6 6&*H] # + + +H%U_55  $777M )!%% 8 8 8 4 m_ =)))_---  _.<-H-O-OQ],^H( )  >>> >>>3I )-!%%4 4 4 0- =)))_--- {e''' ;- ' 'JJ*J ,?FF  A x @ @ @ [- //ri)rjrmrnrrZcddlm}m}|p t}t |t rBt tt |}t||} | itfSt td|}d|j j p|j i} ||} | r| | d<| |t||||||\} } } | | |j j}t!|r:t#||jdkr || n"|| |n| || | | fS) au Used by ``model_schema()``, you probably should be using that function. Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The sub-models of the returned schema will be referenced, but their definitions will not be included in the schema. All the definitions are returned as the second value. r)getdocr^r`rXrkrlrr)rarr^rbrUr r#r enum_process_schema __config__rkrrmodel_type_schemar| schema_extracallablerrc)rtrjrwrmrnrrZrr^rdocrrrrs rgr{r{'s"*)))))))(355L%&&T$Z'' U 3 3 3"cee| k"E * *E %"(:EN;A &--C -U-> %!! ...*Hm]HHX#0L  yy&&1 2 2a 7 7 LOOOO LE " " " "  m] **ri)rmc i}g}i}t} |jD]\} } t| |||||\} } }n6#t$r)}t j|jtYd}~Nd}~wwxYw| | | ||r,| || j <| j r| | j | || <| j r| | t|vr$|t}|jjp|j|d<n d|d}|r||d<|jjdkrd|d <||| fS) a) You probably should be using ``model_schema()``, this function is indirectly used by that function. Take a single ``model`` and generate the schema for its type only, not including additional information as title, etc. Also return additional schema definitions, from sub-models. rNrkr)r propertiesrforbidFr)rbrrr[ SkipFieldwarningswarnmessage UserWarningr|rrrrSrrkrr)rtrjrwrnrmrr rrxrrfrrrskip out_schemas rgrrYsJH"$K!eeM &&((##1 7C!-%)) 888 4Hm__    M$, 4 4 4 HHHH  =)))_---  #"*Jqw z )((($JqMz #""":) #.4F 7&jAA  .%-Jz " ))-2 )* {M 11sA BA<<Br`enumcddl}|j||jpddt t t |Dd}t||t|dd}|rt||||S)z Take a single `enum` and generate its schema. This is similar to the `model_process_schema` function, but applies to ``Enum`` objects. rNzAn enumeration.cg|] }|j Srr)value)rsitems rgruz'enum_process_schema..sCCCCCCri)rkrlrr) rarcleandoc__doc__r#rr add_field_type_to_schemarrh)rrZrarrYs rgrrs NNN'' (I8IJJCCXd^T(B(BCCC GT7+++D"5t<.sQFFF&'y1>*95zF b 9 95Kb4""Ao   4 4B=N!-!1%))>>> :J):    / / / 4Gz$9$9(03   _g$>>>///  $ $Z 0 0 0  !2 3 3 3 3=N, 9''':+},,ristringpath)rformatz date-timerrnumberz time-delta ipv4network ipv6network ipv4interface ipv6interfaceipv4ipv6rbinarybooleanintegeruuidrrr)rrrfield_class_to_schemaz json-stringrrctD]>\}}t||s||cxur turn&||dS?dS)a Update the given `schema` with the type-specific metadata for the given `field_type`. This function looks through `field_class_to_schema` for a class that matches the given `field_type`, and then modifies the given `schema` with the information from that type. N)rErUrr|)rrrt_schemas rgrr$ss1x j% 0 0 J%4R4R4R4R74R4R4R4R4R NN8 $ $ $ EE5SrinamecX|rd||zi}nd||i}|rd|gin|S)Nrrr)r9)rHrmrnr schema_refs rgrr2sM?j4/0 l111==> &6 FGj\ " "JFric ddlm}i}t} |j} |jr9|jr |jjst| |st|||||||S| tus/| tus&| j tkst| turi|| fSt| rddi|| fSt!| rt#d|jdi} |j|jjr |j| d <t)| rt+d t-| D} t/d | Dd kr#t1t3| |||||| S| dj } t5| | d<t7| | n6t| t8rU|| } t;||\} }| t?| |||tA| ||| <ntC| rytE| j#||||||^}}t5|d$}| d|t/|t/|dnDtK| ds4t7| | tM| dd}|rtO||| | r| || fSttM| dd|r| j#} tQ| |r|| }| |vrJtE| ||||||\}}}|||||<| |n| )|t?||||}||| fStU| }||stV| j,vr| || fSt[d|)z This function is indirectly used by ``field_schema()``, you should probably be using that function. Take a single Pydantic ``ModelField``, and return its schema and any additional definitions from sub-models. rrWrrnullz Callable zC was excluded from schema since JSON schema has no equivalent type.Nrc3RK|]"}t|tr|jn|V#dSN)rr r)rsxs rg z)field_singleton_schema..hs7ee*Q"5"5z)field_singleton_schema..js,,, ,,,rirrrr`)rjrwrmrnrrZr r)rrrrrrz.Value not declarable with JSON Schema, field: ).rrXrbrrrrrUr6rrrRr!rLrrQrNrrHrrOtuplerJrr[#multitypes_literal_field_for_schemalistrr rr|rrrPr{rrr)rrhrrrKr __bases__ ValueError)rZrjrwrnrrmrrXrxrrrr enum_namerr items_schemasrYrr4r5rJrfs rgrr:s+*****"$K!eeMJ      #.4  =OPZ\e=f=f  1 )-!%%    SJ&00J4HG4S4SWablWmWmquWuWu; --J<m;; ##usEJsssttt!H #(8(>#!Mz"".AeeFXYcFdFdeeeee ,,V,,, - - 1 13FEBB!-%))  AY( << X6666 J - -A":. %:5BR%S%S""y*lL\]]^^^!4Zu!M!M!M I z " "A-  ))!%%    QZ 5<<>>?? & .. ..       Z!5 6 6A X666 ,?FF  A x @ @ @4m33'*.BDII9UU32 *i((6#J/ \ ) )=Q!-%))>>> :J):    / / /&0K #  !2 3 3 3 3   j ) ) )#J LJZ[[ ; 55 J  D J4H)H)Hm33 MeMM N NNrirc Ttt}|D]"}||j|#d|D}t |jtt||j |j |j |j |j |jS)z To support `Literal` with values of different types, we split it into multiple `Literal` with same type e.g. `Literal['qwe', 'asd', 1, 2]` becomes `Union[Literal['qwe', 'asd'], Literal[1, 2]]` c3JK|]}tt|VdSrN)r&rU)rssame_type_valuess rgrPz6multitypes_literal_field_for_schema..s1rr>N'7!8!89rrrrrrri)rHrclass_validators model_configrrrr)rrWrRrrr3rHr"rUr_r`rrrr)rrZliteral_distinct_typesrdistinct_literalss rgrVrVs ).. 66q{+2215555rrRhRoRoRqRqrrr  ZE+,,-/' k#    ridftc.ddlm}t||st|rt dt |}t|t rd|DSt|tr|j St|tttfr|St|ttfr2|j}d|D}t!|r||n ||S|dSt |S)NrrWzdict[str, Any]cNi|]"\}}t|t|#Srrrrs rgrz"encode_default..s.MMMAq!!>!#4#4MMMric34K|]}t|VdSrNrfrSs rgrPz!encode_default..s*33!N1%%333333ri)rrXrrr#r4dictrr rintfloatrrWrUrRrP)rcrXrseq_argss rgrrs!******#y!!<\#%6%6<#%5c%:%:;;#t %MMMMMM C   %y C#uc* + + % C$ ' '% M33s333,Q//@qq(||QQx[[@ t$$$ri_map_types_constraint annotationr field_namevalidate_assignmentc|}t}|rt||\}}|r|d||z }|r)t d|dd|d|S)a= Get an annotation with validation implemented for numbers and strings based on the field_info. :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr`` :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema :param field_name: name of the field for use in error messages :param validate_assignment: default False, flag for BaseModel Config value of validate_assignment :return: the same ``annotation`` if unmodified or a new annotation with validation in place allow_mutationz On field "z<" the following field constraints are set but not enforced: z, z\. For more details see https://docs.pydantic.dev/usage/schema/#unenforced-field-constraints)get_constraintsrbget_annotation_with_constraintsrrYjoin)rmrrnro constraintsused_constraintsunused_constraintss rgget_annotation_from_field_inforxs,,..K!$_'FzS]'^'^$ $/-...$'77  k k kyy+,, k k k   rictdtdttffd |fS)a Get an annotation with used constraints implemented for numbers and strings based on the field_info. :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr`` :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema :return: the same ``annotation`` if unmodified or a new annotation along with the used constraints. rr\c  ts7tts"ttt t frSt}|Dt}td|DrS|tur |dSt|r&tt fd|DSt|tr]jjjH hdt) |djjjSt|t*rPjjB ddht- |djjSt|t.rPjjB ddht1 |djjSt2tt*t.t4fD]5}t||r#|t fd |DcS6t|t6r$t6|d |d fSd}d}tt8rtt:t<fr#d }d t>d t@t>ffd }nxttBr]ttDtFfsAd}ttHr!d t>d t@t>ffd }ntJ}nttLr?d}ttNr d t>d t@t>ffd }ntP}nttRrttTtVtXtt t tZfscd}tt\r|dz }tt^r|dz }tafdtRD}tb|}|rf te|dfd|DD}|r*tgthdt8f|}|di|SS)Nc3@K|]}t|tVdSrN)rr)rsas rgrPz>get_annotation_with_constraints..go..s,;;:a,,;;;;;;rirc3.K|]}|VdSrNrrrsr|gos rgrPz>get_annotation_with_constraints..go..!+"7"7Q22a55"7"7"7"7"7"7ri> max_items min_items unique_items)rrrrr)rrc3.K|]}|VdSrNrrr~s rgrPz>get_annotation_with_constraints..go..:rrir)rrkwr\c2tjf|SrNrrrrs rgconstraint_funczDget_annotation_with_constraints..go..constraint_funcEs"===ri)rrrc2tjf|SrNrrs rgrzDget_annotation_with_constraints..go..constraint_funcL#ENUHbAAAric2tjf|SrNrrs rgrzDget_annotation_with_constraints..go..constraint_funcUrri)rrrrr) allow_inf_nan) max_digitsdecimal_placesc3<K|]}t||VdSrN)r)rsrrs rgrPz>get_annotation_with_constraints..go..ls3#U#U! 5RS@T@T#UA#U#U#U#U#U#Urici|] \}}||| SrNrr)rsrrs rgrz?get_annotation_with_constraints..go..qs.#It#4###ric3<K|]}|t|fVdSrN)r)rsrrs rgrPz>get_annotation_with_constraints..go..ss3'k'kXaGJ 4R4R(S'k'k'k'k'k'kri.rr)5rOrrrUr;r<r9rLrKanyr%rRr"rUrrrrrr|rGrrHrrErrrrr?r>rr rr6r5rArIrr@rBrr:r8r7rrjr nextrlrbr#r) roriginrfrattrsr numeric_typer_rrrvs ` rgrz+get_annotation_with_constraints..gosk E " " %,, "%/>K_)`aa  LE""  $,UOOD;;d;;;;;  ""r$q'{{" 9U"7"7"7"7$"7"7"77788&$'' $0'3*6 ''(R(R(RSSSBtAwKK(2(2!+!8 &#&& kJ,@,LPZPdPp ''k(BCCCbbakkZ5IU_Uijjjj&),, q*2F2RV`VjVv ''k(BCCC#BBtAwKK:;O[e[oppppT3 8< 9 9fa((9U"7"7"7"7$"7"7"77788889&$'' 2DGRRQ[[011+/9= eT " ", F%)[!9::+ F4>#>$s)>>>>>>>E3''% F 58VBT0U0U% F=eY//-BcBd3iBBBBBBB'-OOE5)) F=e[11/BcBd3iBBBBBBB'/OOE=11 F*"$&#"( ; ; F@eU++0//EeW-->==E##U#U#U#U}#U#U#UUU "7 "E  1  # #CJJ / / /'k'k'k'kej'k'k'kF  1"&xT ':O"L"L&00000 ri)rbrr )rmrrrvs `@@rgrsrssl"%i#i$s)iiiiiiiiV 2j>>+ ++ric.tjdd|S)zU Normalizes the given name. This can be applied to either a model *or* enum. z[^a-zA-Z0-9.\-_]r)resub)rHs rgrr~s 6%sD 1 11ric"eZdZdZdeddfdZdS)rz? 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