Ë
    T^(h×}  ã                   óL  — d Z ddlZddlZddlZddlZddlZddlmZmZ ddl	m
Z
mZ ddlmZmZ ddlmZmZ ddlmZ ddlmZmZ dd	lmZmZmZmZ ddlZdd
lmZ ddl m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&  e%«       rddl'Z' G d„ de(«      Z)d„ Z*d„ Z+d„ Z,d„ Z-d„ Z.d„ Z/d„ Z0d„ Z1d„ Z2d„ Z3d„ Z4d„ Z5d„ Z6d„ Z7d„ Z8d„ Z9d„ Z:d „ Z;d!„ Z<d"„ Z=d#„ Z>d$„ Z? G d%„ d&e«      Z@ e%«       r¦ddlAmBc mCZD d'e@d(eEeFe   d)f   fd*„ZG	 d]d+e
e   d,d)d(e@fd-„ZH ej’                   e!«       «       ej’                  d.«      k\  r8 eDj”                  e@eG eeHe@¬/«      e@j–                  › d0e@j˜                  › �¬1«       n eDjš                  e@eG eeHe@¬/«      «        G d2„ d3eNe«      ZO G d4„ d5eO«      ZP G d6„ d7eO«      ZQ G d8„ d9«      ZRd:„ ZSd;„ ZTd^d<ed=eNd>eNfd?„ZUed_dAeVfdB„«       ZWd]dC„ZXdD„ ZYd]dE„ZZdF„ Z[dG„ Z\dH„ Z]dI„ Z^dJ„ Z_dK„ Z`dL„ Zad]dMeeF   fdN„Zb G dO„ dPed@¬Q«      ZcdRedeNef   d(eVfdS„ZedTeNd(eVfdU„ZfdVdWdXeNdYefdZ„ZgdVdWdXeNfd[„Zhd\„ Ziy)`z
Generic utilities
é    N)ÚOrderedDictÚUserDict)ÚIterableÚMutableMapping)Ú	ExitStackÚcontextmanager)ÚfieldsÚis_dataclass)ÚEnum)ÚpartialÚwraps)ÚAnyÚContextManagerÚOptionalÚ	TypedDict)Úversioné   )Úget_torch_versionÚis_flax_availableÚis_mlx_availableÚis_tf_availableÚis_torch_availableÚis_torch_fx_proxyc                   ó   — e Zd ZdZdd„Zy)Úcached_propertyzš
    Descriptor that mimics @property but caches output in member variable.

    From tensorflow_datasets

    Built-in in functools from Python 3.8.
    Nc                 óÈ   — |€| S | j                   €t        d«      ‚d| j                   j                  z   }t        ||d «      }|€| j                  |«      }t	        |||«       |S )Nzunreadable attributeÚ	__cached_)ÚfgetÚAttributeErrorÚ__name__ÚgetattrÚsetattr)ÚselfÚobjÚobjtypeÚattrÚcacheds        úX/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/utils/generic.pyÚ__get__zcached_property.__get__:   sg   € àˆ;ØˆKØ�9‰9ÐÜ Ð!7Ó8Ð8Ø˜TŸY™Y×/Ñ/Ñ/ˆÜ˜˜d DÓ)ˆØˆ>Ø—Y‘Y˜s“^ˆFÜ�C˜˜vÔ&Øˆó    ©N)r    Ú
__module__Ú__qualname__Ú__doc__r)   © r*   r(   r   r   1   s   „ ñôr*   r   c                 óR   — | j                  «       } | dv ry| dv ryt        d| ›�«      ‚)zïConvert a string representation of truth to true (1) or false (0).

    True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values are 'n', 'no', 'f', 'false', 'off', and '0'.
    Raises ValueError if 'val' is anything else.
    >   Ú1ÚtÚyÚonÚyesÚtruer   >   Ú0ÚfÚnÚnoÚoffÚfalser   zinvalid truth value )ÚlowerÚ
ValueError)Úvals    r(   Ú	strtoboolr@   I   s:   € ð �)‰)‹+€CØ
Ð2Ñ2ØØ
Ð3Ñ3ØÜ
Ð+¨C¨7Ð3Ó
4Ð4r*   c                 óà   — t        t        | «      «      }|j                  d«      ry|j                  d«      ry|j                  d«      ry|j                  d«      ry|j                  d	«      ry
y)zÃ
    Tries to guess the framework of an object `x` from its repr (brittle but will help in `is_tensor` to try the
    frameworks in a smart order, without the need to import the frameworks).
    z<class 'torch.Úptz<class 'tensorflow.Útfz<class 'jaxÚjaxz<class 'numpy.Únpz<class 'mlx.ÚmlxN)ÚstrÚtypeÚ
startswith)ÚxÚrepresentations     r(   Úinfer_framework_from_reprrL   W   sn   € ô
 œ˜a›“\€NØ× Ñ Ð!1Ô2ØØ	×	"Ñ	"Ð#8Ô	9ØØ	×	"Ñ	" =Ô	1ØØ	×	"Ñ	"Ð#3Ô	4ØØ	×	"Ñ	" >Ô	2Øð 
3r*   c                 ó  — t         t        t        t        t        dœ}t        | «      }|€g n|g}|dk7  r|j                  d«       |j                  |D �cg c]  }||dfvsŒ
|‘Œ c}«       |D �ci c]  }|||   “Œ
 c}S c c}w c c}w )z¿
    Returns an (ordered since we are in Python 3.7+) dictionary framework to test function, which places the framework
    we can guess from the repr first, then Numpy, then the others.
    )rB   rC   rD   rE   rF   rE   )Úis_torch_tensorÚis_tf_tensorÚis_jax_tensorÚis_numpy_arrayÚis_mlx_arrayrL   ÚappendÚextend)rJ   Úframework_to_testÚpreferred_frameworkÚ
frameworksr8   s        r(   Ú_get_frameworks_and_test_funcrX   i   s›   € ô ÜÜÜÜñÐô 4°AÓ6Ðà*Ð2‘Ð9LÐ8M€JØ˜dÒ"Ø×Ñ˜$ÔØ×ÑÐ"3Ö\˜Q°qÐATÐVZÐ@[Ò7[’qÒ\Ô]Ø-7Ö8¨ˆAÐ  Ñ#Ñ#Ò8Ð8ùò ]ùÚ8s   ÁA?Á A?Á/Bc                 óª   — t        | «      }|j                  «       D ]  } || «      sŒ y t        | «      ryt        «       rddlm} t        | |«      ryy)z°
    Tests if `x` is a `torch.Tensor`, `tf.Tensor`, `jaxlib.xla_extension.DeviceArray`, `np.ndarray` or `mlx.array`
    in the order defined by `infer_framework_from_repr`
    Tr   )ÚTracerF)rX   Úvaluesr   r   Újax.corerZ   Ú
isinstance)rJ   Úframework_to_test_funcÚ	test_funcrZ   s       r(   Ú	is_tensorr`   ~   sW   € ô ;¸1Ó=ÐØ+×2Ñ2Ó4ò ˆ	Ù�Q�<Ùðô
 ˜ÔØäÔÝ#ä�a˜Ô Øàr*   c                 ó6   — t        | t        j                  «      S r+   )r]   rE   Úndarray©rJ   s    r(   Ú	_is_numpyrd   –   s   € Ü�aœŸ™Ó$Ð$r*   c                 ó   — t        | «      S )z/
    Tests if `x` is a numpy array or not.
    )rd   rc   s    r(   rQ   rQ   š   s   € ô �Q‹<Ðr*   c                 ó6   — dd l }t        | |j                  «      S ©Nr   )Útorchr]   ÚTensor©rJ   rh   s     r(   Ú	_is_torchrk   ¡   ó   € Ûä�a˜Ÿ™Ó&Ð&r*   c                 ó0   — t        «       sdS t        | «      S )z]
    Tests if `x` is a torch tensor or not. Safe to call even if torch is not installed.
    F)r   rk   rc   s    r(   rN   rN   §   s   € ô +Ô,ˆ5Ð>´)¸A³,Ð>r*   c                 ó6   — dd l }t        | |j                  «      S rg   )rh   r]   Údevicerj   s     r(   Ú_is_torch_devicerp   ®   rl   r*   c                 ó0   — t        «       sdS t        | «      S )z]
    Tests if `x` is a torch device or not. Safe to call even if torch is not installed.
    F)r   rp   rc   s    r(   Úis_torch_devicerr   ´   s   € ô +Ô,ˆ5ÐEÔ2BÀ1Ó2EÐEr*   c                 óŠ   — dd l }t        | t        «      rt        || «      rt	        || «      } nyt        | |j
                  «      S )Nr   F)rh   r]   rG   Úhasattrr!   Údtyperj   s     r(   Ú_is_torch_dtyperv   »   s9   € Ûä�!”SÔÜ�5˜!ÔÜ˜˜qÓ!‰AàÜ�a˜Ÿ™Ó%Ð%r*   c                 ó0   — t        «       sdS t        | «      S )z\
    Tests if `x` is a torch dtype or not. Safe to call even if torch is not installed.
    F)r   rv   rc   s    r(   Úis_torch_dtyperx   Æ   s   € ô +Ô,ˆ5ÐD´/À!Ó2DÐDr*   c                 ó6   — dd l }t        | |j                  «      S rg   )Ú
tensorflowr]   ri   ©rJ   rC   s     r(   Ú_is_tensorflowr|   Í   s   € Ûä�a˜Ÿ™Ó#Ð#r*   c                 ó0   — t        «       sdS t        | «      S )zg
    Tests if `x` is a tensorflow tensor or not. Safe to call even if tensorflow is not installed.
    F)r   r|   rc   s    r(   rO   rO   Ó   s   € ô (Ô)ˆ5Ð@¬~¸aÓ/@Ð@r*   c                 óp   — dd l }t        |d«      r|j                  | «      S t        | |j                  «      S )Nr   Úis_symbolic_tensor)rz   rt   r   r]   ri   r{   s     r(   Ú_is_tf_symbolic_tensorr€   Ú   s4   € Ûô ˆrÐ'Ô(Ø×$Ñ$ QÓ'Ð'Ü�a˜Ÿ™Ó#Ð#r*   c                 ó0   — t        «       sdS t        | «      S )z„
    Tests if `x` is a tensorflow symbolic tensor or not (ie. not eager). Safe to call even if tensorflow is not
    installed.
    F)r   r€   rc   s    r(   Úis_tf_symbolic_tensorr‚   ã   s   € ô
 (Ô)ˆ5ÐHÔ/EÀaÓ/HÐHr*   c                 ó:   — dd l m} t        | |j                  «      S rg   )Ú	jax.numpyÚnumpyr]   rb   )rJ   Újnps     r(   Ú_is_jaxr‡   ë   s   € Ýä�a˜Ÿ™Ó%Ð%r*   c                 ó0   — t        «       sdS t        | «      S )zY
    Tests if `x` is a Jax tensor or not. Safe to call even if jax is not installed.
    F)r   r‡   rc   s    r(   rP   rP   ñ   s   € ô *Ô+ˆ5Ð;´¸³Ð;r*   c                 ó:   — dd l m} t        | |j                  «      S rg   )Úmlx.coreÚcorer]   Úarray)rJ   Úmxs     r(   Ú_is_mlxrŽ   ø   s   € Ýä�a˜Ÿ™Ó"Ð"r*   c                 ó0   — t        «       sdS t        | «      S )zZ
    Tests if `x` is a mlx array or not. Safe to call even when mlx is not installed.
    F)r   rŽ   rc   s    r(   rR   rR   þ   s   € ô )Ô*ˆ5Ð:´¸³
Ð:r*   c                 ó.  — t        | t        t        f«      r| S t        | t        t        f«      r-| j                  «       D ��ci c]  \  }}|t        |«      “Œ c}}S t        | t        t        f«      rœ	 t        j                  | «      }t        j                  |j                  t        j                  «      s.t        j                  |j                  t        j                  «      r|j                  «       S 	 | D �cg c]  }t        |«      ‘Œ c}S d„ d„ d„ d„ dœ}t#        | «      }|j                  «       D ]  \  }} || «      sŒ ||   | «      c S  t        | t        j$                  «      r| j                  «       S | S c c}}w # t         $ r Y Œ™w xY wc c}w )zc
    Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list.
    c                 ó"   — | j                  «       S r+   ©Útolist©r$   s    r(   ú<lambda>zto_py_obj.<locals>.<lambda>  ó   € ˜#Ÿ*™*›,€ r*   c                 ó>   — | j                  «       j                  «       S r+   )r…   r“   r”   s    r(   r•   zto_py_obj.<locals>.<lambda>  s   € ˜#Ÿ)™)›+×,Ñ,Ó.€ r*   c                 óH   — t        j                  | «      j                  «       S r+   )rE   Úasarrayr“   r”   s    r(   r•   zto_py_obj.<locals>.<lambda>  s   € œ2Ÿ:™: c›?×1Ñ1Ó3€ r*   c                 ó"   — | j                  «       S r+   r’   r”   s    r(   r•   zto_py_obj.<locals>.<lambda>  r–   r*   ©rB   rC   rD   rE   )r]   ÚintÚfloatÚdictr   ÚitemsÚ	to_py_objÚlistÚtuplerE   rŒ   Ú
issubdtyperu   ÚintegerÚfloatingr“   Ú	ExceptionrX   Únumber)	r$   ÚkÚvÚarrÚoÚframework_to_py_objr^   Ú	frameworkr_   s	            r(   r    r      sV  € ô �#œœU�|Ô$Øˆ
Ü	�Cœ$¤Ð)Ô	*Ø,/¯I©I«K×8¡D A q�”9˜Q“<‘Ó8Ð8Ü	�Cœ$¤˜Ô	'ð	Ü—(‘(˜3“-ˆCÜ�}‰}˜SŸY™Y¬¯
©
Ô3´r·}±}ÀSÇYÁYÔPR×P[ÑP[Ô7\Ø—z‘z“|Ð#ð 8]ð '*Ö* ”	˜!•Ò*Ð*ñ 'Ù.Ù3Ù&ñ	Ðô ;¸3Ó?ÐØ 6× <Ñ <Ó >ò 7Ñˆ	�9Ù�S�>Ø1Ð& yÑ1°#Ó6Ò6ð7ô
 �#”r—y‘yÔ!Ø�z‰z‹|Ðàˆ
ùó7 9øô ò 	Ùð	üâ*s   ÁE=Á3B F Ã9FÆ	FÆFc                 ót  — d„ d„ d„ d„ dœ}t        | t        t        f«      r-| j                  «       D ��ci c]  \  }}|t	        |«      “Œ c}}S t        | t
        t        f«      rt        j                  | «      S t        | «      }|j                  «       D ]  \  }} || «      sŒ ||   | «      c S  | S c c}}w )zc
    Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a Numpy array.
    c                 óZ   — | j                  «       j                  «       j                  «       S r+   )ÚdetachÚcpur…   r”   s    r(   r•   zto_numpy.<locals>.<lambda>0  s   € ˜#Ÿ*™*›,×*Ñ*Ó,×2Ñ2Ó4€ r*   c                 ó"   — | j                  «       S r+   )r…   r”   s    r(   r•   zto_numpy.<locals>.<lambda>1  s   € ˜#Ÿ)™)›+€ r*   c                 ó,   — t        j                  | «      S r+   )rE   r™   r”   s    r(   r•   zto_numpy.<locals>.<lambda>2  s   € œ2Ÿ:™: c›?€ r*   c                 ó   — | S r+   r/   r”   s    r(   r•   zto_numpy.<locals>.<lambda>3  s   € ˜#€ r*   r›   )
r]   rž   r   rŸ   Úto_numpyr¡   r¢   rE   rŒ   rX   )r$   Úframework_to_numpyr¨   r©   r^   r­   r_   s          r(   rµ   rµ   *  s¸   € ñ 5Ù%Ù*Ùñ	Ðô �#œœhÐ'Ô(Ø+.¯9©9«;×7¡4 1 a�”8˜A“;‘Ó7Ð7Ü	�Cœ$¤˜Ô	'Ü�x‰x˜‹}Ðô ;¸3Ó?ÐØ 6× <Ñ <Ó >ò 6Ñˆ	�9Ù�S�>Ø0Ð% iÑ0°Ó5Ò5ð6ð €Jùó 8s   µB4c                   ó~   ‡ — e Zd ZdZdd„Zˆ fd„Zd„ Zd„ Zd„ Zd„ Z	d	„ Z
d
„ Zˆ fd„Zˆ fd„Zˆ fd„Zdee   fd„Zˆ xZS )ÚModelOutputa±  
    Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
    tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
    python dictionary.

    <Tip warning={true}>

    You can't unpack a `ModelOutput` directly. Use the [`~utils.ModelOutput.to_tuple`] method to convert it to a tuple
    before.

    </Tip>
    Úreturnc                 ó`  — t        «       r¤t        j                  t        «       «      t        j                  d«      k\  rFt	        j
                  | t        t        t        | ¬«      | j                  › d| j                  › �¬«       yt	        j                  | t        t        t        | ¬«      «       yy)zìRegister subclasses as pytree nodes.

        This is necessary to synchronize gradients when using `torch.nn.parallel.DistributedDataParallel` with
        `static_graph=True` with modules that output `ModelOutput` subclasses.
        ú2.2©Úoutput_typeú.©Úserialized_type_nameN)r   r   Úparser   Ú_torch_pytreeÚregister_pytree_nodeÚ_model_output_flattenr   Ú_model_output_unflattenr,   r    Ú_register_pytree_node)Úclss    r(   Ú__init_subclass__zModelOutput.__init_subclass__R  s„   € ô ÔÜ�}‰}Ô.Ó0Ó1´W·]±]À5Ó5IÒIÜ×2Ñ2ØÜ)ÜÔ3ÀÔEØ,/¯N©NÐ+;¸1¸S¿\¹\¸NÐ)Kö	ô ×3Ñ3ØÜ)ÜÔ3ÀÔEõð  r*   c                 óÄ   •— t        ‰| �  |i |¤Ž | j                  t        k7  }|r;t	        | «      s/t        | j                  › d| j                  j                  › d�«      ‚y y )Nr¾   z` is not a dataclass. This is a subclass of ModelOutput and so must use the @dataclass decorator.)ÚsuperÚ__init__Ú	__class__r¸   r
   Ú	TypeErrorr,   r    )r#   ÚargsÚkwargsÚis_modeloutput_subclassrÌ   s       €r(   rË   zModelOutput.__init__g  sl   ø€ Ü‰Ñ˜$Ð) &Ò)ð #'§.¡.´KÑ"?Ðá"¬<¸Ô+=ÜØ—?‘?Ð# 1 T§^¡^×%<Ñ%<Ð$=ð >_ð _óð ð ,>Ð"r*   c                 ó   ‡ — t        ‰ «      }t        |«      s"t        ‰ j                  j                  › d�«      ‚t        d„ |dd D «       «      s"t        ‰ j                  j                  › d�«      ‚t        ‰ |d   j                  «      }t        ˆ fd„|dd D «       «      }|rìt        |«      sát        |t        «      r|j                  «       }d}n	 t        |«      }d}|r˜t        «      D ]‰  \  }}t        |t        t         f«      r!t        |«      d
k(  rt        |d   t"        «      s)|dk(  r|‰ |d   j                  <   nt        d|› d�«      ‚ yt%        ‰ |d   |d   «       |d   €Œ|d   ‰ |d   <   Œ‹ y|�|‰ |d   j                  <   yy|D ]*  }t        ‰ |j                  «      }	|	€Œ|	‰ |j                  <   Œ, y# t        $ r d	}Y Œíw xY w)zeCheck the ModelOutput dataclass.

        Only occurs if @dataclass decorator has been used.
        z has no fields.c              3   ó8   K  — | ]  }|j                   d u –— Œ y ­wr+   )Údefault)Ú.0Úfields     r(   ú	<genexpr>z,ModelOutput.__post_init__.<locals>.<genexpr>€  s   è ø€ ÒG¨U�5—=‘= DÔ(ÑGùs   ‚r   Nz. should not have more than one required field.r   c              3   óN   •K  — | ]  }t        ‰|j                  «      d u –— Œ y ­wr+   ©r!   Úname©rÔ   rÕ   r#   s     €r(   rÖ   z,ModelOutput.__post_init__.<locals>.<genexpr>„  s!   øè ø€ Ò#dÈ%¤G¨D°%·*±*Ó$=ÀÔ$EÑ#dùs   ƒ"%TFé   zCannot set key/value for z&. It needs to be a tuple (key, value).)r	   Úlenr>   rÌ   r    Úallr!   rÙ   r`   r]   rž   rŸ   ÚiterrÍ   Ú	enumerater¡   r¢   rG   r"   )
r#   Úclass_fieldsÚfirst_fieldÚother_fields_are_noneÚiteratorÚfirst_field_iteratorÚidxÚelementrÕ   r©   s
   `         r(   Ú__post_init__zModelOutput.__post_init__v  só  ø€ ô
 ˜d“|ˆô �<Ô Ü §¡× 7Ñ 7Ð8¸ÐHÓIÐIÜÑG°lÀ1À2Ð6FÔGÔGÜ §¡× 7Ñ 7Ð8Ð8fÐgÓhÐhä˜d L°¡O×$8Ñ$8Ó9ˆÜ #Ó#dÐS_Ð`aÐ`bÐScÔ#dÓ dÐá ¬°;Ô)?Ü˜+¤tÔ,Ø&×,Ñ,Ó.�Ø'+Ñ$ð1Ü# KÓ0�HØ+/Ð(ñ $Ü$-¨hÓ$7ò 6‘L�C˜ä& w´´u°Ô>Ü" 7›|¨qÒ0Ü)¨'°!©*´cÔ:à !š8à9D˜D ¨a¡×!5Ñ!5Ò6ô #-Ø";¸G¸9ÐDjÐ kó#ð ñ Ü˜D '¨!¡*¨g°a©jÔ9Ø˜q‘zÑ-Ø+2°1©:˜˜W Q™ZÒ(ñ#6ð$ Ð(Ø-8��\ !‘_×)Ñ)Ò*ð )ð &ò )�Ü˜D %§*¡*Ó-�Ø‘=Ø'(�D˜Ÿ™Ò$ñ)øô7 !ò 1Ø+0Ò(ð1ús   ÃF? Æ?GÇGc                 óH   — t        d| j                  j                  › d�«      ‚)Nz$You cannot use ``__delitem__`` on a ú
 instance.©r¦   rÌ   r    ©r#   rÎ   rÏ   s      r(   Ú__delitem__zModelOutput.__delitem__®  s#   € ÜÐ>¸t¿~¹~×?VÑ?VÐ>WÐWaÐbÓcÐcr*   c                 óH   — t        d| j                  j                  › d�«      ‚)Nz#You cannot use ``setdefault`` on a ré   rê   rë   s      r(   Ú
setdefaultzModelOutput.setdefault±  s#   € ÜÐ=¸d¿n¹n×>UÑ>UÐ=VÐV`ÐaÓbÐbr*   c                 óH   — t        d| j                  j                  › d�«      ‚)NzYou cannot use ``pop`` on a ré   rê   rë   s      r(   ÚpopzModelOutput.pop´  s"   € ÜÐ6°t·~±~×7NÑ7NÐ6OÈzÐZÓ[Ð[r*   c                 óH   — t        d| j                  j                  › d�«      ‚)NzYou cannot use ``update`` on a ré   rê   rë   s      r(   ÚupdatezModelOutput.update·  s#   € ÜÐ9¸$¿.¹.×:QÑ:QÐ9RÐR\Ð]Ó^Ð^r*   c                 ó„   — t        |t        «      rt        | j                  «       «      }||   S | j	                  «       |   S r+   )r]   rG   rž   rŸ   Úto_tuple)r#   r¨   Ú
inner_dicts      r(   Ú__getitem__zModelOutput.__getitem__º  s7   € Ü�aœÔÜ˜dŸj™j›lÓ+ˆJØ˜a‘=Ð à—=‘=“? 1Ñ%Ð%r*   c                 ón   •— || j                  «       v r|�t        ‰| �	  ||«       t        ‰| �  ||«       y r+   )ÚkeysrÊ   Ú__setitem__Ú__setattr__)r#   rÙ   ÚvaluerÌ   s      €r(   rú   zModelOutput.__setattr__Á  s4   ø€ Ø�4—9‘9“;Ñ 5Ð#4ä‰GÑ  eÔ,Ü‰Ñ˜D %Õ(r*   c                 óF   •— t         ‰| �  ||«       t         ‰| �	  ||«       y r+   )rÊ   rù   rú   )r#   Úkeyrû   rÌ   s      €r(   rù   zModelOutput.__setitem__Ç  s!   ø€ ä‰Ñ˜C Ô'ä‰Ñ˜C Õ'r*   c                 ó¢   •‡ — t        ‰ «      st        ‰‰ �	  «       S t        ‰‰ �	  «       ^}}}t        ˆ fd„t	        ‰ «      D «       «      }||g|¢­S )Nc              3   óJ   •K  — | ]  }t        ‰|j                  «      –— Œ y ­wr+   rØ   rÚ   s     €r(   rÖ   z)ModelOutput.__reduce__.<locals>.<genexpr>Ñ  s   øè ø€ ÒI°5”W˜T 5§:¡:×.ÑIùs   ƒ #)r
   rÊ   Ú
__reduce__r¢   r	   )r#   ÚcallableÚ_argsÚ	remainingrÎ   rÌ   s   `    €r(   r   zModelOutput.__reduce__Í  sQ   ù€ Ü˜DÔ!Ü‘7Ñ%Ó'Ð'Ü&+¡gÑ&8Ó&:Ð#ˆ�%˜)ÜÓI¼FÀ4»LÔIÓIˆØ˜Ð) 	Ñ)Ð)r*   c                 óH   ‡ — t        ˆ fd„‰ j                  «       D «       «      S )za
        Convert self to a tuple containing all the attributes/keys that are not `None`.
        c              3   ó(   •K  — | ]	  }‰|   –— Œ y ­wr+   r/   )rÔ   r¨   r#   s     €r(   rÖ   z'ModelOutput.to_tuple.<locals>.<genexpr>Ø  s   øè ø€ Ò2 �T˜!•WÑ2ùs   ƒ)r¢   rø   )r#   s   `r(   rô   zModelOutput.to_tupleÔ  s   ø€ ô Ó2 d§i¡i£kÔ2Ó2Ð2r*   )r¹   N)r    r,   r-   r.   rÈ   rË   rç   rì   rî   rð   rò   rö   rú   rù   r   r¢   r   rô   Ú__classcell__)rÌ   s   @r(   r¸   r¸   D  sT   ø„ ñóô*ò6)òpdòcò\ò_ò&ô)ô(ô*ð3˜% ™*÷ 3r*   r¸   Úoutputr¹   z_torch_pytree.Contextc                 óf   — t        | j                  «       «      t        | j                  «       «      fS r+   )r¡   r[   rø   )r  s    r(   rÄ   rÄ   Þ  s#   € Ü�F—M‘M“OÓ$¤d¨6¯;©;«=Ó&9Ð9Ð9r*   r[   Úcontextc           
      ó8   —  |di t        t        || «      «      ¤ŽS )Nr/   )rž   Úzip)r[   r	  r½   s      r(   rÅ   rÅ   á  s   € ñ
 Ñ8œT¤# g¨vÓ"6Ó7Ñ8Ð8r*   r»   r¼   r¾   r¿   c                   ó    — e Zd ZdZed„ «       Zy)ÚExplicitEnumzC
    Enum with more explicit error message for missing values.
    c           
      ó~   — t        |› d| j                  › dt        | j                  j	                  «       «      › �«      ‚)Nz is not a valid z, please select one of )r>   r    r¡   Ú_value2member_map_rø   )rÇ   rû   s     r(   Ú	_missing_zExplicitEnum._missing_ü  s?   € äØˆgÐ% c§l¡l ^Ð3JÌ4ÐPS×PfÑPf×PkÑPkÓPmÓKnÐJoÐpó
ð 	
r*   N)r    r,   r-   r.   Úclassmethodr  r/   r*   r(   r  r  ÷  s   „ ñð ñ
ó ñ
r*   r  c                   ó   — e Zd ZdZdZdZdZy)ÚPaddingStrategyz†
    Possible values for the `padding` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an
    IDE.
    ÚlongestÚ
max_lengthÚ
do_not_padN)r    r,   r-   r.   ÚLONGESTÚ
MAX_LENGTHÚ
DO_NOT_PADr/   r*   r(   r  r    s   „ ñð
 €GØ€JØ�Jr*   r  c                   ó$   — e Zd ZdZdZdZdZdZdZy)Ú
TensorTypez�
    Possible values for the `return_tensors` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for
    tab-completion in an IDE.
    rB   rC   rE   rD   rF   N)	r    r,   r-   r.   ÚPYTORCHÚ
TENSORFLOWÚNUMPYÚJAXÚMLXr/   r*   r(   r  r    s"   „ ñð
 €GØ€JØ€EØ
€CØ
�Cr*   r  c                   ó.   — e Zd ZdZdee   fd„Zd„ Zd„ Zy)ÚContextManagerszš
    Wrapper for `contextlib.ExitStack` which enters a collection of context managers. Adaptation of `ContextManagers`
    in the `fastcore` library.
    Úcontext_managersc                 ó0   — || _         t        «       | _        y r+   )r#  r   Ústack)r#   r#  s     r(   rË   zContextManagers.__init__!  s   € Ø 0ˆÔÜ“[ˆ�
r*   c                 ó\   — | j                   D ]  }| j                  j                  |«       Œ y r+   )r#  r%  Úenter_context)r#   Úcontext_managers     r(   Ú	__enter__zContextManagers.__enter__%  s)   € Ø#×4Ñ4ò 	6ˆOØ�J‰J×$Ñ$ _Õ5ñ	6r*   c                 ó<   —  | j                   j                  |i |¤Ž y r+   )r%  Ú__exit__rë   s      r(   r+  zContextManagers.__exit__)  s   € Øˆ�
‰
×Ñ˜TÐ, VÓ,r*   N)	r    r,   r-   r.   r¡   r   rË   r)  r+  r/   r*   r(   r"  r"    s"   „ ñð
!¨¨nÑ)=ó !ò6ó-r*   r"  c                 óT  — t        | «      }|dk(  r t        j                  | j                  «      }nD|dk(  r t        j                  | j                  «      }nt        j                  | j
                  «      }|j                  D ]%  }|dk(  sŒ	|j                  |   j                  du sŒ% y y)zr
    Check if a given model can return loss.

    Args:
        model_class (`type`): The class of the model.
    rC   rB   Úreturn_lossTF)Úinfer_frameworkÚinspectÚ	signatureÚcallÚforwardÚ__call__Ú
parametersrÓ   )Úmodel_classr­   r0  Úps       r(   Úcan_return_lossr7  -  s�   € ô   Ó,€IØ�DÒÜ×%Ñ% k×&6Ñ&6Ó7‰	Ø	�dÒ	Ü×%Ñ% k×&9Ñ&9Ó:‰	ä×%Ñ% k×&:Ñ&:Ó;ˆ	à×!Ñ!ò ˆØ�Ó )×"6Ñ"6°qÑ"9×"AÑ"AÀTÒ"IÙðð r*   c                 ó¢  — | j                   }t        | «      }|dk(  r t        j                  | j                  «      }nD|dk(  r t        j                  | j
                  «      }nt        j                  | j                  «      }d|v r#|j                  D �cg c]  }d|v s|dv sŒ|‘Œ c}S |j                  D �cg c]	  }d|v sŒ|‘Œ c}S c c}w c c}w )zq
    Find the labels used by a given model.

    Args:
        model_class (`type`): The class of the model.
    rC   rB   ÚQuestionAnsweringÚlabel)Ústart_positionsÚend_positions)r    r.  r/  r0  r1  r2  r3  r4  )r5  Ú
model_namer­   r0  r6  s        r(   Úfind_labelsr>  C  s¾   € ð ×%Ñ%€JÜ Ó,€IØ�DÒÜ×%Ñ% k×&6Ñ&6Ó7‰	Ø	�dÒ	Ü×%Ñ% k×&9Ñ&9Ó:‰	ä×%Ñ% k×&:Ñ&:Ó;ˆ	à˜jÑ(Ø$×/Ñ/Öm�a°7¸a±<À1ÐHlÒCl’ÒmÐmà$×/Ñ/Ö@�a°7¸a²<’Ò@Ð@ùò nùâ@s   ÂCÂ!CÂ6	CÃ CÚdÚ
parent_keyÚ	delimiterc                 ó0   — dd„}t         || ||«      «      S )z/Flatten a nested dict into a single level dict.c              3   óö   K  — | j                  «       D ]`  \  }}|rt        |«      |z   t        |«      z   n|}|r5t        |t        «      r%t	        |||¬«      j                  «       E d {  –—†  Œ[||f–— Œb y 7 Œ­w)N)rA  )rŸ   rG   r]   r   Úflatten_dict)r?  r@  rA  r¨   r©   rý   s         r(   Ú_flatten_dictz#flatten_dict.<locals>._flatten_dict\  sp   è ø€ Ø—G‘G“Iò 	‰DˆAˆqÙ:D”#�j“/ IÑ-´°A³Ò6È!ˆCÙ”Z ¤>Ô2Ü'¨¨3¸)ÔD×JÑJÓL×LÑLà˜1�f“ñ	ð Mús   ‚A&A9Á(A7Á)A9©Ú r¾   )rž   )r?  r@  rA  rE  s       r(   rD  rD  Y  s   € óô ‘˜a ¨YÓ7Ó8Ð8r*   FÚuse_temp_dirc              #   ót   K  — |r"t        j                  «       5 }|–— d d d «       y | –— y # 1 sw Y   y xY w­wr+   )ÚtempfileÚTemporaryDirectory)Úworking_dirrH  Útmp_dirs      r(   Úworking_or_temp_dirrN  g  s=   è ø€ áÜ×(Ñ(Ó*ð 	¨gØŠM÷	ð 	ð Ó÷	ð 	üs   ‚8™,ž8¬5±8c                 óR  — t        | «      rt        j                  | |¬«      S t        | «      r|€| j                  S  | j
                  |Ž S t        | «      rddl}|j                  | |¬«      S t        | «      rddl	m
} |j                  | |¬«      S t        dt        | «      › d�«      ‚)z…
    Framework-agnostic version of `numpy.transpose` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    )ÚaxesNr   )Úpermz"Type not supported for transpose: r¾   )rQ   rE   Ú	transposerN   ÚTÚpermuterO   rz   rP   r„   r…   r>   rH   )rŒ   rP  rC   r†   s       r(   rR  rR  p  s˜   € ô
 �eÔÜ�|‰|˜E¨Ô-Ð-Ü	˜Ô	Ø˜,ˆu�w‰wÐ@¨M¨E¯M©M¸4Ð,@Ð@Ü	�eÔ	Ûà�|‰|˜E¨ˆ|Ó-Ð-Ü	�uÔ	Ýà�}‰}˜U¨ˆ}Ó.Ð.äÐ=¼dÀ5»k¸]È!ÐLÓMÐMr*   c                 ó0  — t        | «      rt        j                  | |«      S t        | «      r | j                  |Ž S t	        | «      rddl}|j                  | |«      S t        | «      rddlm} |j                  | |«      S t        dt        | «      › d�«      ‚)zƒ
    Framework-agnostic version of `numpy.reshape` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    r   Nz Type not supported for reshape: r¾   )rQ   rE   ÚreshaperN   rO   rz   rP   r„   r…   r>   rH   )rŒ   ÚnewshaperC   r†   s       r(   rV  rV  …  s‡   € ô
 �eÔÜ�z‰z˜% Ó*Ð*Ü	˜Ô	Øˆu�}‰}˜hÐ'Ð'Ü	�eÔ	Ûà�z‰z˜% Ó*Ð*Ü	�uÔ	Ýà�{‰{˜5 (Ó+Ð+äÐ;¼DÀ»K¸=ÈÐJÓKÐKr*   c                 ó`  — t        | «      rt        j                  | |¬«      S t        | «      r$|€| j                  «       S | j                  |¬«      S t	        | «      rddl}|j                  | |¬«      S t        | «      rddlm} |j                  | |¬«      S t        dt        | «      › d�«      ‚)zƒ
    Framework-agnostic version of `numpy.squeeze` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    ©ÚaxisN©Údimr   z Type not supported for squeeze: r¾   )rQ   rE   ÚsqueezerN   rO   rz   rP   r„   r…   r>   rH   ©rŒ   rZ  rC   r†   s       r(   r]  r]  š  sš   € ô
 �eÔÜ�z‰z˜% dÔ+Ð+Ü	˜Ô	Ø"& ,ˆu�}‰}‹ÐK°E·M±MÀd°MÓ4KÐKÜ	�eÔ	Ûà�z‰z˜% dˆzÓ+Ð+Ü	�uÔ	Ýà�{‰{˜5 tˆ{Ó,Ð,äÐ;¼DÀ»K¸=ÈÐJÓKÐKr*   c                 ó:  — t        | «      rt        j                  | |«      S t        | «      r| j	                  |¬«      S t        | «      rddl}|j                  | |¬«      S t        | «      rddlm	} |j                  | |¬«      S t        dt        | «      › d�«      ‚)z‡
    Framework-agnostic version of `numpy.expand_dims` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    r[  r   NrY  z$Type not supported for expand_dims: r¾   )rQ   rE   Úexpand_dimsrN   Ú	unsqueezerO   rz   rP   r„   r…   r>   rH   r^  s       r(   r`  r`  ¯  s‹   € ô
 �eÔÜ�~‰~˜e TÓ*Ð*Ü	˜Ô	Ø�‰ 4ˆÓ(Ð(Ü	�eÔ	Ûà�~‰~˜e¨$ˆ~Ó/Ð/Ü	�uÔ	Ýà�‰˜u¨4ˆÓ0Ð0äÐ?ÄÀUÃ¸}ÈAÐNÓOÐOr*   c                 ó  — t        | «      rt        j                  | «      S t        | «      r| j	                  «       S t        | «      rddl}|j                  | «      S t        | «      r| j                  S t        dt        | «      › d�«      ‚)z|
    Framework-agnostic version of `numpy.size` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays.
    r   Nz$Type not supported for tensor_size: r¾   )
rQ   rE   ÚsizerN   ÚnumelrO   rz   rP   r>   rH   )rŒ   rC   s     r(   Útensor_sizere  Ä  sr   € ô �eÔÜ�w‰w�u‹~ÐÜ	˜Ô	Ø�{‰{‹}ÐÜ	�eÔ	Ûà�w‰w�u‹~ÐÜ	�uÔ	Ø�z‰zÐäÐ?ÄÀUÃ¸}ÈAÐNÓOÐOr*   c                 óÒ   — | j                  «       D ]N  \  }}t        |t        t        f«      r!|D �cg c]  }|�d|vr|› d|› �n|‘Œ c}| |<   Œ=|€Œ@d|vsŒE|› d|› �| |<   ŒP | S c c}w )zB
    Adds the information of the repo_id to a given auto map.
    ú--)rŸ   r]   r¢   r¡   )Úauto_mapÚrepo_idrý   rû   r©   s        r(   Úadd_model_info_to_auto_maprj  Ö  s‹   € ð —n‘nÓ&ò 2‰
ˆˆUÜ�eœe¤T˜]Ô+ØdiÖjÐ_`°1°=ÀTÐQRÁ] ˜y¨¨1¨#Ñ.ÐYZÑZÒjˆH�SŠMØÑ 4¨uÒ#4Ø&˜i r¨%¨Ð1ˆH�SŠMð	2ð €Oùò	 ks   ±A$c                 ót   — | j                  «       D ]$  }d| |   v sŒ| |   d   }d|vsŒ|› d|› �| |   d<   Œ& | S )zI
    Adds the information of the repo_id to a given custom pipeline.
    Úimplrg  )rø   )Úcustom_pipelineri  ÚtaskÚmodules       r(   Ú"add_model_info_to_custom_pipelinesrp  ã  sd   € ð
  ×$Ñ$Ó&ò GˆØ�_ TÑ*Ò*Ø$ TÑ*¨6Ñ2ˆFØ˜6Ò!Ø3:°)¸2¸f¸XÐ0F� Ñ% fÒ-ð	Gð
 Ðr*   c                 óX  — t        j                  | «      D ]„  }|j                  }|j                  }|j	                  d«      s|j	                  d«      s|dk(  r y|j	                  d«      s|dk(  r y|j	                  d«      s|j	                  d	«      s|d
k(  sŒ„ y t        d| › d�«      ‚)z 
    Infers the framework of a given model without using isinstance(), because we cannot guarantee that the relevant
    classes are imported or available.
    rz   ÚkerasÚTFPreTrainedModelrC   rh   ÚPreTrainedModelrB   ÚflaxrD   ÚFlaxPreTrainedModelz%Could not infer framework from class r¾   )r/  Úgetmror,   r    rI   rÍ   )r5  Ú
base_classro  rÙ   s       r(   r.  r.  ð  s°   € ô
 —n‘n [Ó1ò 
Pˆ
Ø×&Ñ&ˆØ×"Ñ"ˆØ×Ñ˜\Ô*¨f×.?Ñ.?ÀÔ.HÈDÐTgÒLgÙØ×Ñ˜wÔ'¨4Ð3DÒ+DÙØ×Ñ˜vÔ&¨&×*;Ñ*;¸EÔ*BÀdÐNcÓFcÙð
Pô Ð?À¸}ÈAÐNÓOÐOr*   c                 óà   — t        «       st        | «      S ddl}|j                  j	                  «       r1t        | |j                  «      r| j                  |j                  «      S t        | «      S )zk
    Casts an input to a torch int64 tensor if we are in a tracing context, otherwise to a Python int.
    r   N)	r   rœ   rh   ÚjitÚ
is_tracingr]   ri   ÚtoÚint64rj   s     r(   Ú	torch_intr~    sQ   € ô ÔÜ�1‹vˆãà %§	¡	× 4Ñ 4Ô 6¼:ÀaÈÏÉÔ;Vˆ1�4‰4�—‘ÓÐbÔ\_Ð`aÓ\bÐbr*   c                 óà   — t        «       st        | «      S ddl}|j                  j	                  «       r1t        | |j                  «      r| j                  |j                  «      S t        | «      S )zo
    Casts an input to a torch float32 tensor if we are in a tracing context, otherwise to a Python float.
    r   N)	r   rœ   rh   rz  r{  r]   ri   r|  Úfloat32rj   s     r(   Útorch_floatr�    sQ   € ô ÔÜ�1‹vˆãà"'§)¡)×"6Ñ"6Ô"8¼ZÈÈ5Ï<É<Ô=Xˆ1�4‰4�—‘ÓÐdÔ^aÐbcÓ^dÐdr*   Úextrac                 ó4   ‡— | xs g } t        | «      Šˆfd„}|S )aI  
    Decorator to filter out named arguments that are not in the function signature.

    This decorator ensures that only the keyword arguments that match the function's signature, or are specified in the
    `extra` list, are passed to the function. Any additional keyword arguments are filtered out and a warning is issued.

    Parameters:
        extra (`Optional[list]`, *optional*):
            A list of extra keyword argument names that are allowed even if they are not in the function's signature.

    Returns:
        Callable:
            A decorator that wraps the function and filters out invalid keyword arguments.

    Example usage:

        ```python
        @filter_out_non_signature_kwargs(extra=["allowed_extra_arg"])
        def my_function(arg1, arg2, **kwargs):
            print(arg1, arg2, kwargs)

        my_function(arg1=1, arg2=2, allowed_extra_arg=3, invalid_arg=4)
        # This will print: 1 2 {"allowed_extra_arg": 3}
        # And issue a warning: "The following named arguments are not valid for `my_function` and were ignored: 'invalid_arg'"
        ```
    c                 óì   •‡ ‡‡‡— t        j                  ‰ «      }t        |j                  j	                  «       «      }|j                  ‰«      Šd|v Šd|v Šd‰ _        t        ‰ «      ˆ ˆˆˆfd„«       }|S )Nr#   rÇ   Tc                  ó¤  •— i }i }|j                  «       D ]  \  }}|‰v r|||<   Œ|||<   Œ |r–|j                  «       D �cg c]  }d|› d�‘Œ
 }}dj                  |«      }‰
r| d   j                  j                  dz   }n‰	r| d   j                  dz   }nd}t        j                  d|› ‰j                  › d|› �t        d¬	«        ‰| i |¤ŽS c c}w )
Nú'z, r   r¾   rG  z1The following named arguments are not valid for `z` and were ignored: rÛ   )Ú
stacklevel)rŸ   rø   ÚjoinrÌ   r    ÚwarningsÚwarnÚUserWarning)rÎ   rÏ   Úvalid_kwargsÚinvalid_kwargsr¨   r©   Úinvalid_kwargs_namesÚ
cls_prefixÚfuncÚis_class_methodÚis_instance_methodÚvalid_kwargs_to_passs           €€€€r(   ÚwrapperzCfilter_out_non_signature_kwargs.<locals>.decorator.<locals>.wrapperD  s  ø€ àˆLØˆNàŸ™›ò *‘��1ØÐ,Ñ,Ø&'�L ’Oà()�N 1Ò%ð	*ñ Ø:H×:MÑ:MÓ:OÖ'P°Q¨!¨A¨3¨aªÐ'PÐ$Ð'PØ'+§y¡yÐ1EÓ'FÐ$ñ &Ø!% a¡×!2Ñ!2×!;Ñ!;¸cÑ!A‘JÙ$Ø!% a¡×!1Ñ!1°CÑ!7‘Jà!#�Jä—‘ØGÈ
À|ÐTX×TaÑTaÐSbð c*Ø*>Ð)?ðAäØ õ	ñ ˜Ð. Ñ.Ð.ùò% (Qs   ÁC)r/  r0  Úsetr4  rø   ÚunionÚ _filter_out_non_signature_kwargsr   )r�  ÚsigÚfunction_named_argsr”  r‘  r’  r“  Úextra_params_to_passs   `   @@@€r(   Ú	decoratorz2filter_out_non_signature_kwargs.<locals>.decorator8  s}   ü€ Ü×Ñ Ó%ˆÜ! #§.¡.×"5Ñ"5Ó"7Ó8ÐØ2×8Ñ8Ð9MÓNÐð $Ð':Ð:ÐØÐ#6Ð6ˆð 15ˆÔ-ä	ˆt‹ö	/ó 
ð	/ð> ˆr*   )r•  )r‚  r›  rš  s     @r(   Úfilter_out_non_signature_kwargsrœ    s&   ø€ ð6 ŠK�R€EÜ˜u›:Ðô,ð\ Ðr*   c                   ó"   — e Zd ZU dZee   ed<   y)Ú
LossKwargszû
    Keyword arguments to be passed to the loss function

    Attributes:
        num_items_in_batch (`int`, *optional*):
            Number of items in the batch. It is recommended to pass it when
            you are doing gradient accumulation.
    Únum_items_in_batchN)r    r,   r-   r.   r   rœ   Ú__annotations__r/   r*   r(   rž  rž  i  s   … ñð ! ™Ô%r*   rž  )ÚtotalÚconfig_dictc                 ó
   — d| v S )z3Checks whether a config dict is a timm config dict.Úpretrained_cfgr/   )r¢  s    r(   Úis_timm_config_dictr¥  v  s   € à˜{Ð*Ð*r*   Úpretrained_model_pathc                 ó¤  — | €yt        | «      } t        j                  j                  | «      }t        j                  j	                  | «      }|rE| j                  d«      r4t        | «      5 }t        j                  |«      }ddd«       t        |«      S |r�t        j                  j                  t        j                  j                  | d«      «      rRt        t        j                  j                  | d«      «      5 }t        j                  |«      }ddd«       t        |«      S y# 1 sw Y   t        «      S xY w# 1 sw Y   t        «      S xY w)zA
    Checks whether a checkpoint is a timm model checkpoint.
    NFz.jsonzconfig.json)rG   ÚosÚpathÚisfileÚisdirÚendswithÚopenÚjsonÚloadr¥  Úexistsrˆ  )r¦  Úis_fileÚis_dirr8   r¢  s        r(   Úis_timm_local_checkpointr³  {  s  € ð Ð$Øô  Ð 5Ó6Ðä�g‰g�n‰nÐ2Ó3€GÜ�W‰W�]‰]Ð0Ó1€Fñ Ð(×1Ñ1°'Ô:ÜÐ'Ó(ð 	'¨AÜŸ)™) A›,ˆK÷	'ä" ;Ó/Ð/ñ ”"—'‘'—.‘.¤§¡§¡Ð.CÀ]Ó!SÔTÜ”"—'‘'—,‘,Ð4°mÓDÓEð 	'ÈÜŸ)™) A›,ˆK÷	'ä" ;Ó/Ð/à÷	'ä" ;Ó/Ð/ú÷	'ä" ;Ó/Ð/ús   Á+D&Ã<D<Ä&D9Ä<Ero  ztorch.nn.Modulerý   rû   c                 ób   — t        | ||«       | j                  «       D ]  }t        |||«       Œ y)z5
    Set a value to a module and all submodules.
    N)r"   ÚchildrenÚset_attribute_for_modules)ro  rý   rû   Ú	submodules       r(   r¶  r¶  —  s2   € ô ˆF�C˜ÔØ—_‘_Ó&ò 9ˆ	Ü! )¨S°%Õ8ñ9r*   c                 óv   — t        | |«      rt        | |«       | j                  «       D ]  }t        ||«       Œ y)z:
    Delete a value from a module and all submodules.
    N)rt   Údelattrrµ  Údel_attribute_from_modules)ro  rý   r·  s      r(   rº  rº     s8   € ô
 ˆv�sÔÜ�˜Ôà—_‘_Ó&ò 3ˆ	Ü" 9¨cÕ2ñ3r*   c                 ó.   ‡ — t        ‰ «      ˆ fd„«       }|S )zö
    Decorator to wrap model method, to call output.to_tuple() if return_dict=False passed as a kwarg or
    use_return_dict=False is set in the config.

    Note:
        output.to_tuple() convert output to tuple skipping all `None` values.
    c                 óL  •— |j                  dd«      du }t        | d«      r| j                  j                  du nd}t	        | dd«      }|r|rt        | dd«       	  ‰| g|¢­i |¤Ž}|s|r|r|j                  «       }|r|rt        | d«       |S # |r|rt        | d«       w w w xY w)NÚreturn_dictTFÚconfigÚ_is_top_level_module)rð   rt   r¾  Úuse_return_dictr!   r¶  rô   rº  )r#   rÎ   rÏ   Úis_requested_to_return_tupleÚis_configured_to_return_tupleÚis_top_level_moduler  r�  s          €r(   r”  z!can_return_tuple.<locals>.wrapperµ  sÆ   ø€ à'-§z¡z°-ÀÓ'FÈ%Ð'OÐ$ÜPWÐX\Ð^fÔPg¨¯©×(CÑ(CÀuÑ(LÐmrÐ%ô & dÐ,BÀDÓIÐÙ(Ñ-@Ü% dÐ,BÀEÔJð	IÙ˜$Ð0 Ò0¨Ñ0ˆFÙ+Ñ0MÑReØŸ™Ó*�ñ -Ñ1DÜ*¨4Ð1GÔHàˆøñ -Ñ1DÜ*¨4Ð1GÕHð 2EÐ,ús   Á"B ÂB#)r   )r�  r”  s   ` r(   Úcan_return_tuplerÄ  ¬  s"   ø€ ô ˆ4ƒ[óó ðð2 €Nr*   r+   rF  )F)jr.   r/  r®  r¨  rJ  r‰  Úcollectionsr   r   Úcollections.abcr   r   Ú
contextlibr   r   Údataclassesr	   r
   Úenumr   Ú	functoolsr   r   Útypingr   r   r   r   r…   rE   Ú	packagingr   Úimport_utilsr   r   r   r   r   r   rh   Úpropertyr   r@   rL   rX   r`   rd   rQ   rk   rN   rp   rr   rv   rx   r|   rO   r€   r‚   r‡   rP   rŽ   rR   r    rµ   r¸   Útorch.utils._pytreeÚutilsÚ_pytreerÂ   r¢   r¡   rÄ   rÅ   rÁ   rÃ   r,   r    rÆ   rG   r  r  r  r"  r7  r>  rD  ÚboolrN  rR  rV  r]  r`  re  rj  rp  r.  r~  r�  rœ  rž  rž   r¥  r³  r¶  rº  rÄ  r/   r*   r(   ú<module>rÓ     så  ðñó Û Û 	Û Û ß -ß 4ß 0ß ,Ý ß $ß ;Ó ;ã Ý ÷÷ ñ Ôãô�hô ò05òò$9ò*ò0%òò'ò?ò'òFò&òEò$òAò$òIò&ò<ò#ò;ò"òJô4T3�+ô T3ñn Ôß/Ð/ð: kð :°e¸DÀ¹IÐG^Ð<^Ñ6_ó :ð ñ9Ø˜‘ð9à(ð9ð 
ó	9ð €w‡}�}Ñ&Ó(Ó)¨]¨W¯]©]¸5Ó-AÒAØ*ˆ×*Ñ*ØØ!ÙÐ+¸ÔEØ$/×$:Ñ$:Ð#;¸1¸[×=QÑ=QÐ<RÐ!Sö		
ð 	,ˆ×+Ñ+ØØ!ÙÐ+¸ÔEô	
ô	
�3˜ô 	
ô�lô ô
�ô 
÷-ñ -ò$ò,Añ,9�Nð 9°ð 9ÀSó 9ð ñ°4ò ó ðóNò*Ló*Lò*Pò*Pò$
ò
òPò$	cò	eñL¨8°D©>ó Lô^
&� %õ 
&ð+ T¨#¨s¨(¡^ð +¸ó +ð
°Cð ¸Dó ð89Ð&7ð 9¸cð 9È#ó 9ð	3Ð'8ð 	3¸só 	3ó#r*   