
    Z j&                        S r SSKrSSKrSSKrSSKrSSKJr  SSKJr  SSK	J
r
Jr  SSKJr  SSKJr  SS	KJrJr  SS
KJrJrJrJrJrJrJrJrJr  SSKJrJrJ r   \" 5       (       a  SSK!J"r"  \RF                  " \$5      r%\" S5      r&\'\(\
   S-  \(\
   S-  4   r)Sr*Sr+Sr,S r- " S S5      r. " S S\.5      r/S!S\04S jjr1S"S\0S\04S jjr2S r3S r4S r5 " S S\\(\   \)4   5      r6S /r7g)#z-Factory function to build auto-model classes.    N)OrderedDict)Iterator)AnyTypeVar)repo_exists   )PreTrainedConfig)get_class_from_dynamic_moduleresolve_trust_remote_code)	CONFIG_NAMEcached_file	copy_funcextract_commit_hashfind_adapter_config_fileis_peft_availableis_torch_availableloggingrequires_backends   )
AutoConfigmodel_type_to_module_name!replace_list_option_in_docstrings)GenerationMixin_TaJ  
    This is a generic model class that will be instantiated as one of the model classes of the library when created
    with the [`~BaseAutoModelClass.from_pretrained`] class method or the [`~BaseAutoModelClass.from_config`] class
    method.

    This class cannot be instantiated directly using `__init__()` (throws an error).
a  
        Instantiates one of the model classes of the library from a configuration.

        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use [`~BaseAutoModelClass.from_pretrained`] to load the model weights.

        Args:
            config ([`PreTrainedConfig`]):
                The model class to instantiate is selected based on the configuration class:

                List options
            attn_implementation (`str`, *optional*):
                The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using [`F.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html)), `"flash_attention_2"` (using [Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention)), or `"flash_attention_3"` (using [Dao-AILab/flash-attention/hopper](https://github.com/Dao-AILab/flash-attention/tree/main/hopper)). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

        Examples:

        ```python
        >>> from transformers import AutoConfig, BaseAutoModelClass

        >>> # Download configuration from huggingface.co and cache.
        >>> config = AutoConfig.from_pretrained("checkpoint_placeholder")
        >>> model = BaseAutoModelClass.from_config(config)
        ```
a  
        Instantiate one of the model classes of the library from a pretrained model.

        The model class to instantiate is selected based on the `model_type` property of the config object (either
        passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
        falling back to using pattern matching on `pretrained_model_name_or_path`:

        List options

        The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are
        deactivated). To train the model, you should first set it back in training mode with `model.train()`

        Args:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                    - A path to a *directory* containing model weights saved using
                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
            model_args (additional positional arguments, *optional*):
                Will be passed along to the underlying model `__init__()` method.
            config ([`PreTrainedConfig`], *optional*):
                Configuration for the model to use instead of an automatically loaded configuration. Configuration can
                be automatically loaded when:

                    - The model is a model provided by the library (loaded with the *model id* string of a pretrained
                      model).
                    - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
                      save directory.
                    - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
                      configuration JSON file named *config.json* is found in the directory.
            state_dict (*dict[str, torch.Tensor]*, *optional*):
                A state dictionary to use instead of a state dictionary loaded from saved weights file.

                This option can be used if you want to create a model from a pretrained configuration but load your own
                weights. In this case though, you should check if using [`~PreTrainedModel.save_pretrained`] and
                [`~PreTrainedModel.from_pretrained`] is not a simpler option.
            cache_dir (`str` or `os.PathLike`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            proxies (`dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether or not to only look at local files (e.g., not try downloading the model).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
            trust_remote_code (`bool`, *optional*, defaults to `False`):
                Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
                should only be set to `True` for repositories you trust and in which you have read the code, as it will
                execute code present on the Hub on your local machine.
            code_revision (`str`, *optional*, defaults to `"main"`):
                The specific revision to use for the code on the Hub, if the code leaves in a different repository than
                the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
                system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier
                allowed by git.
            kwargs (additional keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
                automatically loaded:

                    - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
                      underlying model's `__init__` method (we assume all relevant updates to the configuration have
                      already been done)
                    - If a configuration is not provided, `kwargs` will be first passed to the configuration class
                      initialization function ([`~PreTrainedConfig.from_pretrained`]). Each key of `kwargs` that
                      corresponds to a configuration attribute will be used to override said attribute with the
                      supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
                      will be passed to the underlying model's `__init__` function.

        Examples:

        ```python
        >>> from transformers import AutoConfig, BaseAutoModelClass

        >>> # Download model and configuration from huggingface.co and cache.
        >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")

        >>> # Update configuration during loading
        >>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
        >>> model.config.output_attentions
        True
        ```
c                     U[        U 5         n[        U[        [        45      (       d  U$ U Vs0 s H  o3R                  U_M     nn[        U S/ 5      nU H  nXd;   d  M
  XF   s  $    US   $ s  snf )Narchitecturesr   )type
isinstancelisttuple__name__getattr)configmodel_mappingsupported_modelsmodelname_to_modelr   archs          v/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py_get_model_classr*      s    $T&\2&u668HI8Hu^^U*8HMIFOR8M  &&  A Js   A.c                       \ rS rSrSrSS jr\S 5       r\S\S\4S j5       r	\S\
\R                  \
   -  4S	 j5       r\SSS
 jj5       rSrg)_BaseAutoModelClass   Nreturnc                     [        U R                  R                   SU R                  R                   SU R                  R                   S35      e)Nz+ is designed to be instantiated using the `z5.from_pretrained(pretrained_model_name_or_path)` or `z.from_config(config)` methods.)OSError	__class__r!   )selfargskwargss      r)   __init___BaseAutoModelClass.__init__   sR    ~~&&' (..112 3''((FH
 	
    c                    UR                  SS 5      n[        US5      =(       a    U R                  UR                  ;   n[	        U5      U R
                  ;   nU=(       a3    [        XR
                  5      R                  R                  S5      (       + nU(       aK  UR                  U R                     nSU;   a  UR                  S5      S   nOS n[        X1R                  XTUS9nU(       a  U(       a  U(       d  SW;   a  UR                  S5      u  pOUR                  n	[        Xy40 UD6n
U(       d*  U R                  UR                  U
SS9  U
R!                  U S	9  UR                  S
S 5      n[#        U
5      n
U
R$                  " U40 UD6$ U(       az  [        XR
                  5      n
U
R&                  UR(                  R+                  SS 5      :X  a(  UnUR-                  5       n[/        USS 5      nUb  Xl        U
R$                  " U40 UD6$ [3        SUR                   SU R                   SSR5                  S U R
                   5       5       S35      e)Ntrust_remote_codeauto_maptransformers.--r   upstream_repoTexist_ok
auto_classcode_revisiontext_configquantization_config!Unrecognized configuration class  for this kind of AutoModel: .
Model type should be one of , c              3   8   #    U  H  oR                   v   M     g 7fNr!   .0cs     r)   	<genexpr>2_BaseAutoModelClass.from_config.<locals>.<genexpr>        4\I[AZZI[   .)pophasattrr!   r:   r   _model_mappingr*   
__module__
startswithsplitr   _name_or_pathname_or_pathr
   registerr1   register_for_auto_class$add_generation_mixin_to_remote_model_from_configconfig_classsub_configsgetget_text_configr"   rE   
ValueErrorjoin)clsr#   r4   r9   has_remote_codehas_local_codeexplicit_local_code	class_refr>   repo_idmodel_class_parent_configparent_quants                 r)   from_config_BaseAutoModelClass.from_config   s4   "JJ':DA!&*5Y#,,&//:Yf););;, 15E&&6

*ZZ021 5Iy  ) 5a 8 $ 9!#7#7hu! 09Ly %.__T%:" --7	UfUK "V--{TJ33s3C

?D1A>{KK++F=f==*63E3EFK''6+=+=+A+A-QU+VV !'//1  '}6KTR+1=.++F=f==/0@0@/AA^_b_k_k^l m++/994\I[I[4\+\*]]^`
 	
r7   r#   c                     U$ )z`Additional autoclass-specific config post-loading manipulation. May be overridden in subclasses. )rg   r#   s     r)   _prepare_config_for_auto_class2_BaseAutoModelClass._prepare_config_for_auto_class  s	     r7   pretrained_model_name_or_pathc                 *
   UR                  SS 5      nUR                  S5      nSUS'   / SQnU Vs0 s H  owU;   d  M
  XsR                  U5      _M     nnUR                  SS 5      n	UR                  SS 5      n
UR                  SS 5      nUR                  S	S 5      nUb  XS	'   U
cC  [        U[        5      (       d!  [	        U[
        4S
S
S
S.UD6n[        X5      n
O[        USS 5      n
[        5       (       a  Uc  0 nUR                  5       nUb  XS	'   [        U4SU
0UD6nUb  [        USSS9 n[        R                  " U5      nXS'   [        R                  R!                  U5      (       aF  [        R                  R!                  [        R                  R#                  U[
        5      5      (       d  US   nS S S 5        [        U[        5      (       d  [        R$                  " U5      nUR                  S5      S:X  a  UR                  S5      nUR                  S5      S:X  a  UR                  S5      nUR                  S5      b  UR                  S5      n[&        R(                  " U4SU	U
S.UDUD6u  pCUR                  SS 5      S:X  a  SUS'   UR                  SS 5      S:X  a  SUS'   UR                  SS 5      b  US   US'   [+        US5      =(       a    U R,                  UR.                  ;   n[1        U5      U R2                  ;   nU=(       a3    [5        X@R2                  5      R6                  R9                  S5      (       + nS nU(       a3  UR.                  U R,                     nSU;   a  UR;                  S5      S   n[=        UUUUUS9nXSS'   XS'   U(       a  U(       a  U(       dy  [?        WU4SU	0UDUD6nUR                  SS 5      nU(       d*  U RA                  URB                  USS9  URE                  U S9  [G        U5      nUR(                  " U/UQ7SU0UDUD6$ U(       a  [5        X@R2                  5      nURH                  URJ                  R                  SS 5      :X  a)  UnURM                  5       n[        USS 5      nUb  UUl'        UR(                  " U/UQ7SU0UDUD6$ [Q        SURB                   SU R,                   S S!R#                  S" U R2                   5       5       S#35      es  snf ! , (       d  f       GNO= f)$Nr#   r9   T
_from_auto)	cache_dirforce_downloadlocal_files_onlyproxiesrevision	subfoldertokenrC   _commit_hashadapter_kwargsr   F) _raise_exceptions_for_gated_repo%_raise_exceptions_for_missing_entries'_raise_exceptions_for_connection_errorsrzutf-8)encoding_adapter_model_pathbase_model_name_or_pathtorch_dtypeautodtyperE   )return_unused_kwargsrC   r   r:   r;   r<   r   r=   r?   rA   rD   rF   rG   rH   rI   c              3   8   #    U  H  oR                   v   M     g 7frK   rL   rM   s     r)   rP   6_BaseAutoModelClass.from_pretrained.<locals>.<genexpr>  rR   rS   rT   ))rU   rc   r   r	   r   r   r   r"   r   copyr   openjsonloadospathexistsrf   deepcopyr   from_pretrainedrV   r!   r:   r   rW   r*   rX   rY   rZ   r   r
   r]   r1   r^   r_   ra   rb   rd   rE   re   )rg   rw   
model_argsr4   r#   r9   hub_kwargs_namesname
hub_kwargsrC   commit_hashr   r   resolved_config_filemaybe_adapter_pathfadapter_configkwargs_origrn   rh   ri   rj   r>   rk   rm   ro   rp   s                              r)   r   #_BaseAutoModelClass.from_pretrained  sr   Hd+"JJ':;#|
 :J\9IU[^,dJJt,,9I
\

?D9jj6$4d;w-"'wf&677'21( 6;:?<A( !($ 22FT%fndC%!#+002N */w'!9-"<G"KY" "-,cGD%)YYq\N<Y#89
 77>>*GHHPRPWPWP^P^%BKPQ Q 9GG`8a5 E &"233--/K zz-(F2JJ}-zz'"f,JJw'zz/0<JJ45'77-%)+(	
  NF }d3v=(.}%w-7"(w4d;G0;<Q0R,-!&*5Y#,,&//:Yf););;, 15E&&6

*ZZ021 5Iy  ) 5a 85)'
 '8"# $2 09L78HUYcgmK 5A "V--{TJ33s3C>{KK..-0:CIMW[a  *63E3EFK''6+=+=+A+A-QU+VV !'//1  '}6KTR+1=F...-0:CIMW[a  /0@0@/AA^_b_k_k^l m++/994\I[I[4\+\*]]^`
 	
G ]J EDs   	S>S>1B
T
Tc                     [        US5      (       a@  UR                  R                  UR                  :w  a  [        SUR                   SU S35      eU R                  R                  XUS9  g)z
Register a new model for this class.

Args:
    config_class ([`PreTrainedConfig`]):
        The configuration corresponding to the model to register.
    model_class ([`PreTrainedModel`]):
        The model to register.
ra   zThe model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has z and you passed z!. Fix one of those so they match!r?   N)rV   ra   r!   re   rW   r]   )rg   ra   rm   r@   s       r)   r]   _BaseAutoModelClass.register  sx     ;//K4L4L4U4UYeYnYn4n66A6N6N5OO_`l_m n.. 
 	##L#Qr7   rt   r.   NF)r!   rX   __qualname____firstlineno__rW   r5   classmethodrq   r	   ru   strr   PathLiker   r]   __static_attributes__rt   r7   r)   r,   r,      s    N
 1
 1
f 4D IY   S
C"++cBR<R S
 S
j R Rr7   r,   c                   L   ^  \ rS rSrSr\U 4S j5       r\U 4S j5       rSrU =r	$ )_BaseAutoBackboneClassi  Nc                   > [        U SS/5        SSKJn  UR                  SU" 5       5      nUR	                  S5      b  [        S5      eUR	                  SS	5      (       a  [        S
5      eUR                  SUR                  5      nUR                  SUR                  5      nUR                  SUR                  5      nU" UUUUS9nUR                  SS 5        [        T	U ](  " U4SS0UD6$ )Nvisiontimmr   )TimmBackboneConfigr#   out_featuresz0Cannot specify `out_features` for timm backbonesoutput_loading_infoFz@Cannot specify `output_loading_info=True` when loading from timmnum_channelsfeatures_onlyout_indices)backboner   r   r   use_pretrained_backbone
pretrainedT)r   models.timm_backboner   rU   rc   re   r   r   r   superrq   )
rg   rw   r   r4   r   r#   r   r   r   r1   s
            r)   #_load_timm_backbone_from_pretrained:_BaseAutoBackboneClass._load_timm_backbone_from_pretrained  s    #&12>H&8&:;::n%1OPP::+U33_``zz.&2E2EF

?F4H4HIjj0B0BC#2%'#	
 	

,d3w"6EdEfEEr7   c                    > UR                  SS 5        [        U5      (       d  U R                  " U/UQ70 UD6$ [        TU ]  " U/UQ70 UD6$ )Nuse_timm_backbone)rU   r   r   r   r   )rg   rw   r   r4   r1   s       r)   r   &_BaseAutoBackboneClass.from_pretrained  sX    

&-899::;Xp[epioppw&'D\z\U[\\r7   rt   )
r!   rX   r   r   rW   r   r   r   r   __classcell__)r1   s   @r)   r   r     s2    NF F2 ] ]r7   r   head_docc                 p    [        U5      S:  a  U R                  SSU S35      $ U R                  SS5      $ )Nr   z(one of the model classes of the library z0one of the model classes of the library (with a z head) z-one of the base model classes of the library )lenreplace)	docstringr   s     r)   insert_head_docr     sK    
8}q  6>xjP
 	
 24c r7   checkpoint_for_examplec                    U R                   nU R                  n[        [        US9nUR	                  SU5      U l        [        [        R                  5      n[        [        US9nUR	                  SU5      nUR	                  SU5      nXvl        [        UR                   SS9" U5      n[        U5      U l        [        n[        [        R                  5      n	[        XS9nUR	                  SU5      nUR	                  SU5      nUR                  S5      S   R                  S5      S	   n
UR	                  S
U
5      nXl        [        UR                   5      " U	5      n	[        U	5      U l        U $ )N)r   BaseAutoModelClasscheckpoint_placeholderF)use_model_types/-r   shortcut_placeholder)rW   r!   r   CLASS_DOCSTRINGr   __doc__r   r,   rq   FROM_CONFIG_DOCSTRINGr   r   FROM_PRETRAINED_TORCH_DOCSTRINGr   rZ   )rg   r   r   r$   r   class_docstringrq   from_config_docstringfrom_pretrained_docstringr   shortcuts              r)   auto_class_updater     sb   &&M<<D%oIO!))*>ECK /;;<K+,AHU199:NPTU199:RTjk/3M4P4PbghituK!+.CO ? 3 C CDO /0I ] 9 A ABVX\ ] 9 A ABZ\r s%++C04::3?BH 9 A ABXZb c778T8TUVefO%o6CJr7   c                     / nU R                  5        H?  n[        U[        [        45      (       a  U[        U5      -  nM.  UR	                  U5        MA     U$ rK   )valuesr   r   r    append)r$   resultr&   s      r)   
get_valuesr     sL    F%%'edE]++d5k!FMM% 	 ( Mr7   c           
        ^  Uc  g [        U[        5      (       a  [        U 4S jU 5       5      $ [        U[        5      (       a0  UR                  5        VVs0 s H  u  p#U[	        T U5      _M     snn$ [        T U5      (       a  [        T U5      $ [        R                  " S5      nT U:w  a   [	        XA5      $ [        SU SU S35      es  snnf ! [         a    [        SU ST  SU S35      ef = f)Nc              3   <   >#    U  H  n[        TU5      v   M     g 7frK   )getattribute_from_module)rN   amodules     r)   rP   +getattribute_from_module.<locals>.<genexpr>  s     G$Q-fa88$s   transformerszCould not find z neither in z nor in !z in )
r   r    dictitemsr   rV   r"   	importlibimport_modulere   )r   attrkvtransformers_modules   `    r)   r   r     s   |$G$GGG$CG::<P<41+FA66<PPvtvt$$ $11.A$$	i+,?FF ?4&5H4IKLL Q  	itfLQdPeefghh	is   C/
C  C2c                    S[        U R                  5      ;  a  U $ S[        U R                  5      ;   a  U $ [        U S5      =(       a    S[        [	        U S5      5      ;  n[        U S5      =(       a    S[        [	        U S5      5      ;  nU(       d  U(       a+  [        U R                  U [        40 U R                  E5      nU$ U $ )a  
Adds `GenerationMixin` to the inheritance of `model_class`, if `model_class` is a PyTorch model.

This function is used for backwards compatibility purposes: in v4.45, we've started a deprecation cycle to make
`PreTrainedModel` stop inheriting from `GenerationMixin`. Without this function, older models dynamically loaded
from the Hub may not have the `generate` method after we remove the inheritance.
ztorch.nn.modules.module.Moduler   generateprepare_inputs_for_generation)	r   __mro__	__bases__rV   r"   r   r!   r   __dict__)rm   has_custom_generate_in_classhas_custom_prepare_inputs!model_class_with_generation_mixins       r)   r_   r_   "  s     (s;3F3F/GG C 5 566 $+;
#C $HYadZ(b I  !(5T U !Zksv<=t [ $'@,0  ;"@BZ[EYEYBZ-
) 10r7   c                      \ rS rSrSrSS jrS\4S jrS\\	   S\
4S jrS	 rS\\\	      4S
 jrS\\	   S\S\
\-  4S jrS\4S jrS\\
   4S jrS\\\\	   \
4      4S jrS\\\	      4S jrS\S\4S jrSS\\	   S\
SS4S jjrSrg)_LazyAutoMappingiB  z
A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed.

Args:
    - config_mapping: The map model type to config class
    - model_mapping: The map model type to model (or tokenizer) class
r.   Nc                     Xl         UR                  5        VVs0 s H  u  p4XC_M	     snnU l        X l        X R                  l        0 U l        0 U l        g s  snnf rK   )_config_mappingr   _reverse_config_mappingrW   _extra_content_modules)r2   config_mappingr$   r   r   s        r)   r5   _LazyAutoMapping.__init__K  sU    -9G9M9M9O'P9O9O'P$+-1* 	 (Qs   Ac                     [        U R                  R                  5       5      R                  U R                  R                  5       5      n[        U5      [        U R                  5      -   $ rK   )setr   keysintersectionrW   r   r   )r2   common_keyss     r)   __len___LazyAutoMapping.__len__S  sP    $..3356CCDDWDWD\D\D^_;#d&9&9":::r7   keyc                    XR                   ;   a  U R                   U   $ U R                  UR                     nX R                  ;   a   U R                  U   nU R	                  X#5      $ U R
                  R                  5        VVs/ s H  u  pEXQR                  :X  d  M  UPM     nnnU H4  nXpR                  ;   d  M  U R                  U   nU R	                  Xs5      s  $    [        U5      es  snnf rK   )r   r   r!   rW   _load_attr_from_moduler   r   KeyError)r2   r
  
model_type
model_namer   r   model_typesmtypes           r)   __getitem___LazyAutoMapping.__getitem__W  s    %%%&&s++11#,,?
,,,,,Z8J..zFF &*%9%9%?%?%AW%ATQQ,,EVq%AW E+++!007
225EE ! sm Xs   C+C+c                     [        U5      nX0R                  ;  a'  [        R                  " SU 3S5      U R                  U'   [	        U R                  U   U5      $ )NrT   ztransformers.models)r   r   r   r   r   )r2   r  r   module_names       r)   r  '_LazyAutoMapping._load_attr_from_moduleg  sO    /
;mm+)2)@)@1[MARTi)jDMM+&'k(BDIIr7   c                     U R                   R                  5        VVs/ s H'  u  pXR                  ;   d  M  U R                  X5      PM)     nnnU[	        U R
                  R                  5       5      -   $ s  snnf rK   )r   r   rW   r  r   r   r  )r2   r
  r   mapping_keyss       r)   r  _LazyAutoMapping.keysm  st     "11779
9	))) 3D''29 	 

 d4#6#6#;#;#=>>>

   A5A5defaultc                 J     U R                  U5      $ ! [         a    Us $ f = frK   )r  r  )r2   r
  r  s      r)   rc   _LazyAutoMapping.getu  s,    	##C(( 	N	s    ""c                 4    [        U R                  5       5      $ rK   )boolr  r2   s    r)   __bool___LazyAutoMapping.__bool__{      DIIK  r7   c                     U R                   R                  5        VVs/ s H'  u  pXR                  ;   d  M  U R                  X5      PM)     nnnU[	        U R
                  R                  5       5      -   $ s  snnf rK   )rW   r   r   r  r   r   r   )r2   r
  r   mapping_valuess       r)   r   _LazyAutoMapping.values~  st     "00668
8	*** 3D''28 	 

 T%8%8%?%?%A BBB
r  c           	      &   U R                    Vs/ s HP  nXR                  ;   d  M  U R                  XR                  U   5      U R                  XR                   U   5      4PMR     nnU[        U R                  R                  5       5      -   $ s  snf rK   )rW   r   r  r   r   r   )r2   r
  mapping_itemss      r)   r   _LazyAutoMapping.items  s     **

 +***++C1E1Ec1JK++C1D1DS1IJ + 	 
 tD$7$7$=$=$?@@@
s   BA Bc                 4    [        U R                  5       5      $ rK   )iterr  r   s    r)   __iter___LazyAutoMapping.__iter__  r#  r7   itemc                     XR                   ;   a  g[        US5      (       a  UR                  U R                  ;  a  gU R                  UR                     nX R                  ;   $ )NTr!   F)r   rV   r!   r   rW   )r2   r.  r  s      r)   __contains___LazyAutoMapping.__contains__  sU    &&&tZ((DMMA]A],]11$--@
0000r7   valuec                     [        US5      (       aX  UR                  U R                  ;   a>  U R                  UR                     nX@R                  ;   a  U(       d  [	        SU S35      eX R
                  U'   g)z'
Register a new model in this mapping.
r!   'z*' is already used by a Transformers model.N)rV   r!   r   rW   re   r   )r2   r
  r2  r@   r  s        r)   r]   _LazyAutoMapping.register  sg     3
##8T8T(T55cllCJ000 1SE)S!TUU#(C r7   )r   r   rW   r   r   r   r   )r!   rX   r   r   r   r5   intr  r   r	   _LazyAutoMappingValuer  r  r   r  r   rc   r  r!  r   r    r   r   r,  r0  r]   r   rt   r7   r)   r   r   B  s   ; ;t$45 :O  J?d4 012 ?t,-  ?TWY?Y !$ !C23 C	AtE$'7"8:O"OPQ 	A!(4(8#9: !1 1$ 1	)D!12 	);P 	)ei 	) 	)r7   r   r   ) )zgoogle-bert/bert-base-casedr8  )8r   r   r   r   r   collectionsr   collections.abcr   typingr   r   huggingface_hubr   configuration_utilsr	   dynamic_module_utilsr
   r   utilsr   r   r   r   r   r   r   r   r   configuration_autor   r   r   
generationr   
get_loggerr!   loggerr   r    r   r7  r   r   r   r*   r,   r   r   r   r   r   r   r_   r   __all__rt   r7   r)   <module>rE     s"   4    	 # $  ' 3 \
 
 
 i h - 
		H	%T]d3i$.S	D0@@A  4Z# z kR kR\$]0 $]N 3 be <M,@c){4(8#9;P#PQ c)L .r7   