
    qiz                        d Z ddlZddlZddlZddlZddlmZ ddlmZ ddl	m
Z
mZ ddl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mZmZmZmZmZ ddlmZmZm Z   e       rddl!m"Z"  ejF                  e$      Z% ed      Z&e'e(e
   dz  e(e
   dz  f   Z)dZ*dZ+dZ,d Z- G d d      Z. G d de.      Z/d!de0fdZ1d"de0de0fdZ2d Z3d Z4d Z5 G d dee(e   e)f         Z6d gZ7y)#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                     |t        |          }t        |t        t        f      s|S |D ci c]  }|j                  | }}t        | dg       }|D ]  }||v s||   c S  |d   S c c}w )Narchitecturesr   )type
isinstancelisttuple__name__getattr)configmodel_mappingsupported_modelsmodelname_to_modelr   archs          W/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py_get_model_classr*      s    $T&\2&u68HIuU^^U*IMIFOR8M '=  &&' A Js   A%c                       e Zd ZdZd
dZed        Zededefd       Zede	e
j                  e	   z  fd       Zedd
d	       Zy)_BaseAutoModelClassNreturnc                     t        | j                  j                   d| j                  j                   d| j                  j                   d      )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__z_BaseAutoModelClass.__init__   sR    ~~&&' (..112 3''((FH
 	
    c                    |j                  dd       }t        |d      xr | j                  |j                  v }t	        |      | j
                  v }|rN|j                  | j                     }d|v r|j                  d      d   }nd }t        ||j                  |||      }|r|rdv r|j                  d      \  }}n|j                  }t        ||fi |}	|s0| j                  |j                  |	d       |	j                  |        |j                  d	d       }
t        |	      }	 |	j                  |fi |S t	        |      | j
                  v r)t!        || j
                        }	 |	j                  |fi |S t#        d
|j                   d| j                   ddj%                  d | j
                  D               d      )Ntrust_remote_codeauto_map--r   upstream_repoTexist_ok
auto_classcode_revision!Unrecognized configuration class  for this kind of AutoModel: .
Model type should be one of , c              3   4   K   | ]  }|j                     y wNr!   .0cs     r)   	<genexpr>z2_BaseAutoModelClass.from_config.<locals>.<genexpr>        4\AQZZ4\   .)pophasattrr!   r8   r   _model_mappingsplitr   _name_or_pathname_or_pathr
   registerr0   register_for_auto_class$add_generation_mixin_to_remote_model_from_configr*   
ValueErrorjoin)clsr#   r3   r7   has_remote_codehas_local_code	class_refr;   repo_idmodel_class_s              r)   from_configz_BaseAutoModelClass.from_config   s   "JJ':DA!&*5Y#,,&//:Yf););;5Iy  ) 5a 8 $ 9!6#7#7hu! 0y %.__T%:" --7	7UfUK "V--{TJ33s3C

?D1A>{KK+;++F=f==&\S///*633E3EFK+;++F=f==/0@0@/AA^_b_k_k^l m++/994\I[I[4\+\*]]^`
 	
r5   r#   c                     |S )z`Additional autoclass-specific config post-loading manipulation. May be overridden in subclasses. )r[   r#   s     r)   _prepare_config_for_auto_classz2_BaseAutoModelClass._prepare_config_for_auto_class   s	     r5   pretrained_model_name_or_pathc                 ^   |j                  dd       }|j                  d      }d|d<   g d}|D ci c]  }||v s||j                  |       }}|j                  dd       }	|j                  dd       }
|j                  dd       }|j                  d	d       }|||d	<   |
?t        |t              s"t	        |t
        fd
d
d
d|}t        ||
      }
nt        |dd       }
t               ra|i }|j                         }|||d	<   t        |fd|
i|}|6t        |dd      5 }t        j                  |      }||d<   |d   }d d d        t        |t              st        j                  |      }|j                  d      dk(  r|j                  d      }|j                  d      dk(  r|j                  d      }|j                  d      |j                  d      }t        j                   |fd|	|
d||\  }}|j                  dd       dk(  rd|d<   |j                  dd       dk(  rd|d<   |j                  dd       |d   |d<   t#        |d      xr | j$                  |j&                  v }t)        |      | j*                  v }d }|r1|j&                  | j$                     }d|v r|j-                  d      d   }t/        |||||      }||d<   ||d<   |r||rzt1        |fd|	i||}|j                  dd       }|s0| j3                  |j4                  |d       |j7                  |        t9        |      } |j                   |g|d|i||S t)        |      | j*                  v rit;        || j*                        }|j<                  |j>                  j                  dd       k(  r|jA                         } |j                   |g|d|i||S tC        d|j4                   d| j$                   dd jE                  d! | j*                  D               d"      c c}w # 1 sw Y   xY w)#Nr#   r7   T
_from_auto)	cache_dirforce_downloadlocal_files_onlyproxiesrevision	subfoldertokenr@   _commit_hashadapter_kwargsro   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dtypequantization_config)return_unused_kwargsr@   rp   r8   r9   r   r:   r<   r>   text_configrA   rB   rC   rD   c              3   4   K   | ]  }|j                     y wrF   rG   rH   s     r)   rK   z6_BaseAutoModelClass.from_pretrained.<locals>.<genexpr>{  rL   rM   rN   )#rO   getr   r	   r   r   r   r"   r   copyr   openjsonloaddeepcopyr   from_pretrainedrP   r!   r8   r   rQ   rR   r   r
   rU   r0   rV   rW   r*   config_classsub_configsget_text_configrY   rZ   )r[   rf   
model_argsr3   r#   r7   hub_kwargs_namesname
hub_kwargsr@   commit_hashrq   ro   resolved_config_filemaybe_adapter_pathfadapter_configkwargs_origra   r\   r]   r;   r^   r`   s                           r)   r   z#_BaseAutoModelClass.from_pretrained   s   Hd+"JJ':;#|
 :J\TU[^dFJJt,,\
\

?D9jj6$4d;w-"'Jwf&67'21( 6;:?<A( !($ 22FT%fndC%!#+002N */w'!9-"<G"KY" "-,cGD ^%)YYq\N<YN#894BC\4]1	^ &"23--/K zz-(F2JJ}-zz'"f,JJw'zz/0<JJ45'77-%)+(	
  NFF }d3v=(.}%w-7"(w4d;G0;<Q0R,-!&*5Y#,,&//:Yf););;5Iy  ) 5a 85)'
 '8"# $2 078HUYcgmK 5A "V--{TJ33s3C>{KK.;..-0:CIMW[a  &\S///*633E3EFK''6+=+=+A+A-QU+VV//1.;..-0:CIMW[a  /0@0@/AA^_b_k_k^l m++/994\I[I[4\+\*]]^`
 	
c ]J^ ^s   	PP$ P""P,c                     t        |d      r?|j                  j                  |j                  k7  rt        d|j                   d| d      | j                  j                  |||       y)a  
        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.
        r   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)rP   r   r!   rY   rQ   rU   )r[   r   r`   r=   s       r)   rU   z_BaseAutoModelClass.register~  sw     ;/K4L4L4U4UYeYnYn4n66A6N6N5OO_`l_m n.. 
 	##L+#Qr5   r-   NF)r!   
__module____qualname__rQ   r4   classmethodrb   r	   re   strosPathLiker   rU   rd   r5   r)   r,   r,      s    N
 $
 $
L 4D IY   A
C"++cBR<R A
 A
F R Rr5   r,   c                   @     e Zd ZdZe fd       Ze fd       Z xZS )_BaseAutoBackboneClassNc                    t        | ddg       ddlm} |j                  d |             }|j	                  d      t        d      |j	                  dd	      rt        d
      |j                  d|j                        }|j                  d|j                        }|j                  d|j                        } |||||      }|j                  dd        t        	| (  |fddi|S )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   rO   r   rY   r   r   r   superrb   )
r[   rf   r   r3   r   r#   r   r   r   r0   s
            r)   #_load_timm_backbone_from_pretrainedz:_BaseAutoBackboneClass._load_timm_backbone_from_pretrained  s    #&12>H&8&:;::n%1OPP::+U3_``zz.&2E2EF

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

,d3w"6EdEfEEr5   c                     |j                  dd        t        |      s | j                  |g|i |S t        |   |g|i |S )Nuse_timm_backbone)rO   r   r   r   r   )r[   rf   r   r3   r0   s       r)   r   z&_BaseAutoBackboneClass.from_pretrained  sW    

&-89:3::;Xp[epioppw&'D\z\U[\\r5   )r!   r   r   rQ   r   r   r   __classcell__)r0   s   @r)   r   r     s2    NF F2 ] ]r5   r   head_docc                 n    t        |      dkD  r| j                  dd| d      S | j                  dd      S )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 r5   checkpoint_for_examplec                    | j                   }| j                  }t        t        |      }|j	                  d|      | _        t        t        j                        }t        t        |      }|j	                  d|      }|j	                  d|      }||_         t        |j                   d      |      }t        |      | _        t        }t        t        j                        }	t        ||      }|j	                  d|      }|j	                  d|      }|j                  d      d   j                  d      d	   }
|j	                  d
|
      }||	_         t        |j                         |	      }	t        |	      | _        | S )N)r   BaseAutoModelClasscheckpoint_placeholderF)use_model_types/-r   shortcut_placeholder)rQ   r!   r   CLASS_DOCSTRINGr   __doc__r   r,   rb   FROM_CONFIG_DOCSTRINGr   r   FROM_PRETRAINED_TORCH_DOCSTRINGr   rR   )r[   r   r   r$   r   class_docstringrb   from_config_docstringfrom_pretrained_docstringr   shortcuts              r)   auto_class_updater     so   &&M<<D%oIO!))*>ECK /;;<K+,AHU199:NPTU199:RTjk/Kh3M4P4PbghituK!+.CO ? 3 C CDO /0IT\ ] 9 A ABVX\ ] 9 A ABZ\r s%++C04::3?BH 9 A ABXZb c7OU78T8TUVefO%o6CJr5   c                     g }| j                         D ]8  }t        |t        t        f      r|t        |      z  }(|j	                  |       : |S rF   )valuesr   r   r    append)r$   resultr&   s      r)   
get_valuesr     sN    F%%' !edE]+d5k!FMM% 	! Mr5   c           
      8    |y t        |t              rt         fd|D              S t         |      rt         |      S t	        j
                  d      } |k7  r	 t        ||      S t        d| d| d      # t        $ r t        d| d  d| d      w xY w)Nc              3   6   K   | ]  }t        |        y wrF   )getattribute_from_module)rI   amodules     r)   rK   z+getattribute_from_module.<locals>.<genexpr>  s     GQ-fa8Gs   transformerszCould not find z neither in z nor in !z in )r   r    rP   r"   	importlibimport_moduler   rY   )r   attrtransformers_modules   `  r)   r   r     s    |$G$GGGvtvt$$ $11.A$$	i+,?FF ?4&5H4IKLL  	itfLQdPeefghh	is   A: :Bc                 \   dt        | j                        vr| S dt        | j                        v r| S t        | d      xr dt        t	        | d            v}t        | d      xr dt        t	        | d            v}|s|r+t        | j                  | t        fi | j                        }|S | S )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__rP   r"   r   r!   r   __dict__)r`   has_custom_generate_in_classhas_custom_prepare_inputs!model_class_with_generation_mixins       r)   rW   rW     s     (s;3F3F/GG C 5 566 $+;
#C $HYadZ(b I  !(5T U !Zksv<=t [ $'@,0  ;"@BZ[EYEYBZ-
) 10r5   c                       e Zd ZdZddZdefdZdee   de	fdZ
d Zdeee      fd	Zdee   d
ede	ez  fdZdefdZdee	   fdZdeeee   e	f      fdZdeee      fdZdedefdZddee   de	ddfdZy)_LazyAutoMappinga  
    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                     || _         |j                         D ci c]  \  }}||
 c}}| _        || _        | | j                  _        i | _        i | _        y c c}}w rF   )_config_mappingitems_reverse_config_mappingrQ   _extra_content_modules)r1   config_mappingr$   kvs        r)   r4   z_LazyAutoMapping.__init__*  sY    -9G9M9M9O'PA1'P$+-1* 	 (Qs   Ac                     t        | j                  j                               j                  | j                  j                               }t        |      t        | j                        z   S rF   )setr   keysintersectionrQ   r   r   )r1   common_keyss     r)   __len__z_LazyAutoMapping.__len__2  sP    $..3356CCDDWDWD\D\D^_;#d&9&9":::r5   keyc                    || j                   v r| j                   |   S | j                  |j                     }|| j                  v r!| j                  |   }| j	                  ||      S | j
                  j                         D cg c]  \  }}||j                  k(  s| }}}|D ]3  }|| j                  v s| j                  |   }| j	                  ||      c S  t        |      c c}}w rF   )r   r   r!   rQ   _load_attr_from_moduler   r   KeyError)r1   r   
model_type
model_namer   r   model_typesmtypes           r)   __getitem__z_LazyAutoMapping.__getitem__6  s    $%%%&&s++11#,,?
,,,,,Z8J..z:FF &*%9%9%?%?%AWTQQ#,,EVqWW  	FE+++!007
225*EE	F sm Xs   C%C%c                     t        |      }|| j                  vr&t        j                  d| d      | j                  |<   t	        | j                  |   |      S )NrN   ztransformers.models)r   r   r   r   r   )r1   r   r   module_names       r)   r   z'_LazyAutoMapping._load_attr_from_moduleF  sQ    /
;dmm+)2)@)@1[MARTi)jDMM+&'k(BDIIr5   c                     | j                   j                         D cg c]%  \  }}|| j                  v r| j                  ||      ' }}}|t	        | j
                  j                               z   S c c}}w rF   )r   r   rQ   r   r   r   r   )r1   r   r   mapping_keyss       r)   r   z_LazyAutoMapping.keysL  su     "11779
Td))) ''T2
 

 d4#6#6#;#;#=>>>
   *A1defaultc                 H    	 | j                  |      S # t        $ r |cY S w xY wrF   )r   r   )r1   r   r  s      r)   r   z_LazyAutoMapping.getT  s,    	##C(( 	N	s    !!c                 4    t        | j                               S rF   )boolr   r1   s    r)   __bool__z_LazyAutoMapping.__bool__Z      DIIK  r5   c                     | j                   j                         D cg c]%  \  }}|| j                  v r| j                  ||      ' }}}|t	        | j
                  j                               z   S c c}}w rF   )rQ   r   r   r   r   r   r   )r1   r   r   mapping_valuess       r)   r   z_LazyAutoMapping.values]  su     "00668
Td*** ''T2
 

 T%8%8%?%?%A BBB
r   c           	         | j                   D cg c]N  }|| j                  v r>| j                  || j                  |         | j                  || j                   |         fP }}|t        | j                  j                               z   S c c}w rF   )rQ   r   r   r   r   r   )r1   r   mapping_itemss      r)   r   z_LazyAutoMapping.itemse  s     **

 d***	 ++C1E1Ec1JK++C1D1DS1IJ
 
 tD$7$7$=$=$?@@@
s   AB
c                 4    t        | j                               S rF   )iterr   r  s    r)   __iter__z_LazyAutoMapping.__iter__p  r  r5   itemc                     || j                   v ryt        |d      r|j                  | j                  vry| j                  |j                     }|| j                  v S )NTr!   F)r   rP   r!   r   rQ   )r1   r  r   s      r)   __contains__z_LazyAutoMapping.__contains__s  sV    4&&&tZ(DMMA]A],]11$--@
T0000r5   valuec                     t        |d      rP|j                  | j                  v r8| j                  |j                     }|| j                  v r|st	        d| d      || j
                  |<   y)z7
        Register a new model in this mapping.
        r!   'z*' is already used by a Transformers model.N)rP   r!   r   rQ   rY   r   )r1   r   r  r=   r   s        r)   rU   z_LazyAutoMapping.register{  sh     3
#8T8T(T55cllCJT000 1SE)S!TUU#(C r5   r   r   )r!   r   r   r   r4   intr   r   r	   _LazyAutoMappingValuer   r   r   r   r   r   r  r  r   r    r   r   r  r  rU   rd   r5   r)   r   r   !  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 	)r5   r   r   ) )zgoogle-bert/bert-base-casedr  )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   r  r   r   r   r*   r,   r   r   r   r   r   r   rW   r   __all__rd   r5   r)   <module>r$     s!   4    	 # $  ' 3 \
 
 
 i h - 
		H	%T]d3i$.S	D0@@A  4Z# z LR LR^$]0 $]N 3 be <M(@c){4(8#9;P#PQ c)L .r5   