
    ^j                        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mZ dd	lmZ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jD                  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e/fdZ0d!de/de/fdZ1d Z2d Z3d Z4 G d dee'e   e(f         Z5dgZ6y)"z-Factory function to build auto-model classes.    N)OrderedDict)Iterator)AnyTypeVar   )PreTrainedConfig)get_class_from_dynamic_moduleresolve_trust_remote_code)
CONFIG_NAMEcached_file	copy_funcextract_commit_hashfind_adapter_config_filehf_api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          p/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py_get_model_classr*      s    $T&\2&u68HIuU^^U*IMIFOR8M '=  &&' A Js   A%c                       e Zd ZdZd
dZed        Zededefd       Zede	e
j                  e	   z  fd       Zedd
d	       Zy)_BaseAutoModelClassNreturnc                     t        | j                  j                   d| j                  j                   d| j                  j                   d      )Nz+ is designed to be instantiated using the `z5.from_pretrained(pretrained_model_name_or_path)` or `z.from_config(config)` methods.)OSError	__class__r!   )selfargskwargss      r)   __init__z_BaseAutoModelClass.__init__   sR    ~~&&' (..112 3''((FH
 	
    c                 l   |j                  dd       }t        |d      xr | j                  |j                  v }t	        |      | j
                  v }|xr0 t        || j
                        j                  j                  d       }|rN|j                  | j                     }d|v r|j                  d      d   }nd }t        ||j                  |||      }|r|r|sdv r|j                  d      \  }	}n|j                  }	t        ||	fi |}
| j                  |j                  |
d       |
j!                  | 	       |j                  d
d       }t#        |
      }
 |
j$                  |fi |S |rt        || j
                        }
|j&                  j)                  dd       }|8t+        |
dd       |k(  r(|}|j-                         }t+        |dd       }|||_         |
j$                  |fi |S t1        d|j                   d| j                   ddj3                  d | j
                  D               d      )Ntrust_remote_codeauto_maptransformers.--r   upstream_repoTexist_ok
auto_classcode_revisiontext_configconfig_classquantization_config!Unrecognized configuration class  for this kind of AutoModel: .
Model type should be one of , c              3   4   K   | ]  }|j                     y wNr!   .0cs     r)   	<genexpr>z2_BaseAutoModelClass.from_config.<locals>.<genexpr>        4\AQZZ4\   .)pophasattrr!   r8   r   _model_mappingr*   
__module__
startswithsplitr
   _name_or_pathname_or_pathr	   registerr0   register_for_auto_class$add_generation_mixin_to_remote_model_from_configsub_configsgetr"   get_text_configrD   
ValueErrorjoin)clsr#   r3   r7   has_remote_codehas_local_codeexplicit_local_code	class_refr<   repo_idmodel_class_text_config_classparent_configparent_quants                  r)   from_configz_BaseAutoModelClass.from_config   sN   "JJ':DA!&*5Y#,,&//:Yf););;, 15EC&&6

*ZZ021 5Iy  ) 5a 8 $ 9!6#7#7hu! 09Ly %.__T%:" --7	7UfUKLL));LF//3/?

?D1A>{KK+;++F=f==*633E3EFK & 2 2 6 6}d K ,nVZ1[_p1p !'//1  '}6KTR+1=F.+;++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. )rd   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               r|i }|j                         }|||d	<   t        |fd|
i|}|t        |dd      5 }t        j                  |      }||d<   t        j                  j!                  |      rAt        j                  j!                  t        j                  j#                  |t
                    s|d   }d d d        t        |t              st        j$                  |      }|j                  d      dk(  r|j                  d      }|j                  d      dk(  r|j                  d      }|j                  d      |j                  d      }t'        j(                  |fd|	|
d||\  }}|j                  dd       |d   |d<   |j                  dd       |d   |d<   |j                  dd       |d   |d<   t+        |d      xr | j,                  |j.                  v }t1        |      | j2                  v }|xr0 t5        || j2                        j6                  j9                  d       }d }|r1|j.                  | j,                     }d|v r|j;                  d      d   }t=        |||||      }||d<   ||d<   |r||rz|sxt?        |fd|	i||}|j                  dd       }| jA                  |jB                  |d       |jE                  |        tG        |      } |j(                  |g|d|i||S |rt5        || j2                        }|jH                  j                  dd       }|8t        |dd       |k(  r(|}|jK                         }t        |dd       }|||_&         |j(                  |g|d|i||S tO        d|jB                   d | j,                   d!d"j#                  d# | j2                  D               d$      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tokenrA   _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dtyperD   )return_unused_kwargsrA   r}   r8   r9   r:   r   r;   r=   r?   rB   rC   rE   rF   rG   rH   c              3   4   K   | ]  }|j                     y wrJ   rK   rL   s     r)   rO   z6_BaseAutoModelClass.from_pretrained.<locals>.<genexpr>  rP   rQ   rR   )(rS   r`   r   r   r   r   r   r"   r   copyr   openjsonloadospathexistsrc   deepcopyr   from_pretrainedrT   r!   r8   r   rU   r*   rV   rW   rX   r
   r	   r[   r0   r\   r]   r_   ra   rD   rb   )rd   rs   
model_argsr3   r#   r7   hub_kwargs_namesname
hub_kwargsrA   commit_hashr~   r|   resolved_config_filemaybe_adapter_pathfadapter_configkwargs_origrk   re   rf   rg   r<   rh   rj   rl   rm   rn   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 b%)YYq\N<YN#89
 77>>*GHPRPWPWP^P^%BKPQ 9GG`8a5b &"23--/K zz-(F2JJ}-zz'"f,JJw'zz/0<JJ45'77-%)+(	
  NFF }d3?(3M(B}%w-9"-g"6w4d;G0;<Q0R,-!&*5Y#,,&//:Yf););;, 15EC&&6

*ZZ021 5Iy  ) 5a 85)'
 '8"# $2 09L78HUYcgmK 5ALL));LF//3/?>{KK.;..-0:CIMW[a  *633E3EFK & 2 2 6 6}d K ,nVZ1[_p1p !'//1  '}6KTR+1=F..;..-0:CIMW[a  /0@0@/AA^_b_k_k^l m++/994\I[I[4\+\*]]^`
 	
G ]Jb bs   	R9R9$B R>>S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.
        rC   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)rT   rC   r!   rb   rU   r[   )rd   rC   rj   r>   s       r)   r[   z_BaseAutoModelClass.register  sw     ;/K4L4L4U4UYeYnYn4n66A6N6N5OO_`l_m n.. 
 	##L+#Qr5   r-   NF)r!   rV   __qualname__rU   r4   classmethodro   r   rr   strr   PathLiker   r[   rq   r5   r)   r,   r,      s    N
 /
 /
b 4D IY   S
C"++cBR<R S
 S
j 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   rS   r`   rb   r   r   r   superro   )
rd   rs   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               j                  |      s | j                  |g|i |S t	        |   |g|i |S )Nuse_timm_backbone)rS   r   repo_existsr   r   r   )rd   rs   r   r3   r0   s       r)   r   z&_BaseAutoBackboneClass.from_pretrained  s_    

&-x##$AB:3::;Xp[epioppw&'D\z\U[\\r5   )r!   rV   r   rU   r   r   r   __classcell__)r0   s   @r)   r   r     s2    NF F2 ] ]r5   r   head_docc                 n    t        |      dkD  r| j                  dd| d      S | j                  dd      S )Nr   z(one of the model classes of the library z0one of the model classes of the library (with a z head) z-one of the base model classes of the library )lenreplace)	docstringr   s     r)   insert_head_docr     sK    
8}q  6>xjP
 	
 24c r5   checkpoint_for_examplec                    | j                   }| j                  }t        t        |      }|j	                  d|      | _        t        t        j                        }t        t        |      }|j	                  d|      }|j	                  d|      }||_         t        |j                   d      |      }t        |      | _        t        }t        t        j                        }	t        ||      }|j	                  d|      }|j	                  d|      }|j                  d      d   j                  d      d	   }
|j	                  d
|
      }||	_         t        |j                         |	      }	t        |	      | _        | S )N)r   BaseAutoModelClasscheckpoint_placeholderF)use_model_types/-r   shortcut_placeholder)rU   r!   r   CLASS_DOCSTRINGr   __doc__r   r,   ro   FROM_CONFIG_DOCSTRINGr   r   FROM_PRETRAINED_TORCH_DOCSTRINGr   rX   )rd   r   r   r$   r   class_docstringro   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 rJ   )valuesr   r   r    append)r$   resultr&   s      r)   
get_valuesr     sN    F%%' !edE]+d5k!FMM% 	! Mr5   c           
          |y t        |t              rt         fd|D              S t        |t              r.|j                         D ci c]  \  }}|t	         |       c}}S t         |      rt         |      S t        j                  d      } |k7  r	 t	        ||      S t        d| d| d      c c}}w # t        $ r t        d| d  d| d      w xY w)Nc              3   6   K   | ]  }t        |        y wrJ   )getattribute_from_module)rM   amodules     r)   rO   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    dictitemsr   rT   r"   	importlibimport_modulerb   )r   attrkvtransformers_modules   `    r)   r   r   	  s    |$G$GGG$CG::<P41a+FA66PPvtvt$$ $11.A$$	i+,?FF ?4&5H4IKLL Q  	itfLQdPeefghh	is   B8B> >C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__rT   r"   r   r!   r   __dict__)rj   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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   ez  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 rJ   )_config_mappingr   _reverse_config_mappingrU   _extra_content_modules)r1   config_mappingr$   r   r   s        r)   r4   z_LazyAutoMapping.__init__H  sY    -9G9M9M9O'PA1'P$+-1* 	 (Qs   Ac                     t        | j                  j                               j                  | j                  j                               }t        |      t        | j                        z   S rJ   )setr   keysintersectionrU   r   r   )r1   common_keyss     r)   __len__z_LazyAutoMapping.__len__P  sP    $..3356CCDDWDWD\D\D^_;#d&9&9":::r5   keyc                    || j                   v r| j                   |   S | j                  |j                     }|| j                  v r!| j                  |   }| j	                  ||      S | j
                  j                         D cg c]  \  }}||j                  k(  s| }}}|D ]3  }|| j                  v s| j                  |   }| j	                  ||      c S  t        |      c c}}w rJ   )r   r   r!   rU   _load_attr_from_moduler   r   KeyError)r1   r   
model_type
model_namer   r   model_typesmtypes           r)   __getitem__z_LazyAutoMapping.__getitem__T  s    $%%%&&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 )NrR   ztransformers.models)r   r   r   r   r   )r1   r  r   module_names       r)   r   z'_LazyAutoMapping._load_attr_from_moduled  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 rJ   )r   r   rU   r   r   r   r   )r1   r   r   mapping_keyss       r)   r   z_LazyAutoMapping.keysj  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rJ   )r  r   )r1   r   r  s      r)   r`   z_LazyAutoMapping.getr  s,    	##C(( 	N	s    !!c                 4    t        | j                               S rJ   )boolr   r1   s    r)   __bool__z_LazyAutoMapping.__bool__x      DIIK  r5   c                     | j                   j                         D cg c]%  \  }}|| j                  v r| j                  ||      ' }}}|t	        | j
                  j                               z   S c c}}w rJ   )rU   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 rJ   )rU   r   r   r   r   r   )r1   r   mapping_itemss      r)   r   z_LazyAutoMapping.items  s     **

 d***	 ++C1E1Ec1JK++C1D1DS1IJ
 
 tD$7$7$=$=$?@@@
s   AB
c                 4    t        | j                               S rJ   )iterr   r  s    r)   __iter__z_LazyAutoMapping.__iter__  r  r5   itemc                     || j                   v ryt        |d      r|j                  | j                  vry| j                  |j                     }|| j                  v S )NTr!   F)r   rT   r!   r   rU   )r1   r  r  s      r)   __contains__z_LazyAutoMapping.__contains__  sV    4&&&tZ(DMMA]A],]11$--@
T0000r5   valuec                    t        |d      rP|j                  | j                  v r8| j                  |j                     }|| j                  v r|st	        d| d      t        |dd      j                  d      ry|| j                  |<   y)z7
        Register a new model in this mapping.
        r!   'z*' is already used by a Transformers model.rV    r9   N)rT   r!   r   rU   rb   r"   rW   r   )r1   r   r  r>   r  s        r)   r[   z_LazyAutoMapping.register  s     3
#8T8T(T55cllCJT000 1SE)S!TUU 3b)44_E $)C r5   r   r   )r!   rV   r   r   r4   intr   r   r   _LazyAutoMappingValuer  r   r   r   r   r`   r  r  r   r    r   r   r  r  r   r[   rq   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2S8 )AV )ko )r5   r   r   )r  )zgoogle-bert/bert-base-casedr  )7r   r   r   r   r   collectionsr   collections.abcr   typingr   r   configuration_utilsr   dynamic_module_utilsr	   r
   utilsr   r   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   r]   r   __all__rq   r5   r)   <module>r-     s   4    	 # $  3 \   i h - 
		H	%T]d3i$.S	D0@@A  4Z# z iR iRX$]0 $]N 3 be <M,@m){4(8#9;P#PQ m)` .r5   