7iAdZddlmZddlZddlZddlZddlZddlmZm Z ddl m Z m Z m Z ddlmZmZmZddlmZmZddlmZdd lmZmZdd lmZmZmZddlZd d l m!Z!d dl"m#Z#m$Z$m%Z%dZ&e$rddl'Z'ddl(m)Z)d dl*m+Z+dZ&erddl'm,Z,e!jZe.Z/dZ0e#rdZ0dZ1dZ2dZ3dZ4dZ5dZ6dZ7dZ8dZ9 dI dJdZ:dZ;dZd"Z?dMd#Z@Gd$d%eZAe&rOddlBm cmCZDdNd&ZE dO dPd'ZFeDjeAeEeeFeA(eAjd)eAj\*Gd+d,eIeZJGd-d.eJZKGd/d0eJZLGd1d2ZMd3ZNd4ZOdQdRd5ZPdOd6ZQd7ZRdOd8ZSd9ZTd:ZUd;ZVd<ZWdOdSd=ZXGd>d?ed@ZYdTdAZZdUdBZ[dVdCZ\dWdDZ]dEZ^dFZ_GdGdHe Z`y)Xz Generic utilities ) annotationsN) OrderedDictUserDict)CallableIterableMutableMapping)AbstractContextManager ExitStack nullcontext)fields is_dataclass)Enum)partialwraps) TYPE_CHECKINGAny TypedDict)logging)is_mlx_availableis_torch_availableis_torch_fx_proxyF)_dtype)model_addition_debugger_contextT)nncR|j}|dvry|dvrytd|)zConvert a string representation of truth to true (1) or false (0). True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values are 'n', 'no', 'f', 'false', 'off', and '0'. 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Which makes graph splitting in `torch.compile` more flexible as it removes the requirement that partition IDs be monotonically increasing. )rXenabled cache_enabled)rOis_autocast_enabledautocastr ) device_typerXrerfs r-maybe_autocastrjs2    -~~kWdee}r/c:ddlm}t||jS)Nr)mlx.corecorerJarray)r7mxs r-_is_mlxrps a ""r/c(tsdSt|S)zZ Tests if `x` is a mlx array or not. Safe to call even when mlx is not installed. F)_is_mlx_availablerprLs r-r=r=s*59wqz9r/cP| | td||j}d|vS|}d|vS)a Checks whether some flavor of flash attention is requested or not. This is checked against one of the two arguments, i.e. either the `config` or the directly passed value `requested_attention_implementation`. Otherwise, an error will be raised (ambiguity). 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Args: model_class (`type`): The class of the model. return_lossTF)inspect signatureforward parametersr) model_classrps r-can_return_lossr"sW!!+"5"56I  ! ! )"6"6q"9"A"AT"I r/c|j}tj|j}d|vr#|jDcgc] }d|vs|dvs |c}S|jDcgc] }d|vs| c}Scc}wcc}w)zq Find the labels used by a given model. Args: model_class (`type`): The class of the model. QuestionAnsweringlabel)start_positions end_positions)rrrrr)r  model_namerr!s r- find_labelsr) sx%%J!!+"5"56Ij($//ma7a<1HlClmm$//@a7a<@@n@s A2 A2! 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This decorator ensures that only the keyword arguments that match the function's signature, or are specified in the `extra` list, are passed to the function. Any additional keyword arguments are filtered out and a warning is issued. Parameters: extra (`Optional[list]`, *optional*): A list of extra keyword argument names that are allowed even if they are not in the function's signature. Returns: Callable: A decorator that wraps the function and filters out invalid keyword arguments. Example usage: ```python @filter_out_non_signature_kwargs(extra=["allowed_extra_arg"]) def my_function(arg1, arg2, **kwargs): print(arg1, arg2, kwargs) my_function(arg1=1, arg2=2, allowed_extra_arg=3, invalid_arg=4) # This will print: 1 2 {"allowed_extra_arg": 3} # And issue a warning: "The following named arguments are not valid for `my_function` and were ignored: 'invalid_arg'" ``` ctj}t|jj }|j d|vd|vd_tfd}|S)NrrTci}i}|jD]\}}| vr|||<|||<|r|Dcgc]}d|d }}dj|} r|djjdz}n r|djdz}nd}t j d|jd|t d |i|Scc}w) N'z, rrr2z1The following named arguments are not valid for `z` and were ignored: r) stacklevel)rjoinrrwarningswarn UserWarning) rr valid_kwargsinvalid_kwargsrrinvalid_kwargs_names cls_prefixfuncis_class_methodis_instance_methodvalid_kwargs_to_passs r-wrapperzCfilter_out_non_signature_kwargs..decorator..wrappersLN  *1,,&'LO()N1%  * :H'IQ!A3a'I$'I'+yy1E'F$&!%a!2!2!;!;c!AJ$!%a!1!1C!7J!#J G |TXTaTaSbc**>)?A .. .%(Js B?)rrsetrrunion _filter_out_non_signature_kwargsr)r^sigfunction_named_argsrbr_r`raextra_params_to_passs` @@@r- decoratorz2filter_out_non_signature_kwargs..decorators}%!#.."5"5"782889MN$'::#6615- t /  />r/)rc)extrarirhs @r-filter_out_non_signature_kwargsrks&6 KREu:,\ r/cveZdZUdZded<ded<ded<ded<ded <ded <d ed <d ed <ded<ded<y)TransformersKwargsa Keyword arguments to be passed to the forward pass of a `PreTrainedModel`. Attributes: num_items_in_batch (`Optional[torch.Tensor]`, *optional*): Number of items in the batch. It is recommended to pass it when you are doing gradient accumulation. output_hidden_states (`Optional[bool]`, *optional*): Most of the models support outputting all hidden states computed during the forward pass. output_attentions (`Optional[bool]`, *optional*): Turn this on to return the intermediary attention scores. output_router_logits (`Optional[bool]`, *optional*): For MoE models, this allows returning the router logits to compute the loss. cu_seq_lens_q (`torch.LongTensor`, *optional*) Gets cumulative sequence length for query state. cu_seq_lens_k (`torch.LongTensor`, *optional*) Gets cumulative sequence length for key state. max_length_q (`int`, *optional*): Maximum sequence length for query state. max_length_k (`int`, *optional*): Maximum sequence length for key state. position_ids (`torch.LongTensor`, *optional*) Indices of positions of each input sequence tokens. is_causal (`bool`, *optional*) Can be set to False to enable bi-directional attention, i.e. use decoder Attention modules as encoders. ztorch.Tensor | Nonenum_items_in_batch bool | Noneoutput_hidden_statesoutput_attentionsoutput_router_logitsztorch.LongTensor | None cu_seq_lens_q cu_seq_lens_kz int | None max_length_q max_length_k position_ids is_causalN)rrrr__annotations__rr/r-rmrmsE4,+%%""%%****))r/rm)totalc d|vS)z3Checks whether a config dict is a timm config dict.pretrained_cfgr)rs r-is_timm_config_dictr}s { **r/c|yt|}tjj|}tjj |}|rE|j dr4t |5}tj|}dddt|S|rtjjtjj|drRt tjj|d5}tj|}dddt|Sy#1swYtSxYw#1swYtSxYw)zA Checks whether a checkpoint is a timm model checkpoint. NFz.jsonz config.json) r4ospathisfileisdirendswithrrloadr}existsrV)pretrained_model_pathis_fileis_dirr%rs r-is_timm_local_checkpointr s$ 56ggnn23G WW]]0 1F(11': ' ( 'A))A,K '";//"''...C]!ST "'',,4mD E '))A,K '";//  '";// '";//s+D&<D<&D9<Ecbt||||jD]}t|||y)z5 Set a value to a module and all submodules. N)rchildrenset_attribute_for_modules)modulerrc submodules r-rr%s2 FC__&9 !)S%89r/cvt||r t|||jD]}t||y)z: Delete a value from a module and all submodules. N)rVdelattrrdel_attribute_from_modules)rrrs r-rr.s8 vs__&3 "9c23r/c.tfd}|S)z Decorator to wrap model method, to call output.to_tuple() if return_dict=False passed as a kwarg or use_return_dict=False is set in the config. Note: output.to_tuple() convert output to tuple skipping all `None` values. ct|dr|jjnd}|jd|}||}|g|i|}|s t |t s|j }|S)NrvT return_dict)rVrvrrrJr`r)rrrrreturn_dict_passedrr^s r-rbz!can_return_tuple..wrapperCsj18x1Hdkk--d #ZZ {C  ),Kd,T,V,:fe#<__&F r/rr^rbs` r-can_return_tupler:s" 4[ Nr/c.tfd}|S)z Decorator using config field (if they exist) as default value for some args and kwargs. Precedence is always given to the args/kwargs that are explicitly passed. cgd}|D]}d}| jjvr( jjj|dz }|t||kDr ||||}n.|j |||}nt |j |d}||dk(r3t |ddrE|jr9|r7tjdd}n|dk(rdd g}||vrtd |d |d |+t||kDrt|}|||<t|}|||<|j d t |j d d}|Dt|j d } | r|j j} ||j _||d < |j ddrBt||j dd|j d5 |g|i|} dddn  |g|i|} |! r |j _ S|j ` S#1swY.xYw#|! r |j _w|j `wwxYw)N) use_cachevision_feature_layervision_feature_select_strategyvision_aspect_ratiorrgradient_checkpointingFzX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.rrfullz%`Unexpected select feature strategy: z. Please select from rrxdebug_io debug_io_dir model_debug prune_layers)__code__ co_varnamesindexragetrWrvtraininglogger warning_oncer+r_r`rVrxr) rrrargs_with_config_defaultsarg_name arg_index arg_valuevalid_strategiesrxis_causal_in_configis_causal_original_valuerr^s r-rbz+merge_with_config_defaults..wrapperWs% ! 2 1HI4==444 MM55;;HEI $TY)>4 ?C^ O H%1"8, #DKK4@ ${*t%=uE$--\e++v%* !AA(16':$ (88(CI;Ncdtcuuvw(SY-B:D&/DO ;D'0F8$A 1FJJ{GDKKd,ST  ")$++{"C "+/;;+@+@($-DKK !"+F;  .zz*e,4&**^]CVZZP^E_9"$888F99 d4T4V4$&,DDKK)  - 99$&,DDKK) - %s$>H% HH%H"H%%%I rrs` r-merge_with_config_defaultsrQs%  4[DDL Nr/cReZdZdZiZdZdZdZdZdZ dZ e d dZ d d Z y ) GeneralInterfacez Dict-like object keeping track of a class-wide mapping, as well as a local one. 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