ޘhdZddlZddlZddlZddlZddlZddlmZmZm Z ddl m Z m Z ddl mZmZmZddlmZmZmZddlmZddlmZmZdd lmZmZmZmZddlZ d d l!m"Z"d d l#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)iZ*e"j+e,Z-e'r ddl.Z.d dl/m0Z0Gdde1Z2dZ3dZ4dZ5dZ6dZ7dZ8dZ9dZ:dZ;dZdZ?dZ@dZAd ZBd!ZCd"ZDd#ZEd$ZFd%ZGd&ZHGd'd(eZIe'r_ddlJm!cmKZLd)eId*eMeNed+ffd,ZO ddd-e ed.d+d*eIfd/ZPeLjQeIeOeePeI0eIjRd1eIj,2Gd3d4eSeZTGd5d6eTZUGd7d8eTZVGd9d:ZWd;ZXd<ZYded>e d?eSd@eSfdAZZedfdCe[fdDZ\dddEZ]dFZ^dddGZ_dHZ`dIZadJZbdKZcdLZddddMeeNfdNZeGdOdPedBQZfdRegeSefd*e[fdSZhdTeSd*e[fdUZidVdWdXeSdYefdZZjdVdWdXeSfd[Zkd\Zlee)d]^Gd_d`ZmdaZnGdbdce ZodS)gz Generic utilities N) OrderedDictUserDict defaultdict)IterableMutableMapping)AbstractContextManager ExitStackcontextmanager) dataclassfields is_dataclass)Enum)partialwraps)AnyCallableOptional TypedDict)logging)is_flax_availableis_mlx_availableis_tf_availableis_torch_availableis_torch_fx_proxyrequires)model_addition_debugger_contextceZdZdZddZdS)cached_propertyz Descriptor that mimics @property but caches output in member variable. 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Example usage: ```python @filter_out_non_signature_kwargs(extra=["allowed_extra_arg"]) def my_function(arg1, arg2, **kwargs): print(arg1, arg2, kwargs) my_function(arg1=1, arg2=2, allowed_extra_arg=3, invalid_arg=4) # This will print: 1 2 {"allowed_extra_arg": 3} # And issue a warning: "The following named arguments are not valid for `my_function` and were ignored: 'invalid_arg'" ``` ctj}t|j}|d|vd|vd_tfd}|S)Nr(rTc`i}i}|D]\}}| vr|||<|||<|rwd|D}d|} r|djjdz}n r|djdz}nd}t jd|jd|t d |i|S) Ncg|]}d|d S)'r4)rTrs r-rVzWfilter_out_non_signature_kwargs..decorator..wrapper..,s 'I'I'IQA'I'I'Ir/z, rrrMz1The following named arguments are not valid for `z` and were ignored: r) stacklevel)rjoinrr%warningswarn UserWarning) rr valid_kwargsinvalid_kwargsrrinvalid_kwargs_names cls_prefixfuncis_class_methodis_instance_methodvalid_kwargs_to_passs r-wrapperzCfilter_out_non_signature_kwargs..decorator..wrapper s LN  * *1,,,&'LOO()N1%% 'I'I.'I'I'I$'+yy1E'F'F$&$!%a!2!;c!AJJ$$!%a!1C!7JJ!#J A ATXTaAA*>AA 4... .r/)r:r;setr?runion _filter_out_non_signature_kwargsr)rsigfunction_named_argsrrrrextra_params_to_passs` @@@r- decoratorz2filter_out_non_signature_kwargs..decorators%%!#."5"5"7"7882889MNN$'::#6615- t / / / / / / /  />r/)r)rrrs @r-filter_out_non_signature_kwargsrs<6 KREu::,,,,,\ r/ceZdZUdZeded<eeed<eeed<eeed<eded<eded <eeed <eeed <d S) TransformersKwargsai Keyword arguments to be passed to the loss function 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. z torch.Tensornum_items_in_batchoutput_hidden_statesoutput_attentionsoutput_router_logitsztorch.LongTensor cu_seq_lens_q cu_seq_lens_k max_length_q max_length_kN)r%r1r2r3r__annotations__boolrr4r/r-rrEs.!0000"4.(((~%%%"4.(((.////.////3-3-r/r)total config_dictc d|vS)z3Checks whether a config dict is a timm config dict.pretrained_cfgr4)rs r-is_timm_config_dictrgs { **r/pretrained_model_pathc|dSt|}tj|}tj|}|r_|drJt |5}tj|}dddn #1swxYwYt|S|rtj tj |drht tj |d5}tj|}dddn #1swxYwYt|SdS)zA Checks whether a checkpoint is a timm model checkpoint. 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