ZjdSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) zFSMT configuration)strict)PreTrainedConfig)auto_docstringzfacebook/wmt19-en-ru) checkpointc\^\rSrSr%SrSrSSSSS.rS r\\ \ \ S 4-\ S 'S r \ \ S 'S r\ \ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\\ -\ S'Sr\ \ S'Sr\ \ S'Sr\ \ S'Sr\\ -\ S'Sr\\ -\ S'Sr\\ -\ S 'Sr\\ -\ S!'S"r\\ S#'S$r \ S%-\ S&'S'r!\"\ S('S'r#\"\ S)'S*r$\"\ S+'S,r%\ \ S-'S.r&\\ S/'S*r'\"\ S0'S'r(\"\ S1'S2r)\ S%-\ S3'S4r*\ S%-\ S5'S$r+\ \\ -S%-\ S6'S$r,\ \\ -S%-\ S7'U4S8jr-S9r.U=r/$): FSMTConfiga langs (`list[str]`): A list with source language and target_language (e.g., ['en', 'ru']). src_vocab_size (`int`): Vocabulary size of the encoder. Defines the number of different tokens that can be represented by the `inputs_ids` passed to the forward method in the encoder. tgt_vocab_size (`int`): Vocabulary size of the decoder. Defines the number of different tokens that can be represented by the `inputs_ids` passed to the forward method in the decoder. max_length (`int`, *optional*, defaults to 200): Maximum length to generate. num_beams (`int`, *optional*, defaults to 5): Number of beams for beam search that will be used by default in the `generate` method of the model. 1 means no beam search. length_penalty (`float`, *optional*, defaults to 1): Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while `length_penalty` < 0.0 encourages shorter sequences. early_stopping (`bool`, *optional*, defaults to `False`): Flag that will be used by default in the `generate` method of the model. Whether to stop the beam search when at least `num_beams` sentences are finished per batch or not. Examples: ```python >>> from transformers import FSMTConfig, FSMTModel >>> # Initializing a FSMT facebook/wmt19-en-ru style configuration >>> config = FSMTConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = FSMTModel(config) >>> # Accessing the model configuration >>> configuration = model.config ```fsmtencoder_attention_headsd_modeltgt_vocab_sizeencoder_layers)num_attention_heads hidden_size vocab_sizenum_hidden_layers)ende.langsi(src_vocab_sizereluactivation_functioni max_lengthmax_position_embeddingsiencoder_ffn_dim gencoder_layerdropdecoder_ffn_dimdecoder_layersdecoder_attention_headsdecoder_layerdropattention_dropoutg?dropoutactivation_dropoutg{Gz?init_stdNdecoder_start_token_idTis_encoder_decoderscale_embeddingFtie_word_embeddings num_beamsg?length_penaltyearly_stopping use_cache pad_token_idr bos_token_id eos_token_idforced_eos_token_idc J>URSS5 [TU]"S0UD6 g)Ndecoder)popsuper __post_init__)selfkwargs __class__s |/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/fsmt/configuration_fsmt.pyr=FSMTConfig.__post_init__gs! 9d# ''r:)0__name__ __module__ __qualname____firstlineno____doc__ model_type attribute_maprliststrtuple__annotations__rintrrr rrrrr r floatr!r"r#r$r%r&r'r(r*r+boolr,r-r/r0r1r2r4r5r6r7r=__static_attributes__ __classcell__)r@s@rAr r s$ LJ8 &- M*6E49uS#X &5NCNC%%GSJ#'S'OSNC#%S%%(us{(OSNC#%S%%(us{(%(us{(GUS[&) )He)*C$J*## OT %%IsNE ND It L#*  L#* +,L#S /D(,23tCy4/3((rCr N) rHhuggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__r:rCrArXsJ.3#12Q(!Q(3Q(h .rC