U ,-e!!@spdZddlmZddlmZddlmZddlmZe e Z ddd d d Z Gd d d eZ GdddeZdS)z LongT5 model configuration)Mapping)PretrainedConfig)OnnxSeq2SeqConfigWithPast)loggingzFhttps://huggingface.co/google/long-t5-local-base/blob/main/config.jsonzGhttps://huggingface.co/google/long-t5-local-large/blob/main/config.jsonzHhttps://huggingface.co/google/long-t5-tglobal-base/blob/main/config.jsonzIhttps://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json)zgoogle/long-t5-local-basezgoogle/long-t5-local-largezgoogle/long-t5-tglobal-basezgoogle/long-t5-tglobal-largecs8eZdZdZdZdgZddddZdfdd ZZS) LongT5Configa This is the configuration class to store the configuration of a [`LongT5Model`] or a [`FlaxLongT5Model`]. It is used to instantiate a LongT5 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LongT5 [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: vocab_size (`int`, *optional*, defaults to 32128): Vocabulary size of the LongT5 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LongT5Model`]. d_model (`int`, *optional*, defaults to 512): Size of the encoder layers and the pooler layer. d_kv (`int`, *optional*, defaults to 64): Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model // num_heads`. d_ff (`int`, *optional*, defaults to 2048): Size of the intermediate feed forward layer in each `LongT5Block`. num_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. num_decoder_layers (`int`, *optional*): Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. num_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. local_radius (`int`, *optional*, defaults to 127) Number of tokens to the left/right for each token to locally self-attend in a local attention mechanism. global_block_size (`int`, *optional*, defaults to 16) Lenght of blocks an input sequence is divided into for a global token representation. Used only for `encoder_attention_type = "transient-global"`. relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. relative_attention_max_distance (`int`, *optional*, defaults to 128): The maximum distance of the longer sequences for the bucket separation. dropout_rate (`float`, *optional*, defaults to 0.1): The ratio for all dropout layers. layer_norm_eps (`float`, *optional*, defaults to 1e-6): The epsilon used by the layer normalization layers. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). feed_forward_proj (`string`, *optional*, defaults to `"relu"`): Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. LongT5v1.1 uses the `"gated-gelu"` feed forward projection. Original LongT5 implementation uses `"gated-gelu"`. encoder_attention_type (`string`, *optional*, defaults to `"local"`): Type of encoder attention to be used. Should be one of `"local"` or `"transient-global"`, which are supported by LongT5 implementation. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). 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