sgddlmZmZmZddlZddlmZddlm Z ejjGdde Z ejjGdde Z ejjGd d e ZejjGd d e ZejjGd de ZejjGdde ZejjGdde ZejjGdde ZejjGdde ZejjGdde ZejjGdde ZeZejjGdde ZejjGdde ZejjGdd e ZejjGd!d"e ZejjGd#d$e ZejjGd%d&e ZejjGd'd(e ZejjGd)d*e Zy)+)DictOptionalTupleN) ModelOutputceZdZUdZdZej ed<dZe e ej ed<dZ e e ej ed<y)FlaxBaseModelOutputan Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nlast_hidden_state hidden_states attentions) __name__ __module__ __qualname____doc__r jnpndarray__annotations__r rrr U/var/www/html/venv/lib/python3.12/site-packages/transformers/modeling_flax_outputs.pyr r sH&&*s{{)26M8E#++./6/3Js{{+,3rr cbeZdZUdZdZej ed<dZe e ej ed<y)"FlaxBaseModelOutputWithNoAttentiona Base class for model's outputs, with potential hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. Nr r ) r rrrr rrrr rrrrrrr0s0 &*s{{)26M8E#++./6rrceZdZUdZdZej ed<dZej ed<dZ e e ej ed<y),FlaxBaseModelOutputWithPoolingAndNoAttentiona Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`): Last layer hidden-state after a pooling operation on the spatial dimensions. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. Nr pooler_outputr ) r rrrr rrrrr rrrrrrrBs> &*s{{)!%M3;;%26M8E#++./6rrcbeZdZUdZdZej ed<dZe e ej ed<y)(FlaxImageClassifierOutputWithNoAttentiona Base class for outputs of image classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage. Nlogitsr ) r rrrrrrrr rrrrrrrWs/ FCKK26M8E#++./6rrceZdZUdZdZej ed<dZe e e ej fed<dZ e e ej ed<dZe e ej ed<y)FlaxBaseModelOutputWithPasta Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. past_key_values (`Dict[str, jnp.ndarray]`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nr past_key_valuesr r )r rrrr rrrr!rrstrr rr rrrr r jse,&*s{{)8