gddlmZmZmZddlZddlmZddlm Z ej j Gdde Z ej j Gdde Z ej j Gd d e Zej j Gd d e Zej j Gd de Zej j Gdde Zej j Gdde Zej j Gdde Zej j Gdde Zej j Gdde Zej j Gdde ZeZej j Gdde Zej j Gdde Zej j Gdd e Zej j Gd!d"e Zej j Gd#d$e Zej j Gd%d&e Zej j Gd'd(e Zej j Gd)d*e ZdS)+)DictOptionalTupleN) ModelOutputceZdZUdZdZejed<dZe e ejed<dZ e e ejed<dS)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 ^/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/modeling_flax_outputs.pyr r si&&*s{)))26M8E#+./666/3Js{+,33333rr c\eZdZUdZdZejed<dZe e ejed<dS)"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 rrrrrrr0sM  &*s{)))26M8E#+./66666rrcteZdZUdZdZejed<dZejed<dZ e e ejed<dS),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#+./66666rrc\eZdZUdZdZejed<dZe e ejed<dS)(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 rrrrrrrWsL  FCK26M8E#+./66666rrceZdZUdZdZejed<dZe e e ejfed<dZ e e ejed<dZe e ejed<dS)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 js,&*s{)))8