U ,-eʴ@sdZddlZddlZddlZddlmZddlmZmZm Z ddl Z ddl Z ddl m Z ddl mZmZmZddlmZdd lmZmZmZmZdd lmZdd lmZmZmZmZdd lm Z m!Z!m"Z"m#Z#m$Z$d dl%m&Z&e#'e(Z)ese)*de j+ddZ,dZ-ddddddddddddddd d!d"d#d$d%d&d'd(d)d*gZ.d+Z/d,Z0eGd-d.d.e Z1d/d0Z2Gd1d2d2e j3Z4Gd3d4d4e j3Z5Gd5d6d6e j3Z6Gd7d8d8e j3Z7Gd9d:d:e j3Z8Gd;d<dd>e j3Z:Gd?d@d@e j3Z;GdAdBdBe j3ZGdGdHdHe j3Z?GdIdJdJeZ@dKZAdLZBe!dMeAGdNdOdOe@ZCe!dPeAGdQdRdRe@ZDe!dSeAGdTdUdUe@ZEe!dVeAGdWdXdXe@ZFGdYdZdZeGejHZIGd[d\d\eJZKGd]d^d^eKZLdd`daZMddcddZNddfdeZOdgdhZPddjdkZQddmdnZRddpdqZSddsdtZTdudvZUdwdxZVdydzZWd{d|ZXd}d~ZYddZZddZ[ddZ\de]dddZ^ddZ_dS)zPyTorch TAPAS model.N) dataclass)OptionalTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BaseModelOutputBaseModelOutputWithPoolingMaskedLMOutputSequenceClassifierOutput)PreTrainedModel)apply_chunking_to_forward find_pruneable_heads_and_indices#is_torch_greater_or_equal_than_1_12prune_linear_layer) ModelOutputadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) TapasConfigzYou are using torch==zH, but torch>=1.12.0 is required to use TapasModel. Please upgrade torch.rzgoogle/tapas-basezgoogle/tapas-largez google/tapas-large-finetuned-sqaz google/tapas-large-finetuned-wtqz/google/tapas-large-finetuned-wikisql-supervisedz$google/tapas-large-finetuned-tabfactzgoogle/tapas-base-finetuned-sqazgoogle/tapas-base-finetuned-wtqz.google/tapas-base-finetuned-wikisql-supervisedz#google/tapas-base-finetuned-tabfactzgoogle/tapas-smallz google/tapas-small-finetuned-sqaz google/tapas-small-finetuned-wtqz/google/tapas-small-finetuned-wikisql-supervisedz$google/tapas-small-finetuned-tabfactzgoogle/tapas-minizgoogle/tapas-mini-finetuned-sqazgoogle/tapas-mini-finetuned-wtqz.google/tapas-mini-finetuned-wikisql-supervisedz#google/tapas-mini-finetuned-tabfactzgoogle/tapas-tinyzgoogle/tapas-tiny-finetuned-sqazgoogle/tapas-tiny-finetuned-wtqz.google/tapas-tiny-finetuned-wikisql-supervisedz#google/tapas-tiny-finetuned-tabfactg|=gc@sleZdZUdZdZeejed<dZ ejed<dZ ejed<dZ ee ejed<dZ ee ejed<dS)TableQuestionAnsweringOutputa Output type of [`TapasForQuestionAnswering`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` (and possibly `answer`, `aggregation_labels`, `numeric_values` and `numeric_values_scale` are provided)): Total loss as the sum of the hierarchical cell selection log-likelihood loss and (optionally) the semi-supervised regression loss and (optionally) supervised loss for aggregations. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Prediction scores of the cell selection head, for every token. logits_aggregation (`torch.FloatTensor`, *optional*, of shape `(batch_size, num_aggregation_labels)`): Prediction scores of the aggregation head, for every aggregation operator. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (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(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (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. Nlosslogitslogits_aggregation hidden_states attentions)__name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rrr rr!r)r)i/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.pyras rc Cszddl}ddl}ddl}Wn tk r<tdYnXtj|}t d||j |}g}g} |D]@\} } t d| d| |j || } | | | | qrt|| D]\} } | d} tdd | Drt d d| qt|tr4td d | Dr4t d d| qt|trltd d | Drlt d d| qt|trtd d | Drt d d| q| ddkrd| d<|} | D]}|d|r|d|}n|g}|ddks|ddkrt| d} nv|ddkr0t| d} nZ|ddkrdt|tsVt| d} n t| d} n&|ddkrt| d} n |ddkrt| d} n|ddkrt| d} n|ddkrt| d} t| d} n|ddkrt| d} t| d} n|ddkr t| d} t| d} nj|dd krDt| d} t| d} nFzt| |d} Wn2tk rt d d| YqYnXt|d!krt|d"}| |} q|d#dd$krt| d} n@|d%dd&d'td(Dkrt| d} n|dkr|| } z,| j| jkr8td)| jd*| jd+Wn<tk rv}z|j | j| jf7_ W5d}~XYnXt d,| |!| r|"| } t#$| | _%q|S)-z Load tf checkpoints in a PyTorch model. 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