o  h. @sRddlmZddlmZddlmZmZmZeedddd Gd d d eZd S) )Dict)add_end_docstrings) GenericTensorPipelinebuild_pipeline_init_argsTF) has_tokenizersupports_binary_outputa tokenize_kwargs (`dict`, *optional*): Additional dictionary of keyword arguments passed along to the tokenizer. return_tensors (`bool`, *optional*): If `True`, returns a tensor according to the specified framework, otherwise returns a list.csReZdZdZdddZdeeeffddZdd Z dd d Z fd dZ Z S)FeatureExtractionPipelinea Feature extraction pipeline uses no model head. This pipeline extracts the hidden states from the base transformer, which can be used as features in downstream tasks. Example: ```python >>> from transformers import pipeline >>> extractor = pipeline(model="google-bert/bert-base-uncased", task="feature-extraction") >>> result = extractor("This is a simple test.", return_tensors=True) >>> result.shape # This is a tensor of shape [1, sequence_length, hidden_dimension] representing the input string. torch.Size([1, 8, 768]) ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier: `"feature-extraction"`. All models may be used for this pipeline. See a list of all models, including community-contributed models on [huggingface.co/models](https://huggingface.co/models). NcKsN|duri}|durd|vrtd||d<|}i}|dur"||d<|i|fS)N truncationz\truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)return_tensors) ValueError)selfr tokenize_kwargsr kwargspreprocess_paramspostprocess_paramsr}/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/transformers/pipelines/feature_extraction.py_sanitize_parameters(s z.FeatureExtractionPipeline._sanitize_parametersreturncKs|j|fd|ji|}|S)Nr ) tokenizer framework)rinputsr model_inputsrrr preprocess;sz$FeatureExtractionPipeline.preprocesscCs|jdi|}|S)Nr)model)rr model_outputsrrr_forward?sz"FeatureExtractionPipeline._forwardFcCs@|r|dS|jdkr|dS|jdkr|dSdS)Nrpttf)rtolistnumpy)rrr rrr postprocessCs   z%FeatureExtractionPipeline.postprocesscstj|i|S)a Extract the features of the input(s). Args: args (`str` or `List[str]`): One or several texts (or one list of texts) to get the features of. Return: A nested list of `float`: The features computed by the model. )super__call__)rargsr __class__rrr&Ls z"FeatureExtractionPipeline.__call__)NNN)F) __name__ __module__ __qualname____doc__rrstrrrrr$r& __classcell__rrr(rr s   r N) typingrutilsrbaserrrr rrrrs