o
    0j                     @   s   d dl mZmZmZmZmZ d dlZddlm	Z	 ddl
mZmZ ddlmZ ddlmZ d	d
lmZ d	dlmZ ejG dd deZe	dG dd deZdS )    )AnyDictListOptionalUnionN   )pipeline_requires_extra   )	HPIConfigPaddlePredictorOption)
TopkResult)	benchmark   )(AutoParallelImageSimpleInferencePipeline)BasePipelinec                       s   e Zd ZdZddddddddedee dee deeeef  d	ee d
e	dee
eeef ef  ddf fddZde
eee ejeej f defddZ  ZS )_ImageClassificationPipelinezImage Classification PipelineNFdeviceengineengine_config	pp_optionuse_hpip
hpi_configconfigr   r   r   r   r   r   returnc             	      sr   t  jd||||||d| |d d }	i }
|	dd }dur&d|i}
| j|	fi |
| _|	dd| _dS )a  Initializes the image classification pipeline.

        Args:
            config (Dict): Configuration dictionary containing model and other parameters.
            device (Optional[str], optional): The device to use for prediction. Defaults to `None`.
            engine (Optional[str], optional): Inference engine. Defaults to `None`.
            engine_config (Optional[Dict[str, Any]], optional): Engine-specific config. Defaults to `None`.
            pp_option (Optional[PaddlePredictorOption], optional): Paddle predictor options.
                Defaults to `None`.
            use_hpip (bool, optional): Whether to use HPIP. Defaults to `False`.
            hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional):
                HPIP configuration. Defaults to `None`.
        r   
SubModulesImageClassificationtopkN    )super__init__getZcreate_modelimage_classification_modelr   )selfr   r   r   r   r   r   r   kwargsZ!image_classification_model_configZmodel_kwargsr   	__class__r   z/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddlex/inference/pipelines/image_classification/pipeline.pyr!      s(   
z%_ImageClassificationPipeline.__init__inputc                 k   s(    | d| j}| j||dE dH  dS )af  Predicts image classification results for the given input.

        Args:
            input (Union[str, list[str], np.ndarray, list[np.ndarray]]): The input image(s) or path(s) to the images.
            **kwargs: Additional keyword arguments that can be passed to the function.

        Returns:
            TopkResult: The predicted top k results.
        r   )r   N)popr   r#   )r$   r)   r%   r   r   r   r(   predictK   s   z$_ImageClassificationPipeline.predict)__name__
__module____qualname____doc__r   r   strr   r   boolr   r
   r!   r   npZndarrayr   r+   __classcell__r   r   r&   r(   r      s<    	,r   Zcvc                   @   s$   e Zd ZdZedd Zdd ZdS )ImageClassificationPipelineZimage_classificationc                 C   s   t S )N)r   )r$   r   r   r(   _pipeline_cls`   s   z)ImageClassificationPipeline._pipeline_clsc                 C   s   |d d  ddS )Nr   r   Z
batch_size   )r"   )r$   r   r   r   r(   _get_batch_sized   s   z+ImageClassificationPipeline._get_batch_sizeN)r,   r-   r.   entitiespropertyr5   r7   r   r   r   r(   r4   \   s
    
r4   )typingr   r   r   r   r   numpyr2   Z
utils.depsr   modelsr
   r   Z"models.image_classification.resultr   Zutils.benchmarkr   Z	_parallelr   baser   Ztime_methodsr   r4   r   r   r   r(   <module>   s   @