Ë
    µŒj³  ã                   ó‚   — d dl Z d dlmZmZmZmZmZmZ d dlm	Z	 d dl
mZmZ  e j                  e«      Z G d„ de«      Zy)é    N)ÚAnyÚCallableÚDictÚIteratorÚListÚOptional)ÚDocument)Ú	BaseModelÚmodel_validatorc                   ó¼   — e Zd ZU dZdZeed<   dZeed<   dZe	ed<   dZ
eeegef      ed	<   eed
<    ed¬«      ededefd„«       «       Zdee   fd„Zdee   fd„Zy)ÚTensorflowDatasetsav  Access to the TensorFlow Datasets.

    The Current implementation can work only with datasets that fit in a memory.

    `TensorFlow Datasets` is a collection of datasets ready to use, with TensorFlow
    or other Python ML frameworks, such as Jax. All datasets are exposed
    as `tf.data.Datasets`.
    To get started see the Guide: https://www.tensorflow.org/datasets/overview and
    the list of datasets: https://www.tensorflow.org/datasets/catalog/
                                               overview#all_datasets

    You have to provide the sample_to_document_function: a function that
       a sample from the dataset-specific format to the Document.

    Attributes:
        dataset_name: the name of the dataset to load
        split_name: the name of the split to load. Defaults to "train".
        load_max_docs: a limit to the number of loaded documents. Defaults to 100.
        sample_to_document_function: a function that converts a dataset sample
          to a Document

    Example:
        .. code-block:: python

            from langchain_community.utilities import TensorflowDatasets

            def mlqaen_example_to_document(example: dict) -> Document:
                return Document(
                    page_content=decode_to_str(example["context"]),
                    metadata={
                        "id": decode_to_str(example["id"]),
                        "title": decode_to_str(example["title"]),
                        "question": decode_to_str(example["question"]),
                        "answer": decode_to_str(example["answers"]["text"][0]),
                    },
                )

            tsds_client = TensorflowDatasets(
                    dataset_name="mlqa/en",
                    split_name="train",
                    load_max_docs=MAX_DOCS,
                    sample_to_document_function=mlqaen_example_to_document,
                )

    Ú Údataset_nameÚtrainÚ
split_nameéd   Úload_max_docsNÚsample_to_document_functionÚdatasetÚbefore)ÚmodeÚvaluesÚreturnc                 óÒ   — 	 ddl }	 ddl}|d   €t        d«      ‚|j	                  |d   |d   ¬	«      |d
<   |S # t        $ r t        d«      ‚w xY w# t        $ r t        d«      ‚w xY w)z7Validate that the python package exists in environment.r   Nz\Could not import tensorflow python package. Please install it with `pip install tensorflow`.znCould not import tensorflow_datasets python package. Please install it with `pip install tensorflow-datasets`.r   zmsample_to_document_function is None. Please provide a function that converts a dataset sample to  a Document.r   r   )Úsplitr   )Ú
tensorflowÚImportErrorÚtensorflow_datasetsÚ
ValueErrorÚload)Úclsr   r   r   s       ú{/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/utilities/tensorflow_datasets.pyÚvalidate_environmentz'TensorflowDatasets.validate_environment?   s«   € ð	Ûð	Û&ð Ð/Ñ0Ð8Üð óð ð
 0×4Ñ4Ø�>Ñ"¨&°Ñ*>ð 5ó 
ˆˆyÑð ˆøô- ò 	ÜðCóð ð	ûô ò 	ÜðLóð ð	ús   ‚9 ‡A ¹AÁA&c                 ó`   ‡ — ˆ fd„‰ j                   j                  ‰ j                  «      D «       S )zYDownload a selected dataset lazily.

        Returns: an iterator of Documents.

        c              3   óX   •K  — | ]!  }‰j                   �‰j                  |«      –— Œ# y ­w)N)r   )Ú.0ÚsÚselfs     €r"   Ú	<genexpr>z/TensorflowDatasets.lazy_load.<locals>.<genexpr>c   s0   øè ø€ ð 
á:�Ø×/Ñ/Ð;ð ×,Ñ,¨Q×/Ù:ùs   ƒ'*)r   Útaker   ©r(   s   `r"   Ú	lazy_loadzTensorflowDatasets.lazy_load]   s*   ø€ ó
à—\‘\×&Ñ& t×'9Ñ'9Ô:ó
ð 	
ó    c                 ó4   — t        | j                  «       «      S )zMDownload a selected dataset.

        Returns: a list of Documents.

        )Úlistr,   r+   s    r"   r    zTensorflowDatasets.loadi   s   € ô �D—N‘NÓ$Ó%Ð%r-   )Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   ÚstrÚ__annotations__r   r   Úintr   r   r   r   r	   r   r   Úclassmethodr#   r   r,   r   r    © r-   r"   r   r   
   s—   … ñ,ð\ €L�#ÓØ€J�ÓØ€M�3ÓØHLÐ ¨(°D°6¸8Ð3CÑ*DÑ!EÓLØƒLá˜(Ô#Øð¨$ð °3ò ó ó $ðð8

˜8 HÑ-ó 

ð&�d˜8‘nô &r-   r   )ÚloggingÚtypingr   r   r   r   r   r   Úlangchain_core.documentsr	   Úpydanticr
   r   Ú	getLoggerr0   Úloggerr   r8   r-   r"   Ú<module>r?      s4   ðÛ ß @× @å -ß /à	ˆ×	Ñ	˜8Ó	$€ôe&˜õ e&r-   