U
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  d dl Z d dlmZ d dlm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 eeZeG dd	 d	ZeG d
d dZG dd deZG dd deZG dd deZeeeef  e
eee f dddZdd Zeee eeeef  dddZdS )    N)defaultdict)	dataclass)AnyDictListOptionalTupleUnion)	yaml_dump   )
get_loggerc                   @   s  e Zd ZU dZeed< eed< eed< eed< eed< dZee ed< dZ	ee ed	< dZ
ee ed
< dZee ed< dZeeeef  ed< dZee ed< dZee ed< dZeeeef  ed< dZee ed< dZee ed< eedddZd edddZdS )
EvalResultu
  
    Flattened representation of individual evaluation results found in model-index of Model Cards.

    For more information on the model-index spec, see https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1.

    Args:
        task_type (`str`):
            The task identifier. Example: "image-classification".
        dataset_type (`str`):
            The dataset identifier. Example: "common_voice". Use dataset id from https://hf.co/datasets.
        dataset_name (`str`):
            A pretty name for the dataset. Example: "Common Voice (French)".
        metric_type (`str`):
            The metric identifier. Example: "wer". Use metric id from https://hf.co/metrics.
        metric_value (`Any`):
            The metric value. Example: 0.9 or "20.0 ± 1.2".
        task_name (`str`, *optional*):
            A pretty name for the task. Example: "Speech Recognition".
        dataset_config (`str`, *optional*):
            The name of the dataset configuration used in `load_dataset()`.
            Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info:
            https://hf.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
        dataset_split (`str`, *optional*):
            The split used in `load_dataset()`. Example: "test".
        dataset_revision (`str`, *optional*):
            The revision (AKA Git Sha) of the dataset used in `load_dataset()`.
            Example: 5503434ddd753f426f4b38109466949a1217c2bb
        dataset_args (`Dict[str, Any]`, *optional*):
            The arguments passed during `Metric.compute()`. Example for `bleu`: `{"max_order": 4}`
        metric_name (`str`, *optional*):
            A pretty name for the metric. Example: "Test WER".
        metric_config (`str`, *optional*):
            The name of the metric configuration used in `load_metric()`.
            Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
            See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
        metric_args (`Dict[str, Any]`, *optional*):
            The arguments passed during `Metric.compute()`. Example for `bleu`: max_order: 4
        verified (`bool`, *optional*):
            Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
        verify_token (`str`, *optional*):
            A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
    	task_typedataset_typedataset_namemetric_typemetric_valueN	task_namedataset_configdataset_splitdataset_revisiondataset_argsmetric_namemetric_configmetric_argsverifiedverify_tokenreturnc                 C   s   | j | j| j| j| jfS )z9Returns a tuple that uniquely identifies this evaluation.)r   r   r   r   r   self r!   \/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/huggingface_hub/repocard_data.pyunique_identifier|   s    zEvalResult.unique_identifier)otherr   c                 C   sD   | j  D ]4\}}|dkrq
|dkr
t| |t||kr
 dS q
dS )zx
        Return True if `self` and `other` describe exactly the same metric but with a
        different value.
        r   r   FT)__dict__itemsgetattr)r    r$   key_r!   r!   r"   is_equal_except_value   s    z EvalResult.is_equal_except_value)__name__
__module____qualname____doc__str__annotations__r   r   r   r   r   r   r   r   r   r   r   r   boolr   propertytupler#   r*   r!   r!   r!   r"   r      s&   
/
r   c                   @   s   e Zd ZdZdedddZeeef dddZ	d	d
 Z
dedddZdd ZdeeedddZd eeedddZeedddZeeddddZeedddZdS )!CardDataa  Structure containing metadata from a RepoCard.

    [`CardData`] is the parent class of [`ModelCardData`] and [`DatasetCardData`].

    Metadata can be exported as a dictionary or YAML. Export can be customized to alter the representation of the data
    (example: flatten evaluation results). `CardData` behaves as a dictionary (can get, pop, set values) but do not
    inherit from `dict` to allow this export step.
    F)ignore_metadata_errorsc                 K   s   | j | d S N)r%   update)r    r5   kwargsr!   r!   r"   __init__   s    zCardData.__init__r   c                 C   s   t | j}| | t|S )zConverts CardData to a dict.

        Returns:
            `dict`: CardData represented as a dictionary ready to be dumped to a YAML
            block for inclusion in a README.md file.
        )copydeepcopyr%   _to_dict_remove_noner    Z	data_dictr!   r!   r"   to_dict   s    
zCardData.to_dictc                 C   s   dS )zUse this method in child classes to alter the dict representation of the data. Alter the dict in-place.

        Args:
            data_dict (`dict`): The raw dict representation of the card data.
        Nr!   r>   r!   r!   r"   r<      s    zCardData._to_dictNc                 C   s   t |  d|d S )a
  Dumps CardData to a YAML block for inclusion in a README.md file.

        Args:
            line_break (str, *optional*):
                The line break to use when dumping to yaml.

        Returns:
            `str`: CardData represented as a YAML block.
        F)	sort_keys
line_break)r
   r?   strip)r    rA   r!   r!   r"   to_yaml   s    
zCardData.to_yamlc                 C   s   |   S r6   )rC   r   r!   r!   r"   __repr__   s    zCardData.__repr__)r(   defaultr   c                 C   s   | j ||S z#Get value for a given metadata key.)r%   getr    r(   rE   r!   r!   r"   rG      s    zCardData.getc                 C   s   | j ||S )z#Pop value for a given metadata key.)r%   poprH   r!   r!   r"   rI      s    zCardData.pop)r(   r   c                 C   s
   | j | S rF   r%   r    r(   r!   r!   r"   __getitem__   s    zCardData.__getitem__)r(   valuer   c                 C   s   || j |< dS )z#Set value for a given metadata key.NrJ   )r    r(   rM   r!   r!   r"   __setitem__   s    zCardData.__setitem__c                 C   s
   || j kS )z%Check if a given metadata key is set.rJ   rK   r!   r!   r"   __contains__   s    zCardData.__contains__)F)N)N)N)r+   r,   r-   r.   r1   r9   r   r/   r   r?   r<   rC   rD   rG   rI   rL   rN   rO   r!   r!   r!   r"   r4      s   	r4   c                       s   e Zd ZdZdddddddddd	eeeee f  ee ee eee  eee  eee  eee  ee e	d	 fddZ
dd Z  ZS )	ModelCardDataa  Model Card Metadata that is used by Hugging Face Hub when included at the top of your README.md

    Args:
        language (`Union[str, List[str]]`, *optional*):
            Language of model's training data or metadata. It must be an ISO 639-1, 639-2 or
            639-3 code (two/three letters), or a special value like "code", "multilingual". Defaults to `None`.
        license (`str`, *optional*):
            License of this model. Example: apache-2.0 or any license from
            https://huggingface.co/docs/hub/repositories-licenses. Defaults to None.
        library_name (`str`, *optional*):
            Name of library used by this model. Example: keras or any library from
            https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts.
            Defaults to None.
        tags (`List[str]`, *optional*):
            List of tags to add to your model that can be used when filtering on the Hugging
            Face Hub. Defaults to None.
        datasets (`List[str]`, *optional*):
            List of datasets that were used to train this model. Should be a dataset ID
            found on https://hf.co/datasets. Defaults to None.
        metrics (`List[str]`, *optional*):
            List of metrics used to evaluate this model. Should be a metric name that can be found
            at https://hf.co/metrics. Example: 'accuracy'. Defaults to None.
        eval_results (`Union[List[EvalResult], EvalResult]`, *optional*):
            List of `huggingface_hub.EvalResult` that define evaluation results of the model. If provided,
            `model_name` is used to as a name on PapersWithCode's leaderboards. Defaults to `None`.
        model_name (`str`, *optional*):
            A name for this model. It is used along with
            `eval_results` to construct the `model-index` within the card's metadata. The name
            you supply here is what will be used on PapersWithCode's leaderboards. If None is provided
            then the repo name is used as a default. Defaults to None.
        ignore_metadata_errors (`str`):
            If True, errors while parsing the metadata section will be ignored. Some information might be lost during
            the process. Use it at your own risk.
        kwargs (`dict`, *optional*):
            Additional metadata that will be added to the model card. Defaults to None.

    Example:
        ```python
        >>> from huggingface_hub import ModelCardData
        >>> card_data = ModelCardData(
        ...     language="en",
        ...     license="mit",
        ...     library_name="timm",
        ...     tags=['image-classification', 'resnet'],
        ... )
        >>> card_data.to_dict()
        {'language': 'en', 'license': 'mit', 'library_name': 'timm', 'tags': ['image-classification', 'resnet']}

        ```
    NF)	languagelicenselibrary_nametagsdatasetsmetricseval_results
model_namer5   c       	      
      s   || _ || _|| _|| _|| _|| _|| _|| _|
dd }|rzt	|\}}|| _|| _W nB t
k
r } z$|	r~td ntd| dW 5 d }~X Y nX t jf |
 | jrt| jtkr| jg| _| jd krtdd S )Nmodel-indexz<Invalid model-index. Not loading eval results into CardData.z=Invalid `model_index` in metadata cannot be parsed: KeyError z. Pass `ignore_metadata_errors=True` to ignore this error while loading a Model Card. Warning: some information will be lost. Use it at your own risk.z7Passing `eval_results` requires `model_name` to be set.)rQ   rR   rS   rT   rU   rV   rW   rX   rI   model_index_to_eval_resultsKeyErrorloggerwarning
ValueErrorsuperr9   typer   )r    rQ   rR   rS   rT   rU   rV   rW   rX   r5   r8   model_indexerror	__class__r!   r"   r9     s4    



zModelCardData.__init__c                 C   s,   | j dk	r(t| j| j |d< |d= |d= dS )z[Format the internal data dict. In this case, we convert eval results to a valid model indexNrY   rW   rX   )rW   eval_results_to_model_indexrX   r>   r!   r!   r"   r<   ?  s    
zModelCardData._to_dict)r+   r,   r-   r.   r   r	   r/   r   r   r1   r9   r<   __classcell__r!   r!   rc   r"   rP      s,   6



/rP   c                       s  e Zd ZdZdddddddddddddddeeeee f  eeeee f  eeeee f  eeeee f  eeeee f  eeeee f  eee  eeeee f  eeeee f  ee ee ee eeeee f  e	d fddZ
dd Z  ZS )	DatasetCardDataa	  Dataset Card Metadata that is used by Hugging Face Hub when included at the top of your README.md

    Args:
        language (`List[str]`, *optional*):
            Language of dataset's data or metadata. It must be an ISO 639-1, 639-2 or
            639-3 code (two/three letters), or a special value like "code", "multilingual".
        license (`Union[str, List[str]]`, *optional*):
            License(s) of this dataset. Example: apache-2.0 or any license from
            https://huggingface.co/docs/hub/repositories-licenses.
        annotations_creators (`Union[str, List[str]]`, *optional*):
            How the annotations for the dataset were created.
            Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'no-annotation', 'other'.
        language_creators (`Union[str, List[str]]`, *optional*):
            How the text-based data in the dataset was created.
            Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'other'
        multilinguality (`Union[str, List[str]]`, *optional*):
            Whether the dataset is multilingual.
            Options are: 'monolingual', 'multilingual', 'translation', 'other'.
        size_categories (`Union[str, List[str]]`, *optional*):
            The number of examples in the dataset. Options are: 'n<1K', '1K<n<10K', '10K<n<100K',
            '100K<n<1M', '1M<n<10M', '10M<n<100M', '100M<n<1B', '1B<n<10B', '10B<n<100B', '100B<n<1T', 'n>1T', and 'other'.
        source_datasets (`List[str]]`, *optional*):
            Indicates whether the dataset is an original dataset or extended from another existing dataset.
            Options are: 'original' and 'extended'.
        task_categories (`Union[str, List[str]]`, *optional*):
            What categories of task does the dataset support?
        task_ids (`Union[str, List[str]]`, *optional*):
            What specific tasks does the dataset support?
        paperswithcode_id (`str`, *optional*):
            ID of the dataset on PapersWithCode.
        pretty_name (`str`, *optional*):
            A more human-readable name for the dataset. (ex. "Cats vs. Dogs")
        train_eval_index (`Dict`, *optional*):
            A dictionary that describes the necessary spec for doing evaluation on the Hub.
            If not provided, it will be gathered from the 'train-eval-index' key of the kwargs.
        config_names (`Union[str, List[str]]`, *optional*):
            A list of the available dataset configs for the dataset.
    NF)rQ   rR   annotations_creatorslanguage_creatorsmultilingualitysize_categoriessource_datasetstask_categoriestask_idspaperswithcode_idpretty_nametrain_eval_indexconfig_namesr5   c                   sl   || _ || _|| _|| _|| _|| _|| _|| _|	| _|
| _	|| _
|| _|pV|dd | _t jf | d S )Ntrain-eval-index)rh   ri   rQ   rR   rj   rk   rl   rm   rn   ro   rp   rr   rI   rq   r_   r9   )r    rQ   rR   rh   ri   rj   rk   rl   rm   rn   ro   rp   rq   rr   r5   r8   rc   r!   r"   r9   n  s    zDatasetCardData.__init__c                 C   s   | d|d< d S )Nrq   rs   )rI   r>   r!   r!   r"   r<     s    zDatasetCardData._to_dict)r+   r,   r-   r.   r   r	   r/   r   r   r1   r9   r<   rf   r!   r!   rc   r"   rg   F  s@   *
$rg   c                       s   e Zd ZdZdddddddddddddee ee ee ee ee ee ee ee eee  eee  eee  ed fddZ	  Z
S )SpaceCardDataa	  Space Card Metadata that is used by Hugging Face Hub when included at the top of your README.md

    To get an exhaustive reference of Spaces configuration, please visit https://huggingface.co/docs/hub/spaces-config-reference#spaces-configuration-reference.

    Args:
        title (`str`, *optional*)
            Title of the Space.
        sdk (`str`, *optional*)
            SDK of the Space (one of `gradio`, `streamlit`, `docker`, or `static`).
        sdk_version (`str`, *optional*)
            Version of the used SDK (if Gradio/Streamlit sdk).
        python_version (`str`, *optional*)
            Python version used in the Space (if Gradio/Streamlit sdk).
        app_file (`str`, *optional*)
            Path to your main application file (which contains either gradio or streamlit Python code, or static html code).
            Path is relative to the root of the repository.
        app_port (`str`, *optional*)
            Port on which your application is running. Used only if sdk is `docker`.
        license (`str`, *optional*)
            License of this model. Example: apache-2.0 or any license from
            https://huggingface.co/docs/hub/repositories-licenses.
        duplicated_from (`str`, *optional*)
            ID of the original Space if this is a duplicated Space.
        models (List[`str`], *optional*)
            List of models related to this Space. Should be a dataset ID found on https://hf.co/models.
        datasets (`List[str]`, *optional*)
            List of datasets related to this Space. Should be a dataset ID found on https://hf.co/datasets.
        tags (`List[str]`, *optional*)
            List of tags to add to your Space that can be used when filtering on the Hub.
        ignore_metadata_errors (`str`):
            If True, errors while parsing the metadata section will be ignored. Some information might be lost during
            the process. Use it at your own risk.
        kwargs (`dict`, *optional*):
            Additional metadata that will be added to the space card.

    Example:
        ```python
        >>> from huggingface_hub import SpaceCardData
        >>> card_data = SpaceCardData(
        ...     title="Dreambooth Training",
        ...     license="mit",
        ...     sdk="gradio",
        ...     duplicated_from="multimodalart/dreambooth-training"
        ... )
        >>> card_data.to_dict()
        {'title': 'Dreambooth Training', 'sdk': 'gradio', 'license': 'mit', 'duplicated_from': 'multimodalart/dreambooth-training'}
        ```
    NF)titlesdksdk_versionpython_versionapp_fileapp_portrR   duplicated_frommodelsrU   rT   r5   c                   sT   || _ || _|| _|| _|| _|| _|| _|| _|	| _|
| _	|| _
t jf | d S r6   )ru   rv   rw   rx   ry   rz   rR   r{   r|   rU   rT   r_   r9   )r    ru   rv   rw   rx   ry   rz   rR   r{   r|   rU   rT   r5   r8   rc   r!   r"   r9     s    zSpaceCardData.__init__)r+   r,   r-   r.   r   r/   intr   r1   r9   rf   r!   r!   rc   r"   rt     s6   4


rt   )ra   r   c                 C   s  g }| D ]}|d }|d }|D ]}|d d }|d  d}|d d }|d d }	|d  d}
|d  d}|d  d}|d  d	}|d
 D ]v}|d }|d }| d}| d	}| d}| d}| d}t|||	||||
||||||||d}|| qq"q||fS )a  Takes in a model index and returns the model name and a list of `huggingface_hub.EvalResult` objects.

    A detailed spec of the model index can be found here:
    https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1

    Args:
        model_index (`List[Dict[str, Any]]`):
            A model index data structure, likely coming from a README.md file on the
            Hugging Face Hub.

    Returns:
        model_name (`str`):
            The name of the model as found in the model index. This is used as the
            identifier for the model on leaderboards like PapersWithCode.
        eval_results (`List[EvalResult]`):
            A list of `huggingface_hub.EvalResult` objects containing the metrics
            reported in the provided model_index.

    Example:
        ```python
        >>> from huggingface_hub.repocard_data import model_index_to_eval_results
        >>> # Define a minimal model index
        >>> model_index = [
        ...     {
        ...         "name": "my-cool-model",
        ...         "results": [
        ...             {
        ...                 "task": {
        ...                     "type": "image-classification"
        ...                 },
        ...                 "dataset": {
        ...                     "type": "beans",
        ...                     "name": "Beans"
        ...                 },
        ...                 "metrics": [
        ...                     {
        ...                         "type": "accuracy",
        ...                         "value": 0.9
        ...                     }
        ...                 ]
        ...             }
        ...         ]
        ...     }
        ... ]
        >>> model_name, eval_results = model_index_to_eval_results(model_index)
        >>> model_name
        'my-cool-model'
        >>> eval_results[0].task_type
        'image-classification'
        >>> eval_results[0].metric_type
        'accuracy'

        ```
    nameresultstaskr`   datasetconfigsplitrevisionargsrV   rM   r   verifyToken)r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   )rG   r   append)ra   rW   elemr~   r   resultr   r   r   r   r   r   r   r   Zmetricr   r   r   r   r   r   r   eval_resultr!   r!   r"   rZ     sP    8





rZ   c                 C   sR   t | tttfr&t| dd | D S t | trJt| dd |  D S | S dS )zk
    Recursively remove `None` values from a dict. Borrowed from: https://stackoverflow.com/a/20558778
    c                 s   s   | ]}|d k	rt |V  qd S r6   r=   ).0xr!   r!   r"   	<genexpr>P  s      z_remove_none.<locals>.<genexpr>c                 s   s2   | ]*\}}|d k	r|d k	rt |t |fV  qd S r6   r   )r   kvr!   r!   r"   r   R  s       N)
isinstancelistr3   setr`   dictr&   )objr!   r!   r"   r=   K  s
    
r=   )rX   rW   r   c           	   	   C   s   t t}|D ]}||j | qg }| D ]P}|d }|j|jd|j|j|j	|j
|j|jddd |D d}|| q.| |dg}t|S )a  Takes in given model name and list of `huggingface_hub.EvalResult` and returns a
    valid model-index that will be compatible with the format expected by the
    Hugging Face Hub.

    Args:
        model_name (`str`):
            Name of the model (ex. "my-cool-model"). This is used as the identifier
            for the model on leaderboards like PapersWithCode.
        eval_results (`List[EvalResult]`):
            List of `huggingface_hub.EvalResult` objects containing the metrics to be
            reported in the model-index.

    Returns:
        model_index (`List[Dict[str, Any]]`): The eval_results converted to a model-index.

    Example:
        ```python
        >>> from huggingface_hub.repocard_data import eval_results_to_model_index, EvalResult
        >>> # Define minimal eval_results
        >>> eval_results = [
        ...     EvalResult(
        ...         task_type="image-classification",  # Required
        ...         dataset_type="beans",  # Required
        ...         dataset_name="Beans",  # Required
        ...         metric_type="accuracy",  # Required
        ...         metric_value=0.9,  # Required
        ...     )
        ... ]
        >>> eval_results_to_model_index("my-cool-model", eval_results)
        [{'name': 'my-cool-model', 'results': [{'task': {'type': 'image-classification'}, 'dataset': {'name': 'Beans', 'type': 'beans'}, 'metrics': [{'type': 'accuracy', 'value': 0.9}]}]}]

        ```
    r   )r`   r~   )r~   r`   r   r   r   r   c              
   S   s.   g | ]&}|j |j|j|j|j|j|jd qS ))r`   rM   r~   r   r   r   r   )r   r   r   r   r   r   r   )r   r   r!   r!   r"   
<listcomp>  s   
z/eval_results_to_model_index.<locals>.<listcomp>)r   r   rV   )r~   r   )r   r   r#   r   valuesr   r   r   r   r   r   r   r   r=   )	rX   rW   Ztask_and_ds_types_mapr   Zmodel_index_datar   Zsample_resultdatara   r!   r!   r"   re   W  s4    %
re   )r:   collectionsr   dataclassesr   typingr   r   r   r   r   r	   Zhuggingface_hub.utilsr
   Zutils.loggingr   r+   r\   r   r4   rP   rg   rt   r/   rZ   r=   re   r!   r!   r!   r"   <module>   s"     EjPQ(d