
    sg"+                     f    d Z ddlZddlmZ ddlZddlmZ ddlm	Z	 ddl
mZmZmZ  G d d	e      Zy)
z(
Image/Text processor class for OWL-ViT
    N)List   )ProcessorMixin)BatchEncoding)is_flax_availableis_tf_availableis_torch_availablec                   z     e Zd ZdZddgZdZdZd fd	ZddZd Z	d	 Z
d
 Zd Zd Zed        Zed        Z xZS )OwlViTProcessora3  
    Constructs an OWL-ViT processor which wraps [`OwlViTImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`]
    into a single processor that interits both the image processor and tokenizer functionalities. See the
    [`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.

    Args:
        image_processor ([`OwlViTImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`], *optional*):
            The tokenizer is a required input.
    image_processor	tokenizerOwlViTImageProcessor)CLIPTokenizerCLIPTokenizerFastc                     d }d|v r+t        j                  dt               |j                  d      }||n|}|t	        d      |t	        d      t
        |   ||       y )Nfeature_extractorzhThe `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor` instead.z)You need to specify an `image_processor`.z"You need to specify a `tokenizer`.)warningswarnFutureWarningpop
ValueErrorsuper__init__)selfr   r   kwargsr   	__class__s        _/var/www/html/venv/lib/python3.12/site-packages/transformers/models/owlvit/processing_owlvit.pyr   zOwlViTProcessor.__init__.   sw     &(MM
 !'

+> ?-<-H/N_"HIIABB)4    c                    |||t        d      |{t        |t              s#t        |t              r+t        |d   t              s | j                  |f||d|g}nt        |t              rt        |d   t              rvg }t        |D cg c]  }t        |       c}      }	|D ]L  }t        |      |	k7  r|dg|	t        |      z
  z  z   } | j                  |f||d|}
|j                  |
       N nt        d      |dk(  rRt        j                  |D 
cg c]  }
|
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d	      }n!|dk(  rYt               rOddlm} |j                  |D 
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   	 c}
d	      }n|dk(  rWt               rMddl}|j!                  |D 
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d      }ng|dk(  rWt#               rMddl}|j'                  |D 
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<   |.t)               }
 | j*                  |fd|i|j,                  }||
d<   | | j*                  |fd|i|}||j,                  
d<   |
S ||j,                  
d<   |
S ||
S t)        t/        di |      S c c}w c c}
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  
        Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and
        `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode:
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
        of the above two methods for more information.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`,
            `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            query_images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The query image to be prepared, one query image is expected per target image to be queried. Each image
                can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image
                should be of shape (C, H, W), where C is a number of channels, H and W are image height and width.
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:
                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.
        Returns:
            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
        NzXYou have to specify at least one text or query image or image. All three cannot be none.r   )paddingreturn_tensors zLInput text should be a string, a list of strings or a nested list of stringsnp	input_ids)axisattention_maskjaxpt)dimtfz/Target return tensor type could not be returnedr!   query_pixel_valuespixel_values)datatensor_type )r   
isinstancestrr   r   maxlenappend	TypeErrorr#   concatenater   	jax.numpynumpyr	   torchcatr   
tensorflowstackr   r   r,   dict)r   textimagesquery_imagesr    r!   r   	encodingstmax_num_queriesencodingr$   r&   jnpr9   r*   r+   image_featuress                     r   __call__zOwlViTProcessor.__call__@   s   H <L0V^j  $$D$)?
SWXYSZ\`Ha+T^^Dk'R`kdjkl	D$'JtAw,E	 #&t&<!s1v&<"=  /A1v03q6)A BB-t~~ajQ_jcijH$$X./   noo%NNR[+\hH[,A+\cde	!#\e0fPX:J1K0fmn!o5(->-@'OOS\,]xXk-B,]deOf	!$]f1gQY(;K2L1gno!p4',>,@!IIY&Wx'<&W]^I_	!&W`+a8H5E,F+agh!i4'O,='HHI%Vh{&;%V]^H_	!#V_*`(84D+E*`gh!i !!RSS$H$-H[!)7H%&#$H!5!5!5"-;"?E"l  .@H)*1T11&bb[abN 2'5'B'BH^$O%&*<'5'B'BH^$O!9O d&<^&<.YYy '= ,]0f
 -^1g
 'X+a
 &W*`s6   LL"L'?L,#L1L6?L;7M Mc                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to [`OwlViTImageProcessor.post_process`]. Please refer to the docstring
        of this method for more information.
        )r   post_processr   argsr   s      r   rI   zOwlViTProcessor.post_process   s"    
 1t##00$A&AAr   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to [`OwlViTImageProcessor.post_process_object_detection`]. Please refer
        to the docstring of this method for more information.
        )r   post_process_object_detectionrJ   s      r   rM   z-OwlViTProcessor.post_process_object_detection   s#    
 Bt##AA4R6RRr   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to [`OwlViTImageProcessor.post_process_one_shot_object_detection`].
        Please refer to the docstring of this method for more information.
        )r   #post_process_image_guided_detectionrJ   s      r   rO   z3OwlViTProcessor.post_process_image_guided_detection   s$    
 Ht##GGXQWXXr   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        )r   batch_decoderJ   s      r   rQ   zOwlViTProcessor.batch_decode   s     
 +t~~**D;F;;r   c                 :     | j                   j                  |i |S )z
        This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        )r   decoderJ   s      r   rS   zOwlViTProcessor.decode   s     
 %t~~$$d5f55r   c                 N    t        j                  dt               | j                  S )Nzg`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.)r   r   r   image_processor_classr   s    r   feature_extractor_classz'OwlViTProcessor.feature_extractor_class   s"    u	
 )))r   c                 N    t        j                  dt               | j                  S )Nz[`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.)r   r   r   r   rV   s    r   r   z!OwlViTProcessor.feature_extractor   s"    i	
 ###r   )NN)NNN
max_lengthr#   )__name__
__module____qualname____doc__
attributesrU   tokenizer_classr   rG   rI   rM   rO   rQ   rS   propertyrW   r   __classcell__)r   s   @r   r   r      sp    
 $[1J2<O5$mZ^BSY<6 * * $ $r   r   )r]   r   typingr   r8   r#   processing_utilsr   tokenization_utils_baser   utilsr   r   r	   r   r/   r   r   <module>rf      s/       . 4 K KC$n C$r   