§
    ‚Štj2  ã                   óø   — d dl Z d dlZd dlZd dlmZ ddlmZmZ  ej        e¦  «        Z	e G d„ d¦  «        ¦   «         Z
 ed¬¦  «         G d	„ d
¦  «        ¦   «         Z G d„ d¦  «        Z G d„ de¦  «        ZdS )é    N)Ú	dataclassé   )Úis_torch_availableÚloggingc                   óV   — e Zd ZU dZeed<   eed<   dZedz  ed<   dZedz  ed<   d„ ZdS )ÚInputExamplea5  
    A single training/test example for simple sequence classification.

    Args:
        guid: Unique id for the example.
        text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
        text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
        label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
    ÚguidÚtext_aNÚtext_bÚlabelc                 óX   — t          j        t          j        | ¦  «        d¬¦  «        dz   S )ú*Serializes this instance to a JSON string.é   )Úindentú
©ÚjsonÚdumpsÚdataclassesÚasdict©Úselfs    ú`/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/data/processors/utils.pyÚto_json_stringzInputExample.to_json_string/   s'   € åŒz�+Ô,¨TÑ2Ô2¸1Ð=Ñ=Ô=ÀÑDÐDó    )	Ú__name__Ú
__module__Ú__qualname__Ú__doc__ÚstrÚ__annotations__r   r   r   © r   r   r   r      sp   € € € € € € ðð ð €I€I�IØ€K€K�KØ€FˆC�$‰JÐÐÑØ€Eˆ3�‰:ÐÐÑðEð Eð Eð Eð Er   r   T)Úfrozenc                   óŠ   — e Zd ZU dZee         ed<   dZee         dz  ed<   dZee         dz  ed<   dZ	ee
z  dz  ed<   d„ ZdS )ÚInputFeaturesa¿  
    A single set of features of data. Property names are the same names as the corresponding inputs to a model.

    Args:
        input_ids: Indices of input sequence tokens in the vocabulary.
        attention_mask: Mask to avoid performing attention on padding token indices.
            Mask values selected in `[0, 1]`: Usually `1` for tokens that are NOT MASKED, `0` for MASKED (padded)
            tokens.
        token_type_ids: (Optional) Segment token indices to indicate first and second
            portions of the inputs. Only some models use them.
        label: (Optional) Label corresponding to the input. Int for classification problems,
            float for regression problems.
    Ú	input_idsNÚattention_maskÚtoken_type_idsr   c                 óT   — t          j        t          j        | ¦  «        ¦  «        dz   S )r   r   r   r   s    r   r   zInputFeatures.to_json_stringI   s"   € åŒz�+Ô,¨TÑ2Ô2Ñ3Ô3°dÑ:Ð:r   )r   r   r   r   ÚlistÚintr!   r'   r(   r   Úfloatr   r"   r   r   r%   r%   4   sŒ   € € € € € € ðð ð �CŒyÐÐÑØ'+€N�D˜”I Ñ$Ð+Ð+Ñ+Ø'+€N�D˜”I Ñ$Ð+Ð+Ñ+Ø $€Eˆ3�‰;˜ÑÐ$Ð$Ñ$ð;ð ;ð ;ð ;ð ;r   r%   c                   óN   — e Zd ZdZd„ Zd„ Zd„ Zd„ Zd„ Zd„ Z	e
d
d	„¦   «         ZdS )ÚDataProcessorzEBase class for data converters for sequence classification data sets.c                 ó   — t          ¦   «         ‚)z·
        Gets an example from a dict.

        Args:
            tensor_dict: Keys and values should match the corresponding Glue
                tensorflow_dataset examples.
        ©ÚNotImplementedError)r   Útensor_dicts     r   Úget_example_from_tensor_dictz*DataProcessor.get_example_from_tensor_dictQ   s   € õ "Ñ#Ô#Ð#r   c                 ó   — t          ¦   «         ‚)z8Gets a collection of [`InputExample`] for the train set.r0   ©r   Údata_dirs     r   Úget_train_examplesz DataProcessor.get_train_examples[   ó   € å!Ñ#Ô#Ð#r   c                 ó   — t          ¦   «         ‚)z6Gets a collection of [`InputExample`] for the dev set.r0   r5   s     r   Úget_dev_exampleszDataProcessor.get_dev_examples_   r8   r   c                 ó   — t          ¦   «         ‚)z7Gets a collection of [`InputExample`] for the test set.r0   r5   s     r   Úget_test_exampleszDataProcessor.get_test_examplesc   r8   r   c                 ó   — t          ¦   «         ‚)z*Gets the list of labels for this data set.r0   r   s    r   Ú
get_labelszDataProcessor.get_labelsg   r8   r   c                 ó²   — t          |                      ¦   «         ¦  «        dk    r1|                      ¦   «         t          |j        ¦  «                 |_        |S )z¦
        Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
        examples to the correct format.
        é   )Úlenr>   r+   r   )r   Úexamples     r   Útfds_mapzDataProcessor.tfds_mapk   sF   € õ
 ˆt�ŠÑ Ô Ñ!Ô! AÒ%Ð%Ø ŸOšOÑ-Ô-­c°'´-Ñ.@Ô.@ÔAˆGŒMØˆr   Nc                 ó¢   — t          |dd¬¦  «        5 }t          t          j        |d|¬¦  «        ¦  «        cddd¦  «         S # 1 swxY w Y   dS )z!Reads a tab separated value file.Úrz	utf-8-sig)Úencodingú	)Ú	delimiterÚ	quotecharN)Úopenr*   ÚcsvÚreader)ÚclsÚ
input_filerI   Úfs       r   Ú	_read_tsvzDataProcessor._read_tsvt   s©   € õ �*˜c¨KÐ8Ñ8Ô8ð 	L¸AÝ�œ
 1°À	ÐJÑJÔJÑKÔKð	Lð 	Lð 	Lð 	Lñ 	Lô 	Lð 	Lð 	Lð 	Lð 	Lð 	Lð 	Løøøð 	Lð 	Lð 	Lð 	Lð 	Lð 	Ls   “$AÁAÁA©N)r   r   r   r   r3   r7   r:   r<   r>   rC   ÚclassmethodrP   r"   r   r   r.   r.   N   sš   € € € € € ØOÐOð$ð $ð $ð$ð $ð $ð$ð $ð $ð$ð $ð $ð$ð $ð $ðð ð ð ðLð Lð Lñ „[ðLð Lð Lr   r.   c                   óŠ   — e Zd ZdZdd„Zd„ Zd„ Ze	 dd„¦   «         Zedd„¦   «         Z		 	 	 	 	 	 	 dd„Z
	 dd„Z	 	 	 	 	 dd„ZdS )Ú%SingleSentenceClassificationProcessorz@Generic processor for a single sentence classification data set.NÚclassificationFc                 óN   — |€g n|| _         |€g n|| _        || _        || _        d S rQ   )ÚlabelsÚexamplesÚmodeÚverbose)r   rW   rX   rY   rZ   s        r   Ú__init__z.SingleSentenceClassificationProcessor.__init__~   s3   € Ø"˜N�b�b°ˆŒØ&Ð.˜˜°HˆŒØˆŒ	ØˆŒˆˆr   c                 ó*   — t          | j        ¦  «        S rQ   )rA   rX   r   s    r   Ú__len__z-SingleSentenceClassificationProcessor.__len__„   s   € Ý�4”=Ñ!Ô!Ð!r   c                 óˆ   — t          |t          ¦  «        r!t          | j        | j        |         ¬¦  «        S | j        |         S )N)rW   rX   )Ú
isinstanceÚslicerT   rW   rX   )r   Úidxs     r   Ú__getitem__z1SingleSentenceClassificationProcessor.__getitem__‡   sA   € Ý�c�5Ñ!Ô!ð 	jÝ8ÀÄÐVZÔVcÐdgÔVhÐiÑiÔiÐiØŒ}˜SÔ!Ð!r   Ú r   r@   c           
      óP   —  | di |¤Ž}|                      ||||||dd¬¦  «         |S )NT)Ú
split_nameÚcolumn_labelÚcolumn_textÚ	column_idÚskip_first_rowÚoverwrite_labelsÚoverwrite_examplesr"   )Úadd_examples_from_csv)	rM   Ú	file_namere   rf   rg   rh   ri   ÚkwargsÚ	processors	            r   Úcreate_from_csvz5SingleSentenceClassificationProcessor.create_from_csvŒ   sR   € ð �C�M�M˜&�M�Mˆ	Ø×'Ò'ØØ!Ø%Ø#ØØ)Ø!Ø#ð 	(ñ 		
ô 		
ð 		
ð Ðr   c                 óD   —  | di |¤Ž}|                      ||¬¦  «         |S )N)rW   r"   )Úadd_examples)rM   Útexts_or_text_and_labelsrW   rn   ro   s        r   Úcreate_from_examplesz:SingleSentenceClassificationProcessor.create_from_examples�   s3   € à�C�M�M˜&�M�Mˆ	Ø×ÒÐ7ÀÐÑGÔGÐGØÐr   c	                 ó°  — |                       |¦  «        }	|r
|	dd …         }	g }
g }g }t          |	¦  «        D ]†\  }}|
                     ||         ¦  «         |                     ||         ¦  «         |�|                     ||         ¦  «         ŒY|r|› d|› �nt          |¦  «        }|                     |¦  «         Œ‡|                      |
||||¬¦  «        S )Nr@   ú-)rj   rk   )rP   Ú	enumerateÚappendr    rr   )r   rm   re   rf   rg   rh   ri   rj   rk   ÚlinesÚtextsrW   ÚidsÚiÚliner	   s                   r   rl   z;SingleSentenceClassificationProcessor.add_examples_from_csv£   s  € ð —’˜yÑ)Ô)ˆØð 	Ø˜!˜"˜"”IˆEØˆØˆØˆÝ  Ñ'Ô'ð 	!ð 	!‰GˆAˆtØ�LŠL˜˜kÔ*Ñ+Ô+Ð+Ø�MŠM˜$˜|Ô,Ñ-Ô-Ð-ØÐ$Ø—
’
˜4 	œ?Ñ+Ô+Ð+Ð+à.8ÐD˜*Ð*Ð* qÐ*Ð*Ð*½cÀ!¹f¼f�Ø—
’
˜4Ñ Ô Ð Ð à× Ò Ø�6˜3Ð1AÐVhð !ñ 
ô 
ð 	
r   c           	      ó¢  — |�Ot          |¦  «        t          |¦  «        k    r/t          dt          |¦  «        › dt          |¦  «        › �¦  «        ‚|�Ot          |¦  «        t          |¦  «        k    r/t          dt          |¦  «        › dt          |¦  «        › �¦  «        ‚|€d gt          |¦  «        z  }|€d gt          |¦  «        z  }g }t          ¦   «         }t          |||¦  «        D ]g\  }}	}
t	          |t
          t          f¦  «        r|	€|\  }}	n|}|                     |	¦  «         |                     t          |
|d |	¬¦  «        ¦  «         Œh|r|| _
        n| j
                             |¦  «         |rt          |¦  «        | _        n9t          t          | j        ¦  «                             |¦  «        ¦  «        | _        | j
        S )Nz(Text and labels have mismatched lengths z and z%Text and ids have mismatched lengths )r	   r
   r   r   )rA   Ú
ValueErrorÚsetÚzipr_   Útupler*   Úaddrx   r   rX   ÚextendrW   Úunion)r   rs   rW   r{   rj   rk   rX   Úadded_labelsÚtext_or_text_and_labelr   r	   Útexts               r   rr   z2SingleSentenceClassificationProcessor.add_examplesÁ   sñ  € ð Ð¥#Ð&>Ñ"?Ô"?Å3ÀvÁ;Ä;Ò"NÐ"NÝØl½3Ð?WÑ;XÔ;XÐlÐlÕ_bÐciÑ_jÔ_jÐlÐlñô ð ð ˆ?�sÐ#;Ñ<Ô<ÅÀCÁÄÒHÐHÝÐsÅSÐIaÑEbÔEbÐsÐsÕilÐmpÑiqÔiqÐsÐsÑtÔtÐtØˆ;Ø�&�3Ð7Ñ8Ô8Ñ8ˆCØˆ>Ø�V�cÐ":Ñ;Ô;Ñ;ˆFØˆÝ‘u”uˆÝ36Ð7OÐQWÐY\Ñ3]Ô3]ð 	\ð 	\Ñ/Ð" E¨4ÝÐ0µ5½$°-Ñ@Ô@ð .ÀUÀ]Ø4‘��e�eà-�Ø×Ò˜UÑ#Ô#Ð#Ø�OŠO�L¨d¸4ÈÐTYÐZÑZÔZÑ[Ô[Ð[Ð[ð ð 	+Ø$ˆDŒMˆMàŒM× Ò  Ñ*Ô*Ð*ð ð 	EÝ˜|Ñ,Ô,ˆDŒKˆKå�s 4¤;Ñ/Ô/×5Ò5°lÑCÔCÑDÔDˆDŒKàŒ}Ðr   Tc           	      óŽ  — |€|j         }d„ t          | j        ¦  «        D ¦   «         }g }t          | j        ¦  «        D ]p\  }	}
|	dz  dk    rt                               d|	› �¦  «         |                     |
j        dt          ||j         ¦  «        ¬¦  «        }| 	                    |¦  «         Œqt          d„ |D ¦   «         ¦  «        }g }t          t          || j        ¦  «        ¦  «        D �][\  }	\  }}
|	dz  dk    r2t                               d	|	› d
t          | j        ¦  «        › �¦  «         |rdndgt          |¦  «        z  }|t          |¦  «        z
  }|r|g|z  |z   }|rdndg|z  |z   }n||g|z  z   }||rdndg|z  z   }t          |¦  «        |k    r"t          dt          |¦  «        › d|› �¦  «        ‚t          |¦  «        |k    r"t          dt          |¦  «        › d|› �¦  «        ‚| j        dk    r||
j                 }n4| j        dk    rt!          |
j        ¦  «        }nt          | j        ¦  «        ‚|	dk     rÝ| j        rÖt                               d¦  «         t                               d|
j        › �¦  «         t                               dd                     d„ |D ¦   «         ¦  «        › �¦  «         t                               dd                     d„ |D ¦   «         ¦  «        › �¦  «         t                               d|
j        › d|› d�¦  «         | 	                    t)          |||¬¦  «        ¦  «         �Œ]|€|S |dk    råt+          ¦   «         st-          d¦  «        ‚ddl}ddlm} |                     d„ |D ¦   «         |j        ¬ ¦  «        }|                     d!„ |D ¦   «         |j        ¬ ¦  «        }| j        dk    r'|                     d"„ |D ¦   «         |j        ¬ ¦  «        }n1| j        dk    r&|                     d#„ |D ¦   «         |j        ¬ ¦  «        } ||||¦  «        }|S t          d$¦  «        ‚)%aÕ  
        Convert examples in a list of `InputFeatures`

        Args:
            tokenizer: Instance of a tokenizer that will tokenize the examples
            max_length: Maximum example length
            pad_on_left: If set to `True`, the examples will be padded on the left rather than on the right (default)
            pad_token: Padding token
            mask_padding_with_zero: If set to `True`, the attention mask will be filled by `1` for actual values
                and by `0` for padded values. If set to `False`, inverts it (`1` for padded values, `0` for actual
                values)

        Returns:
            Will return a list of task-specific `InputFeatures` which can be fed to the model.

        Nc                 ó   — i | ]\  }}||“Œ	S r"   r"   )Ú.0r|   r   s      r   ú
<dictcomp>zFSingleSentenceClassificationProcessor.get_features.<locals>.<dictcomp>  s   € ÐEÐEÐE¡( ! U�U˜AÐEÐEÐEr   i'  r   zTokenizing example T)Úadd_special_tokensÚ
max_lengthc              3   ó4   K  — | ]}t          |¦  «        V — Œd S rQ   )rA   )r‹   r&   s     r   ú	<genexpr>zESingleSentenceClassificationProcessor.get_features.<locals>.<genexpr>  s(   è è € ÐIÐI¨i�3˜y™>œ>ÐIÐIÐIÐIÐIÐIr   zWriting example ú/r@   zError with input length z vs rU   Ú
regressioné   z*** Example ***zguid: zinput_ids: ú c                 ó,   — g | ]}t          |¦  «        ‘ŒS r"   ©r    ©r‹   Úxs     r   ú
<listcomp>zFSingleSentenceClassificationProcessor.get_features.<locals>.<listcomp>2  s   € Ð3NÐ3NÐ3N¸qµC¸±F´FÐ3NÐ3NÐ3Nr   zattention_mask: c                 ó,   — g | ]}t          |¦  «        ‘ŒS r"   r–   r—   s     r   r™   zFSingleSentenceClassificationProcessor.get_features.<locals>.<listcomp>3  s   € Ð8XÐ8XÐ8XÀA½¸Q¹¼Ð8XÐ8XÐ8Xr   zlabel: z (id = ú))r&   r'   r   Úptz8return_tensors set to 'pt' but PyTorch can't be imported)ÚTensorDatasetc                 ó   — g | ]	}|j         ‘Œ
S r"   )r&   ©r‹   rO   s     r   r™   zFSingleSentenceClassificationProcessor.get_features.<locals>.<listcomp>@  s   € Ð)HÐ)HÐ)H¸!¨!¬+Ð)HÐ)HÐ)Hr   )Údtypec                 ó   — g | ]	}|j         ‘Œ
S r"   )r'   rŸ   s     r   r™   zFSingleSentenceClassificationProcessor.get_features.<locals>.<listcomp>A  s   € Ð.RÐ.RÐ.RÀA¨qÔ/?Ð.RÐ.RÐ.Rr   c                 ó   — g | ]	}|j         ‘Œ
S r"   ©r   rŸ   s     r   r™   zFSingleSentenceClassificationProcessor.get_features.<locals>.<listcomp>C  ó   € Ð*EÐ*EÐ*E°q¨1¬7Ð*EÐ*EÐ*Er   c                 ó   — g | ]	}|j         ‘Œ
S r"   r£   rŸ   s     r   r™   zFSingleSentenceClassificationProcessor.get_features.<locals>.<listcomp>E  r¤   r   z)return_tensors should be `'pt'` or `None`)Úmax_lenrw   rW   rX   ÚloggerÚinfoÚencoder
   Úminrx   Úmaxr�   rA   r   rY   r   r,   rZ   r	   Újoinr%   r   ÚRuntimeErrorÚtorchÚtorch.utils.datar�   ÚtensorÚlong)r   Ú	tokenizerrŽ   Úpad_on_leftÚ	pad_tokenÚmask_padding_with_zeroÚreturn_tensorsÚ	label_mapÚall_input_idsÚex_indexrB   r&   Úbatch_lengthÚfeaturesr'   Úpadding_lengthr   r®   r�   Úall_attention_maskÚ
all_labelsÚdatasets                         r   Úget_featuresz2SingleSentenceClassificationProcessor.get_featuresæ   sÑ  € ð2 ÐØ"Ô*ˆJàEÐE­i¸¼Ñ.DÔ.DÐEÑEÔEˆ	àˆÝ!*¨4¬=Ñ!9Ô!9ð 		,ð 		,ÑˆH�gØ˜%Ñ 1Ò$Ð$Ý—’Ð<°(Ð<Ð<Ñ=Ô=Ð=à!×(Ò(Ø”Ø#'Ý˜z¨9Ô+<Ñ=Ô=ð )ñ ô ˆIð
 × Ò  Ñ+Ô+Ð+Ð+åÐIÐI¸=ÐIÑIÔIÑIÔIˆàˆÝ.7½¸MÈ4Ì=Ñ8YÔ8YÑ.ZÔ.Zð #	lñ #	lÑ*ˆHÑ*�y 'Ø˜%Ñ 1Ò$Ð$Ý—’ÐN¨xÐNÐN½#¸d¼mÑ:LÔ:LÐNÐNÑOÔOÐOð $:Ð@˜a˜a¸qÐAÅCÈ	ÁNÄNÑRˆNð *­C°	©N¬NÑ:ˆNØð jØ'˜[¨>Ñ9¸YÑF�	Ø(>Ð#E 1 1ÀAÐ"FÈÑ"WÐ[iÑ!i��à%¨)¨°~Ñ)EÑF�	Ø!/Ð9OÐ4V°A°AÐUVÐ3WÐZhÑ3hÑ!i�å�9‰~Œ~ Ò-Ð-Ý Ð!^½CÀ	¹N¼NÐ!^Ð!^ÐP\Ð!^Ð!^Ñ_Ô_Ð_Ý�>Ñ"Ô" lÒ2Ð2Ý Ð!c½CÀÑ<OÔ<OÐ!cÐ!cÐUaÐ!cÐ!cÑdÔdÐdàŒyÐ,Ò,Ð,Ø! '¤-Ô0��Ø”˜lÒ*Ð*Ý˜gœmÑ,Ô,��å  ¤Ñ+Ô+Ð+à˜!Š|ˆ| ¤ˆ|Ý—’Ð-Ñ.Ô.Ð.Ý—’Ð3 W¤\Ð3Ð3Ñ4Ô4Ð4Ý—’ÐQ¨#¯(ª(Ð3NÐ3NÀIÐ3NÑ3NÔ3NÑ*OÔ*OÐQÐQÑRÔRÐRÝ—’Ð[¨s¯xªxÐ8XÐ8XÈÐ8XÑ8XÔ8XÑ/YÔ/YÐ[Ð[Ñ\Ô\Ð\Ý—’ÐD g¤mÐDÐD¸EÐDÐDÐDÑEÔEÐEà�OŠO�M°IÈnÐdiÐjÑjÔjÑkÔkÐkÑkàÐ!ØˆOØ˜tÒ#Ð#Ý%Ñ'Ô'ð _Ý"Ð#]Ñ^Ô^Ð^ØˆLˆLˆLØ6Ð6Ð6Ð6Ð6Ð6à!ŸLšLÐ)HÐ)H¸xÐ)HÑ)HÔ)HÐPUÔPZ˜LÑ[Ô[ˆMØ!&§¢Ð.RÐ.RÈÐ.RÑ.RÔ.RÐZ_ÔZd Ñ!eÔ!eÐØŒyÐ,Ò,Ð,Ø"Ÿ\š\Ð*EÐ*E¸HÐ*EÑ*EÔ*EÈUÌZ˜\ÑXÔX�
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Ø”˜lÒ*Ð*Ø"Ÿ\š\Ð*EÐ*E¸HÐ*EÑ*EÔ*EÈUÌ[˜\ÑYÔY�
à#�m MÐ3EÀzÑRÔRˆGØˆNåÐHÑIÔIÐIr   )NNrU   F)rc   r   r@   NFrQ   )rc   r   r@   NFFF)NNFF)NFr   TN)r   r   r   r   r[   r]   rb   rR   rp   rt   rl   rr   rÀ   r"   r   r   rT   rT   {   s  € € € € € ØJÐJðð ð ð ð"ð "ð "ð"ð "ð "ð
 àejðð ð ñ „[ðð  ðð ð ñ „[ðð ØØØØØØ ð
ð 
ð 
ð 
ð> kpð#ð #ð #ð #ðP ØØØ#ØðdJð dJð dJð dJð dJð dJr   rT   )rK   r   r   r   Úutilsr   r   Ú
get_loggerr   r§   r   r%   r.   rT   r"   r   r   ú<module>rÃ      sZ  ðð  €
€
€
Ø Ð Ð Ð Ø €€€Ø !Ð !Ð !Ð !Ð !Ð !à 0Ð 0Ð 0Ð 0Ð 0Ð 0Ð 0Ð 0ð 
ˆÔ	˜HÑ	%Ô	%€ð ðEð Eð Eð Eð Eñ Eô Eñ „ðEð0 €�$ÐÑÔð;ð ;ð ;ð ;ð ;ñ ;ô ;ñ Ôð;ð2*Lð *Lð *Lð *Lð *Lñ *Lô *Lð *LðZOJð OJð OJð OJð OJ¨Mñ OJô OJð OJð OJð OJr   