U
    Kc`                     @   s   d dl Z d dlZd dlmZ d dlZd dlmZ G dd deZe Z	dddZ
dd	 ZG d
d deZd ddZd!ddZd"ddZdd Zdd Zdd ZddddZddddZddddZdS )#    N)Optional)infc                   @   sN   e Zd ZU dZeed< dZeed< dZeed< dZ	eed< d	Z
ee ed
< d	S )__PrinterOptions   	precision  	threshold   	edgeitemsP   	linewidthNsci_mode)__name__
__module____qualname__r   int__annotations__r   floatr
   r   r   r   bool r   r   5/tmp/pip-unpacked-wheel-gikjz4vx/torch/_tensor_str.pyr   	   s
   
r   c                 C   s   |dk	rl|dkr*dt _dt _dt _dt _nB|dkrLdt _dt _dt _dt _n |d	krldt _tt _dt _dt _| dk	rz| t _|dk	r|t _|dk	r|t _|dk	r|t _|t _dS )
a  Set options for printing. Items shamelessly taken from NumPy

    Args:
        precision: Number of digits of precision for floating point output
            (default = 4).
        threshold: Total number of array elements which trigger summarization
            rather than full `repr` (default = 1000).
        edgeitems: Number of array items in summary at beginning and end of
            each dimension (default = 3).
        linewidth: The number of characters per line for the purpose of
            inserting line breaks (default = 80). Thresholded matrices will
            ignore this parameter.
        profile: Sane defaults for pretty printing. Can override with any of
            the above options. (any one of `default`, `short`, `full`)
        sci_mode: Enable (True) or disable (False) scientific notation. If
            None (default) is specified, the value is defined by
            `torch._tensor_str._Formatter`. This value is automatically chosen
            by the framework.

    Example::

        >>> # Limit the precision of elements
        >>> torch.set_printoptions(precision=2)
        >>> torch.tensor([1.12345])
        tensor([1.12])
        >>> # Limit the number of elements shown
        >>> torch.set_printoptions(threshold=5)
        >>> torch.arange(10)
        tensor([0, 1, 2, ..., 7, 8, 9])
        >>> # Restore defaults
        >>> torch.set_printoptions(profile='default')
        >>> torch.tensor([1.12345])
        tensor([1.1235])
        >>> torch.arange(10)
        tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

    Ndefaultr   r   r	   r   short   full)
PRINT_OPTSr   r   r
   r   r   r   )r   r   r
   r   Zprofiler   r   r   r   set_printoptions   s2    -r   c                 C   s   | j rtjntj}| j|dS )N)dtype)Zis_mpstorchr   doubleto)tr   r   r   r   tensor_totype^   s    r"   c                   @   s$   e Zd Zdd Zdd Zdd ZdS )
_Formatterc           	   	   C   s  |j j| _d| _d| _d| _t  |d}W 5 Q R X | jsj|D ] }d	|}t
| jt|| _qDnt|t||d@ }| dkrd S t| }t| }t|
 }|D ]}|t|krd| _ qq| jr`|| dks|dkr2d| _|D ]*}d		tj	|}t
| jt|| _qn,|D ]&}d
	|}t
| jt|d | _q6n|| dks|dks|dk rd| _|D ]*}d		tj	|}t
| jt|| _qn0|D ]*}d	tj	|}t
| jt|| _qtjd k	rtj| _d S )NTF   {}r   g     @@g    חAz	{{:.{}e}}{:.0f}g-C6?	{{:.{}f}})r   Zis_floating_pointfloating_dtypeint_moder   	max_widthr   no_gradZreshapeformatmaxlenZmasked_selectisfinitenenumelr"   absminceilr   r   )	selftensorZtensor_viewvalueZ	value_strZnonzero_finite_valsZnonzero_finite_absZnonzero_finite_minZnonzero_finite_maxr   r   r   __init__d   sj    


 


z_Formatter.__init__c                 C   s   | j S )N)r+   r6   r   r   r   width   s    z_Formatter.widthc                 C   s   | j rf| jr$d| jtj|}qp| jrRd|}t|sdt	|sd|d7 }qpdtj|}n
d|}| jt
| d | S )Nz{{:{}.{}e}}r'   .r(   r&    )r)   r   r-   r+   r   r   r*   mathisinfisnanr/   )r6   r8   retr   r   r   r-      s      


z_Formatter.formatN)r   r   r   r9   r;   r-   r   r   r   r   r#   c   s   Ir#   c                 C   sh   |d k	rVt | j|}t | j|d  }|d dks@|d dkrH|| S |d | S n||  S d S Njr   +-)_scalar_strrealimaglstripr-   item)r6   
formatter1
formatter2real_strimag_strr   r   r   rF      s    rF   c                    s  |  d }|d k	r$||  d 7 }tdtttj| | ||fdd |r| ddtj kr fdd| d tj 	 D dg  fd	d| tj d  	 D  n fd
d| 	 D fddt
dtD }dd |D }ddd|d   | d S )Nr   r$   c                 S   sd   |d k	rV| | j}| | jd  }|d dks@|d dkrH|| S |d | S n
| | S d S rB   )r-   rG   rH   rI   )valrK   rL   rM   rN   r   r   r   _val_formatter   s    z#_vector_str.<locals>._val_formatterr   c                    s   g | ]} |qS r   r   .0rO   rP   r   r   
<listcomp>   s     z_vector_str.<locals>.<listcomp>z ...c                    s   g | ]} |qS r   r   rQ   rS   r   r   rT      s     c                    s   g | ]} |qS r   r   rQ   rS   r   r   rT      s     c                    s   g | ]} ||  qS r   r   rR   i)dataelements_per_liner   r   rT      s    c                 S   s   g | ]}d  |qS ), )joinrR   liner   r   r   rT      s     [,
r=   ])r;   r.   r   r>   floorr   r   sizer
   tolistranger/   rZ   )r6   indent	summarizerK   rL   Zelement_lengthZ
data_lineslinesr   )rP   rW   rX   r   _vector_str   s*      rg   c                    s     }|dkrt S |dkr4t S rddtj kr fddtdtjD dg  fddtttj tD  }n& fddtddD }d	d
|d   dd   |}d| d S )Nr   r$   r   c                    s$   g | ]}t | d   qS r$   _tensor_str_with_formatterrU   rK   rL   rd   r6   re   r   r   rT     s       z._tensor_str_with_formatter.<locals>.<listcomp>...c                    s$   g | ]}t | d   qS rh   ri   rU   rk   r   r   rT     s       c                    s$   g | ]}t | d   qS rh   ri   rU   rk   r   r   rT     s       ,
r=   r]   r_   )	dimrF   rg   ra   r   r
   rc   r/   rZ   )r6   rd   re   rK   rL   ro   Zslices
tensor_strr   rk   r   rj      s*    
"rj   c                 C   s   |   dkrdS |  r"| d } |   tjk}|  r@|  } |  rP|  } | j	t
jksh| j	t
jkrp|  } | j	t
jkr|  } | j	jr|  } t|rt| jn| j}t|rt| jn| j}t| ||||S t|rt| n| }t| |||S d S )Nr   [])r2   	has_namesrenamer   r   Z_is_zerotensorcloneZis_negZresolve_negr   r   Zfloat16Zbfloat16r   Z	complex32cfloatZ
is_complexZresolve_conjr#   get_summarized_datarG   rH   rj   )r6   rd   re   Zreal_formatterZimag_formatter	formatterr   r   r   _tensor_str   s<    
    rx   c                 C   s   | g}t | | d d }|D ]`}t |}|sB|| d tjkrf|dd|  |  || }d}q |d|  ||d 7 }q |d d	|S )
Nrn   r$   r   r^   r=   FrY   ) )r/   rfindr   r   appendrZ   )rp   suffixesrd   force_newlineZtensor_strsZlast_line_lensuffixZ
suffix_lenr   r   r   _add_suffixesL  s    
r   c                    s      }|dkr S |dkrX ddtj krTt d tj  tj d  fS  S  ddtj krć fddtdtjD } fddtt tj t D }tdd || D S tdd  D S d S )	Nr   r$   r   c                    s   g | ]} | qS r   r   rU   r:   r   r   rT   h  s     z'get_summarized_data.<locals>.<listcomp>c                    s   g | ]} | qS r   r   rU   r:   r   r   rT   i  s     c                 S   s   g | ]}t |qS r   rv   rR   xr   r   r   rT   j  s     c                 S   s   g | ]}t |qS r   r   r   r   r   r   rT   l  s     )	ro   ra   r   r
   r   catrc   r/   stack)r6   ro   startendr   r:   r   rv   \  s    &rv   tensor_contentsc             	      s  t jj| rt| |dS t| t jkp6t| t jjk}| j	rDd}n|rNd}nt| j
 d}t| g }|d k	}|rz|}t jj| \}}|jjt j ks|jjdkrt j |jjks|jjdkr|dt|j d  |jjd	kr|d
}t  t jkrt jnt j}	|jt  |	t jt jfk}
|jrR|dtt|j   ddl!m"} |j#st$||s|dt|%   |
s|dt|j  |sd}|& ' }t(| t| }|) dkr|dtt|j  7 }d}|* ' }t(| t| }|) dkr.|dtt|j  7 }|| d d   | | d }n|j+t j,t j-t j.t j/hkr`|dtt|j   |dt|%   |
s|dt|j  |st j,t jj0t jj1ft j-t jj2t jj3ft j.t jj0t jj1ft j/t jj2t jj3fi|j+ \}}|j+t j,t j.hkr0d\}}nd\}}d|d d  d}||' }t(| t| }|) dkr|dtt|j  7 }|d d  d}||' }t(| t| }|) dkr|dtt|j  7 }d}|4 ' }t(| t| }|) dkr(|dtt|j  7 }|| d d   | | d d   | | d }n|j5r|dtt|j   |
s|dt|j  |dt|6   |6 t j7ks|6 t j8kr|dt|9   |dt|:   nr|6 t j;ks.|6 t j<ks.|6 t j=krp|dt|>   |dt|?   |dt|@   |st(|A  }nt|j	r|sdd  d!B fd"d#t jCjDjEF|dD }d$| d%}n&t G|rd&}tHt I|}n|j#rH|dtt|j   |jt  kr<|dt|j  |sd'}n|) dkr|js|J d(kr|dtt|j   |jt  kr|dt|j  |sd)}nH|
s|dt|j  |s|j+t jKkrt(|L  }n
t(| }|j+t jKkr|d*t|j+  | jMd k	rht| jMj
}|d+krV| jMN Od,d(d- }|d.P| n| jQrz|d/ |R r|d0P|jS |d k	r|d1P| tT|| | |jd2}t$|t jjr|sd3| d}|S )4Nr   znested_tensor(ztensor((cudaZmpszdevice='')ZxlaZlazyZipucpuzsize=r   )
FakeTensorznnz=zdtype=zindices=tensor(z, size=zvalues=tensor(z),
r=   ry   )rowcolumn)r   r   cr	   z_indices=tensor(zquantization_scheme=zscale=zzero_point=zaxis=c                 S   s   d dd | dD S )Nrn   c                 s   s   | ]}d | V  qdS )z  Nr   r[   r   r   r   	<genexpr>  s     z4_str_intern.<locals>.indented_str.<locals>.<genexpr>)rZ   split)srd   r   r   r   indented_str  s    z!_str_intern.<locals>.indented_strr^   c                 3   s    | ]}t | d  V  qdS )r$   N)str)rR   r!   rd   r   r   r   r     s   z_str_intern.<locals>.<genexpr>z[
z
]z_to_functional_tensor(rl   r$   rq   zlayout=ZCppFunctionz::r%   zgrad_fn=<{}>zrequires_grad=Trueznames={}z
tangent={})r~   z
Parameter()Ur   _C
_functorchZis_functorch_wrapped_tensor_functorch_wrapper_str_interntypeZTensornn	ParameterZ	is_nestedr   r/   ZautogradZ
forward_adZunpack_dualZdeviceZ_get_default_devicer   Zcurrent_deviceindexr|   r   r    Zget_default_dtyper   Zcdoubleru   r   Zint64r   Z	is_sparsetupleshapeZtorch._subclasses.fake_tensorr   is_meta
isinstanceZ_nnzZ_indicesdetachrx   r2   Z_valuesZlayoutZ
sparse_csrZ
sparse_cscZ
sparse_bsrZ
sparse_bscZcrow_indicesZcol_indicesZccol_indicesZrow_indicesvaluesZis_quantizedZqschemeZper_tensor_affineZper_tensor_symmetricZq_scaleZq_zero_pointZper_channel_affineZper_channel_symmetricZ per_channel_affine_float_qparamsZq_per_channel_scalesZq_per_channel_zero_pointsZq_per_channel_axisZ
dequantizerZ   opsZatenZunbindr   Z_is_functional_tensorreprZ_from_functional_tensorro   ZstridedZto_denseZgrad_fnnamersplitr-   Zrequires_gradrr   namesr   )inpr   Zis_plain_tensorprefixr}   Zcustom_contents_providedrp   r6   ZtangentZ_default_complex_dtypeZhas_default_dtyper   Zindices_prefixindicesZindices_strZvalues_prefixr   Z
values_strZcompressed_indices_methodZplain_indices_methodZcdimnameZpdimnameZcompressed_indices_prefixZcompressed_indicesZcompressed_indices_strZplain_indices_prefixZplain_indicesZplain_indices_strstrsr   Zstring_reprr   r   r   _str_interno  s   



	    
 
 
	





   r   c                C   s   t jj| }|dkstt jj| r2t |  t jj| }t|}t	
|d}t jj| rt jj| }|dks|td| d| d| dS t jj| rd| d| dS t jj| rd| d	| d
S tdd S )Nr%   z    zBatchedTensor(lvl=z, bdim=z	, value=
z
)zGradTrackingTensor(lvl=zFunctionalTensor(lvl=z
, value=\
ry   z8We don't know how to print this, please file us an issue)r   r   r   Zmaybe_get_levelAssertionErrorZis_functionaltensorZ_syncZget_unwrappedr   textwraprd   Zis_batchedtensorZmaybe_get_bdimZis_gradtrackingtensor
ValueError)r7   r   levelr8   Z
value_reprZindented_value_reprZbdimr   r   r   r   [  s$    
r   c             
   C   s6   t  $ t j }t| |dW  5 Q R  S Q R X d S )Nr   )r   r,   r   Z_DisableFuncTorchr   )r6   r   Zguardr   r   r   _strz  s    

r   )NNNNNN)N)N)N)r>   r   typingr   r   Z
torch._sixr   objectr   r   r   r"   r#   rF   rg   rj   rx   r   rv   r   r   r   r   r   r   r   <module>   s2         
I`

+
%, m