a
    hao                     @   s  d dl Z d dlZd dlZd dlZd dlmZmZ d dlZd dlmZ ej	G dd dZ
e
 Zd$ddZeeef dd	d
Ze jdd Zdd ZG dd d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)Any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   ?/var/www/auris/lib/python3.9/site-packages/torch/_tensor_str.pyr      s
   
r   c                 C   s   |du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urz| t _|dur|t _|dur|t _|du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   )returnc                   C   s
   t tS )zyGets the current options for printing, as a dictionary that
    can be passed as ``**kwargs`` to set_printoptions().
    )dataclassesZasdictr   r   r   r   r   get_printoptionsb   s    r    c               
   k   sB   t  }tf i |  zdV  W tf i | ntf i | 0 dS )zyContext manager that temporarily changes the print options.  Accepted
    arguments are same as :func:`set_printoptions`.N)r    r   )kwargsZ
old_kwargsr   r   r   printoptionsi   s
    r"   c                 C   s:   | j s"| jrtj| jjr"| jr(tjntj	}| j
|dS )N)dtype)Zis_mpsZis_xputorchZxpuZget_device_propertiesdeviceZhas_fp64Zis_maiar   doubleto)tr#   r   r   r   tensor_totypeu   s    	r)   c                   @   s$   e Zd Zdd Zdd Zdd ZdS )
_Formatterc           	      C   sF  |j j| _d| _d| _d| _t  |d}W d    n1 sD0    Y  | jsz|D ]}| }t	| jt
|| _qXn|j tjkr|tj}t|t||d@ }| dkrd S |j tjkr| }t| }t| }t|	 }|D ]}|t|krd| _ qq| jr|| dks:|dkrtd| _|D ],}dtj d	|}t	| jt
|| _qDn*|D ]$}|d
}t	| jt
|d | _qxn|| dks|dks|dk rd| _|D ],}dtj d	|}t	| jt
|| _qn2|D ],}dtj d|}t	| jt
|| _q tjd urBtj| _d S )NTF   r   g     @@g    חA{:.e}.0fg-C6?f})r#   Zis_floating_pointfloating_dtypeint_moder   	max_widthr$   no_gradZreshapemaxlenfloat4_e2m1fn_x2viewuint8Zmasked_selectisfinitenenumelZfloat8_e8m0fnur   r)   absminceilr   r   format)	selftensorZtensor_viewvalueZ	value_strZnonzero_finite_valsZnonzero_finite_absZnonzero_finite_minZnonzero_finite_maxr   r   r   __init__   sj    

(

z_Formatter.__init__c                 C   s   | j S )N)r3   rA   r   r   r   width   s    z_Formatter.widthc                 C   s   | j rl| jr*d| j dtj d|}qr| jrV|d}t|sjt	|sj|d7 }qrdtj d|}n| }| jt
| d | S )Nz{:.r.   r/   r-   r0    )r1   r   r3   r   r   r@   r2   mathisinfisnanr6   )rA   rC   retr   r   r   r@      s    
z_Formatter.formatN)r   r   r   rD   rF   r@   r   r   r   r   r*      s   Tr*   c                 C   sh   |d urVt | j|}t | j|d  }|d dks@|d dkrH|| S |d | S n||  S d S Njr   +-)_scalar_strrealimaglstripr@   item)rA   
formatter1
formatter2real_strimag_strr   r   r   rQ      s    rQ   c                    s>  |  d }|d ur$||  d 7 }tdtttj| | ||fdd | jtj	krh| 
tj} |rztjszdgnx|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 urV| | j}| | jd  }|d dks@|d dkrH|| S |d | S n
| | S d S rM   )r@   rR   rS   rT   )valrV   rW   rX   rY   r   r   r   _val_formatter  s    z#_vector_str.<locals>._val_formatter...r   c                    s   g | ]} |qS r   r   .0rZ   r[   r   r   
<listcomp>      z_vector_str.<locals>.<listcomp>z ...c                    s   g | ]} |qS r   r   r]   r_   r   r   r`     ra   c                    s   g | ]} |qS r   r   r]   r_   r   r   r`     ra   c                    s   g | ]} ||  qS r   r   r^   i)dataelements_per_liner   r   r`   !  s   c                 S   s   g | ]}d  |qS ), )joinr^   liner   r   r   r`   $  ra   [,
rH   ])rF   r5   r   rI   floorr   r   r#   r$   r7   r8   r9   r   sizetolistranger6   rg   )rA   indent	summarizerV   rW   Zelement_lengthZ
data_lineslinesr   )r[   rd   re   r   _vector_str   s0    
 rt   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$   g | ]}t | d   qS r+   _tensor_str_with_formatterrb   rV   rW   rq   rA   rr   r   r   r`   6  s   z._tensor_str_with_formatter.<locals>.<listcomp>r\   c                    s$   g | ]}t | d   qS ru   rv   rb   rx   r   r   r`   =  s   c                    s$   g | ]}t | d   qS ru   rv   rb   rx   r   r   r`   E  s   ,
rH   rj   rl   )	dimrQ   rt   rn   r   r   rp   r6   rg   )rA   rq   rr   rV   rW   r{   Zslices
tensor_strr   rx   r   rw   +  s*    
"rw   c                 C   s   |   dkrdS |  r"| d } |   tjk}|  r@|  } |  rP|  } | j	t
jt
jt
jt
jfv rr|  } | j	jr|  } t|rt| jn| j}t|rt| jn| j}t| ||||S t|rt| n| }t| |||S d S )Nr   [])r<   	has_namesrenamer   r	   Z_is_zerotensorcloneZis_negZresolve_negr#   r$   Zfloat8_e5m2Zfloat8_e5m2fnuzZfloat8_e4m3fnZfloat8_e4m3fnuzZhalfZ
is_complexZresolve_conjr*   get_summarized_datarR   rS   rw   )rA   rq   rr   Zreal_formatterZimag_formatter	formatterr   r   r   _tensor_strP  s:    

r   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 )
Nrz   r+   r   rk   rH   Frf   ) )r6   rfindr   r   appendrg   )r|   suffixesrq   force_newlineZtensor_strsZlast_line_lensuffixZ
suffix_lenr   r   r   _add_suffixes  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 tjsr dg    S  ddtj krއ 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   rb   rE   r   r   r`     ra   z'get_summarized_data.<locals>.<listcomp>c                    s   g | ]} | qS r   r   rb   rE   r   r   r`     ra   c                 S   s   g | ]}t |qS r   r   r^   xr   r   r   r`     ra   c                 S   s   g | ]}t |qS r   r   r   r   r   r   r`     ra   )
r{   rn   r   r   r$   catZ	new_emptyrp   r6   stack)rA   r{   startendr   rE   r   r     s     &r   tensor_contentsc          !   	      s  t jj| rt| |dS t| t ju p6t| t jju }| j	rDd}n|rNd}nt| j
 d}t| g }|d u}|rz|}t jj| \}}|jjt j ks|jjdkrt j |jjks|jjdkr|dt|j d  |jjd	v r|d
}t  t jkrt jnt j}	|jt  |	t jt jfv }
|jrz|dtt|j   ddl!m"} |j#pht$||}|s|dt|%   |
s|dt|j  |sd}|& ' }|rd}nt(| t| }|s|) dkr|dtt|j  7 }d}|* ' }|rd}nt(| t| }|s@|) dkrV|dtt|j  7 }|| d d   | | d }n<|j+t j,t j-t j.t j/hv rddl!m"} |dtt|j   |j#pt$||}|s|dt|%   |
s|dt|j  |st j,t jj0t jj1ft j-t jj2t jj3ft j.t jj0t jj1ft j/t jj2t jj3fi|j+ \}}|j+t j,t j.hv r|d\}}nd\}}d|d d  d}||' }|rd}nt(| t| }|) dks|r|dtt|j  7 }|d d  d}||' }|rd}nt(| t| }|) dks<|rR|dtt|j  7 }d}|4 ' }|rnd}nt(| t| }|) dks|r|dtt|j  7 }|| d d   | | d d   | | d }n|j5r
|dtt|j   |
s|dt|j  |dt|6   |6 t j7ksR|6 t j8kr|dt|9   |dt|:   nr|6 t j;ks|6 t j<ks|6 t j=kr|dt|>   |dt|?   |dt|@   |st(|A  }n|j	rX|sd d! d"B fd#d$t jCjDjEF|dD }d%| d&}n^t G|rzd'}tHt I|}n<ddl!m"} |j#st$||r|dtt|j   |jt  kr|dt|j  |sd}n|) dkrN|jsN|J d(kr|dtt|j   |jt  krB|dt|j  |sd)}nhtKjLsn|dtt|j   |
s|dt|j  |s|j+t jMkrt(|N  }n
t(| }|j+t jMkr|d*t|j+  d }z
| jO}W n tPy   d+}Y n0 |d u r<|d ur<t|j
}|d,kr<|Q Rd-d(d. }|d urZ|d/| d0 n| jSrl|d1 |T r|d2|jU  |d ur|d3|  tV|| | |jd4} t$|t jjr|sd5|  d} | S )6Nr   znested_tensor(ztensor((cudaZmpszdevice='')ZxlaZlazyZipuZmtiacpuzsize=r   )
FakeTensorznnz=zdtype=zindices=tensor(r\   z, size=zvalues=tensor(z),
rH   r   )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 )Nrz   c                 s   s   | ]}d | V  qdS )z  Nr   rh   r   r   r   	<genexpr>Z  ra   z4_str_intern.<locals>.indented_str.<locals>.<genexpr>)rg   split)srq   r   r   r   indented_strY  s    z!_str_intern.<locals>.indented_strrk   c                 3   s    | ]}t | d  V  qdS )r+   N)str)r^   r(   rq   r   r   r   r   \  s   z_str_intern.<locals>.<genexpr>z[
z
]z_to_functional_tensor(r+   r}   zlayout=ZInvalidZCppFunctionz::r,   z	grad_fn=<>zrequires_grad=Trueznames=ztangent=)r   z
Parameter()Wr$   _C
_functorchZis_functorch_wrapped_tensor_functorch_wrapper_str_interntypeZTensornn	ParameterZ	is_nestedr   r6   ZautogradZ
forward_adZunpack_dualr%   Z_get_default_devicer   Zcurrent_deviceindexr   r   r'   Zget_default_dtyper&   ZcdoubleZcfloatr#   int64r   Z	is_sparsetupleshapeZtorch._subclasses.fake_tensorr   is_meta
isinstanceZ_nnzZ_indicesdetachr   r<   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
dequantizerg   opsZatenZunbindr   Z_is_functional_tensorreprZ_from_functional_tensorr{   r   r   ZstridedZto_densegrad_fnRuntimeErrornamersplitZrequires_gradr~   namesr   )!inpr   Zis_plain_tensorprefixr   Zcustom_contents_providedr|   rA   ZtangentZ_default_complex_dtypeZhas_default_dtyper   r   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strsZgrad_fn_namer   Zstring_reprr   r   r   _str_intern  s   



	
	









r   c                C   s   t jj| }|dksJ t jj| r2t |  t jj| }t|}t	|d}t jj
| rt jj| }|dks|J 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=\
r   z8We don't know how to print this, please file us an issue)r$   r   r   Zmaybe_get_levelZis_functionaltensorZ_syncZget_unwrappedr   textwraprq   Zis_batchedtensorZmaybe_get_bdimZis_gradtrackingtensor
ValueError)rB   r   levelrC   Z
value_reprZindented_value_reprZbdimr   r   r   r     s"    
r   c             	   C   s|   t  ` t jj 4 t j }t| |dW  d    W  d    S 1 sP0    Y  W d    n1 sn0    Y  d S )Nr   )r$   r4   utilsZ_python_dispatchZ_disable_current_modesr   Z_DisableFuncTorchr   )rA   r   Zguardr   r   r   _str  s    
r   )NNNNNN)N)N)N)
contextlibr   rI   r   typingr   r   r$   r   Z	dataclassr   r   r   dictr   r    contextmanagerr"   r)   r*   rQ   rt   rw   r   r   r   r   r   r   r   r   r   r   <module>   s@         
I
g

5
%/  