o
    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deeef f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   W/home/www/facesmatcher.com/frenv_anti/lib/python3.10/site-packages/torch/_tensor_str.pyr      s   
 r   c                 C   s   |dur6|dkrdt _dt _dt _dt _n!|dkr&dt _dt _dt _dt _n|d	kr6dt _tt _dt _dt _| dur=| t _|durD|t _|durK|t _|durR|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   Z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().
    )dataclassesasdictr   r   r   r   r   get_printoptionsb   s   
r    c               
   k   sB    t  }tdi |  zdV  W tdi | dS tdi | w )zyContext manager that temporarily changes the print options.  Accepted
    arguments are same as :func:`set_printoptions`.Nr   )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$   e Zd Zdd Zdd Zdd ZdS )
_Formatterc           	      C   s4  |j j| _d| _d| _d| _t  |d}W d    n1 s"w   Y  | js<|D ]}| }t	| jt
|| _q,n|j tjkrH|tj}t|t||d@ }| dkr^d S |j tjkrh| }t| }t| }t|	 }|D ]}|t|krd| _ qq|| jr|| dks|dkrd| _|D ]}dtj d	|}t	| jt
|| _qq|D ]}|d
}t	| jt
|d | _qnB|| dks|dks|dk rd| _|D ]}dtj d	|}t	| jt
|| _qn|D ]}dtj d|}t	| jt
|| _qtjd urtj| _d S d S )NTF   r   g     @@g    חA{:.e}.0fg-C6?f})r#   Zis_floating_pointfloating_dtypeint_moder   	max_widthr$   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__   sx   



z_Formatter.__init__c                 C   s   | j S N)r3   rA   r   r   r   width   s   z_Formatter.widthc                 C   s   | j r6| jrd| j dtj d|}q9| jr+|d}t|s*t	|s*|d7 }q9d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   rG   r@   r   r   r   r   r*      s    Tr*   c                 C   sb   |d ur*t | j|}t | j|d  }|d dks |d dkr$|| S |d | S ||  S Njr   +-)_scalar_strrealimaglstripr@   item)rA   
formatter1
formatter2real_strimag_strr   r   r   rR      s   rR   c                    s>  |  d }|d ur||  d 7 }tdtttj| | ||fdd | jtj	kr4| 
tj} |r=tjs=dgn<|rn| ddtj krn fdd| d tj  D d	g  fd
d| tj d   D  n fdd|  D fddtdtD }dd |D }ddd|d   | d S )Nr   r+   c                 S   s^   |d ur*| | j}| | jd  }|d dks |d dkr$|| S |d | S | | S rN   )r@   rS   rT   rU   )valrW   rX   rY   rZ   r   r   r   _val_formatter  s   
z#_vector_str.<locals>._val_formatter...r   c                       g | ]} |qS r   r   .0r[   r\   r   r   
<listcomp>      z_vector_str.<locals>.<listcomp>z ...c                    r^   r   r   r_   ra   r   r   rb     rc   c                    r^   r   r   r_   ra   r   r   rb     rc   c                    s   g | ]
} ||  qS r   r   r`   i)dataelements_per_liner   r   rb   !  s    c                 S   s   g | ]}d  |qS ), )joinr`   liner   r   r   rb   $  s    [,
rI   ])rG   r5   r   rJ   floorr   r   r#   r$   r7   r8   r9   r   sizetolistranger6   ri   )rA   indent	summarizerW   rX   Zelement_lengthZ
data_lineslinesr   )r\   rf   rg   r   _vector_str   s0   
 rv   c                    s     }|dkrt S |dkrt S rRddtj krR 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                    $   g | ]}t | d   qS r+   _tensor_str_with_formatterrd   rW   rX   rs   rA   rt   r   r   rb   6      z._tensor_str_with_formatter.<locals>.<listcomp>r]   c                    rw   rx   ry   rd   r{   r   r   rb   =  r|   c                    rw   rx   ry   rd   r{   r   r   rb   E  r|   ,
rI   rl   rn   )	dimrR   rv   rp   r   r   rr   r6   ri   )rA   rs   rt   rW   rX   r   Zslices
tensor_strr   r{   r   rz   +  s*   
"rz   c                 C   s   |   dkrdS |  r| d } |   tjk}|  r |  } |  r(|  } | j	t
jt
jt
jt
jfv r9|  } | j	jra|  } t|rIt| jn| j}t|rUt| jn| j}t| ||||S t|rht| n| }t| |||S )Nr   [])r<   	has_namesrenamer   r	   Z_is_zerotensorcloneZis_negZresolve_negr#   r$   Zfloat8_e5m2Zfloat8_e5m2fnuzZfloat8_e4m3fnZfloat8_e4m3fnuzZhalfZ
is_complexZresolve_conjr*   get_summarized_datarS   rT   rz   )rA   rs   rt   Zreal_formatterZimag_formatter	formatterr   r   r   _tensor_strP  s:   

r   c                 C   s   | g}t | | d d }|D ]0}t |}|s!|| d tjkr3|dd|  |  || }d}q|d|  ||d 7 }q|d d	|S )
Nr~   r+   r   rm   rI   Frh   ) )r6   rfindr   r   appendri   )r   suffixesrs   force_newlineZtensor_strsZlast_line_lensuffixZ
suffix_lenr   r   r   _add_suffixes  s   

r   c                    s      }|dkr
 S |dkr, ddtj kr*t d tj  tj d  fS  S tjs9 dg    S  ddtj kro 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 )	Nr   r+   r   c                       g | ]} | qS r   r   rd   rF   r   r   rb     rc   z'get_summarized_data.<locals>.<listcomp>c                    r   r   r   rd   rF   r   r   rb     rc   c                 S      g | ]}t |qS r   r   r`   xr   r   r   rb     rc   c                 S   r   r   r   r   r   r   r   rb     rc   )
r   rp   r   r   r$   catZ	new_emptyrr   r6   stack)rA   r   startendr   rF   r   r     s    &r   tensor_contentsc          !   	      s  t jj| rt| |dS t| t ju pt| t jju }| j	r"d}n|r'd}nt| j
 d}t| g }|d u}|r=|}t jj| \}}|jjt j ksd|jjdkr^t j |jjksd|jjdkrp|dt|j d  |jjd	v r{|d
}t  t jkrt jnt j}	|jt  |	t jt jfv }
|jr6|dtt|j   ddl!m"} |j#pt$||}|s|dt|%   |
s|dt|j  |s4d}|& ' }|rd}n	t(| t| }|s|) dkr|dtt|j  7 }d}|* ' }|rd}n	t(| t| }|s|) dkr$|dtt|j  7 }|| d d   | | d }n|j+t j,t j-t j.t j/hv rjddl!m"} |dtt|j   |j#p^t$||}|sm|dt|%   |
sz|dt|j  |sht 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 rd\}}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|r"|dtt|j  7 }d}|4 ' }|r0d}n	t(| t| }|) dksC|rN|dtt|j  7 }|| d d   | | d d   | | d }ng|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|:   n9|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	r#|s"d d! d"B fd#d$t jCjDjEF|dD }d%| d&}nt G|r3d'}tHt I|}nddl!m"} |j#sCt$||rg|dtt|j   |jt  kra|dt|j  |sfd}nj|) dkr|js|J d(kr|dtt|j   |jt  kr|dt|j  |sd)}n4tKjLs|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w |d u r|d urt|j
}|d,kr|Q Rd-d(d. }|d ur#|d/| d0 n	| jSr,|d1 |T r:|d2|jU  |d urG|d3|  tV|| | |jd4} t$|t jjrc|scd5|  d} | S )6Nr   znested_tensor(ztensor((cudaZmpszdevice='')ZxlaZlazyZipuZmtiacpuzsize=r   )
FakeTensorznnz=zdtype=zindices=tensor(r]   z, size=zvalues=tensor(z),
rI   r   )rowcolumn)r   r   cr
   z_indices=tensor(zquantization_scheme=zscale=zzero_point=zaxis=c                 S   s   d dd | dD S )Nr~   c                 s   s    | ]}d | V  qdS )z  Nr   rj   r   r   r   	<genexpr>Z  s    z4_str_intern.<locals>.indented_str.<locals>.<genexpr>)ri   split)srs   r   r   r   indented_strY  s   z!_str_intern.<locals>.indented_strrm   c                 3   s"    | ]}t | d  V  qdS )r+   N)str)r`   r(   rs   r   r   r   r   \  s
    
z_str_intern.<locals>.<genexpr>z[
z
]z_to_functional_tensor(r+   r   zlayout=ZInvalidZCppFunctionz::r,   z	grad_fn=<>zrequires_grad=Trueznames=ztangent=)r   z
Parameter()Wr$   _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#   Z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
dequantizeri   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| rt |  t jj| }t|}t	|d}t jj
| rJt jj| }|dks>J d| d| d| dS t jj| rZd| d| dS t jj| rjd| d	| d
S td)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$   r   r   Zmaybe_get_levelZis_functionaltensorZ_syncZget_unwrappedr   textwraprs   Zis_batchedtensorZmaybe_get_bdimZis_gradtrackingtensor
ValueError)rB   r   levelrC   
value_reprZindented_value_reprZbdimr   r   r   r     s"   
r   c             	   C   s~   t  1 t jj  t j }t| |dW  d    W  d    S 1 s(w   Y  W d    d S 1 s8w   Y  d S )Nr   )r$   r4   utilsZ_python_dispatchZ_disable_current_modesr   Z_DisableFuncTorchr   )rA   r   guardr   r   r   _str  s   

Rr   )NNNNNNrE   )
contextlibr   rJ   r   typingr   r   r$   r   	dataclassr   r   r   dictr   r    contextmanagerr"   r)   r*   rR   rv   rz   r   r   r   r   r   r   r   r   r   r   <module>   sB   
I

g

5%/  