o
    hc                     @   s  d dl Z d dlZd dlZd dlZd dlmZ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Dict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   G/var/www/html/ai/venv/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   short   full)
PRINT_OPTSr   r
   r   r   r   r   )r   r
   r   r   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_printoptionsa   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   )kwargs
old_kwargsr   r   r   printoptionsh   s   "r&   c                 C   s   | j rtjntj}| j|dS )N)dtype)is_mpstorchr   doubleto)tr'   r   r   r   tensor_totypet   s   r-   c                   @   s$   e Zd Zdd Zdd Zdd ZdS )
_Formatterc           	      C   s  |j j| _d| _d| _d| _t  |d}W d    n1 s"w   Y  | js<|D ]}| }t	| jt
|| _q,nt|t||d@ }| dkrRd S t| }t| }t|	 }|D ]}|t|krtd| _ nqf| jr|| dks|dkrd| _|D ]}dtj d	|}t	| jt
|| _qnW|D ]}|d
}t	| jt
|d | _qnB|| dks|dks|dk rd| _|D ]}dtj d	|}t	| jt
|| _qn|D ]}dtj d|}t	| jt
|| _qtjd urtj| _d S d S )NTF   r   g     @@g    חA{:.e}.0fg-C6?f})r'   is_floating_pointfloating_dtypeint_moder   	max_widthr)   no_gradreshapemaxlenmasked_selectisfinitenenumelr-   absminceilr   r   format)	selftensortensor_viewvalue	value_strnonzero_finite_valsnonzero_finite_absnonzero_finite_minnonzero_finite_maxr   r   r   __init__z   sf   

z_Formatter.__init__c                 C   s   | j S N)r8   rE   r   r   r   width   s   z_Formatter.widthc                 C   s   | j r6| jrd| j dtj d|}n$| jr+|d}t|s*t	|s*|d7 }ndtj d|}n| }| jt
| d | S )Nz{:.r2   r3   r1   r4    )r6   r   r8   r   r   rD   r7   mathisinfisnanr<   )rE   rH   retr   r   r   rD      s   z_Formatter.formatN)r   r   r   rN   rQ   rD   r   r   r   r   r.   y   s    Cr.   c                 C   sb   |d ur*t | j|}t | j|d  }|d dks |d dkr$|| S |d | S ||  S Njr   +-)_scalar_strrealimaglstriprD   item)rE   
formatter1
formatter2real_strimag_strr   r   r   r\      s   r\   c                    s&  |  d }|d ur||  d 7 }tdtttj| | ||fdd |r1tjs1dgn<|rb| ddtj krb fdd| d tj 	 D d	g  fd
d| tj d  	 D  n fdd| 	 D fddt
dtD }dd |D }ddd|d   | d S )Nr   r/   c                 S   s^   |d ur*| | j}| | jd  }|d dks |d dkr$|| S |d | S | | S rX   )rD   r]   r^   r_   )valra   rb   rc   rd   r   r   r   _val_formatter   s   
z#_vector_str.<locals>._val_formatter...r   c                       g | ]} |qS r   r   .0re   rf   r   r   
<listcomp>       z_vector_str.<locals>.<listcomp>z ...c                    rh   r   r   ri   rk   r   r   rl      rm   c                    rh   r   r   ri   rk   r   r   rl      rm   c                    s   g | ]
} ||  qS r   r   rj   i)dataelements_per_liner   r   rl      s    c                 S   s   g | ]}d  |qS ), )joinrj   liner   r   r   rl     s    [,
rS   ])rQ   r;   r   rT   floorr   r   r   sizetolistranger<   rs   )rE   indent	summarizera   rb   element_length
data_lineslinesr   )rf   rp   rq   r   _vector_str   s,   
 r   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_formatterrn   ra   rb   r}   rE   r~   r   r   rl         z._tensor_str_with_formatter.<locals>.<listcomp>rg   c                    r   r   r   rn   r   r   r   rl     r   c                    r   r   r   rn   r   r   r   rl   #  r   ,
rS   rv   rx   )	dimr\   r   rz   r   r   r|   r<   rs   )rE   r}   r~   ra   rb   r   slices
tensor_strr   r   r   r   	  s*   
"r   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	t
ju rC|  } | j	jrk|  } t|rSt| jn| j}t|r_t| jn| j}t| ||||S t|rrt| n| }t| |||S )Nr   [])r@   	has_namesrenamer   r
   _is_zerotensorcloneis_negresolve_negr'   r)   float16bfloat16float8_e5m2float8_e4m3fnr   	complex32cfloat
is_complexresolve_conjr.   get_summarized_datar]   r^   r   )rE   r}   r~   real_formatterimag_formatter	formatterr   r   r   _tensor_str.  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   rw   rS   Frr   ) )r<   rfindr   r   appendrs   )r   suffixesr}   force_newlinetensor_strslast_line_lensuffix
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   rn   rP   r   r   rl   }  rm   z'get_summarized_data.<locals>.<listcomp>c                    r   r   r   rn   rP   r   r   rl   ~  rm   c                 S      g | ]}t |qS r   r   rj   xr   r   r   rl     rm   c                 S   r   r   r   r   r   r   r   rl     rm   )
r   rz   r   r   r)   cat	new_emptyr|   r<   stack)rE   r   startendr   rP   r   r   o  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r$|dtt|j   ddl!m"} |j#st$||s|dt|%   |
s|dt|j  |s"d}|& ' }t(| t| }|) dkr|dtt|j  7 }d}|* ' }t(| t| }|) dkr|dtt|j  7 }|| d d   | | d }nn|j+t j,t j-t j.t j/hv r+|dtt|j   |dt|%   |
sV|dt|j  |s)t j,t jj0t jj1ft j-t jj2t jj3ft j.t jj0t jj1ft j/t jj2t jj3fi|j+ \}}|j+t j,t j.hv rd\}}nd\}}d|d d  d}||' }t(| t| }|) dkr|dtt|j  7 }|d d  d}||' }t(| t| }|) dkr|dtt|j  7 }d}|4 ' }t(| t| }|) dkr|dtt|j  7 }|| d d   | | d d   | | d }ng|j5r|dtt|j   |
sH|dt|j  |dt|6   |6 t j7ksc|6 t j8krz|dt|9   |dt|:   n9|6 t j;ks|6 t j<ks|6 t j=kr|dt|>   |dt|?   |dt|@   |st(|A  }n|j	r|sdd  d!B fd"d#t jCjDjEF|dD }d$| d%}nt G|rd&}tHt I|}nddl!m"} |j#st$||r(|dtt|j   |jt  kr"|dt|j  |s'd'}nj|) dkr^|js^|J d(krF|dtt|j   |jt  krX|dt|j  |s]d)}n4tKjLsn|dtt|j   |
s{|dt|j  |s|j+t jMkrt(|N  }nt(| }|j+t jMkr|d*t|j+  | jOd urt| jOj
}|d+kr| jOP Qd,d(d- }|d.| d/ n	| jRr|d0 |S r|d1|jT  |d ur|d2|  tU|| | |jd3}t$|t jjr	|s	d4| d}|S )5Nr   znested_tensor(ztensor((cudampszdevice='')xlalazyipumtiacpuzsize=r   )
FakeTensorznnz=zdtype=zindices=tensor(z, size=zvalues=tensor(z),
rS   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   rt   r   r   r   	<genexpr>&  s    z4_str_intern.<locals>.indented_str.<locals>.<genexpr>)rs   split)sr}   r   r   r   indented_str%  s   z!_str_intern.<locals>.indented_strrw   c                 3   s"    | ]}t | d  V  qdS )r/   N)str)rj   r,   r}   r   r   r   r   (  s
    
z_str_intern.<locals>.<genexpr>z[
z
]z_to_functional_tensor(rg   r/   r   zlayout=CppFunctionz::r0   z	grad_fn=<>zrequires_grad=Trueznames=ztangent=)r   z
Parameter()Vr)   _C
_functorchis_functorch_wrapped_tensor_functorch_wrapper_str_interntypeTensornn	Parameter	is_nestedr   r<   autograd
forward_adunpack_dualdevice_get_default_devicer   current_deviceindexr   r   r+   get_default_dtyper*   cdoubler   r'   int64r   	is_sparsetupleshapetorch._subclasses.fake_tensorr   is_meta
isinstance_nnz_indicesdetachr   r@   _valueslayout
sparse_csr
sparse_csc
sparse_bsr
sparse_bsccrow_indicescol_indicesccol_indicesrow_indicesvaluesis_quantizedqschemeper_tensor_affineper_tensor_symmetricq_scaleq_zero_pointper_channel_affineper_channel_symmetric per_channel_affine_float_qparamsq_per_channel_scalesq_per_channel_zero_pointsq_per_channel_axis
dequantizers   opsatenunbindr   _is_functional_tensorrepr_from_functional_tensorr   r   r   stridedto_densegrad_fnnamersplitrequires_gradr   namesr   )inpr   is_plain_tensorprefixr   custom_contents_providedr   rE   tangent_default_complex_dtypehas_default_dtyper   indices_prefixindicesindices_strvalues_prefixr   
values_strcompressed_indices_methodplain_indices_methodcdimnamepdimnamecompressed_indices_prefixcompressed_indicescompressed_indices_strplain_indices_prefixplain_indicesplain_indices_strstrsr  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)Nr0   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   maybe_get_levelis_functionaltensor_syncget_unwrappedr  textwrapr}   is_batchedtensormaybe_get_bdimis_gradtrackingtensor
ValueError)rF   r   levelrH   
value_reprindented_value_reprbdimr   r   r   r   v  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)   r9   utils_python_dispatch_disable_current_modesr   _DisableFuncTorchr'  )rE   r   guardr   r   r   _str  s   

Rr:  )NNNNNNrO   )
contextlibr!   rT   r,  typingr   r   r   r)   r   	dataclassr   r   r   r   r#   contextmanagerr&   r-   r.   r\   r   r   r   r   r   r'  r   r:  r   r   r   r   <module>   s@    
I

V

-%1 s