
    \
qiu                     T   d dl Z d dlZd dlmZ ej        d             Zej        d             Zej        d             Zej        d             Z ej        ddg	          d
             Z	ej        d             Z
ej        d             Zej        d             Zej        d             Zej        d             Zej        d             Z ej        ddg	          d             Z ej        d d d d gg d          d             Z ej        ddg	          d             Z ej        ddg	          d             Z ej        ddg	          d             Z ej        ddg	          d              Z ej        ddg	          d!             Zej        d"             ZdS )#    N)Seriesc                      t           )z3A fixture providing the ExtensionDtype to validate.NotImplementedError     /var/www/html/bestrading.cuttalo.com/services/ml-inference/venv/lib/python3.11/site-packages/pandas/tests/extension/conftest.pydtyper
      
     r   c                      t           )z
    Length-10 array for this type.

    * data[0] and data[1] should both be non missing
    * data[0] and data[1] should not be equal
    r   r   r   r	   datar      
     r   c                 b    | j         s"| j        dk    st          j        |  d           t          )z
    Length-10 array in which all the elements are two.

    Call pytest.skip in your fixture if the dtype does not support divmod.
    mz is not a numeric dtype)_is_numerickindpytestskipr   r
   s    r	   data_for_twosr      s<      7s!2!2 	u555666
r   c                      t           )zLength-2 array with [NA, Valid]r   r   r   r	   data_missingr   (   r   r   r   r   )paramsc                 :    | j         dk    r|S | j         dk    r|S dS )z5Parametrized fixture giving 'data' and 'data_missing'r   r   Nparam)requestr   r   s      r	   all_datar   .   s1     }	.	(	( 
)	(r   c                       fd}|S )a  
    Generate many datasets.

    Parameters
    ----------
    data : fixture implementing `data`

    Returns
    -------
    Callable[[int], Generator]:
        A callable that takes a `count` argument and
        returns a generator yielding `count` datasets.
    c              3   8   K   t          |           D ]}V  d S N)range)count_r   s     r	   genzdata_repeated.<locals>.genG   s1      u 	 	AJJJJ	 	r   r   )r   r%   s   ` r	   data_repeatedr&   7   s#          Jr   c                      t           )z
    Length-3 array with a known sort order.

    This should be three items [B, C, A] with
    A < B < C

    For boolean dtypes (for which there are only 2 values available),
    set B=C=True
    r   r   r   r	   data_for_sortingr(   N   s
     r   c                      t           )z{
    Length-3 array with a known sort order.

    This should be three items [B, NA, A] with
    A < B and NA missing.
    r   r   r   r	   data_missing_for_sortingr*   \   r   r   c                      t           j        S )z
    Binary operator for comparing NA values.

    Should return a function of two arguments that returns
    True if both arguments are (scalar) NA for your type.

    By default, uses ``operator.is_``
    )operatoris_r   r   r	   na_cmpr.   g   s     <r   c                     | j         S )z
    The scalar missing value for this type. Default dtype.na_value.

    TODO: can be removed in 3.x (see https://github.com/pandas-dev/pandas/pull/54930)
    )na_valuer   s    r	   r0   r0   t   s     >r   c                      t           )z
    Data for factorization, grouping, and unique tests.

    Expected to be like [B, B, NA, NA, A, A, B, C]

    Where A < B < C and NA is missing.

    If a dtype has _is_boolean = True, i.e. only 2 unique non-NA entries,
    then set C=B.
    r   r   r   r	   data_for_groupingr2   ~   s
     r   TFc                     | j         S )z#Whether to box the data in a Seriesr   r   s    r	   box_in_seriesr5      s     =r   c                     dS N   r   xs    r	   <lambda>r;          ! r   c                 (    dgt          |           z  S r7   )lenr9   s    r	   r;   r;      s    1#A, r   c                 B    t          dgt          |           z            S r7   )r   r>   r9   s    r	   r;   r;      s    &!s1vv&& r   c                     | S r!   r   r9   s    r	   r;   r;      r<   r   )scalarlistseriesobject)r   idsc                     | j         S )z,
    Functions to test groupby.apply().
    r   r4   s    r	   groupby_apply_oprG      s     =r   c                     | j         S )zU
    Boolean fixture to support Series and Series.to_frame() comparison testing.
    r   r4   s    r	   as_framerI          
 =r   c                     | j         S )zL
    Boolean fixture to support arr and Series(arr) comparison testing.
    r   r4   s    r	   	as_seriesrL      rJ   r   c                     | j         S )zd
    Boolean fixture to support comparison testing of ExtensionDtype array
    and numpy array.
    r   r4   s    r	   	use_numpyrN           =r   ffillbfillc                     | j         S )zl
    Parametrized fixture giving method parameters 'ffill' and 'bfill' for
    Series.<method> testing.
    r   r4   s    r	   fillna_methodrS      rO   r   c                     | j         S )zR
    Boolean fixture to support ExtensionDtype _from_sequence method testing.
    r   r4   s    r	   as_arrayrU      rJ   r   c                 @    t                               t                     S )z
    A scalar that *cannot* be held by this ExtensionArray.

    The default should work for most subclasses, but is not guaranteed.

    If the array can hold any item (i.e. object dtype), then use pytest.skip.
    )rD   __new__)r   s    r	   invalid_scalarrX      s     >>&!!!r   )r,   r   pandasr   fixturer
   r   r   r   r   r&   r(   r*   r.   r0   r2   r5   rG   rI   rL   rN   rS   rU   rX   r   r   r	   <module>r[      s             
         
 /000  10   , 
 
 
    	 	 	       e}%%%  &%
 &&	 	/..     e}%%%  &% e}%%%  &% e}%%%  &% )***  +* e}%%%  &% " " " " "r   