

from .number_theory.prime_numbers import next_prime






class     __magic_name__     :


 def __init__(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			int | None = None      ,    lowerCAmelCase__		:			float | None = None      ,    )     ->   None:
   UpperCAmelCase          =	size_table
   UpperCAmelCase          =	[None] * self.size_table
   UpperCAmelCase          =	0.75 if lim_charge is None else lim_charge
   UpperCAmelCase          =	1 if charge_factor is None else charge_factor
   UpperCAmelCase          =	[]
   UpperCAmelCase          =	{}


 def _UpperCamelCase						(    self		:			Any     )     ->   Any:
   return self._keys


 def _UpperCamelCase						(    self		:			str     )     ->   Any:
   return sum(1 for slot in self.values if slot is not None     ) / (
       self.size_table * self.charge_factor
   )


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Optional[int]     )     ->   Tuple:
   return key % self.size_table


 def _UpperCamelCase						(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Tuple     )     ->   List[str]:
   print(f"step {step_ord}"     )
   print(list(range(len(self.values     )     )     )     )
   print(self.values     )


 def _UpperCamelCase						(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   Optional[int]:
   UpperCAmelCase          =	1
   UpperCAmelCase          =	values
   for value in values:
     self.insert_data(lowerCAmelCase__     )
     self._step_by_step(lowerCAmelCase__     )
     i += 1


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			List[str]      ,    lowerCAmelCase__		:			List[Any]     )     ->   Optional[int]:
   UpperCAmelCase          =	data
   UpperCAmelCase          =	data


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			str=None     )     ->   List[str]:
   UpperCAmelCase          =	self.hash_function(key + 1     )

   while self.values[new_key] is not None and self.values[new_key] != key:
     if self.values.count(lowerCAmelCase__     ) > 0:
       UpperCAmelCase          =	self.hash_function(new_key + 1     )
     else:
       UpperCAmelCase          =	None
       break

   return new_key


 def _UpperCamelCase						(    self		:			str     )     ->   str:
   UpperCAmelCase          =	[value for value in self.values if value is not None]
   UpperCAmelCase          =	next_prime(self.size_table      ,    factor=2     )
   self._keys.clear()
   UpperCAmelCase          =	[None] * self.size_table  # hell's pointers D: don't DRY ;/
   for value in survivor_values:
     self.insert_data(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			List[str]     )     ->   Any:
   UpperCAmelCase          =	self.hash_function(lowerCAmelCase__     )

   if self.values[key] is None:
     self._set_value(lowerCAmelCase__      ,    lowerCAmelCase__     )

   elif self.values[key] == data:
     pass

   else:
     UpperCAmelCase          =	self._collision_resolution(lowerCAmelCase__      ,    lowerCAmelCase__     )
     if collision_resolution is not None:
       self._set_value(lowerCAmelCase__      ,    lowerCAmelCase__     )
     else:
       self.rehashing()
       self.insert_data(lowerCAmelCase__     )






import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class     __magic_name__     (							TensorFormatter[Mapping, np.ndarray, Mapping]     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str=None      ,    **lowerCAmelCase__		:			int     )     ->   Optional[Any]:
   super().__init__(features=lowerCAmelCase__     )
   UpperCAmelCase          =	np_array_kwargs


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Dict     )     ->   Union[str, Any]:
   if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
     if column and all(
         isinstance(lowerCAmelCase__      ,    np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
       return np.stack(lowerCAmelCase__     )
     else:
       # don't use np.array(column, dtype=object)
       # since it fails in certain cases
       # see https://stackoverflow.com/q/51005699
       UpperCAmelCase          =	np.empty(len(lowerCAmelCase__     )      ,    dtype=lowerCAmelCase__     )
       UpperCAmelCase          =	column
       return out
   return column


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   if isinstance(lowerCAmelCase__      ,    (str, bytes, type(lowerCAmelCase__     ))     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    np.number     ):
     return value

   UpperCAmelCase          =	{}

   if isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.integer     ):
     UpperCAmelCase          =	{"dtype": np.intaa}
   elif isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.floating     ):
     UpperCAmelCase          =	{"dtype": np.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowerCAmelCase__      ,    PIL.Image.Image     ):
       return np.asarray(lowerCAmelCase__      ,    **self.np_array_kwargs     )

   return np.asarray(lowerCAmelCase__      ,    **{**default_dtype, **self.np_array_kwargs}     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			str     )     ->   Optional[int]:
   # support for torch, tf, jax etc.
   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowerCAmelCase__      ,    torch.Tensor     ):
       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
   if hasattr(lowerCAmelCase__      ,    "__array__"     ) and not isinstance(lowerCAmelCase__      ,    (np.ndarray, np.character, np.number)     ):
     UpperCAmelCase          =	data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(lowerCAmelCase__      ,    np.ndarray     ):
     if data_struct.dtype == object:
       return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   if isinstance(lowerCAmelCase__      ,    (list, tuple)     ):
     return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   return self._tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			dict     )     ->   Union[str, Any]:
   return map_nested(self._recursive_tensorize      ,    lowerCAmelCase__      ,    map_list=lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_row(lowerCAmelCase__     )
   return self.recursive_tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			pa.Table     )     ->   np.ndarray:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_column(lowerCAmelCase__      ,    pa_table.column_names[0]     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   UpperCAmelCase          =	self._consolidate(lowerCAmelCase__     )
   return column


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   for column_name in batch:
     UpperCAmelCase          =	self._consolidate(batch[column_name]     )
   return batch



