





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
 import torch


class     __magic_name__     (							TensorFormatter[Mapping, '''torch.Tensor''', Mapping]							):





     """simple docstring"""



     def __init__(							self				:str	,					snake_case				:str=None	,					**snake_case				:int     ):





        '''simple docstring'''




        super().__init__(features=snake_case     )
        A_    : int								=	torch_tensor_kwargs
        import torch  # noqa import torch at initialization



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:List[Any]     ):





        '''simple docstring'''




        import torch

        if isinstance(snake_case	,					snake_case     ) and column:
           if all(
               isinstance(snake_case	,					torch.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype
               for x in column     ):
              return torch.stack(snake_case     )
        return column



     def   SCREAMING_SNAKE_CASE						(							self				:str	,					snake_case				:Dict     ):





        '''simple docstring'''




        import torch

        if isinstance(snake_case	,					(str, bytes, type(snake_case     ))     ):
           return value
        elif isinstance(snake_case	,					(np.character, np.ndarray)     ) and np.issubdtype(value.dtype	,					np.character     ):
           return value.tolist()

        A_    : Union[str, Any]								=	{}

        if isinstance(snake_case	,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype	,					np.integer     ):
           A_    : Optional[Any]								=	{'dtype': torch.intaa}
        elif isinstance(snake_case	,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype	,					np.floating     ):
           A_    : List[Any]								=	{'dtype': torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(snake_case	,					PIL.Image.Image     ):
              A_    : List[str]								=	np.asarray(snake_case     )
        return torch.tensor(snake_case	,					**{**default_dtype, **self.torch_tensor_kwargs}     )



     def   SCREAMING_SNAKE_CASE						(							self				:str	,					snake_case				:Tuple     ):





        '''simple docstring'''




        import torch

        # support for torch, tf, jax etc.
        if hasattr(snake_case	,					"__array__"     ) and not isinstance(snake_case	,					torch.Tensor     ):
           A_    : Optional[int]								=	data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(snake_case	,					np.ndarray     ):
           if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
              return self._consolidate([self.recursive_tensorize(snake_case     ) for substruct in data_struct]     )
        elif isinstance(snake_case	,					(list, tuple)     ):
           return self._consolidate([self.recursive_tensorize(snake_case     ) for substruct in data_struct]     )
        return self._tensorize(snake_case     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:dict     ):





        '''simple docstring'''




        return map_nested(self._recursive_tensorize	,					snake_case	,					map_list=snake_case     )



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple	,					snake_case				:pa.Table     ):





        '''simple docstring'''




        A_    : Tuple								=	self.numpy_arrow_extractor().extract_row(snake_case     )
        A_    : List[Any]								=	self.python_features_decoder.decode_row(snake_case     )
        return self.recursive_tensorize(snake_case     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:pa.Table     ):





        '''simple docstring'''




        A_    : Optional[int]								=	self.numpy_arrow_extractor().extract_column(snake_case     )
        A_    : Optional[Any]								=	self.python_features_decoder.decode_column(snake_case	,					pa_table.column_names[0]     )
        A_    : int								=	self.recursive_tensorize(snake_case     )
        A_    : List[Any]								=	self._consolidate(snake_case     )
        return column





     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]	,					snake_case				:pa.Table     ):





        '''simple docstring'''




        A_    : Optional[int]								=	self.numpy_arrow_extractor().extract_batch(snake_case     )
        A_    : List[Any]								=	self.python_features_decoder.decode_batch(snake_case     )
        A_    : Any								=	self.recursive_tensorize(snake_case     )
        for column_name in batch:
           A_    : Optional[Any]								=	self._consolidate(batch[column_name]     )
        return batch







from itertools import count


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 50 )			->     int:
    lowerCamelCase__    :      Optional[Any]					 =		[1] * min_block_length

    for n in count(_UpperCAmelCase ):
        fill_count_functions.append(1 )

        for block_length in range(_UpperCAmelCase						,	n + 1 ):
            for block_start in range(n - block_length ):
                fill_count_functions[n] += fill_count_functions[
                    n - block_start - block_length - 1
                ]

            fill_count_functions[n] += 1

        if fill_count_functions[n] > 100_0000:
            break

    return n


if __name__ == "__main__":
    print(F"""{solution() = }""")





