

'''simple docstring'''



# 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 				_snake_case					(   TensorFormatter[Mapping, '''torch.Tensor''', Mapping]			):

  def __init__(				self      , _SCREAMING_SNAKE_CASE=None      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(features=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							torch_tensor_kwargs
      import torch  # noqa import torch at initialization

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      import torch

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

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      import torch

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

      lowerCAmelCase 						=							{}

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

          if isinstance(_SCREAMING_SNAKE_CASE      , PIL.Image.Image	):
              lowerCAmelCase 						=							np.asarray(_SCREAMING_SNAKE_CASE	)
      return torch.tensor(_SCREAMING_SNAKE_CASE      , **{**default_dtype, **self.torch_tensor_kwargs}	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      import torch

      # support for torch, tf, jax etc.
      if hasattr(_SCREAMING_SNAKE_CASE      , '__array__'	) and not isinstance(_SCREAMING_SNAKE_CASE      , torch.Tensor	):
          lowerCAmelCase 						=							data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_SCREAMING_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(_SCREAMING_SNAKE_CASE	) for substruct in data_struct]	)
      elif isinstance(_SCREAMING_SNAKE_CASE      , (list, tuple)	):
          return self._consolidate([self.recursive_tensorize(_SCREAMING_SNAKE_CASE	) for substruct in data_struct]	)
      return self._tensorize(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.numpy_arrow_extractor().extract_row(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.python_features_decoder.decode_row(_SCREAMING_SNAKE_CASE	)
      return self.recursive_tensorize(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.numpy_arrow_extractor().extract_column(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.python_features_decoder.decode_column(_SCREAMING_SNAKE_CASE      , pa_table.column_names[0]	)
      lowerCAmelCase 						=							self.recursive_tensorize(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self._consolidate(_SCREAMING_SNAKE_CASE	)
      return column


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.numpy_arrow_extractor().extract_batch(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.python_features_decoder.decode_batch(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.recursive_tensorize(_SCREAMING_SNAKE_CASE	)
      for column_name in batch:
          lowerCAmelCase 						=							self._consolidate(batch[column_name]	)
      return batch








'''simple docstring'''



def       snake_case   (	snake_case      : list			) ->  list:


    """simple docstring"""


    if any(not isinstance(snake_case					,		snake_case			) or x < 0 for x in sequence			):
        raise TypeError('Sequence must be list of non-negative integers'			)
    for _ in range(len(snake_case			)			):
        for i, (rod_upper, rod_lower) in enumerate(zip(snake_case					,		sequence[1:]			)			):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





