




import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
       import jax
       import jaxlib

lowerCamelCase			:			Tuple								=       get_logger()

lowerCamelCase			:			List[Any]								=       None




class 		__lowercase						(TensorFormatter[Mapping, """jax.Array""", Mapping]				):




      """simple docstring"""





      def __init__( self							, A=None							, A=None							, **A      )						->							Optional[int]:
         super().__init__(features=__lowerCamelCase      )
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__lowerCamelCase							, __lowerCamelCase      ):
            raise ValueError(
                f"""Expected {device} to be a `str` not {type(__lowerCamelCase      )}, as `jaxlib.xla_extension.Device` """
                """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
                """the device with `str()` to get its string identifier that will be internally mapped """
                """to the actual `jaxlib.xla_extension.Device`."""      )
         snake_case :							List[Any]           =			device if isinstance(__lowerCamelCase							, __lowerCamelCase      ) else str(jax.devices()[0]      )
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
            snake_case :							Tuple           =			self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()      ):
            logger.warning(
                f"""Device with string identifier {self.device} not listed among the available """
                f"""devices: {list(DEVICE_MAPPING.keys()      )}, so falling back to the default """
                f"""device: {str(jax.devices()[0]      )}."""      )
            snake_case :							Dict           =			str(jax.devices()[0]      )
         snake_case :							Union[str, Any]           =			jnp_array_kwargs

      @staticmethod
      def       UpperCAmelCase ( )						->							List[str]:
         import jax

         return {str(__lowerCamelCase      ): device for device in jax.devices()}

      def       UpperCAmelCase ( self							, A      )						->							int:
         import jax
         import jax.numpy as jnp

         if isinstance(__lowerCamelCase							, __lowerCamelCase      ) and column:
            if all(
                isinstance(__lowerCamelCase							, jax.Array      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
               return jnp.stack(__lowerCamelCase							, axis=0      )
         return column

      def       UpperCAmelCase ( self							, A      )						->							Optional[Any]:
         import jax
         import jax.numpy as jnp

         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.tolist()

         snake_case :							Union[str, Any]           =			{}

         if isinstance(__lowerCamelCase							, (np.number, np.ndarray)      ) and np.issubdtype(value.dtype							, np.integer      ):
            # the default int precision depends on the jax config
            # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
            if jax.config.jax_enable_xaa:
               snake_case :							Union[str, Any]           =			{'''dtype''': jnp.intaa}
            else:
               snake_case :							int           =			{'''dtype''': jnp.intaa}
         elif isinstance(__lowerCamelCase							, (np.number, np.ndarray)      ) and np.issubdtype(value.dtype							, np.floating      ):
            snake_case :							Any           =			{'''dtype''': jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(__lowerCamelCase							, PIL.Image.Image      ):
               snake_case :							Optional[int]           =			np.asarray(__lowerCamelCase      )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
            snake_case :							Dict           =			self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]      ):
            # calling jnp.array on a np.ndarray does copy the data
            # see https://github.com/google/jax/issues/4486
            return jnp.array(__lowerCamelCase							, **{**default_dtype, **self.jnp_array_kwargs}      )

      def       UpperCAmelCase ( self							, A      )						->							List[Any]:
         import jax

         # 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							, jax.Array      ):
            snake_case :							Dict           =			data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__lowerCamelCase							, np.ndarray      ):
            if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
               return self._consolidate([self.recursive_tensorize(__lowerCamelCase      ) for substruct in data_struct]      )
         elif isinstance(__lowerCamelCase							, (list, tuple)      ):
            return self._consolidate([self.recursive_tensorize(__lowerCamelCase      ) for substruct in data_struct]      )
         return self._tensorize(__lowerCamelCase      )

      def       UpperCAmelCase ( self							, A      )						->							int:
         return map_nested(self._recursive_tensorize							, __lowerCamelCase							, map_list=__lowerCamelCase      )

      def       UpperCAmelCase ( self							, A      )						->							int:
         snake_case :							Dict           =			self.numpy_arrow_extractor().extract_row(__lowerCamelCase      )
         snake_case :							Optional[Any]           =			self.python_features_decoder.decode_row(__lowerCamelCase      )
         return self.recursive_tensorize(__lowerCamelCase      )

      def       UpperCAmelCase ( self							, A      )						->							List[str]:
         snake_case :							str           =			self.numpy_arrow_extractor().extract_column(__lowerCamelCase      )
         snake_case :							List[str]           =			self.python_features_decoder.decode_column(__lowerCamelCase							, pa_table.column_names[0]      )
         snake_case :							Optional[int]           =			self.recursive_tensorize(__lowerCamelCase      )
         snake_case :							Any           =			self._consolidate(__lowerCamelCase      )
         return column

      def       UpperCAmelCase ( self							, A      )						->							str:
         snake_case :							List[str]           =			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase      )
         snake_case :							Optional[int]           =			self.python_features_decoder.decode_batch(__lowerCamelCase      )
         snake_case :							Tuple           =			self.recursive_tensorize(__lowerCamelCase      )
         for column_name in batch:
            snake_case :							Any           =			self._consolidate(batch[column_name]      )
         return batch












"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->list:



    '''simple docstring'''







    for i in range(len(snake_case_		) - 1							,0							,-1		):
        __A       :		Union[str, Any]							=	False

        for j in range(snake_case_							,0							,-1		):
            if unsorted[j] < unsorted[j - 1]:
                __A					,			__A       :		Union[str, Any]							=	unsorted[j - 1], unsorted[j]
                __A       :		Optional[int]							=	True

        for j in range(snake_case_		):
            if unsorted[j] > unsorted[j + 1]:
                __A					,			__A       :		Optional[Any]							=	unsorted[j + 1], unsorted[j]
                __A       :		Union[str, Any]							=	True

        if not swapped:
            break
    return unsorted


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       a_					=     input("""Enter numbers separated by a comma:\n""").strip()
       a_					=     [int(item) for item in user_input.split(""",""")]
       print(f'''{cocktail_shaker_sort(unsorted) = }''')




