"""simple docstring"""







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

__SCREAMING_SNAKE_CASE    	=get_logger()

__SCREAMING_SNAKE_CASE    	=None







class        UpperCamelCase		(			TensorFormatter[Mapping, 'jax.Array', Mapping]  ):
   def __init__(			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        super().__init__(features=SCREAMING_SNAKE_CASE__			)
        import jax
        from jaxlib.xla_client import Device

        if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__			):
             raise ValueError(
                 f'''Expected {device} to be a `str` not {type(SCREAMING_SNAKE_CASE__			)}, 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`.'			)
        lowercase_	:				Tuple													=	device if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__			) 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:
             lowercase_	:				List[Any]													=	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]			)}.'''			)
             lowercase_	:				Tuple													=	str(jax.devices()[0]			)
        lowercase_	:				Optional[int]													=	jnp_array_kwargs
   @staticmethod
   def 			_UpperCAmelCase   (			)	->      Dict[str, "jaxlib.xla_extension.Device"]:


        '''simple docstring'''


        import jax

        return {str(SCREAMING_SNAKE_CASE__			): device for device in jax.devices()}
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        import jax
        import jax.numpy as jnp

        if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__			) and column:
             if all(
                 isinstance(SCREAMING_SNAKE_CASE__     ,jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                  return jnp.stack(SCREAMING_SNAKE_CASE__     ,axis=0			)
        return column
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        import jax
        import jax.numpy as jnp

        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()

        lowercase_	:				Optional[Any]													=	{}

        if isinstance(SCREAMING_SNAKE_CASE__     ,(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:
                  lowercase_	:				int													=	{"""dtype""": jnp.intaa}
             else:
                  lowercase_	:				Union[str, Any]													=	{"""dtype""": jnp.intaa}
        elif isinstance(SCREAMING_SNAKE_CASE__     ,(np.number, np.ndarray)			) and np.issubdtype(value.dtype     ,np.floating			):
             lowercase_	:				int													=	{"""dtype""": jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(SCREAMING_SNAKE_CASE__     ,PIL.Image.Image			):
                  lowercase_	:				Any													=	np.asarray(SCREAMING_SNAKE_CASE__			)

        # 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:
             lowercase_	:				Optional[int]													=	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(SCREAMING_SNAKE_CASE__     ,**{**default_dtype, **self.jnp_array_kwargs}			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

             if isinstance(SCREAMING_SNAKE_CASE__     ,torch.Tensor			):
                  return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
        if hasattr(SCREAMING_SNAKE_CASE__     ,'__array__'			) and not isinstance(SCREAMING_SNAKE_CASE__     ,jax.Array			):
             lowercase_	:				List[Any]													=	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:  # jax arrays 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 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        return map_nested(self._recursive_tensorize     ,SCREAMING_SNAKE_CASE__     ,map_list=SCREAMING_SNAKE_CASE__			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Mapping:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE__			)
        lowercase_	:				Optional[int]													=	self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE__			)
        return self.recursive_tensorize(SCREAMING_SNAKE_CASE__			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      "jax.Array":


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE__			)
        lowercase_	:				int													=	self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE__     ,pa_table.column_names[0]			)
        lowercase_	:				str													=	self.recursive_tensorize(SCREAMING_SNAKE_CASE__			)
        lowercase_	:				Any													=	self._consolidate(SCREAMING_SNAKE_CASE__			)
        return column







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Mapping:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE__			)
        lowercase_	:				Optional[Any]													=	self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE__			)
        lowercase_	:				Tuple													=	self.recursive_tensorize(SCREAMING_SNAKE_CASE__			)
        for column_name in batch:
             lowercase_	:				Union[str, Any]													=	self._consolidate(batch[column_name]			)
        return batch



"""simple docstring"""




def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [1]

							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   0, 0, 0
							SCREAMING_SNAKE_CASE__						:       List[str]           =   ugly_nums[ia] * 2
							SCREAMING_SNAKE_CASE__						:       int           =   ugly_nums[ia] * 3
							SCREAMING_SNAKE_CASE__						:       Any           =   ugly_nums[ia] * 5

							for _ in range(1   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   min(_snake_case   ,_snake_case   ,_snake_case						)
														ugly_nums.append(_snake_case						)
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ugly_nums[ia] * 2
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   ugly_nums[ia] * 3
														if next_num == next_a:
																					ia += 1
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   ugly_nums[ia] * 5
							return ugly_nums[-1]


if __name__ == "__main__":
					from doctest import testmod

					testmod(verbose=True)
					print(f"""{ugly_numbers(2_0_0) = }""")




