# Lint as: python3
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__		:       List[Any]       						=       get_logger()

SCREAMING_SNAKE_CASE__		:       Optional[dict]       						=       None

class    snake_case       (   TensorFormatter[Mapping, 'jax.Array', Mapping]    ):




  def __init__(   self  :						Any  ,			a_  :						Dict=None  ,			a_  :						Dict=None  ,			**a_  :						Optional[int]					)->							Tuple:




       """simple docstring"""
       super().__init__(features=a_					)
       import jax
       from jaxlib.xla_client import Device

       if isinstance(a_  ,			a_					):
            raise ValueError(
                F'''Expected {device} to be a `str` not {type(a_					)}, 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`.'					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		device if isinstance(a_  ,			a_					) 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:
            SCREAMING_SNAKE_CASE__					:					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]					)}.'''					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		str(jax.devices()[0]					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		jnp_array_kwargs




  @staticmethod
  def 						__lowercase(   )->							Dict[str, "jaxlib.xla_extension.Device"]:




       """simple docstring"""
       import jax

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




  def 						__lowercase(   self  :						str  ,			a_  :						Dict					)->							List[str]:




       """simple docstring"""
       import jax
       import jax.numpy as jnp

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




  def 						__lowercase(   self  :						int  ,			a_  :						Union[str, Any]					)->							Union[str, Any]:




       """simple docstring"""
       import jax
       import jax.numpy as jnp

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

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		{}

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

            if isinstance(a_  ,			PIL.Image.Image					):
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		np.asarray(a_					)

        # 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:
            SCREAMING_SNAKE_CASE__					:					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(a_  ,			**{**default_dtype, **self.jnp_array_kwargs}					)




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						Any					)->							Union[str, Any]:




       """simple docstring"""
       import jax

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

            if isinstance(a_  ,			torch.Tensor					):
                 return self._tensorize(data_struct.detach().cpu().numpy()[()]					)
       if hasattr(a_  ,			'__array__'					) and not isinstance(a_  ,			jax.Array					):
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(a_  ,			np.ndarray					):
            if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                 return self._consolidate([self.recursive_tensorize(a_					) for substruct in data_struct]					)
       elif isinstance(a_  ,			(list, tuple)					):
            return self._consolidate([self.recursive_tensorize(a_					) for substruct in data_struct]					)
       return self._tensorize(a_					)




  def 						__lowercase(   self  :						Union[str, Any]  ,			a_  :						dict					)->							List[str]:




       """simple docstring"""
       return map_nested(self._recursive_tensorize  ,			a_  ,			map_list=a_					)




  def 						__lowercase(   self  :						Dict  ,			a_  :						pa.Table					)->							Mapping:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.numpy_arrow_extractor().extract_row(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.python_features_decoder.decode_row(a_					)
       return self.recursive_tensorize(a_					)




  def 						__lowercase(   self  :						Any  ,			a_  :						pa.Table					)->							"jax.Array":




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.numpy_arrow_extractor().extract_column(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.python_features_decoder.decode_column(a_  ,			pa_table.column_names[0]					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.recursive_tensorize(a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self._consolidate(a_					)
       return column




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						pa.Table					)->							Mapping:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.numpy_arrow_extractor().extract_batch(a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		self.python_features_decoder.decode_batch(a_					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.recursive_tensorize(a_					)
       for column_name in batch:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self._consolidate(batch[column_name]					)
       return batch





"""simple docstring"""





from collections.abc import Callable

import numpy as np



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						np.array:


					"""simple docstring"""





					_UpperCamelCase									=    int(np.ceil((x_end - xa) / step_size      )      )
					_UpperCamelCase									=    np.zeros((n + 1,)      )
					_UpperCamelCase									=    ya
					_UpperCamelCase									=    xa

					for k in range(__snake_case      ):
										_UpperCamelCase									=    y[k] + step_size * ode_func(__snake_case,	y[k]      )
										_UpperCamelCase									=    y[k] + (
										    (step_size / 2) * (ode_func(__snake_case,	y[k]      ) + ode_func(x + step_size,	__snake_case      ))
										)
										x += step_size

					return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()






