
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

a_    :						Tuple    		=      get_logger()

a_    :						List[str]    		=      None




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

    def __init__(			self :      Tuple							,    snake_case__ :      Union[str, Any]=None							,    snake_case__ :      str=None							,    **snake_case__ :      Dict    ):

     """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								=			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								=			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								=			str(jax.devices()[0]    )
     SCREAMING_SNAKE_CASE								=			jnp_array_kwargs

    @staticmethod
    def UpperCamelCase			(			):

     """simple docstring"""






     import jax

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

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Union[str, Any]    ):

     """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 UpperCamelCase			(			self :      Tuple							,    snake_case__ :      Optional[int]    ):

     """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								=			{}

     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								=			{'dtype': jnp.intaa}
      else:
       SCREAMING_SNAKE_CASE								=			{'dtype': jnp.intaa}
     elif isinstance(_A							,    (np.number, np.ndarray)    ) and np.issubdtype(value.dtype							,    np.floating    ):
      SCREAMING_SNAKE_CASE								=			{'dtype': jnp.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
      import PIL.Image

      if isinstance(_A							,    PIL.Image.Image    ):
       SCREAMING_SNAKE_CASE								=			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								=			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 UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      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								=			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 UpperCamelCase			(			self :      Tuple							,    snake_case__ :      Dict    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.numpy_arrow_extractor().extract_row(_A    )
     SCREAMING_SNAKE_CASE								=			self.python_features_decoder.decode_row(_A    )
     return self.recursive_tensorize(_A    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Dict    ):

     """simple docstring"""






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




    def UpperCamelCase			(			self :      Any							,    snake_case__ :      List[str]    ):

     """simple docstring"""






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




import random


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  float		,							_UpperCamelCase		:  bool = False	)		->							dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{i: [] for i in range(_UpperCamelCase	)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(_UpperCamelCase	)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(_UpperCamelCase	):
  for j in range(i + 1		,							_UpperCamelCase	):
   if random.random() < probability:
    graph[i].append(_UpperCamelCase	)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(_UpperCamelCase	)
 return graph


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							dict:
 '''simple docstring'''


 return {
     i: [j for j in range(_UpperCamelCase	) if i != j] for i in range(_UpperCamelCase	)
 }


if __name__ == "__main__":
      import doctest

      doctest.testmod()

