
"""simple docstring"""







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase__				    =       TypeVar('''T''')


class 				SCREAMING_SNAKE_CASE__    (		Generic[T]    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__ = True						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							{}  # dictionary of lists
         lowerCAmelCase					:					Dict								=							directed

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         if not self.directed:  # For undirected graphs
           # if both source vertex and destination vertex are both present in the
           # adjacency list, add destination vertex to source vertex list of adjacent
           # vertices and add source vertex to destination vertex list of adjacent
           # vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
             self.adj_list[source_vertex].append(__lowerCAmelCase						)
             self.adj_list[destination_vertex].append(__lowerCAmelCase						)
           # if only source vertex is present in adjacency list, add destination vertex
           # to source vertex list of adjacent vertices, then create a new vertex with
           # destination vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif source_vertex in self.adj_list:
             self.adj_list[source_vertex].append(__lowerCAmelCase						)
             lowerCAmelCase					:					Any								=							[source_vertex]
           # if only destination vertex is present in adjacency list, add source vertex
           # to destination vertex list of adjacent vertices, then create a new vertex
           # with source vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif destination_vertex in self.adj_list:
             self.adj_list[destination_vertex].append(__lowerCAmelCase						)
             lowerCAmelCase					:					Union[str, Any]								=							[destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and assign a list
           # containing the destination vertex as it's first adjacent vertex also
           # create a new vertex with destination vertex as key and assign a list
           # containing the source vertex as it's first adjacent vertex.
           else:
             lowerCAmelCase					:					Optional[int]								=							[destination_vertex]
             lowerCAmelCase					:					Dict								=							[source_vertex]
         else:  # For directed graphs
           # if both source vertex and destination vertex are present in adjacency
           # list, add destination vertex to source vertex list of adjacent vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
             self.adj_list[source_vertex].append(__lowerCAmelCase						)
           # if only source vertex is present in adjacency list, add destination
           # vertex to source vertex list of adjacent vertices and create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           elif source_vertex in self.adj_list:
             self.adj_list[source_vertex].append(__lowerCAmelCase						)
             lowerCAmelCase					:					Any								=							[]
           # if only destination vertex is present in adjacency list, create a new
           # vertex with source vertex as key and assign a list containing destination
           # vertex as first adjacent vertex
           elif destination_vertex in self.adj_list:
             lowerCAmelCase					:					Optional[Any]								=							[destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and a list containing
           # destination vertex as it's first adjacent vertex. Then create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           else:
             lowerCAmelCase					:					List[str]								=							[destination_vertex]
             lowerCAmelCase					:					Optional[int]								=							[]

         return self







       def __repr__(						self						):






         """simple docstring"""





         return pformat(self.adj_list						)









"""simple docstring"""







# 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

lowerCAmelCase__				    =       get_logger()

lowerCAmelCase__				    =       None


class 				SCREAMING_SNAKE_CASE__    (		TensorFormatter[Mapping, "jax.Array", Mapping]    ):
       """simple docstring"""


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






         """simple docstring"""





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

         if isinstance(snake_case__		,					snake_case__						):
           raise ValueError(
               f"""Expected {device} to be a `str` not {type(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`."						)
         lowerCAmelCase					:					List[str]								=							device if isinstance(snake_case__		,					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:
           lowerCAmelCase					:					Optional[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]						)}."""						)
           lowerCAmelCase					:					Dict								=							str(jax.devices()[0]						)
         lowerCAmelCase					:					Union[str, Any]								=							jnp_array_kwargs

       @staticmethod
       def 	lowercase__							(						):






         """simple docstring"""





         import jax

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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

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

         lowerCAmelCase					:					Tuple								=							{}

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

           if isinstance(snake_case__		,					PIL.Image.Image						):
             lowerCAmelCase					:					Dict								=							np.asarray(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:
           lowerCAmelCase					:					str								=							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(snake_case__		,					**{**default_dtype, **self.jnp_array_kwargs}						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax

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

           if isinstance(snake_case__		,					torch.Tensor						):
             return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
         if hasattr(snake_case__		,					"__array__"						) and not isinstance(snake_case__		,					jax.Array						):
           lowerCAmelCase					:					Dict								=							data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(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(snake_case__						) for substruct in data_struct]						)
         elif isinstance(snake_case__		,					(list, tuple)						):
           return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         return self._tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.numpy_arrow_extractor().extract_row(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.python_features_decoder.decode_row(snake_case__						)
         return self.recursive_tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.numpy_arrow_extractor().extract_column(snake_case__						)
         lowerCAmelCase					:					Dict								=							self.python_features_decoder.decode_column(snake_case__		,					pa_table.column_names[0]						)
         lowerCAmelCase					:					Optional[Any]								=							self.recursive_tensorize(snake_case__						)
         lowerCAmelCase					:					Dict								=							self._consolidate(snake_case__						)
         return column







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.numpy_arrow_extractor().extract_batch(snake_case__						)
         lowerCAmelCase					:					str								=							self.python_features_decoder.decode_batch(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.recursive_tensorize(snake_case__						)
         for column_name in batch:
           lowerCAmelCase					:					Optional[int]								=							self._consolidate(batch[column_name]						)
         return batch





