
# 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							:Union[str, Any]							 =				get_logger()

SCREAMING_SNAKE_CASE							:Optional[dict]							 =				None





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




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: Tuple=None							,A					: List[Any]=None							,**A					: str ):
					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`." )
					__A   			= 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:
									__A   			= 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] )}.''' )
									__A   			= str(jax.devices()[0] )
					__A   			= jnp_array_kwargs






	@staticmethod
	def 				UpperCamelCase_  (       ):
					import jax

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






	def 				UpperCamelCase_  (       self					: Any							,A					: Tuple ):
					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					: int							,A					: int ):
					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()

					__A   			= {}

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

									if isinstance(A							,PIL.Image.Image ):
													__A   			= 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:
									__A   			= 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					: Union[str, Any]							,A					: Union[str, Any] ):
					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 ):
									__A   			= 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					: Any							,A					: dict ):
					return map_nested(self._recursive_tensorize							,A							,map_list=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: pa.Table ):
					__A   			= self.numpy_arrow_extractor().extract_row(A )
					__A   			= self.python_features_decoder.decode_row(A )
					return self.recursive_tensorize(A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: pa.Table ):
					__A   			= self.numpy_arrow_extractor().extract_column(A )
					__A   			= self.python_features_decoder.decode_column(A							,pa_table.column_names[0] )
					__A   			= self.recursive_tensorize(A )
					__A   			= self._consolidate(A )
					return column







	def 				UpperCamelCase_  (       self					: Dict							,A					: pa.Table ):
					__A   			= self.numpy_arrow_extractor().extract_batch(A )
					__A   			= self.python_features_decoder.decode_batch(A )
					__A   			= self.recursive_tensorize(A )
					for column_name in batch:
									__A   			= self._consolidate(batch[column_name] )
					return batch


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
