
"""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

_lowercase							      = get_logger()

_lowercase							      = None







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



						'''simple docstring'''


						def __init__(       self   :					Optional[int]     ,A_   :					Any=None     ,A_   :					int=None     ,**A_   :					str					)   ->      Union[str, Any]:
							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   _SCREAMING_SNAKE_CASE					(       )   ->      Optional[Any]:
							import jax

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					Dict					)   ->      Union[str, Any]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					List[str]					)   ->      Union[str, Any]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					int     ,A_   :					Optional[int]					)   ->      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   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					str					)   ->      int:
							return map_nested(self._recursive_tensorize     ,A_     ,map_list=A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Tuple					)   ->      List[str]:
							A        =      self.numpy_arrow_extractor().extract_row(A_					)
							A        =      self.python_features_decoder.decode_row(A_					)
							return self.recursive_tensorize(A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int     ,A_   :					Optional[int]					)   ->      List[str]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					Dict					)   ->      List[Any]:
							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


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

snake_case_			  = TypeVar('''T''')
snake_case_			  = TypeVar('''U''')


class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self						,   a						,   a):
         lowercase__    :   List[Any]      =       key
         lowercase__    :   List[Any]      =       val
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None





    def __repr__(      self):
         return (
             f"""Node: key: {self.key}, val: {self.val}, """
             f"""has next: {bool(self.next)}, has prev: {bool(self.prev)}"""
         )



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self):
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__      , lowercase__    :   Union[str, Any]      =       self.rear, self.head





    def __repr__(      self):
         lowercase__    :   Any      =       ['DoubleLinkedList']
         lowercase__    :   List[str]      =       self.head
         while node.next is not None:
              rep.append(str(a))
              lowercase__    :   Tuple      =       node.next
         rep.append(str(self.rear))
         return ",\n    ".join(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Optional[Any]      =       self.rear.prev

         # All nodes other than self.head are guaranteed to have non-None previous
         assert previous is not None

         lowercase__    :   Dict      =       node
         lowercase__    :   int      =       previous
         lowercase__    :   Union[str, Any]      =       node
         lowercase__    :   Optional[int]      =       self.rear





    def       snake_case_				(      self						,   a):

         if node.prev is None or node.next is None:
              return None

         lowercase__    :   Union[str, Any]      =       node.next
         lowercase__    :   Tuple      =       node.prev
         lowercase__    :   Union[str, Any]      =       None
         lowercase__    :   List[Any]      =       None
         return node



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):
    __lowerCamelCase						:       dict[Callable[[T], U], LRUCache[T, U]]														=     {}





    def __init__(      self						,   a):
         lowercase__    :   DoubleLinkedList[T, U]      =       DoubleLinkedList()
         lowercase__    :   Optional[Any]      =       capacity
         lowercase__    :   Union[str, Any]      =       0
         lowercase__    :   Tuple      =       0
         lowercase__    :   int      =       0
         lowercase__    :   dict[T, DoubleLinkedListNode[T, U]]      =       {}





    def __repr__(      self):
         return (
             f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
             f"""capacity={self.capacity}, current size={self.num_keys})"""
         )





    def __contains__(      self						,   a):
         return key in self.cache





    def       snake_case_				(      self						,   a):
         # Note: pythonic interface would throw KeyError rather than return None

         if key in self.cache:
              self.hits += 1
              lowercase__    :   DoubleLinkedListNode[T, U]      =       self.cache[key]
              lowercase__    :   str      =       self.list.remove(self.cache[key])
              assert node == value_node

              # node is guaranteed not None because it is in self.cache
              assert node is not None
              self.list.add(a)
              return node.val
         self.miss += 1
         return None





    def       snake_case_				(      self						,   a						,   a):

         if key not in self.cache:
              if self.num_keys >= self.capacity:
                   # delete first node (oldest) when over capacity
                   lowercase__    :   Optional[int]      =       self.list.head.next

                   # guaranteed to have a non-None first node when num_keys > 0
                   # explain to type checker via assertions
                   assert first_node is not None
                   assert first_node.key is not None
                   assert (
                       self.list.remove(a) is not None
                   )  # node guaranteed to be in list assert node.key is not None

                   del self.cache[first_node.key]
                   self.num_keys -= 1
              lowercase__    :   Optional[Any]      =       DoubleLinkedListNode(a						,   a)
              self.list.add(self.cache[key])
              self.num_keys += 1

         else:
              # bump node to the end of the list, update value
              lowercase__    :   Any      =       self.list.remove(self.cache[key])
              assert node is not None  # node guaranteed to be in list
              lowercase__    :   Union[str, Any]      =       value
              self.list.add(a)





    @classmethod
    def       snake_case_				(      cls						,   a = 128):

         def cache_decorator_inner(a) -> Callable[..., U]:
              def cache_decorator_wrapper(*a) -> U:
                   if func not in cls.decorator_function_to_instance_map:
                        lowercase__    :   Dict      =       LRUCache(a)

                   lowercase__    :   str      =       cls.decorator_function_to_instance_map[func].get(args[0])
                   if result is None:
                        lowercase__    :   str      =       func(*a)
                        cls.decorator_function_to_instance_map[func].put(args[0]						,   a)
                   return result

              def cache_info() -> LRUCache[T, U]:
                   return cls.decorator_function_to_instance_map[func]

              setattr(a						,   'cache_info'						,   a)  # noqa: B010

              return cache_decorator_wrapper

         return cache_decorator_inner


if __name__ == "__main__":
       import doctest

       doctest.testmod()

