



# 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		:				List[str]      		=get_logger()

_lowercase		:				Optional[dict]      		=None

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







						"""simple docstring"""




						def __init__(       self ,       __lowercase=None ,       __lowercase=None ,       **__lowercase  ) ->				List[str]:
									"""simple docstring"""




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

									if isinstance(__lowercase ,       __lowercase  ):
												raise ValueError(
												    F'''Expected {device} to be a `str` not {type(__lowercase  )}, 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__       :						List[Any]							=  device if isinstance(__lowercase ,       __lowercase  ) 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__       :						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]  )}.'''  )
												a__       :						Optional[int]							=  str(jax.devices()[0]  )
									a__       :						List[Any]							=  jnp_array_kwargs

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




									import jax

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									import jax
									import jax.numpy as jnp

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									import jax
									import jax.numpy as jnp

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

									a__       :						List[Any]							=  {}

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

												if isinstance(__lowercase ,       PIL.Image.Image  ):
															a__       :						str							=  np.asarray(__lowercase  )

        # 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__       :						Tuple							=  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(__lowercase ,       **{**default_dtype, **self.jnp_array_kwargs}  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									import jax

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

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Mapping:
									"""simple docstring"""




									a__       :						str							=  self.numpy_arrow_extractor().extract_row(__lowercase  )
									a__       :						Optional[Any]							=  self.python_features_decoder.decode_row(__lowercase  )
									return self.recursive_tensorize(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				"jax.Array":
									"""simple docstring"""




									a__       :						Tuple							=  self.numpy_arrow_extractor().extract_column(__lowercase  )
									a__       :						Tuple							=  self.python_features_decoder.decode_column(__lowercase ,       pa_table.column_names[0]  )
									a__       :						List[str]							=  self.recursive_tensorize(__lowercase  )
									a__       :						Optional[Any]							=  self._consolidate(__lowercase  )
									return column

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Mapping:
									"""simple docstring"""




									a__       :						List[str]							=  self.numpy_arrow_extractor().extract_batch(__lowercase  )
									a__       :						Optional[Any]							=  self.python_features_decoder.decode_batch(__lowercase  )
									a__       :						Dict							=  self.recursive_tensorize(__lowercase  )
									for column_name in batch:
												a__       :						Dict							=  self._consolidate(batch[column_name]  )
									return batch









from __future__ import annotations

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       float						,       _lowercase    :       float						,       )							->	tuple[str, float]:







			"""simple docstring"""
			if (stress, tangential_force, area).count(0) != 1:
						raise ValueError("""You cannot supply more or less than 2 values""")
			elif stress < 0:
						raise ValueError("""Stress cannot be negative""")
			elif tangential_force < 0:
						raise ValueError("""Tangential Force cannot be negative""")
			elif area < 0:
						raise ValueError("""Area cannot be negative""")
			elif stress == 0:
						return (
						    "stress",
						    tangential_force / area,
						)
			elif tangential_force == 0:
						return (
						    "tangential_force",
						    stress * area,
						)
			else:
						return (
						    "area",
						    tangential_force / stress,
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




