



# 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

UpperCAmelCase							 =					get_logger()

UpperCAmelCase							 =					None

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







							'''simple docstring'''






							def __init__(							self  ,  a_=None  ,  a_=None  ,  **a_		):
											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 _a      (							):
											import jax

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


							def _a      (							self  ,  a_		):
											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 _a      (							self  ,  a_		):
											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 _a      (							self  ,  a_		):
											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 _a      (							self  ,  a_		):
											return map_nested(self._recursive_tensorize  ,  a_  ,  map_list=a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.numpy_arrow_extractor().extract_row(a_		)
											a__				      =		self.python_features_decoder.decode_row(a_		)
											return self.recursive_tensorize(a_		)


							def _a      (							self  ,  a_		):
											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 _a      (							self  ,  a_		):
											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 math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				if velocity > c:
								raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""		)
				elif velocity < 1:
								# Usually the speed should be much higher than 1 (c order of magnitude)
								raise ValueError("""Speed must be greater than or equal to 1!"""		)

				return velocity / c

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

				"""simple docstring"""
				# Ensure event is not empty
				if event is None:
								a__				      =		np.array([ct, x, y, z]		)  # Symbolic four vector
				else:
								event[0] *= c  # x0 is ct (speed of light * time)

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	print("""Example of four vector: """)
	print(f"""ct' = {four_vector[0]}""")
	print(f"""x' = {four_vector[1]}""")
	print(f"""y' = {four_vector[2]}""")
	print(f"""z' = {four_vector[3]}""")

	# Substitute symbols with numerical values
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

	print(f"""\n{numerical_vector}""")

