


"""simple docstring"""
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							:      Optional[int]						=      get_logger()

__SCREAMING_SNAKE_CASE							:      Optional[dict]						=      None





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





							'''simple docstring'''

							def __init__(			self							: Tuple						,    UpperCAmelCase_							: List[Any]=None						,    UpperCAmelCase_							: int=None						,    **UpperCAmelCase_							: Dict    )   ->int:



														"""simple docstring"""



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

														if isinstance(lowerCAmelCase__						,    lowerCAmelCase__    ):
																					raise ValueError(
																					    F"""Expected {device} to be a `str` not {type(lowerCAmelCase__    )}, 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`."""    )
														snake_case_          =						device if isinstance(lowerCAmelCase__						,    lowerCAmelCase__    ) 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:
																					snake_case_          =						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]    )}."""    )
																					snake_case_          =						str(jax.devices()[0]    )
														snake_case_          =						jnp_array_kwargs



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



														"""simple docstring"""



														import jax

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



							def 						lowerCAmelCase    (			self							: List[Any]						,    UpperCAmelCase_							: List[Any]    )   ->Tuple:



														"""simple docstring"""



														import jax
														import jax.numpy as jnp

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



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: Union[str, Any]    )   ->Optional[Any]:



														"""simple docstring"""



														import jax
														import jax.numpy as jnp

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

														snake_case_          =						{}

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

																					if isinstance(lowerCAmelCase__						,    PIL.Image.Image    ):
																												snake_case_          =						np.asarray(lowerCAmelCase__    )

        # 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:
																					snake_case_          =						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(lowerCAmelCase__						,    **{**default_dtype, **self.jnp_array_kwargs}    )



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: List[str]    )   ->Union[str, Any]:



														"""simple docstring"""



														import jax

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

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



							def 						lowerCAmelCase    (			self							: str						,    UpperCAmelCase_							: str    )   ->Dict:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: List[Any]    )   ->Mapping:



														"""simple docstring"""



														snake_case_          =						self.numpy_arrow_extractor().extract_row(lowerCAmelCase__    )
														snake_case_          =						self.python_features_decoder.decode_row(lowerCAmelCase__    )
														return self.recursive_tensorize(lowerCAmelCase__    )



							def 						lowerCAmelCase    (			self							: Union[str, Any]						,    UpperCAmelCase_							: Optional[int]    )   ->"jax.Array":



														"""simple docstring"""



														snake_case_          =						self.numpy_arrow_extractor().extract_column(lowerCAmelCase__    )
														snake_case_          =						self.python_features_decoder.decode_column(lowerCAmelCase__						,    pa_table.column_names[0]    )
														snake_case_          =						self.recursive_tensorize(lowerCAmelCase__    )
														snake_case_          =						self._consolidate(lowerCAmelCase__    )
														return column



							def 						lowerCAmelCase    (			self							: Union[str, Any]						,    UpperCAmelCase_							: List[str]    )   ->Mapping:



														"""simple docstring"""



														snake_case_          =						self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__    )
														snake_case_          =						self.python_features_decoder.decode_batch(lowerCAmelCase__    )
														snake_case_          =						self.recursive_tensorize(lowerCAmelCase__    )
														for column_name in batch:
																					snake_case_          =						self._consolidate(batch[column_name]    )
														return batch







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





print(fmt.format(*covid_stats()))



