# 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

A     :  Optional[Any]					     =       get_logger()

A     :  Optional[dict]					     =       None


class   lowerCamelCase					(TensorFormatter[Mapping, '''jax.Array''', Mapping]					):
		"""simple docstring"""







		def __init__(							self :       int  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Union[str, Any]=None  ,     **__magic_name__ :       str   )  ->	Tuple:
								super().__init__(features=__magic_name__   )
								import jax
								from jaxlib.xla_client import Device

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







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

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







		def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
								import jax
								import jax.numpy as jnp

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







		def    __A     (							self :       Tuple  ,     __magic_name__ :       int   )  ->	Optional[Any]:
								import jax
								import jax.numpy as jnp

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

								SCREAMING_SNAKE_CASE_          =  {}

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

														if isinstance(__magic_name__  ,     PIL.Image.Image   ):
																				SCREAMING_SNAKE_CASE_          =  np.asarray(__magic_name__   )

        # 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:
														SCREAMING_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(__magic_name__  ,     **{**default_dtype, **self.jnp_array_kwargs}   )







		def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Optional[Any]   )  ->	Union[str, Any]:
								import jax

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

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







		def    __A     (							self :       int  ,     __magic_name__ :       dict   )  ->	Any:
								return map_nested(self._recursive_tensorize  ,     __magic_name__  ,     map_list=__magic_name__   )







		def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
								SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_row(__magic_name__   )
								SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_row(__magic_name__   )
								return self.recursive_tensorize(__magic_name__   )







		def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	"jax.Array":
								SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_column(__magic_name__   )
								SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_column(__magic_name__  ,     pa_table.column_names[0]   )
								SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
								SCREAMING_SNAKE_CASE_          =  self._consolidate(__magic_name__   )
								return column







		def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
								SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_batch(__magic_name__   )
								SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_batch(__magic_name__   )
								SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
								for column_name in batch:
														SCREAMING_SNAKE_CASE_          =  self._consolidate(batch[column_name]   )
								return batch



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = None				):
      if version.parse(hfh.__version__				).release < version.parse("0.11.0"				).release:
            # old versions of hfh don't url-encode the file path
            SCREAMING_SNAKE_CASE_          =  quote(__UpperCamelCase				)
      return hfh.hf_hub_url(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"     ,      revision=__UpperCamelCase				)


