
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

lowerCamelCase_  :	Optional[int] 				=					get_logger()

lowerCamelCase_  :	Optional[dict] 				=					None

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






			'''simple docstring'''

			def __init__(    self	:    Any  ,		snake_case_	:    Dict=None  ,		snake_case_	:    Any=None  ,		**snake_case_	:    Dict ):
							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`.""" )
							UpperCamelCase_:							int		=  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:
											UpperCamelCase_:							Tuple		=  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] )}.''' )
											UpperCamelCase_:							Tuple		=  str(jax.devices()[0] )
							UpperCamelCase_:							int		=  jnp_array_kwargs

			@staticmethod
			def  lowerCAmelCase__		(    ):
							import jax

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

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    Union[str, Any] ):
							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	:    Tuple  ,		snake_case_	:    Tuple ):
							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()

							UpperCamelCase_:							Tuple		=  {}

							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:
															UpperCamelCase_:							List[str]		=  {"dtype": jnp.intaa}
											else:
															UpperCamelCase_:							Union[str, Any]		=  {"dtype": jnp.intaa}
							elif isinstance(lowerCAmelCase__  ,		(np.number, np.ndarray) ) and np.issubdtype(value.dtype  ,		np.floating ):
											UpperCamelCase_:							Union[str, Any]		=  {"dtype": jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
											import PIL.Image

											if isinstance(lowerCAmelCase__  ,		PIL.Image.Image ):
															UpperCamelCase_:							List[Any]		=  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:
											UpperCamelCase_:							str		=  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	:    List[Any]  ,		snake_case_	:    int ):
							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 ):
											UpperCamelCase_:							Tuple		=  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	:    List[Any]  ,		snake_case_	:    Optional[int] ):
							return map_nested(self._recursive_tensorize  ,		lowerCAmelCase__  ,		map_list=lowerCAmelCase__ )

			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    Tuple ):
							UpperCamelCase_:							Dict		=  self.numpy_arrow_extractor().extract_row(lowerCAmelCase__ )
							UpperCamelCase_:							str		=  self.python_features_decoder.decode_row(lowerCAmelCase__ )
							return self.recursive_tensorize(lowerCAmelCase__ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    Tuple ):
							UpperCamelCase_:							str		=  self.numpy_arrow_extractor().extract_column(lowerCAmelCase__ )
							UpperCamelCase_:							Tuple		=  self.python_features_decoder.decode_column(lowerCAmelCase__  ,		pa_table.column_names[0] )
							UpperCamelCase_:							List[Any]		=  self.recursive_tensorize(lowerCAmelCase__ )
							UpperCamelCase_:							Any		=  self._consolidate(lowerCAmelCase__ )
							return column




			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Optional[int] ):
							UpperCamelCase_:							Union[str, Any]		=  self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__ )
							UpperCamelCase_:							Optional[Any]		=  self.python_features_decoder.decode_batch(lowerCAmelCase__ )
							UpperCamelCase_:							List[Any]		=  self.recursive_tensorize(lowerCAmelCase__ )
							for column_name in batch:
											UpperCamelCase_:							str		=  self._consolidate(batch[column_name] )
							return batch








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_  :	Dict 				=					{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							lowerCamelCase_  :	Any 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






