# 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

_lowerCAmelCase 				=     get_logger()

_lowerCAmelCase 				=     None

class    _UpperCAmelCase							(		TensorFormatter[Mapping, '''jax.Array''', Mapping]     ):
      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_     :				Union[str, Any]			      =	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_     :				int			      =	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_     :				Tuple			      =	str(jax.devices()[0]       )
         A_     :				str			      =	jnp_array_kwargs
      @staticmethod
      def 		_lowerCamelCase      (		):
         import jax

         return {str(a__       ): device for device in jax.devices()}
      def 		_lowerCamelCase      (		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 		_lowerCamelCase      (		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_     :				Optional[Any]			      =	{}

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

            if isinstance(a__      ,       PIL.Image.Image       ):
               A_     :				Optional[int]			      =	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_     :				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(a__      ,       **{**default_dtype, **self.jnp_array_kwargs}       )
      def 		_lowerCamelCase      (		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_     :				Optional[Any]			      =	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 		_lowerCamelCase      (		self      ,       a__       ):
         return map_nested(self._recursive_tensorize      ,       a__      ,       map_list=a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[str]			      =	self.numpy_arrow_extractor().extract_row(a__       )
         A_     :				Dict			      =	self.python_features_decoder.decode_row(a__       )
         return self.recursive_tensorize(a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				int			      =	self.numpy_arrow_extractor().extract_column(a__       )
         A_     :				List[str]			      =	self.python_features_decoder.decode_column(a__      ,       pa_table.column_names[0]       )
         A_     :				List[Any]			      =	self.recursive_tensorize(a__       )
         A_     :				Optional[int]			      =	self._consolidate(a__       )
         return column






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[str]			      =	self.numpy_arrow_extractor().extract_batch(a__       )
         A_     :				Optional[int]			      =	self.python_features_decoder.decode_batch(a__       )
         A_     :				Any			      =	self.recursive_tensorize(a__       )
         for column_name in batch:
            A_     :				int			      =	self._consolidate(batch[column_name]       )
         return batch













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase 				=     {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NllbMoeForConditionalGeneration""",
          """NllbMoeModel""",
          """NllbMoePreTrainedModel""",
          """NllbMoeTop2Router""",
          """NllbMoeSparseMLP""",
      ]


if TYPE_CHECKING:
      from .configuration_nllb_moe import (
          NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
          NllbMoeConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nllb_moe import (
                NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
                NllbMoeForConditionalGeneration,
                NllbMoeModel,
                NllbMoePreTrainedModel,
                NllbMoeSparseMLP,
                NllbMoeTopaRouter,
            )


else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






