


# 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

SCREAMING_SNAKE_CASE       :      Any												= get_logger()

SCREAMING_SNAKE_CASE       :      Optional[dict]												= None






class _lowerCamelCase(						TensorFormatter[Mapping, """jax.Array""", Mapping]   ):
       def __init__(					self,      lowerCamelCase=None,      lowerCamelCase=None,      **lowerCamelCase)       ->   List[Any]:




           """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`.')
           _lowercase       :							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:
               _lowercase       :							List[Any]      		=		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])}.''')
               _lowercase       :							Dict      		=		str(jax.devices()[0])
           _lowercase       :							str      		=		jnp_array_kwargs
       @staticmethod
       def 			UpperCamelCase     (					)       ->   Dict[str, "jaxlib.xla_extension.Device"]:




           """simple docstring"""

           import jax

           return {str(lowerCamelCase): device for device in jax.devices()}
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Any:




           """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 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Union[str, 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()

           _lowercase       :							Any      		=		{}

           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:
                   _lowercase       :							Dict      		=		{'dtype': jnp.intaa}
               else:
                   _lowercase       :							List[str]      		=		{'dtype': jnp.intaa}
           elif isinstance(lowerCamelCase,      (np.number, np.ndarray)) and np.issubdtype(value.dtype,      np.floating):
               _lowercase       :							Tuple      		=		{'dtype': jnp.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(lowerCamelCase,      PIL.Image.Image):
                   _lowercase       :							List[str]      		=		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:
               _lowercase       :							Any      		=		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 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Dict:




           """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):
               _lowercase       :							Any      		=		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 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




           """simple docstring"""

           return map_nested(self._recursive_tensorize,      lowerCamelCase,      map_list=lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Mapping:




           """simple docstring"""

           _lowercase       :							Dict      		=		self.numpy_arrow_extractor().extract_row(lowerCamelCase)
           _lowercase       :							Tuple      		=		self.python_features_decoder.decode_row(lowerCamelCase)
           return self.recursive_tensorize(lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   "jax.Array":




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		self.numpy_arrow_extractor().extract_column(lowerCamelCase)
           _lowercase       :							Union[str, Any]      		=		self.python_features_decoder.decode_column(lowerCamelCase,      pa_table.column_names[0])
           _lowercase       :							List[str]      		=		self.recursive_tensorize(lowerCamelCase)
           _lowercase       :							Tuple      		=		self._consolidate(lowerCamelCase)
           return column




       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Mapping:




           """simple docstring"""

           _lowercase       :							str      		=		self.numpy_arrow_extractor().extract_batch(lowerCamelCase)
           _lowercase       :							Dict      		=		self.python_features_decoder.decode_batch(lowerCamelCase)
           _lowercase       :							List[str]      		=		self.recursive_tensorize(lowerCamelCase)
           for column_name in batch:
               _lowercase       :							str      		=		self._consolidate(batch[column_name])
           return batch




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase			:     Any           =   {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     int           =   [
        'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MCTCTForCTC',
        'MCTCTModel',
        'MCTCTPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
    from .feature_extraction_mctct import MCTCTFeatureExtractor
    from .processing_mctct import MCTCTProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

