
"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


a					:	Optional[int]							=		logging.get_logger(__name__)

a					:	int							=		{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}




class 		a_						( __lowerCAmelCase	):
    a      :				Dict     =				'roc_bert'







    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]=3_05_22     ,					__UpperCamelCase			:       int=7_68     ,					__UpperCamelCase			:       Any=12     ,					__UpperCamelCase			:       List[str]=12     ,					__UpperCamelCase			:       Tuple=30_72     ,					__UpperCamelCase			:       int="gelu"     ,					__UpperCamelCase			:       Any=0.1     ,					__UpperCamelCase			:       List[str]=0.1     ,					__UpperCamelCase			:       List[Any]=5_12     ,					__UpperCamelCase			:       Union[str, Any]=2     ,					__UpperCamelCase			:       Tuple=0.0_2     ,					__UpperCamelCase			:       List[Any]=1e-12     ,					__UpperCamelCase			:       Optional[int]=True     ,					__UpperCamelCase			:       List[Any]=0     ,					__UpperCamelCase			:       int="absolute"     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       List[str]=True     ,					__UpperCamelCase			:       Any=True     ,					__UpperCamelCase			:       Tuple=7_68     ,					__UpperCamelCase			:       int=9_10     ,					__UpperCamelCase			:       Optional[Any]=5_12     ,					__UpperCamelCase			:       List[str]=2_48_58     ,					__UpperCamelCase			:       Tuple=True     ,					**__UpperCamelCase			:       Optional[Any]     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   max_position_embeddings
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   intermediate_size
        _UpperCAmelCase          =   hidden_act
        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   type_vocab_size
        _UpperCAmelCase          =   layer_norm_eps
        _UpperCAmelCase          =   use_cache
        _UpperCAmelCase          =   enable_pronunciation
        _UpperCAmelCase          =   enable_shape
        _UpperCAmelCase          =   pronunciation_embed_dim
        _UpperCAmelCase          =   pronunciation_vocab_size
        _UpperCAmelCase          =   shape_embed_dim
        _UpperCAmelCase          =   shape_vocab_size
        _UpperCAmelCase          =   concat_input
        _UpperCAmelCase          =   position_embedding_type
        _UpperCAmelCase          =   classifier_dropout
        super().__init__(pad_token_id=_UpperCamelCase     ,					**_UpperCamelCase     )

"""simple docstring"""



# 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					:	List[Any]							=		get_logger()

a					:	Optional[dict]							=		None




class 		a_						( TensorFormatter[Mapping, 'jax.Array', Mapping]	):







    def __init__(			self			:       int     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Optional[int]=None     ,					**__UpperCamelCase			:       int     )		->Tuple:






        '''simple docstring'''






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

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







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






        '''simple docstring'''






        import jax

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







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Any     )		->Union[str, Any]:






        '''simple docstring'''






        import jax
        import jax.numpy as jnp

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







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     )		->Optional[int]:






        '''simple docstring'''






        import jax
        import jax.numpy as jnp

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

        _UpperCAmelCase          =   {}

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

            if isinstance(__UpperCamelCase     ,					PIL.Image.Image     ):
                _UpperCAmelCase          =   np.asarray(__UpperCamelCase     )

        # 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          =   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(__UpperCamelCase     ,					**{**default_dtype, **self.jnp_array_kwargs}     )







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







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       dict     )		->int:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       pa.Table     )		->Mapping:






        '''simple docstring'''






        _UpperCAmelCase          =   self.numpy_arrow_extractor().extract_row(__UpperCamelCase     )
        _UpperCAmelCase          =   self.python_features_decoder.decode_row(__UpperCamelCase     )
        return self.recursive_tensorize(__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       pa.Table     )		->"jax.Array":






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       pa.Table     )		->Mapping:






        '''simple docstring'''






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