


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

snake_case    :   int	    =					get_logger()

snake_case    :   Optional[dict]	    =					None







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




 def __init__(		self ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  **_lowerCamelCase   ):
       super().__init__(features=snake_case__   )
       import jax
       from jaxlib.xla_client import Device

       if isinstance(snake_case__ ,  snake_case__   ):
             raise ValueError(
                 F'''Expected {device} to be a `str` not {type(snake_case__   )}, 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						:Tuple										=	device if isinstance(snake_case__ ,  snake_case__   ) 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						:List[str]										=	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						:Optional[int]										=	jnp_array_kwargs




 @staticmethod
 def 	SCREAMING_SNAKE_CASE__    (		):
       import jax

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       import jax
       import jax.numpy as jnp

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       import jax
       import jax.numpy as jnp

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

       a						:Tuple										=	{}

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

             if isinstance(snake_case__ ,  PIL.Image.Image   ):
                   a						:List[Any]										=	np.asarray(snake_case__   )

        # 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						:Union[str, 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(snake_case__ ,  **{**default_dtype, **self.jnp_array_kwargs}   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       import jax

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return map_nested(self._recursive_tensorize ,  snake_case__ ,  map_list=snake_case__   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Union[str, Any]										=	self.numpy_arrow_extractor().extract_row(snake_case__   )
       a						:List[str]										=	self.python_features_decoder.decode_row(snake_case__   )
       return self.recursive_tensorize(snake_case__   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	self.numpy_arrow_extractor().extract_column(snake_case__   )
       a						:List[Any]										=	self.python_features_decoder.decode_column(snake_case__ ,  pa_table.column_names[0]   )
       a						:int										=	self.recursive_tensorize(snake_case__   )
       a						:Dict										=	self._consolidate(snake_case__   )
       return column




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









from ...configuration_utils import PretrainedConfig







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'bert-generation'




 def __init__(		self ,  _lowerCamelCase=5_0358 ,  _lowerCamelCase=1024 ,  _lowerCamelCase=24 ,  _lowerCamelCase=16 ,  _lowerCamelCase=4096 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=0 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase="absolute" ,  _lowerCamelCase=True ,  **_lowerCamelCase ,  ):
       super().__init__(pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )

       a						:Optional[int]										=	vocab_size
       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Any										=	num_attention_heads
       a						:List[Any]										=	hidden_act
       a						:Tuple										=	intermediate_size
       a						:Any										=	hidden_dropout_prob
       a						:int										=	attention_probs_dropout_prob
       a						:Dict										=	max_position_embeddings
       a						:int										=	initializer_range
       a						:Union[str, Any]										=	layer_norm_eps
       a						:str										=	position_embedding_type
       a						:int										=	use_cache





