



"""simple docstring"""
from typing import List, Union

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


_lowerCAmelCase					:Any  =      logging.get_logger(__name__)

_lowerCAmelCase					:List[str]  =      {
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}
class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     a__             ="led"
     a__             ={
         "num_attention_heads": "encoder_attention_heads",
         "hidden_size": "d_model",
         "attention_probs_dropout_prob": "attention_dropout",
         "initializer_range": "init_std",
     }





     def __init__(      self ,			A=5_0_2_6_5 ,			A=1_6_3_8_4 ,			A=1_0_2_4 ,			A=1_2 ,			A=4_0_9_6 ,			A=1_6 ,			A=1_2 ,			A=4_0_9_6 ,			A=1_6 ,			A=0.0 ,			A=0.0 ,			A=True ,			A=True ,			A="gelu" ,			A=1_0_2_4 ,			A=0.1 ,			A=0.0 ,			A=0.0 ,			A=0.02 ,			A=2 ,			A=0.0 ,			A=1 ,			A=0 ,			A=2 ,			A = 5_1_2 ,			**A ,			)							->      Optional[int]:
         _UpperCAmelCase							:						str       							=					vocab_size
         _UpperCAmelCase							:						str       							=					max_encoder_position_embeddings
         _UpperCAmelCase							:						Union[str, Any]       							=					max_decoder_position_embeddings
         _UpperCAmelCase							:						str       							=					d_model
         _UpperCAmelCase							:						Tuple       							=					encoder_ffn_dim
         _UpperCAmelCase							:						Union[str, Any]       							=					encoder_layers
         _UpperCAmelCase							:						str       							=					encoder_attention_heads
         _UpperCAmelCase							:						int       							=					decoder_ffn_dim
         _UpperCAmelCase							:						List[str]       							=					decoder_layers
         _UpperCAmelCase							:						Optional[Any]       							=					decoder_attention_heads
         _UpperCAmelCase							:						List[str]       							=					dropout
         _UpperCAmelCase							:						List[str]       							=					attention_dropout
         _UpperCAmelCase							:						Any       							=					activation_dropout
         _UpperCAmelCase							:						Any       							=					activation_function
         _UpperCAmelCase							:						Any       							=					init_std
         _UpperCAmelCase							:						Union[str, Any]       							=					encoder_layerdrop
         _UpperCAmelCase							:						Any       							=					decoder_layerdrop
         _UpperCAmelCase							:						Tuple       							=					classifier_dropout
         _UpperCAmelCase							:						str       							=					use_cache
         _UpperCAmelCase							:						Optional[Any]       							=					encoder_layers
         _UpperCAmelCase							:						List[Any]       							=					attention_window

         super().__init__(
             pad_token_id=A ,			bos_token_id=A ,			eos_token_id=A ,			is_encoder_decoder=A ,			decoder_start_token_id=A ,			**A ,			)







"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import tensorflow as tf
class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):





     '''simple docstring'''



     def __init__(      self ,			A=None ,			**A	)							->      List[str]:
         super().__init__(features=A	)
         _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
         import tensorflow as tf  # noqa: import tf at initialization





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
         import tensorflow as tf

         if isinstance(A ,			A	) and column:
             if all(
                 isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                 return tf.stack(A	)
             elif all(
                 isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                 for x in column	):
                 # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                 return tf.ragged.stack(A	)

         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      int:
         import tensorflow as tf

         if value is None:
             return value

         _UpperCAmelCase							:						Any       							=					{}

         if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
             _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
         elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
             _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(A ,			PIL.Image.Image	):
                 _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)

         return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
         import tensorflow as tf

         # 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 ,			tf.Tensor	):
             _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(A ,			np.ndarray	):
             if data_struct.dtype == object:  # tf tensors 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	)							->      Optional[int]:
         return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
         _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
         return self.recursive_tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
         _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
         _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
         _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
         for column_name in batch:
             _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
         return batch


