'''simple docstring'''




from typing import List, Union

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


__SCREAMING_SNAKE_CASE	:					Dict					=  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:					int					=  {
    """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      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      __UpperCamelCase:  str      =						"led"
      __UpperCamelCase:  int      =						{
          "num_attention_heads": "encoder_attention_heads",
          "hidden_size": "d_model",
          "attention_probs_dropout_prob": "attention_dropout",
          "initializer_range": "init_std",
      }




      def __init__(	self						:    List[str]						, A						:    Optional[int]=50265						, A						:    int=16384						, A						:    Any=1024						, A						:    int=12						, A						:    Optional[int]=4096						, A						:    List[str]=16						, A						:    str=12						, A						:    Optional[Any]=4096						, A						:    Union[str, Any]=16						, A						:    List[Any]=0.0						, A						:    Optional[int]=0.0						, A						:    Optional[Any]=True						, A						:    Optional[Any]=True						, A						:    int="gelu"						, A						:    Optional[Any]=1024						, A						:    Optional[int]=0.1						, A						:    Tuple=0.0						, A						:    Tuple=0.0						, A						:    Any=0.02						, A						:    Dict=2						, A						:    Optional[Any]=0.0						, A						:    Tuple=1						, A						:    int=0						, A						:    Union[str, Any]=2						, A						:    Union[List[int], int] = 512						, **A						:    Union[str, Any]						, ):
             _UpperCAmelCase					:  Optional[int]											=   vocab_size
             _UpperCAmelCase					:  List[str]											=   max_encoder_position_embeddings
             _UpperCAmelCase					:  Optional[Any]											=   max_decoder_position_embeddings
             _UpperCAmelCase					:  Optional[Any]											=   d_model
             _UpperCAmelCase					:  Optional[Any]											=   encoder_ffn_dim
             _UpperCAmelCase					:  Optional[Any]											=   encoder_layers
             _UpperCAmelCase					:  Optional[int]											=   encoder_attention_heads
             _UpperCAmelCase					:  Union[str, Any]											=   decoder_ffn_dim
             _UpperCAmelCase					:  Optional[int]											=   decoder_layers
             _UpperCAmelCase					:  Any											=   decoder_attention_heads
             _UpperCAmelCase					:  Optional[Any]											=   dropout
             _UpperCAmelCase					:  Dict											=   attention_dropout
             _UpperCAmelCase					:  List[Any]											=   activation_dropout
             _UpperCAmelCase					:  int											=   activation_function
             _UpperCAmelCase					:  List[Any]											=   init_std
             _UpperCAmelCase					:  str											=   encoder_layerdrop
             _UpperCAmelCase					:  int											=   decoder_layerdrop
             _UpperCAmelCase					:  int											=   classifier_dropout
             _UpperCAmelCase					:  str											=   use_cache
             _UpperCAmelCase					:  Dict											=   encoder_layers
             _UpperCAmelCase					:  Optional[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'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

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

class      lowerCamelCase_     (TensorFormatter[Mapping, np.ndarray, Mapping]   ):
      '''simple docstring'''

      def __init__(	self						:    str						, A						:    List[str]=None						, **A						:    Optional[Any]	):
             super().__init__(features=A	)
             _UpperCAmelCase					:  Tuple											=   np_array_kwargs




      def 					_A     (	self						:    str						, A						:    Tuple	):
             if isinstance(A						, A	):
                    if column and all(
                        isinstance(A						, np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                           return np.stack(A	)
                    else:
                           # don't use np.array(column, dtype=object)
                           # since it fails in certain cases
                           # see https://stackoverflow.com/q/51005699
                           _UpperCAmelCase					:  List[str]											=   np.empty(len(A	)						, dtype=A	)
                           _UpperCAmelCase					:  int											=   column
                           return out
             return column




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             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
             elif isinstance(A						, np.number	):
                    return value

             _UpperCAmelCase					:  List[str]											=   {}

             if isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.integer	):
                    _UpperCAmelCase					:  str											=   {"dtype": np.intaa}
             elif isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.floating	):
                    _UpperCAmelCase					:  int											=   {"dtype": np.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(A						, PIL.Image.Image	):
                           return np.asarray(A						, **self.np_array_kwargs	)

             return np.asarray(A						, **{**default_dtype, **self.np_array_kwargs}	)




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             # 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						, (np.ndarray, np.character, np.number)	):
                    _UpperCAmelCase					:  Dict											=   data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(A						, np.ndarray	):
                    if data_struct.dtype == object:
                           return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             if isinstance(A						, (list, tuple)	):
                    return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             return self._tensorize(A	)




      def 					_A     (	self						:    List[Any]						, A						:    dict	):
             return map_nested(self._recursive_tensorize						, A						, map_list=A	)




      def 					_A     (	self						:    List[str]						, A						:    pa.Table	):
             _UpperCAmelCase					:  str											=   self.numpy_arrow_extractor().extract_row(A	)
             _UpperCAmelCase					:  Dict											=   self.python_features_decoder.decode_row(A	)
             return self.recursive_tensorize(A	)




      def 					_A     (	self						:    str						, A						:    pa.Table	):
             _UpperCAmelCase					:  List[str]											=   self.numpy_arrow_extractor().extract_column(A	)
             _UpperCAmelCase					:  Optional[Any]											=   self.python_features_decoder.decode_column(A						, pa_table.column_names[0]	)
             _UpperCAmelCase					:  Tuple											=   self.recursive_tensorize(A	)
             _UpperCAmelCase					:  Tuple											=   self._consolidate(A	)
             return column





      def 					_A     (	self						:    Union[str, Any]						, A						:    pa.Table	):
             _UpperCAmelCase					:  Any											=   self.numpy_arrow_extractor().extract_batch(A	)
             _UpperCAmelCase					:  Any											=   self.python_features_decoder.decode_batch(A	)
             _UpperCAmelCase					:  List[str]											=   self.recursive_tensorize(A	)
             for column_name in batch:
                    _UpperCAmelCase					:  Optional[Any]											=   self._consolidate(batch[column_name]	)
             return batch

