




from typing import Callable, Optional, Union

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


_snake_case            = logging.get_logger(__name__)

_snake_case            = {
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}







class 		lowercase							(     UpperCamelCase__	):
     _a											=		"xlm-prophetnet"
     _a											=		["past_key_values"]
     _a											=		{
         "num_attention_heads": "num_encoder_attention_heads",
     }

     def __init__(						self	,  _a = 0.1	,  _a = "gelu"	,  _a = 3_0522	,  _a = 1024	,  _a = 4096	,  _a = 12	,  _a = 16	,  _a = 4096	,  _a = 12	,  _a = 16	,  _a = 0.1	,  _a = 0.1	,  _a = 512	,  _a = 0.02	,  _a = True	,  _a = True	,  _a = 0	,  _a = 2	,  _a = 32	,  _a = 128	,  _a = False	,  _a = 0.0	,  _a = True	,  _a = 0	,  _a = 1	,  _a = 2	,  **_a	,  )		->							int:
           _A							:  List[Any]    =		vocab_size
           _A							:  Dict    =		hidden_size
           _A							:  int    =		encoder_ffn_dim
           _A							:  List[Any]    =		num_encoder_layers
           _A							:  str    =		num_encoder_attention_heads
           _A							:  Optional[int]    =		decoder_ffn_dim
           _A							:  Any    =		num_decoder_layers
           _A							:  List[str]    =		num_decoder_attention_heads
           _A							:  List[str]    =		max_position_embeddings
           _A							:  Dict    =		init_std  # Normal(0, this parameter)
           _A							:  Optional[int]    =		activation_function

           # parameters for xlmprophetnet
           _A							:  Union[str, Any]    =		ngram
           _A							:  List[Any]    =		num_buckets
           _A							:  int    =		relative_max_distance
           _A							:  Dict    =		disable_ngram_loss
           _A							:  Union[str, Any]    =		eps

           # 3 Types of Dropout
           _A							:  int    =		attention_dropout
           _A							:  Optional[Any]    =		activation_dropout
           _A							:  Union[str, Any]    =		dropout

           _A							:  Tuple    =		use_cache

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

     @property
     def       a__			(						self       )		->							int:
           return self.num_encoder_layers + self.num_decoder_layers



     @num_hidden_layers.setter
     def       a__			(						self	,  _a       )		->							Any:
           raise NotImplementedError(
               """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
               """ `num_decoder_layers`."""       )







import operator







def        lowerCAmelCase_			(  snake_case_,snake_case_ = False,snake_case_ = None  ):
      _A							:  str    =		operator.lt if reverse else operator.gt
      _A							:  Optional[Any]    =		solution or []

      if not arr:
            return solution

      _A							:  Dict    =		[arr.pop(0  )]
      for i, item in enumerate(snake_case_  ):
            if _operator(snake_case_,sublist[-1]  ):
                  sublist.append(snake_case_  )
                  arr.pop(snake_case_  )

    #  merging sublist into solution list
      if not solution:
            solution.extend(snake_case_  )
      else:
            while sublist:
                  _A							:  Union[str, Any]    =		sublist.pop(0  )
                  for i, xx in enumerate(snake_case_  ):
                        if not _operator(snake_case_,snake_case_  ):
                              solution.insert(snake_case_,snake_case_  )
                              break
                  else:
                        solution.append(snake_case_  )

      strand_sort(snake_case_,snake_case_,snake_case_  )
      return solution


if __name__ == "__main__":
  assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
  assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]

