



from typing import Callable, Optional, Union

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


UpperCAmelCase_									=							logging.get_logger(__name__)

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





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''xlm-prophetnet'''
   SCREAMING_SNAKE_CASE__							=     ['''past_key_values''']
   SCREAMING_SNAKE_CASE__							=     {
       '''num_attention_heads''': '''num_encoder_attention_heads''',
   }





   def __init__(	self       ,     _lowerCAmelCase = 0.1       ,     _lowerCAmelCase = "gelu"       ,     _lowerCAmelCase = 30_522       ,     _lowerCAmelCase = 1_024       ,     _lowerCAmelCase = 4_096       ,     _lowerCAmelCase = 12       ,     _lowerCAmelCase = 16       ,     _lowerCAmelCase = 4_096       ,     _lowerCAmelCase = 12       ,     _lowerCAmelCase = 16       ,     _lowerCAmelCase = 0.1       ,     _lowerCAmelCase = 0.1       ,     _lowerCAmelCase = 512       ,     _lowerCAmelCase = 0.02       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = 0       ,     _lowerCAmelCase = 2       ,     _lowerCAmelCase = 32       ,     _lowerCAmelCase = 128       ,     _lowerCAmelCase = False       ,     _lowerCAmelCase = 0.0       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = 0       ,     _lowerCAmelCase = 1       ,     _lowerCAmelCase = 2       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  vocab_size
         _lowerCAmelCase 			=  hidden_size
         _lowerCAmelCase 			=  encoder_ffn_dim
         _lowerCAmelCase 			=  num_encoder_layers
         _lowerCAmelCase 			=  num_encoder_attention_heads
         _lowerCAmelCase 			=  decoder_ffn_dim
         _lowerCAmelCase 			=  num_decoder_layers
         _lowerCAmelCase 			=  num_decoder_attention_heads
         _lowerCAmelCase 			=  max_position_embeddings
         _lowerCAmelCase 			=  init_std  # Normal(0, this parameter)
         _lowerCAmelCase 			=  activation_function

         # parameters for xlmprophetnet
         _lowerCAmelCase 			=  ngram
         _lowerCAmelCase 			=  num_buckets
         _lowerCAmelCase 			=  relative_max_distance
         _lowerCAmelCase 			=  disable_ngram_loss
         _lowerCAmelCase 			=  eps

         # 3 Types of Dropout
         _lowerCAmelCase 			=  attention_dropout
         _lowerCAmelCase 			=  activation_dropout
         _lowerCAmelCase 			=  dropout

         _lowerCAmelCase 			=  use_cache

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





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.num_encoder_layers + self.num_decoder_layers





   @num_hidden_layers.setter
   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''			)




from __future__ import annotations
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list				)->list:
      if len(_SCREAMING_SNAKE_CASE				) == 0:
            return []
      _lowerCAmelCase			,   _lowerCAmelCase 			=  min(_SCREAMING_SNAKE_CASE				), max(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  int(max_value - min_value				) + 1
      _lowerCAmelCase 			=  [[] for _ in range(_SCREAMING_SNAKE_CASE				)]

      for i in my_list:
            buckets[int(i - min_value				)].append(_SCREAMING_SNAKE_CASE				)

      return [v for bucket in buckets for v in sorted(_SCREAMING_SNAKE_CASE				)]


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
    assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]