


from typing import Callable, Optional, Union

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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

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


class     __UpperCAmelCase							(       _a							):


     """simple docstring"""
     _lowerCamelCase   =      """xlm-prophetnet"""
     _lowerCamelCase   =      ["""past_key_values"""]
     _lowerCamelCase   =      {
         """num_attention_heads""": """num_encoder_attention_heads""",
     }


     def __init__(	self  ,       __A = 0.1  ,       __A = "gelu"  ,       __A = 30522  ,       __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  ,       ):
        __a					=  vocab_size
        __a					=  hidden_size
        __a					=  encoder_ffn_dim
        __a					=  num_encoder_layers
        __a					=  num_encoder_attention_heads
        __a					=  decoder_ffn_dim
        __a					=  num_decoder_layers
        __a					=  num_decoder_attention_heads
        __a					=  max_position_embeddings
        __a					=  init_std  # Normal(0, this parameter)
        __a					=  activation_function

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

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

        __a					=  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 	snake_case_  (	self    ):
        return self.num_encoder_layers + self.num_decoder_layers


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







"""simple docstring"""

def lowerCAmelCase_   (					UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 if n == 0:
  return 1

 elif n % 2 == 1:
  return (binary_exponentiation(UpperCamelCase__					, n - 1					, UpperCamelCase__			) * a) % mod

 else:
  __lowercase						=						binary_exponentiation(UpperCamelCase__					, n / 2					, UpperCamelCase__			)
  return (b * b) % mod


# a prime number
UpperCAmelCase__					=701

UpperCAmelCase__					=10_0000_0000
UpperCAmelCase__					=10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)

print((a / b) % p == (a * b ** (p - 2)) % p)
