'''simple docstring'''





from typing import Callable, Optional, Union

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


__lowerCamelCase    :				Dict							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Optional[int]							     =	{
    '''microsoft/xprophetnet-large-wiki100-cased''': (
        '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
    ),
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"xlm-prophetnet"
   lowerCAmelCase_														=							["past_key_values"]
   lowerCAmelCase_														=							{
       "num_attention_heads": "num_encoder_attention_heads",
   }




   def __init__(					self    :       Optional[int] ,			_lowercase    :       Optional[float] = 0.1 ,			_lowercase    :       Optional[Union[str, Callable]] = "gelu" ,			_lowercase    :       Optional[int] = 3_05_22 ,			_lowercase    :       Optional[int] = 10_24 ,			_lowercase    :       Optional[int] = 40_96 ,			_lowercase    :       Optional[int] = 12 ,			_lowercase    :       Optional[int] = 16 ,			_lowercase    :       Optional[int] = 40_96 ,			_lowercase    :       Optional[int] = 12 ,			_lowercase    :       Optional[int] = 16 ,			_lowercase    :       Optional[float] = 0.1 ,			_lowercase    :       Optional[float] = 0.1 ,			_lowercase    :       Optional[int] = 5_12 ,			_lowercase    :       Optional[float] = 0.02 ,			_lowercase    :       Optional[bool] = True ,			_lowercase    :       Optional[bool] = True ,			_lowercase    :       Optional[int] = 0 ,			_lowercase    :       Optional[int] = 2 ,			_lowercase    :       Optional[int] = 32 ,			_lowercase    :       Optional[int] = 1_28 ,			_lowercase    :       Optional[bool] = False ,			_lowercase    :       Optional[float] = 0.0 ,			_lowercase    :       Optional[bool] = True ,			_lowercase    :       Optional[int] = 0 ,			_lowercase    :       Optional[int] = 1 ,			_lowercase    :       Optional[int] = 2 ,			**_lowercase    :       Tuple ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   vocab_size
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   encoder_ffn_dim
     SCREAMING_SNAKE_CASE__    							=   num_encoder_layers
     SCREAMING_SNAKE_CASE__    							=   num_encoder_attention_heads
     SCREAMING_SNAKE_CASE__    							=   decoder_ffn_dim
     SCREAMING_SNAKE_CASE__    							=   num_decoder_layers
     SCREAMING_SNAKE_CASE__    							=   num_decoder_attention_heads
     SCREAMING_SNAKE_CASE__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   init_std  # Normal(0, this parameter)
     SCREAMING_SNAKE_CASE__    							=   activation_function

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

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

     SCREAMING_SNAKE_CASE__    							=   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    :       Union[str, Any]   ):



     """simple docstring"""






     return self.num_encoder_layers + self.num_decoder_layers




   @num_hidden_layers.setter
   def 			__a		(					self    :       Tuple ,			_lowercase    :       Dict   ):



     """simple docstring"""






     raise NotImplementedError(
         """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
         """ `num_decoder_layers`."""   )

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_	)
class __snake_case       (		lowerCamelCase_	):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   lowerCAmelCase_														=							field(default="text-classification"     ,	metadata={"include_in_asdict_even_if_is_default": True}	)
   lowerCAmelCase_														=							Features({"text": Value("string"	)}	)
   lowerCAmelCase_														=							Features({"labels": ClassLabel}	)
   lowerCAmelCase_														=							"text"
   lowerCAmelCase_														=							"labels"




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     if self.label_column not in features:
       raise ValueError(f"""Column {self.label_column} is not present in features."""   )
     if not isinstance(features[self.label_column] ,			_lowercase   ):
       raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(self   )
     SCREAMING_SNAKE_CASE__    							=   self.label_schema.copy()
     SCREAMING_SNAKE_CASE__    							=   features[self.label_column]
     SCREAMING_SNAKE_CASE__    							=   label_schema
     return task_template




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return {
         self.text_column: "text",
         self.label_column: "labels",
     }
