

from typing import Callable, Optional, Union

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


A_						:     Dict			  =				logging.get_logger(__name__)

A_						:     Optional[int]			  =				{
    'microsoft/prophetnet-large-uncased': (
        'https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'
    ),
}
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    "prophetnet"
    a__       							=    ["past_key_values"]
    a__       							=    {
        "num_attention_heads": "num_encoder_attention_heads",
    }


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

        # parameters for prophetnet
        __UpperCAmelCase			   =     ngram
        __UpperCAmelCase			   =     num_buckets
        __UpperCAmelCase			   =     relative_max_distance
        __UpperCAmelCase			   =     disable_ngram_loss
        __UpperCAmelCase			   =     eps

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

        __UpperCAmelCase			   =     use_cache

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


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






    @num_hidden_layers.setter
    def        lowerCAmelCase_  (self       , lowercase__					)       -> Tuple:
        raise NotImplementedError(
            '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
            ''' `num_decoder_layers`.'''					)







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




