import warnings

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


_lowercase							:      List[str]      							=logging.get_logger(__name__)

_lowercase							:      int      							={
    '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     str            =				"mvp"
  lowercase		:     Optional[Any]            =				["past_key_values"]
  lowercase		:     Any            =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







  def __init__(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Tuple=5_02_67      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=12      ,	SCREAMING_SNAKE_CASE__    :					Tuple=40_96      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=12      ,	SCREAMING_SNAKE_CASE__    :					int=40_96      ,	SCREAMING_SNAKE_CASE__    :					Tuple=16      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					Dict="gelu"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					int=0.0      ,	SCREAMING_SNAKE_CASE__    :					str=0.0      ,	SCREAMING_SNAKE_CASE__    :					Dict=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					str=0.0      ,	SCREAMING_SNAKE_CASE__    :					Any=False      ,	SCREAMING_SNAKE_CASE__    :					List[str]=True      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=1      ,	SCREAMING_SNAKE_CASE__    :					Dict=0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=True      ,	SCREAMING_SNAKE_CASE__    :					Tuple=2      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=False      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1_00      ,	SCREAMING_SNAKE_CASE__    :					Any=8_00      ,	**SCREAMING_SNAKE_CASE__    :					Any      ,	)    ->				Optional[Any]:
         A						:     Tuple         =vocab_size
         A						:     Dict         =max_position_embeddings
         A						:     Union[str, Any]         =d_model
         A						:     str         =encoder_ffn_dim
         A						:     Any         =encoder_layers
         A						:     str         =encoder_attention_heads
         A						:     Tuple         =decoder_ffn_dim
         A						:     List[Any]         =decoder_layers
         A						:     Tuple         =decoder_attention_heads
         A						:     str         =dropout
         A						:     Union[str, Any]         =attention_dropout
         A						:     Tuple         =activation_dropout
         A						:     Tuple         =activation_function
         A						:     Optional[int]         =init_std
         A						:     str         =encoder_layerdrop
         A						:     int         =decoder_layerdrop
         A						:     Optional[Any]         =classifier_dropout
         A						:     Dict         =use_cache
         A						:     Tuple         =encoder_layers
         A						:     List[str]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     Dict         =use_prompt
         A						:     List[Any]         =prompt_length
         A						:     str         =prompt_mid_dim

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	is_encoder_decoder=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	forced_eos_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'      ,	SCREAMING_SNAKE_CASE__     ):
                A						:     int         =self.bos_token_id
                warnings.warn(
                    f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                    'The config can simply be saved and uploaded again to be fixed.'     )







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






