




'''simple docstring'''







import warnings

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


A     =logging.get_logger(__name__)

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


class 			_a  (	__a ):
       __a				:     Optional[int]   				=					"""mvp"""
       __a				:     Dict   				=					["""past_key_values"""]
       __a				:     int   				=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
       def __init__(	self						:	List[str]   ,				lowercase						:	List[str]=50_267   ,				lowercase						:	List[str]=1_024   ,				lowercase						:	Any=12   ,				lowercase						:	Optional[int]=4_096   ,				lowercase						:	Tuple=16   ,				lowercase						:	Dict=12   ,				lowercase						:	List[str]=4_096   ,				lowercase						:	Union[str, Any]=16   ,				lowercase						:	str=0.0   ,				lowercase						:	List[str]=0.0   ,				lowercase						:	Union[str, Any]="gelu"   ,				lowercase						:	int=1_024   ,				lowercase						:	Union[str, Any]=0.1   ,				lowercase						:	Union[str, Any]=0.0   ,				lowercase						:	Optional[Any]=0.0   ,				lowercase						:	Union[str, Any]=0.02   ,				lowercase						:	str=0.0   ,				lowercase						:	Optional[Any]=False   ,				lowercase						:	Tuple=True   ,				lowercase						:	Tuple=1   ,				lowercase						:	Optional[Any]=0   ,				lowercase						:	List[Any]=2   ,				lowercase						:	Dict=True   ,				lowercase						:	int=2   ,				lowercase						:	Optional[int]=2   ,				lowercase						:	Any=False   ,				lowercase						:	Tuple=100   ,				lowercase						:	int=800   ,				**lowercase						:	str   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		vocab_size
           UpperCAmelCase			       =		max_position_embeddings
           UpperCAmelCase			       =		d_model
           UpperCAmelCase			       =		encoder_ffn_dim
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		encoder_attention_heads
           UpperCAmelCase			       =		decoder_ffn_dim
           UpperCAmelCase			       =		decoder_layers
           UpperCAmelCase			       =		decoder_attention_heads
           UpperCAmelCase			       =		dropout
           UpperCAmelCase			       =		attention_dropout
           UpperCAmelCase			       =		activation_dropout
           UpperCAmelCase			       =		activation_function
           UpperCAmelCase			       =		init_std
           UpperCAmelCase			       =		encoder_layerdrop
           UpperCAmelCase			       =		decoder_layerdrop
           UpperCAmelCase			       =		classifier_dropout
           UpperCAmelCase			       =		use_cache
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		scale_embedding  # scale factor will be sqrt(d_model) if True
           UpperCAmelCase			       =		use_prompt
           UpperCAmelCase			       =		prompt_length
           UpperCAmelCase			       =		prompt_mid_dim

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

           if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''   ,				lowercase				):
               UpperCAmelCase			       =		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.'''				)










'''simple docstring'''







from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('''Unable to interleave an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."     )
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

    if not dsets:
        raise ValueError('''Unable to concatenate an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )
    else:
        return _concatenate_iterable_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )




