



'''simple docstring'''





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


__a          =		logging.get_logger(__name__)

__a          =		{
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"gpt_neox"




    def __init__(    self					:     int ,     snake_case_					:     Union[str, Any]=50_432 ,     snake_case_					:     Union[str, Any]=6_144 ,     snake_case_					:     Union[str, Any]=44 ,     snake_case_					:     Any=64 ,     snake_case_					:     Union[str, Any]=24_576 ,     snake_case_					:     List[Any]="gelu" ,     snake_case_					:     Any=0.25 ,     snake_case_					:     Dict=10_000 ,     snake_case_					:     List[Any]=0.0 ,     snake_case_					:     Tuple=0.0 ,     snake_case_					:     Optional[int]=0.1 ,     snake_case_					:     Any=2_048 ,     snake_case_					:     str=0.02 ,     snake_case_					:     Dict=1E-5 ,     snake_case_					:     Union[str, Any]=True ,     snake_case_					:     Dict=0 ,     snake_case_					:     Optional[Any]=2 ,     snake_case_					:     Dict=False ,     snake_case_					:     Optional[Any]=True ,     snake_case_					:     Optional[Any]=None ,     **snake_case_					:     List[Any] ,     ):
          super().__init__(bos_token_id=snake_case_ ,     eos_token_id=snake_case_ ,     **snake_case_						)
          snake_case__  :    Tuple    						=      vocab_size
          snake_case__  :    Union[str, Any]    						=      max_position_embeddings
          snake_case__  :    Optional[int]    						=      hidden_size
          snake_case__  :    List[Any]    						=      num_hidden_layers
          snake_case__  :    Union[str, Any]    						=      num_attention_heads
          snake_case__  :    List[Any]    						=      intermediate_size
          snake_case__  :    Tuple    						=      hidden_act
          snake_case__  :    str    						=      rotary_pct
          snake_case__  :    Tuple    						=      rotary_emb_base
          snake_case__  :    List[str]    						=      attention_dropout
          snake_case__  :    Tuple    						=      hidden_dropout
          snake_case__  :    Dict    						=      classifier_dropout
          snake_case__  :    Dict    						=      initializer_range
          snake_case__  :    Optional[int]    						=      layer_norm_eps
          snake_case__  :    Optional[int]    						=      use_cache
          snake_case__  :    Union[str, Any]    						=      tie_word_embeddings
          snake_case__  :    str    						=      use_parallel_residual
          snake_case__  :    int    						=      rope_scaling
          self._rope_scaling_validation()

          if self.hidden_size % self.num_attention_heads != 0:
                raise ValueError(
                    """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""						)




    def lowerCamelCase       (    self					:     List[str]						):
          if self.rope_scaling is None:
                return

          if not isinstance(self.rope_scaling ,     snake_case_						) or len(self.rope_scaling						) != 2:
                raise ValueError(
                    """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                    f"got {self.rope_scaling}"						)
          snake_case__  :    Optional[int]    						=      self.rope_scaling.get("""type""" ,     snake_case_						)
          snake_case__  :    int    						=      self.rope_scaling.get("""factor""" ,     snake_case_						)
          if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                raise ValueError(
                    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"						)
          if rope_scaling_factor is None or not isinstance(snake_case_ ,     snake_case_						) or rope_scaling_factor <= 1.0:
                raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"						)


# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__A				='''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
			raise ImportWarning(
			    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
			)

if version.parse(pyarrow.__version__).major < 8:
			raise ImportWarning(
			    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
			    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
			)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__A				=concatenate_datasets
__A				=DownloadConfig
__A				=DownloadManager
__A				=DownloadMode
__A				=DownloadConfig
__A				=DownloadMode
__A				=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


