





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


__lowercase  :   Optional[Any]						      =logging.get_logger(__name__)

__lowercase  :   Optional[int]						      ={
    """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      A       (	__lowercase						):
      _snake_case							     ='''gpt_neox'''



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						Tuple=5_0432		,  _lowerCAmelCase:						int=6144		,  _lowerCAmelCase:						str=44		,  _lowerCAmelCase:						Optional[Any]=64		,  _lowerCAmelCase:						Optional[Any]=2_4576		,  _lowerCAmelCase:						Optional[int]="gelu"		,  _lowerCAmelCase:						str=0.25		,  _lowerCAmelCase:						Tuple=1_0000		,  _lowerCAmelCase:						Dict=0.0		,  _lowerCAmelCase:						Dict=0.0		,  _lowerCAmelCase:						str=0.1		,  _lowerCAmelCase:						List[str]=2048		,  _lowerCAmelCase:						Union[str, Any]=0.02		,  _lowerCAmelCase:						Optional[int]=1e-5		,  _lowerCAmelCase:						Optional[Any]=True		,  _lowerCAmelCase:						List[Any]=0		,  _lowerCAmelCase:						Optional[Any]=2		,  _lowerCAmelCase:						str=False		,  _lowerCAmelCase:						Optional[int]=True		,  _lowerCAmelCase:						Tuple=None		,  **_lowerCAmelCase:						Optional[int]		,  )						->    List[Any]:
         '''simple docstring'''

         super().__init__(bos_token_id=_lowerCAmelCase		,  eos_token_id=_lowerCAmelCase		,  **_lowerCAmelCase       )
         UpperCAmelCase_											=vocab_size
         UpperCAmelCase_											=max_position_embeddings
         UpperCAmelCase_											=hidden_size
         UpperCAmelCase_											=num_hidden_layers
         UpperCAmelCase_											=num_attention_heads
         UpperCAmelCase_											=intermediate_size
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=rotary_pct
         UpperCAmelCase_											=rotary_emb_base
         UpperCAmelCase_											=attention_dropout
         UpperCAmelCase_											=hidden_dropout
         UpperCAmelCase_											=classifier_dropout
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=layer_norm_eps
         UpperCAmelCase_											=use_cache
         UpperCAmelCase_											=tie_word_embeddings
         UpperCAmelCase_											=use_parallel_residual
         UpperCAmelCase_											=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:						Any       )						->    Optional[int]:
         '''simple docstring'''

         if self.rope_scaling is None:
            return

         if not isinstance(self.rope_scaling		,  _lowerCAmelCase       ) 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}'       )
         UpperCAmelCase_											=self.rope_scaling.get("type"		,  _lowerCAmelCase       )
         UpperCAmelCase_											=self.rope_scaling.get("factor"		,  _lowerCAmelCase       )
         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(_lowerCAmelCase		,  _lowerCAmelCase       ) or rope_scaling_factor <= 1.0:
            raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'       )











__lowercase  :   Dict						      ="""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich




