


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


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

A      :    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 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"gpt_neox"





       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			Union[str, Any]=50432					,      __lowerCamelCase							:			List[str]=6144					,      __lowerCamelCase							:			Dict=44					,      __lowerCamelCase							:			str=64					,      __lowerCamelCase							:			Any=24576					,      __lowerCamelCase							:			str="gelu"					,      __lowerCamelCase							:			Dict=0.2_5					,      __lowerCamelCase							:			Optional[Any]=10000					,      __lowerCamelCase							:			Dict=0.0					,      __lowerCamelCase							:			List[str]=0.0					,      __lowerCamelCase							:			Any=0.1					,      __lowerCamelCase							:			Tuple=2048					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			Union[str, Any]=1E-5					,      __lowerCamelCase							:			int=True					,      __lowerCamelCase							:			Union[str, Any]=0					,      __lowerCamelCase							:			Optional[Any]=2					,      __lowerCamelCase							:			Dict=False					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Optional[int]=None					,      **__lowerCamelCase							:			List[str]					,      ):


             '''simple docstring'''



             super().__init__(bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase       )
             lowerCamelCase__		:				List[Any]    =  vocab_size
             lowerCamelCase__		:				Optional[Any]    =  max_position_embeddings
             lowerCamelCase__		:				str    =  hidden_size
             lowerCamelCase__		:				str    =  num_hidden_layers
             lowerCamelCase__		:				int    =  num_attention_heads
             lowerCamelCase__		:				Tuple    =  intermediate_size
             lowerCamelCase__		:				str    =  hidden_act
             lowerCamelCase__		:				Union[str, Any]    =  rotary_pct
             lowerCamelCase__		:				Union[str, Any]    =  rotary_emb_base
             lowerCamelCase__		:				Optional[Any]    =  attention_dropout
             lowerCamelCase__		:				Any    =  hidden_dropout
             lowerCamelCase__		:				Union[str, Any]    =  classifier_dropout
             lowerCamelCase__		:				Any    =  initializer_range
             lowerCamelCase__		:				str    =  layer_norm_eps
             lowerCamelCase__		:				Union[str, Any]    =  use_cache
             lowerCamelCase__		:				Tuple    =  tie_word_embeddings
             lowerCamelCase__		:				Dict    =  use_parallel_residual
             lowerCamelCase__		:				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							:			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}"       )
             lowerCamelCase__		:				Optional[Any]    =  self.rope_scaling.get("type"					,      __lowerCamelCase       )
             lowerCamelCase__		:				Any    =  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}"       )






class 			_lowercase :  # Public class to implement a graph


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  row
             lowerCamelCase__		:				Optional[Any]    =  col
             lowerCamelCase__		:				Union[str, Any]    =  graph





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			list[list[bool]]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowerCamelCase__		:				Tuple    =  [-1, 0, 1, -1, 1, -1, 0, 1]
             lowerCamelCase__		:				List[Any]    =  True  # Make those cells visited
             for k in range(8       ):
                   if self.is_safe(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       ):
                         self.diffs(i + row_nbr[k]					,      j + col_nbr[k]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):  # And finally, count all islands.


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  [[False for j in range(self.COL       )] for i in range(self.ROW       )]
             lowerCamelCase__		:				Optional[Any]    =  0
             for i in range(self.ROW       ):
                   for j in range(self.COL       ):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                               self.diffs(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                               count += 1
             return count


