



"""simple docstring"""





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


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """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__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''gpt_neox'''



       def __init__(							self    :						Optional[Any]	,       _UpperCAmelCase    :						List[str]=50432	,       _UpperCAmelCase    :						Any=6144	,       _UpperCAmelCase    :						Tuple=44	,       _UpperCAmelCase    :						Tuple=64	,       _UpperCAmelCase    :						Dict=24576	,       _UpperCAmelCase    :						List[str]="gelu"	,       _UpperCAmelCase    :						Tuple=0.25	,       _UpperCAmelCase    :						Union[str, Any]=10000	,       _UpperCAmelCase    :						List[str]=0.0	,       _UpperCAmelCase    :						List[str]=0.0	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Optional[Any]=2048	,       _UpperCAmelCase    :						int=0.02	,       _UpperCAmelCase    :						Any=1e-5	,       _UpperCAmelCase    :						Optional[int]=True	,       _UpperCAmelCase    :						Dict=0	,       _UpperCAmelCase    :						List[str]=2	,       _UpperCAmelCase    :						Dict=False	,       _UpperCAmelCase    :						Optional[Any]=True	,       _UpperCAmelCase    :						Optional[Any]=None	,       **_UpperCAmelCase    :						Dict	,       )				->		Optional[Any]:


             '''simple docstring'''




             super().__init__(bos_token_id=_UpperCAmelCase	,       eos_token_id=_UpperCAmelCase	,       **_UpperCAmelCase					)
             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   lowercase__       (							self    :						Optional[Any]					)				->		Dict:


             '''simple docstring'''




             if self.rope_scaling is None:
                   return

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









"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(lowerCAmelCase__   ) == 2 and len(matrix[0]   ) == 2 and len(matrix[1]   ) == 2:
            # Calculate the determinant of the matrix
            UpperCAmelCase_    				=							float(
                d(matrix[0][0]   ) * d(matrix[1][1]   ) - d(matrix[1][0]   ) * d(matrix[0][1]   )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creates a copy of the matrix with swapped positions of the elements
            UpperCAmelCase_    				=							[[0.0, 0.0], [0.0, 0.0]]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							matrix[1][1], matrix[0][0]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							-matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(lowerCAmelCase__   )   ) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(lowerCAmelCase__   ) == 3
          and len(matrix[0]   ) == 3
          and len(matrix[1]   ) == 3
          and len(matrix[2]   ) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            UpperCAmelCase_    				=							float(
                (
                    (d(matrix[0][0]   ) * d(matrix[1][1]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][2]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][2]   ) * d(matrix[1][0]   ) * d(matrix[2][1]   ))
                )
                - (
                    (d(matrix[0][2]   ) * d(matrix[1][1]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][0]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][0]   ) * d(matrix[1][2]   ) * d(matrix[2][1]   ))
                )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creating cofactor matrix
            UpperCAmelCase_    				=							[
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
            ]
            UpperCAmelCase_    				=							(d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
                d(matrix[1][2]   ) * d(matrix[2][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
                d(matrix[1][1]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[1][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
                d(matrix[0][1]   ) * d(matrix[1][0]   )
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        UpperCAmelCase_    				=							cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        inverse_matrix[i][j] /= d(lowerCAmelCase__   )

        # Calculate the inverse of the matrix
            return [[float(d(lowerCAmelCase__   )   ) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError("Please provide a matrix of size 2x2 or 3x3."   )




