'''simple docstring'''






from __future__ import annotations

from decimal import Decimal

from numpy import array





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			list[list[float]]				):
     _A    		=  Decimal

     # Check if the provided matrix has 2 rows and 2 columns
     # since this implementation only works for 2x2 matrices
     if len(A__				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
          # Calculate the determinant of the matrix
          _A    		=  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
          _A    		=  [[0.0, 0.0], [0.0, 0.0]]
          _A		,       _A    		=  matrix[1][1], matrix[0][0]
          _A		,       _A    		=  -matrix[1][0], -matrix[0][1]

          # Calculate the inverse of the matrix
          return [
              [(float(d(A__				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
          ]
     elif (
         len(A__				) == 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
          _A    		=  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
          _A    		=  [
              [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				)],
          ]
          _A    		=  (d(matrix[1][1]				) * d(matrix[2][2]				)) - (
              d(matrix[1][2]				) * d(matrix[2][1]				)
          )
          _A    		=  -(
              (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
          )
          _A    		=  (d(matrix[1][0]				) * d(matrix[2][1]				)) - (
              d(matrix[1][1]				) * d(matrix[2][0]				)
          )
          _A    		=  -(
              (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
          )
          _A    		=  (d(matrix[0][0]				) * d(matrix[2][2]				)) - (
              d(matrix[0][2]				) * d(matrix[2][0]				)
          )
          _A    		=  -(
              (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
          )
          _A    		=  (d(matrix[0][1]				) * d(matrix[1][2]				)) - (
              d(matrix[0][2]				) * d(matrix[1][1]				)
          )
          _A    		=  -(
              (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
          )
          _A    		=  (d(matrix[0][0]				) * d(matrix[1][1]				)) - (
              d(matrix[0][1]				) * d(matrix[1][0]				)
          )

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

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

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






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


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{
    '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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "gpt_neox"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]=50432     , __lowerCamelCase					:			int=6144     , __lowerCamelCase					:			Optional[Any]=44     , __lowerCamelCase					:			Tuple=64     , __lowerCamelCase					:			Optional[int]=24576     , __lowerCamelCase					:			List[str]="gelu"     , __lowerCamelCase					:			Any=0.25     , __lowerCamelCase					:			List[Any]=10000     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[Any]=2048     , __lowerCamelCase					:			Tuple=0.02     , __lowerCamelCase					:			Tuple=1e-5     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int=0     , __lowerCamelCase					:			Optional[Any]=2     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			Optional[int]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   rotary_pct
       SCREAMING_SNAKE_CASE										=   rotary_emb_base
       SCREAMING_SNAKE_CASE										=   attention_dropout
       SCREAMING_SNAKE_CASE										=   hidden_dropout
       SCREAMING_SNAKE_CASE										=   classifier_dropout
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   use_cache
       SCREAMING_SNAKE_CASE										=   tie_word_embeddings
       SCREAMING_SNAKE_CASE										=   use_parallel_residual
       SCREAMING_SNAKE_CASE										=   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      _snake_case		(       self					:			Union[str, Any]			):
       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}"			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("type"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   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}"			)