


from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					NDArray[floataa]			,     SCREAMING_SNAKE_CASE__			:					NDArray[floataa]			,     SCREAMING_SNAKE_CASE__			:					list[int]			,     SCREAMING_SNAKE_CASE__			:					int			,     ):
     __UpperCamelCase							,      __UpperCamelCase    							=coefficient_matrix.shape
     __UpperCamelCase							,      __UpperCamelCase    							=constant_matrix.shape

     if rowsa != colsa:
          __UpperCamelCase    							=F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if colsa != 1:
          __UpperCamelCase    							=F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if rowsa != rowsa:
          __UpperCamelCase    							=(
              'Coefficient and constant matrices dimensions must be nxn and nx1 but '
              F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
          )
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if len(SCREAMING_SNAKE_CASE__				) != rowsa:
          __UpperCamelCase    							=(
              'Number of initial values must be equal to number of rows in coefficient '
              F'matrix but received {len(SCREAMING_SNAKE_CASE__				)} and {rowsa}'
          )
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if iterations <= 0:
          raise ValueError('Iterations must be at least 1'				)

     __UpperCamelCase    							=np.concatenate(
         (coefficient_matrix, constant_matrix)			,     axis=1				)

     __UpperCamelCase							,      __UpperCamelCase    							=table.shape

     strictly_diagonally_dominant(SCREAMING_SNAKE_CASE__				)

     # Iterates the whole matrix for given number of times
     for _ in range(SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=[]
          for row in range(SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=0
               for col in range(SCREAMING_SNAKE_CASE__				):
                    if col == row:
                         __UpperCamelCase    							=table[row][col]
                    elif col == cols - 1:
                         __UpperCamelCase    							=table[row][col]
                    else:
                         temp += (-1) * table[row][col] * init_val[col]
               __UpperCamelCase    							=(temp + val) / denom
               new_val.append(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=new_val

     return [float(SCREAMING_SNAKE_CASE__				) for i in new_val]

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					NDArray[floataa]				):
     __UpperCamelCase							,      __UpperCamelCase    							=table.shape

     __UpperCamelCase    							=True

     for i in range(0			,     SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=0
          for j in range(0			,     cols - 1				):
               if i == j:
                    continue
               else:
                    total += table[i][j]

          if table[i][i] <= total:
               raise ValueError('Coefficient matrix is not strictly diagonally dominant'				)

     return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






"""simple docstring"""






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


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Union[str, Any]							  =	'open-llama'

      def __init__(						self		,       __lowerCAmelCase=10_0000		,       __lowerCAmelCase=4096		,       __lowerCAmelCase=1_1008		,       __lowerCAmelCase=32		,       __lowerCAmelCase=32		,       __lowerCAmelCase="silu"		,       __lowerCAmelCase=2048		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-6		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0		,       __lowerCAmelCase=1		,       __lowerCAmelCase=2		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	vocab_size
            lowercase														=	max_position_embeddings
            lowercase														=	hidden_size
            lowercase														=	intermediate_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	initializer_range
            lowercase														=	rms_norm_eps
            lowercase														=	use_cache
            lowercase														=	kwargs.pop(
                """use_memorry_efficient_attention"""		,       __lowerCAmelCase						)
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_dropout_prob
            lowercase														=	use_stable_embedding
            lowercase														=	shared_input_output_embedding
            lowercase														=	rope_scaling
            self._rope_scaling_validation()

            super().__init__(
                pad_token_id=__lowerCAmelCase		,       bos_token_id=__lowerCAmelCase		,       eos_token_id=__lowerCAmelCase		,       tie_word_embeddings=__lowerCAmelCase		,       **__lowerCAmelCase		,       )







      def 				A__       (						self						):







            """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}'						)
            lowercase														=	self.rope_scaling.get("""type"""		,       __lowerCAmelCase						)
            lowercase														=	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}'						)



