





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


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

lowercase    : int    	=  {}

class  __A(						__UpperCAmelCase ):
     __A	  =       "llama"
     __A	  =       ["past_key_values"]

     def __init__(      self,       A=3_2000,       A=4096,       A=1_1008,       A=32,       A=32,       A=None,       A="silu",       A=2048,       A=0.02,       A=1E-6,       A=True,       A=0,       A=1,       A=2,       A=1,       A=False,       A=None,       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						vocab_size
            _UpperCamelCase              =						max_position_embeddings
            _UpperCamelCase              =						hidden_size
            _UpperCamelCase              =						intermediate_size
            _UpperCamelCase              =						num_hidden_layers
            _UpperCamelCase              =						num_attention_heads

            # for backward compatibility
            if num_key_value_heads is None:
                   _UpperCamelCase              =						num_attention_heads

            _UpperCamelCase              =						num_key_value_heads
            _UpperCamelCase              =						hidden_act
            _UpperCamelCase              =						initializer_range
            _UpperCamelCase              =						rms_norm_eps
            _UpperCamelCase              =						pretraining_tp
            _UpperCamelCase              =						use_cache
            _UpperCamelCase              =						rope_scaling
            self._rope_scaling_validation()

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



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            if self.rope_scaling is None:
                   return

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













from __future__ import annotations

from collections.abc import Iterator

class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						value
            _UpperCamelCase              =						None
            _UpperCamelCase              =						None






class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						tree

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
            )



     def __iter__(      self      ):





            """simple docstring"""

            yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






