





from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

UpperCAmelCase_           =	TypeVar("T")




class 			__UpperCamelCase    (							Generic[T]    ):





     def __init__(     self				,							_UpperCamelCase = True					):
        _UpperCAmelCase           =   {}  # dictionary of lists
        _UpperCAmelCase           =   directed





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase					):

        if not self.directed:  # For undirected graphs
           # if both source vertex and destination vertex are both present in the
           # adjacency list, add destination vertex to source vertex list of adjacent
           # vertices and add source vertex to destination vertex list of adjacent
           # vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
              self.adj_list[source_vertex].append(_UpperCAmelCase					)
              self.adj_list[destination_vertex].append(_UpperCAmelCase					)
           # if only source vertex is present in adjacency list, add destination vertex
           # to source vertex list of adjacent vertices, then create a new vertex with
           # destination vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif source_vertex in self.adj_list:
              self.adj_list[source_vertex].append(_UpperCAmelCase					)
              _UpperCAmelCase           =   [source_vertex]
           # if only destination vertex is present in adjacency list, add source vertex
           # to destination vertex list of adjacent vertices, then create a new vertex
           # with source vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif destination_vertex in self.adj_list:
              self.adj_list[destination_vertex].append(_UpperCAmelCase					)
              _UpperCAmelCase           =   [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and assign a list
           # containing the destination vertex as it's first adjacent vertex also
           # create a new vertex with destination vertex as key and assign a list
           # containing the source vertex as it's first adjacent vertex.
           else:
              _UpperCAmelCase           =   [destination_vertex]
              _UpperCAmelCase           =   [source_vertex]
        else:  # For directed graphs
           # if both source vertex and destination vertex are present in adjacency
           # list, add destination vertex to source vertex list of adjacent vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
              self.adj_list[source_vertex].append(_UpperCAmelCase					)
           # if only source vertex is present in adjacency list, add destination
           # vertex to source vertex list of adjacent vertices and create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           elif source_vertex in self.adj_list:
              self.adj_list[source_vertex].append(_UpperCAmelCase					)
              _UpperCAmelCase           =   []
           # if only destination vertex is present in adjacency list, create a new
           # vertex with source vertex as key and assign a list containing destination
           # vertex as first adjacent vertex
           elif destination_vertex in self.adj_list:
              _UpperCAmelCase           =   [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and a list containing
           # destination vertex as it's first adjacent vertex. Then create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           else:
              _UpperCAmelCase           =   [destination_vertex]
              _UpperCAmelCase           =   []

        return self





     def __repr__(     self					):
        return pformat(self.adj_list					)




'''simple docstring'''

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


__lowerCAmelCase		     =  logging.get_logger(__name__)

__lowerCAmelCase		     =  {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}

class       __magic_name__		(       _UpperCamelCase  ):
							lowerCAmelCase			:       str  							=   'open-llama'







							def __init__(				self   :			str      ,_UpperCAmelCase   :			int=100000      ,_UpperCAmelCase   :			List[str]=4096      ,_UpperCAmelCase   :			Dict=11008      ,_UpperCAmelCase   :			int=32      ,_UpperCAmelCase   :			Union[str, Any]=32      ,_UpperCAmelCase   :			List[str]="silu"      ,_UpperCAmelCase   :			List[Any]=2048      ,_UpperCAmelCase   :			Any=0.02      ,_UpperCAmelCase   :			int=1E-6      ,_UpperCAmelCase   :			Optional[int]=True      ,_UpperCAmelCase   :			Dict=0      ,_UpperCAmelCase   :			Optional[Any]=1      ,_UpperCAmelCase   :			Dict=2      ,_UpperCAmelCase   :			Tuple=False      ,_UpperCAmelCase   :			Dict=True      ,_UpperCAmelCase   :			Dict=0.1      ,_UpperCAmelCase   :			Optional[int]=0.1      ,_UpperCAmelCase   :			List[str]=True      ,_UpperCAmelCase   :			int=True      ,_UpperCAmelCase   :			Union[str, Any]=None      ,**_UpperCAmelCase   :			Optional[int]      ,):
									_a	: str								=				vocab_size
									_a	: str								=				max_position_embeddings
									_a	: List[str]								=				hidden_size
									_a	: Any								=				intermediate_size
									_a	: Union[str, Any]								=				num_hidden_layers
									_a	: Tuple								=				num_attention_heads
									_a	: int								=				hidden_act
									_a	: str								=				initializer_range
									_a	: Any								=				rms_norm_eps
									_a	: Dict								=				use_cache
									_a	: Optional[int]								=				kwargs.pop(
									    'use_memorry_efficient_attention'      ,_UpperCAmelCase						)
									_a	: int								=				hidden_dropout_prob
									_a	: int								=				attention_dropout_prob
									_a	: Union[str, Any]								=				use_stable_embedding
									_a	: str								=				shared_input_output_embedding
									_a	: Any								=				rope_scaling
									self._rope_scaling_validation()

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







							def 				__lowercase     (				self   :			Optional[Any]						):
									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}"""						)
									_a	: Optional[Any]								=				self.rope_scaling.get('type'      ,_UpperCAmelCase						)
									_a	: Optional[Any]								=				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}"""						)




