






'''simple docstring'''







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


_A			:    Tuple					  =logging.get_logger(__name__)

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

class _lowercase			(       _lowercase	):

							'''simple docstring'''





							a          = """open-llama"""




							def __init__(							self:       str    ,							UpperCamelCase__:       Optional[int]=100_000    ,							UpperCamelCase__:       List[str]=4_096    ,							UpperCamelCase__:       Tuple=11_008    ,							UpperCamelCase__:       Union[str, Any]=32    ,							UpperCamelCase__:       str=32    ,							UpperCamelCase__:       str="silu"    ,							UpperCamelCase__:       int=2_048    ,							UpperCamelCase__:       str=0.02    ,							UpperCamelCase__:       Dict=1e-6    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       List[Any]=0    ,							UpperCamelCase__:       int=1    ,							UpperCamelCase__:       int=2    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       Optional[Any]=True    ,							UpperCamelCase__:       List[Any]=0.1    ,							UpperCamelCase__:       Tuple=0.1    ,							UpperCamelCase__:       Optional[Any]=True    ,							UpperCamelCase__:       int=True    ,							UpperCamelCase__:       Dict=None    ,							**UpperCamelCase__:       Optional[int]    ,							):
														lowerCamelCase__      :			List[Any] 						= vocab_size
														lowerCamelCase__      :			Optional[int] 						= max_position_embeddings
														lowerCamelCase__      :			Optional[Any] 						= hidden_size
														lowerCamelCase__      :			List[str] 						= intermediate_size
														lowerCamelCase__      :			str 						= num_hidden_layers
														lowerCamelCase__      :			List[Any] 						= num_attention_heads
														lowerCamelCase__      :			Dict 						= hidden_act
														lowerCamelCase__      :			int 						= initializer_range
														lowerCamelCase__      :			Optional[int] 						= rms_norm_eps
														lowerCamelCase__      :			Optional[Any] 						= use_cache
														lowerCamelCase__      :			str 						= kwargs.pop(
														    """use_memorry_efficient_attention"""    ,							lowercase_  )
														lowerCamelCase__      :			Any 						= hidden_dropout_prob
														lowerCamelCase__      :			str 						= attention_dropout_prob
														lowerCamelCase__      :			List[Any] 						= use_stable_embedding
														lowerCamelCase__      :			Any 						= shared_input_output_embedding
														lowerCamelCase__      :			List[str] 						= rope_scaling
														self._rope_scaling_validation()

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




							def 	lowerCamelCase_       (							self:       Optional[int]  ):







														if self.rope_scaling is None:
																					return

														if not isinstance(self.rope_scaling    ,							lowercase_  ) 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}'''  )
														lowerCamelCase__      :			List[str] 						= self.rope_scaling.get("""type"""    ,							lowercase_  )
														lowerCamelCase__      :			str 						= self.rope_scaling.get("""factor"""    ,							lowercase_  )
														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(lowercase_    ,							lowercase_  ) 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'''







import sys
import turtle





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> tuple[float, float]:
							return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> None:
							my_pen.up()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.down()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )

							if depth == 0:
														return

							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise ValueError(
						    '''Correct format for using this script: '''
						    '''python fractals.py <int:depth_for_fractal>'''
						)
			_A			:    Any					  =turtle.Turtle()
			my_pen.ht()
			my_pen.speed(5)
			my_pen.pencolor('''red''')

			_A			:    Dict					  =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
			triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))

