

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[Any]             =       "gpt_bigcode"
							UpperCAmelCase__			:		Optional[Any]             =       ["past_key_values"]
							UpperCAmelCase__			:		Tuple             =       {
							    "hidden_size": "n_embd",
							    "max_position_embeddings": "n_positions",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}


							def __init__(     self			,				_a=5_0_2_5_7			,				_a=1_0_2_4			,				_a=7_6_8			,				_a=1_2			,				_a=1_2			,				_a=None			,				_a="gelu_pytorch_tanh"			,				_a=0.1			,				_a=0.1			,				_a=0.1			,				_a=1e-5			,				_a=0.02			,				_a=True			,				_a=True			,				_a=5_0_2_5_6			,				_a=5_0_2_5_6			,				_a=True			,				_a=True			,				_a=True			,				**_a			,				)       ->       List[str]:
												_a      :       Optional[int]   =						vocab_size
												_a      :       Union[str, Any]   =						n_positions
												_a      :       Optional[int]   =						n_embd
												_a      :       List[Any]   =						n_layer
												_a      :       Optional[int]   =						n_head
												_a      :       Any   =						n_inner
												_a      :       Optional[int]   =						activation_function
												_a      :       Optional[int]   =						resid_pdrop
												_a      :       Union[str, Any]   =						embd_pdrop
												_a      :       Any   =						attn_pdrop
												_a      :       int   =						layer_norm_epsilon
												_a      :       List[Any]   =						initializer_range
												_a      :       List[str]   =						scale_attn_weights
												_a      :       List[Any]   =						use_cache
												_a      :       List[Any]   =						attention_softmax_in_fpaa
												_a      :       List[str]   =						scale_attention_softmax_in_fpaa
												_a      :       Dict   =						multi_query

												_a      :       List[Any]   =						bos_token_id
												_a      :       str   =						eos_token_id

												super().__init__(bos_token_id=_a			,				eos_token_id=_a			,				**_a			)






def   __UpperCAmelCase					(      __a :     str      )     ->	list:

					"""simple docstring"""

					if n_term == "":
										return []
					_a      :       list   =						[]
					for temp in range(int(__a      )      ):
										series.append(F"""1/{temp + 1}""" if series else '''1'''      )
					return series


if __name__ == "__main__":
			a__												=						input('''Enter the last number (nth term) of the Harmonic Series''')
			print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
			print(harmonic_series(nth_term))



