






'''simple docstring'''

import doctest
from collections import deque

import numpy as np






class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self       )						->  None:
							lowercase_      :   List[Any]        =			[2, 1, 2, -1]
							lowercase_      :   str        =			[1, 2, 3, 4]







	def        lowerCamelCase__		(							self       )						->  list[float]:
							lowercase_      :   Optional[int]        =			len(self.first_signal       )
							lowercase_      :   List[str]        =			len(self.second_signal       )

							lowercase_      :   List[str]        =			max(_lowercase  ,      _lowercase       )

							# create a zero matrix of max_length x max_length
							lowercase_      :   Union[str, Any]        =			[[0] * max_length for i in range(_lowercase       )]

							# fills the smaller signal with zeros to make both signals of same length
							if length_first_signal < length_second_signal:
													self.first_signal += [0] * (max_length - length_first_signal)
							elif length_first_signal > length_second_signal:
													self.second_signal += [0] * (max_length - length_second_signal)
							for i in range(_lowercase       ):
													lowercase_      :   Optional[int]        =			deque(self.second_signal       )
													rotated_signal.rotate(_lowercase       )
													for j, item in enumerate(_lowercase       ):
																			matrix[i][j] += item

        # multiply the matrix with the first signal
							lowercase_      :   List[str]        =			np.matmul(np.transpose(_lowercase       )  ,      np.transpose(self.first_signal       )       )

							# rounding-off to two decimal places
							return [round(_lowercase  ,      2       ) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()








'''simple docstring'''

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


A:      int  				=				logging.get_logger(__name__)

A:      int  				=				{
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  'gpt_bigcode'
	SCREAMING_SNAKE_CASE_					:      int        =  ['past_key_values']
	SCREAMING_SNAKE_CASE_					:      Any        =  {
	    'hidden_size': 'n_embd',
	    'max_position_embeddings': 'n_positions',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}
	def __init__(							self  ,      _lowercase=5_0257  ,      _lowercase=1024  ,      _lowercase=768  ,      _lowercase=12  ,      _lowercase=12  ,      _lowercase=None  ,      _lowercase="gelu_pytorch_tanh"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=1E-5  ,      _lowercase=0.02  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=5_0256  ,      _lowercase=5_0256  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      **_lowercase  ,      )						->  Any:
							lowercase_      :   Tuple        =			vocab_size
							lowercase_      :   str        =			n_positions
							lowercase_      :   List[str]        =			n_embd
							lowercase_      :   str        =			n_layer
							lowercase_      :   Optional[Any]        =			n_head
							lowercase_      :   Optional[int]        =			n_inner
							lowercase_      :   Union[str, Any]        =			activation_function
							lowercase_      :   Dict        =			resid_pdrop
							lowercase_      :   str        =			embd_pdrop
							lowercase_      :   Optional[Any]        =			attn_pdrop
							lowercase_      :   List[Any]        =			layer_norm_epsilon
							lowercase_      :   Optional[int]        =			initializer_range
							lowercase_      :   List[Any]        =			scale_attn_weights
							lowercase_      :   Any        =			use_cache
							lowercase_      :   List[str]        =			attention_softmax_in_fpaa
							lowercase_      :   Any        =			scale_attention_softmax_in_fpaa
							lowercase_      :   Optional[Any]        =			multi_query

							lowercase_      :   Optional[Any]        =			bos_token_id
							lowercase_      :   Optional[Any]        =			eos_token_id

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