




from __future__ import annotations

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				np.ndarray     ):
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       np.shape(SCREAMING_SNAKE_CASE_     )
					if rows != columns:
										__lowerCAmelCase				   =       (
										    "'table' has to be of square shaped array but got a "
										    F"""{rows}x{columns} array:\n{table}"""
										)
										raise ValueError(SCREAMING_SNAKE_CASE_     )

					__lowerCAmelCase				   =       np.zeros((rows, columns)     )
					__lowerCAmelCase				   =       np.zeros((rows, columns)     )
					for i in range(SCREAMING_SNAKE_CASE_     ):
										for j in range(SCREAMING_SNAKE_CASE_     ):
															__lowerCAmelCase				   =       sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_     )     )
															if upper[j][j] == 0:
																				raise ArithmeticError("No LU decomposition exists"     )
															__lowerCAmelCase				   =       (table[i][j] - total) / upper[j][j]
										__lowerCAmelCase				   =       1
										for j in range(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
															__lowerCAmelCase				   =       sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_     )     )
															__lowerCAmelCase				   =       table[i][j] - total
					return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()










"""simple docstring"""






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


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""ctrl"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(  self  ,      lowercase=246534  ,      lowercase=256  ,      lowercase=1280  ,      lowercase=8192  ,      lowercase=48  ,      lowercase=16  ,      lowercase=0.1  ,      lowercase=0.1  ,      lowercase=1E-6  ,      lowercase=0.02  ,      lowercase=True  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Any    =    vocab_size
							_lowerCamelCase     :			Dict    =    n_positions
							_lowerCamelCase     :			Optional[int]    =    n_embd
							_lowerCamelCase     :			str    =    n_layer
							_lowerCamelCase     :			Union[str, Any]    =    n_head
							_lowerCamelCase     :			Any    =    dff
							_lowerCamelCase     :			int    =    resid_pdrop
							_lowerCamelCase     :			Dict    =    embd_pdrop
							_lowerCamelCase     :			Union[str, Any]    =    layer_norm_epsilon
							_lowerCamelCase     :			Tuple    =    initializer_range

							_lowerCamelCase     :			str    =    use_cache

							super().__init__(**lowercase  )