
import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def     a						(							snake_case__: NDArray[floataa]						,       snake_case__: NDArray[floataa] ):


				'''simple docstring'''
				lowercase_  ,     lowercase_					  = np.shape(snake_case__ )

				lowercase_					  = np.zeros((rows, 1)						,       dtype=snake_case__ )
				for row in reversed(range(snake_case__ ) ):
								lowercase_					  = 0
								for col in range(row + 1						,       snake_case__ ):
												total += coefficients[row, col] * x[col]

								lowercase_					  = (vector[row] - total) / coefficients[row, row]

				return x




def     a						(							snake_case__: NDArray[floataa]						,       snake_case__: NDArray[floataa] ):


				'''simple docstring'''
				lowercase_  ,     lowercase_					  = np.shape(snake_case__ )
				if rows != columns:
								return np.array(()						,       dtype=snake_case__ )

				# augmented matrix
				lowercase_					  = np.concatenate((coefficients, vector)						,       axis=1 )
				lowercase_					  = augmented_mat.astype('''float64''' )

				# scale the matrix leaving it triangular
				for row in range(rows - 1 ):
								lowercase_					  = augmented_mat[row, row]
								for col in range(row + 1						,       snake_case__ ):
												lowercase_					  = augmented_mat[col, row] / pivot
												augmented_mat[col, :] -= factor * augmented_mat[row, :]

				lowercase_					  = retroactive_resolution(
				    augmented_mat[:, 0:columns]						,       augmented_mat[:, columns : columns + 1] )

				return x


if __name__ == "__main__":
						import doctest

						doctest.testmod()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a        = {
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2Text2ForCausalLM',
						    'Speech2Text2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
						from .processing_speech_to_text_a import SpeechaTextaProcessor
						from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text_a import (
												    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextaForCausalLM,
												    SpeechaTextaPreTrainedModel,
												)

else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




