
'''simple docstring'''
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						current_set.copy()
    for row_index, row in enumerate(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    int     			=						row[0]
        for column_index, column in enumerate(__SCREAMING_SNAKE_CASE       ):
            if magnitude == 0:
                snake_case_ :    Optional[int]     			=						column
                continue
            snake_case_ :    Optional[Any]     			=						column / magnitude
    # Subtract to cancel term
    snake_case_ :    List[Any]     			=						current_set[0]
    snake_case_ :    Optional[Any]     			=						[first_row]
    snake_case_ :    Union[str, Any]     			=						current_set[1::]
    for row in current_set:
        snake_case_ :    Optional[Any]     			=						[]
        # If first term is 0, it is already in form we want, so we preserve it
        if row[0] == 0:
            final_set.append(__SCREAMING_SNAKE_CASE       )
            continue
        for column_index in range(len(__SCREAMING_SNAKE_CASE       )       ):
            temp_row.append(first_row[column_index] - row[column_index]       )
        final_set.append(__SCREAMING_SNAKE_CASE       )
    # Create next recursion iteration set
    if len(final_set[0]       ) != 3:
        snake_case_ :    Union[str, Any]     			=						final_set[0]
        snake_case_ :    Dict     			=						[]
        snake_case_ :    Optional[Any]     			=						[]
        for row in final_set[1::]:
            current_first_column.append(row[0]       )
            next_iteration.append(row[1::]       )
        snake_case_ :    Dict     			=						simplify(__SCREAMING_SNAKE_CASE       )
        for i in range(len(__SCREAMING_SNAKE_CASE       )       ):
            resultant[i].insert(0,		current_first_column[i]       )
        resultant.insert(0,		__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Any     			=						resultant
    return final_set

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        raise IndexError("solve_simultaneous() requires n lists of length n+1"       )
    snake_case_ :    Optional[Any]     			=						len(__SCREAMING_SNAKE_CASE       ) + 1
    if any(len(__SCREAMING_SNAKE_CASE       ) != _length for item in equations       ):
        raise IndexError("solve_simultaneous() requires n lists of length n+1"       )
    for row in equations:
        if any(not isinstance(__SCREAMING_SNAKE_CASE,		(int, float)       ) for column in row       ):
            raise ValueError("solve_simultaneous() requires lists of integers"       )
    if len(__SCREAMING_SNAKE_CASE       ) == 1:
        return [equations[0][-1] / equations[0][0]]
    snake_case_ :    int     			=						equations.copy()
    if any(0 in row for row in data_set       ):
        snake_case_ :    Tuple     			=						data_set.copy()
        snake_case_ :    List[Any]     			=						[]
        for row_index, row in enumerate(__SCREAMING_SNAKE_CASE       ):
            if 0 not in row:
                snake_case_ :    Tuple     			=						data_set.pop(__SCREAMING_SNAKE_CASE       )
                break
        if not full_row:
            raise ValueError("solve_simultaneous() requires at least 1 full equation"       )
        data_set.insert(0,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Dict     			=						data_set.copy()
    snake_case_ :    Tuple     			=						simplify(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[Any]     			=						simplified[::-1]
    snake_case_ :    list     			=						[]
    for row in simplified:
        snake_case_ :    Tuple     			=						row[-1]
        if not solutions:
            if row[-2] == 0:
                solutions.append(0       )
                continue
            solutions.append(current_solution / row[-2]       )
            continue
        snake_case_ :    Optional[Any]     			=						row.copy()[: len(__SCREAMING_SNAKE_CASE       ) - 1 :]
        while temp_row[0] == 0:
            temp_row.pop(0       )
        if len(__SCREAMING_SNAKE_CASE       ) == 0:
            solutions.append(0       )
            continue
        snake_case_ :    List[Any]     			=						temp_row[1::]
        snake_case_ :    Optional[Any]     			=						temp_row[::-1]
        for column_index, column in enumerate(__SCREAMING_SNAKE_CASE       ):
            current_solution -= column * solutions[column_index]
        solutions.append(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[Any]     			=						[]
    for item in solutions:
        final.append(float(round(__SCREAMING_SNAKE_CASE,		5       )       )       )
    return final[::-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  a_											=						[
      [2, 1, 1, 1, 1, 4],
      [1, 2, 1, 1, 1, 5],
      [1, 1, 2, 1, 1, 6],
      [1, 1, 1, 2, 1, 7],
      [1, 1, 1, 1, 2, 8],
  ]
  print(solve_simultaneous(eq))
  print(solve_simultaneous([[4, 2]]))






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_											=						{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WavLMForAudioFrameClassification",
      "WavLMForCTC",
      "WavLMForSequenceClassification",
      "WavLMForXVector",
      "WavLMModel",
      "WavLMPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




