
def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			list[list]:






						'''simple docstring'''

						_lowercase       :	Dict           =   current_set.copy()
						for row_index, row in enumerate(SCREAMING_SNAKE_CASE_  ):
												_lowercase       :	Any           =   row[0]
												for column_index, column in enumerate(SCREAMING_SNAKE_CASE_  ):
																		if magnitude == 0:
																								_lowercase       :	Tuple           =   column
																								continue
																		_lowercase       :	Union[str, Any]           =   column / magnitude
    # Subtract to cancel term
						_lowercase       :	List[str]           =   current_set[0]
						_lowercase       :	Union[str, Any]           =   [first_row]
						_lowercase       :	str           =   current_set[1::]
						for row in current_set:
												_lowercase       :	Dict           =   []
												# 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:
												_lowercase       :	int           =   final_set[0]
												_lowercase       :	Optional[int]           =   []
												_lowercase       :	Tuple           =   []
												for row in final_set[1::]:
																		current_first_column.append(row[0]  )
																		next_iteration.append(row[1::]  )
												_lowercase       :	int           =   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_  )
												_lowercase       :	Dict           =   resultant
						return final_set



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str]  )   ->			list:






						'''simple docstring'''

						if len(SCREAMING_SNAKE_CASE_  ) == 0:
												raise IndexError("solve_simultaneous() requires n lists of length n+1"  )
						_lowercase       :	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]]
						_lowercase       :	int           =   equations.copy()
						if any(0 in row for row in data_set  ):
												_lowercase       :	Optional[int]           =   data_set.copy()
												_lowercase       :	int           =   []
												for row_index, row in enumerate(SCREAMING_SNAKE_CASE_  ):
																		if 0 not in row:
																								_lowercase       :	Any           =   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_  )
						_lowercase       :	int           =   data_set.copy()
						_lowercase       :	Optional[int]           =   simplify(SCREAMING_SNAKE_CASE_  )
						_lowercase       :	int           =   simplified[::-1]
						_lowercase       :	Optional[int]           =   []
						for row in simplified:
												_lowercase       :	Dict           =   row[-1]
												if not solutions:
																		if row[-2] == 0:
																								solutions.append(0  )
																								continue
																		solutions.append(current_solution / row[-2]  )
																		continue
												_lowercase       :	str           =   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
												_lowercase       :	Dict           =   temp_row[1::]
												_lowercase       :	Optional[int]           =   temp_row[::-1]
												for column_index, column in enumerate(SCREAMING_SNAKE_CASE_  ):
																		current_solution -= column * solutions[column_index]
												solutions.append(SCREAMING_SNAKE_CASE_  )
						_lowercase       :	Dict           =   []
						for item in solutions:
												final.append(float(round(SCREAMING_SNAKE_CASE_ ,			5  )  )  )
						return final[::-1]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							UpperCamelCase_						:     Optional[Any]	       =							[
							    [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]]))







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
