
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list[list])      ->					list[list]:






							'''simple docstring'''





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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list[list])      ->					list:






							'''simple docstring'''





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


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		lowercase						:      Union[str, 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase						:      Dict									= logging.get_logger(__name__)

lowercase						:      Optional[Any]									= {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'speech_to_text_2'
	_A			=  ['past_key_values']
	_A			=  {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(      self	:Dict  ,     a	:Tuple=1_0_0_0_0  ,     a	:Optional[int]=6  ,     a	:List[str]=2_0_4_8  ,     a	:Tuple=4  ,     a	:List[Any]=0.0  ,     a	:str=True  ,     a	:Any="relu"  ,     a	:Any=2_5_6  ,     a	:Optional[int]=0.1  ,     a	:Any=0.0  ,     a	:int=0.0  ,     a	:int=0.02  ,     a	:List[Any]=2  ,     a	:Tuple=True  ,     a	:str=1  ,     a	:Optional[int]=0  ,     a	:List[Any]=2  ,     a	:Any=1_0_2_4  ,     **a	:str  ,     )	->				int:
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     int         =      d_model
								__UpperCamelCase			:     Optional[int]         =      decoder_ffn_dim
								__UpperCamelCase			:     Any         =      decoder_layers
								__UpperCamelCase			:     Any         =      decoder_attention_heads
								__UpperCamelCase			:     Tuple         =      dropout
								__UpperCamelCase			:     Any         =      attention_dropout
								__UpperCamelCase			:     Any         =      activation_dropout
								__UpperCamelCase			:     Dict         =      activation_function
								__UpperCamelCase			:     int         =      init_std
								__UpperCamelCase			:     List[str]         =      decoder_layerdrop
								__UpperCamelCase			:     Optional[Any]         =      use_cache
								__UpperCamelCase			:     int         =      decoder_layers
								__UpperCamelCase			:     Union[str, Any]         =      scale_embedding  # scale factor will be sqrt(d_model) if True
								__UpperCamelCase			:     Dict         =      max_target_positions

								super().__init__(
								    pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     decoder_start_token_id=a  ,     **a  ,     )