



'''simple docstring'''




def        lowerCAmelCase_     (			_lowerCamelCase:     list[list]      ):
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	current_set.copy()
		for row_index, row in enumerate(_lowerCamelCase      ):
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	row[0]
				for column_index, column in enumerate(_lowerCamelCase      ):
						if magnitude == 0:
								__SCREAMING_SNAKE_CASE :	Any					 =	column
								continue
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	column / magnitude
    # Subtract to cancel term
		__SCREAMING_SNAKE_CASE :	Tuple					 =	current_set[0]
		__SCREAMING_SNAKE_CASE :	Tuple					 =	[first_row]
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	current_set[1::]
		for row in current_set:
				__SCREAMING_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(_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:
				__SCREAMING_SNAKE_CASE :	str					 =	final_set[0]
				__SCREAMING_SNAKE_CASE :	Dict					 =	[]
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[]
				for row in final_set[1::]:
						current_first_column.append(row[0]      )
						next_iteration.append(row[1::]      )
				__SCREAMING_SNAKE_CASE :	Any					 =	simplify(_lowerCamelCase      )
				for i in range(len(_lowerCamelCase      )      ):
						resultant[i].insert(0 ,   current_first_column[i]      )
				resultant.insert(0 ,   _lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	resultant
		return final_set


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


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						UpperCamelCase__ :			List[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]]))




'''simple docstring'''




import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin






class 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					):


			'''simple docstring'''

			@register_to_config
			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 1_2_8							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 20_00.0							,  lowerCAmelCase__  :				int = 7_6_8							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 6_4							,  lowerCAmelCase__  :				int = 2_0_4_8							,  lowerCAmelCase__  :				float = 0.1							,  ):





					"""simple docstring"""






					super().__init__()

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Sequential(
					    nn.Linear(lowerCAmelCase__							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  nn.Linear(d_model * 4							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  )

					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Embedding(lowerCAmelCase__							,  lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	False

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	nn.Dropout(p=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.ModuleList()
					for lyr_num in range(lowerCAmelCase__		):
							# FiLM conditional T5 decoder
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	DecoderLayer(d_model=lowerCAmelCase__							,  d_kv=lowerCAmelCase__							,  num_heads=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)
							self.decoders.append(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	TaLayerNorm(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	nn.Dropout(p=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mul(query_input.unsqueeze(-1		)							,  key_input.unsqueeze(-2		)		)
					return mask.unsqueeze(-3		)



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	get_timestep_embedding(
					    decoder_noise_time * self.config.max_decoder_noise_time							,  embedding_dim=self.config.d_model							,  max_period=self.config.max_decoder_noise_time							,  ).to(dtype=self.dtype		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.conditioning_emb(lowerCAmelCase__		).unsqueeze(1		)

					assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	decoder_input_tokens.shape[1]

					# If we want to use relative positions for audio context, we can just offset
					# this sequence by the length of encodings_and_masks.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.broadcast_to(
					    torch.arange(lowerCAmelCase__							,  device=decoder_input_tokens.device		)							,  (batch, seq_length)							,  )

					__SCREAMING_SNAKE_CASE :	str					 =	self.position_encoding(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.continuous_inputs_projection(lowerCAmelCase__		)
					inputs += position_encodings
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dropout(lowerCAmelCase__		)

					# decoder: No padding present.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.ones(
					    decoder_input_tokens.shape[:2]							,  device=decoder_input_tokens.device							,  dtype=inputs.dtype		)

					# Translate encoding masks to encoder-decoder masks.
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[(x, self.encoder_decoder_mask(lowerCAmelCase__							,  lowerCAmelCase__		)) for x, y in encodings_and_masks]

					# cross attend style: concat encodings
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.cat([x[0] for x in encodings_and_encdec_masks]							,  dim=1		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.cat([x[1] for x in encodings_and_encdec_masks]							,  dim=-1		)

					for lyr in self.decoders:
							__SCREAMING_SNAKE_CASE :	Dict					 =	lyr(
							    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  encoder_attention_mask=lowerCAmelCase__							,  )[0]

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.decoder_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.post_dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	self.spec_out(lowerCAmelCase__		)
					return spec_out






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Tuple=1E-6		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.ModuleList()

					# cond self attention: layer 0
					self.layer.append(
					    TaLayerSelfAttentionCond(d_model=lowerCAmelCase__							,  d_kv=lowerCAmelCase__							,  num_heads=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)		)

					# cross attention: layer 1
					self.layer.append(
					    TaLayerCrossAttention(
					        d_model=lowerCAmelCase__							,  d_kv=lowerCAmelCase__							,  num_heads=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__							,  layer_norm_epsilon=lowerCAmelCase__							,  )		)

					# Film Cond MLP + dropout: last layer
					self.layer.append(
					    TaLayerFFCond(d_model=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__							,  layer_norm_epsilon=lowerCAmelCase__		)		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Optional[int]=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer[0](
					    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					if encoder_hidden_states is not None:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.where(encoder_attention_mask > 0							,  0							,  -1E10		).to(
							    encoder_hidden_states.dtype		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[1](
							    lowerCAmelCase__							,  key_value_states=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					# Apply Film Conditional Feed Forward layer
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[-1](lowerCAmelCase__							,  lowerCAmelCase__		)

					return (hidden_states,)






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	TaLayerNorm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	Attention(query_dim=lowerCAmelCase__							,  heads=lowerCAmelCase__							,  dim_head=lowerCAmelCase__							,  out_bias=lowerCAmelCase__							,  scale_qk=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				int=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.layer_norm(lowerCAmelCase__		)

					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	str					 =	self.FiLMLayer(lowerCAmelCase__							,  lowerCAmelCase__		)

					# Self-attention block
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.attention(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	hidden_states + self.dropout(lowerCAmelCase__		)

					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	Attention(query_dim=lowerCAmelCase__							,  heads=lowerCAmelCase__							,  dim_head=lowerCAmelCase__							,  out_bias=lowerCAmelCase__							,  scale_qk=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Any=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.layer_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.attention(
					    lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  attention_mask=attention_mask.squeeze(1		)							,  )
					__SCREAMING_SNAKE_CASE :	Dict					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return layer_output






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Any					 =	TaDenseGatedActDense(d_model=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Tuple=None		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer_norm(lowerCAmelCase__		)
					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.film(lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	self.DenseReluDense(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Tuple							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Dropout(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	NewGELUActivation()



			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.act(self.wi_a(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Any					 =	self.wi_a(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	hidden_gelu * hidden_linear
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.wo(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Tuple=1E-6		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Parameter(torch.ones(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	str					 =	eps



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	hidden_states.to(torch.floataa		).pow(2		).mean(-1							,  keepdim=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

					# convert into half-precision if necessary
					if self.weight.dtype in [torch.floataa, torch.bfloataa]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	hidden_states.to(self.weight.dtype		)

					return self.weight * hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				torch.Tensor		):





					"""simple docstring"""






					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__							,  3.0		))		))






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  out_features * 2							,  bias=lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scale_bias(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.chunk(lowerCAmelCase__							,  2							,  -1		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	x * (1 + scale) + shift
					return x