




'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							current_set.copy()
					for row_index, row in enumerate(lowerCamelCase__      ):
										snake_case_		     =							row[0]
										for column_index, column in enumerate(lowerCamelCase__      ):
															if magnitude == 0:
																				snake_case_		     =							column
																				continue
															snake_case_		     =							column / magnitude
    # Subtract to cancel term
					snake_case_		     =							current_set[0]
					snake_case_		     =							[first_row]
					snake_case_		     =							current_set[1::]
					for row in current_set:
										snake_case_		     =							[]
										# 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:
										snake_case_		     =							final_set[0]
										snake_case_		     =							[]
										snake_case_		     =							[]
										for row in final_set[1::]:
															current_first_column.append(row[0]      )
															next_iteration.append(row[1::]      )
										snake_case_		     =							simplify(lowerCamelCase__      )
										for i in range(len(lowerCamelCase__      )      ):
															resultant[i].insert(0, current_first_column[i]      )
										resultant.insert(0, lowerCamelCase__      )
										snake_case_		     =							resultant
					return final_set


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							a    :							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]]))


"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)  # pylint: disable=invalid-name

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=768  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = proj_size

							lowerCAmelCase__   = CLIPVisionModel(snake_case__  )
							lowerCAmelCase__   = PaintByExampleMapper(snake_case__  )
							lowerCAmelCase__   = nn.LayerNorm(config.hidden_size  )
							lowerCAmelCase__   = nn.Linear(config.hidden_size	,    self.proj_size  )

							# uncondition for scaling
							lowerCAmelCase__   = nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]=False  ):
							lowerCAmelCase__   = self.model(pixel_values=snake_case__  )
							lowerCAmelCase__   = clip_output.pooler_output
							lowerCAmelCase__   = self.mapper(latent_states[:, None]  )
							lowerCAmelCase__   = self.final_layer_norm(snake_case__  )
							lowerCAmelCase__   = self.proj_out(snake_case__  )
							if return_uncond_vector:
									return latent_states, self.uncond_vector

							return latent_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  int	,    snake_case__    :  Dict  ):
							super().__init__()
							lowerCAmelCase__   = (config.num_hidden_layers + 1) // 5
							lowerCAmelCase__   = config.hidden_size
							lowerCAmelCase__   = 1
							lowerCAmelCase__   = nn.ModuleList(
							    [
							        BasicTransformerBlock(snake_case__	,    snake_case__	,    snake_case__	,    activation_fn="""gelu"""	,    attention_bias=snake_case__  )
							        for _ in range(snake_case__  )
							    ]  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[int]  ):
							for block in self.blocks:
									lowerCAmelCase__   = block(snake_case__  )

							return hidden_states




