






'''simple docstring'''
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class      __SCREAMING_SNAKE_CASE      :




 @property
 def      __magic_name__	(      self	:							str      )	->						Tuple:
       return self.get_dummy_input()




 @property
 def      __magic_name__	(      self	:							int      )	->						List[Any]:
       if self.block_type == "down":
             return (4, 32, 16, 16)
       elif self.block_type == "mid":
             return (4, 32, 32, 32)
       elif self.block_type == "up":
             return (4, 32, 64, 64)

       raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."      )




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Any=True					,      __lowercase	:							Any=False					,      __lowercase	:							List[Any]=False					,      __lowercase	:							Dict=False					,      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				str									=4
       SCREAMING_SNAKE_CASE__     :				Tuple									=32
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=(32, 32)

       SCREAMING_SNAKE_CASE__     :				str									=torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.device(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=(batch_size, num_channels) + sizes
       SCREAMING_SNAKE_CASE__     :				Any									=randn_tensor(__lowercase					,      generator=__lowercase					,      device=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									={'''hidden_states''': hidden_states}

       if include_temb:
             SCREAMING_SNAKE_CASE__     :				Any									=1_28
             SCREAMING_SNAKE_CASE__     :				List[Any]									=randn_tensor((batch_size, temb_channels)					,      generator=__lowercase					,      device=__lowercase      )

       if include_res_hidden_states_tuple:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.manual_seed(1      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=(randn_tensor(__lowercase					,      generator=__lowercase					,      device=__lowercase      ),)

       if include_encoder_hidden_states:
             SCREAMING_SNAKE_CASE__     :				Dict									=floats_tensor((batch_size, 32, 32)      ).to(__lowercase      )

       if include_skip_sample:
             SCREAMING_SNAKE_CASE__     :				str									=randn_tensor(((batch_size, 3) + sizes)					,      generator=__lowercase					,      device=__lowercase      )

       return dummy_input




 def      __magic_name__	(      self	:							int      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				int									={
           '''in_channels''': 32,
           '''out_channels''': 32,
           '''temb_channels''': 1_28,
       }
       if self.block_type == "up":
             SCREAMING_SNAKE_CASE__     :				str									=32

       if self.block_type == "mid":
             init_dict.pop('''out_channels'''      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.dummy_input
       return init_dict, inputs_dict




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Union[str, Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				Dict									=self.block_class(**__lowercase      )
       unet_block.to(__lowercase      )
       unet_block.eval()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=unet_block(**__lowercase      )

       if isinstance(__lowercase					,      __lowercase      ):
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=output[0]

       self.assertEqual(output.shape					,      self.output_shape      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=output[0, -1, -3:, -3:]
       SCREAMING_SNAKE_CASE__     :				int									=torch.tensor(__lowercase      ).to(__lowercase      )
       assert torch_all_close(output_slice.flatten()					,      __lowercase					,      atol=5e-3      )




 @unittest.skipIf(torch_device == '''mps'''					,      '''Training is not supported in mps'''      )
 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Dict									=self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				str									=self.block_class(**__lowercase      )
       model.to(__lowercase      )
       model.train()
       SCREAMING_SNAKE_CASE__     :				str									=model(**__lowercase      )

       if isinstance(__lowercase					,      __lowercase      ):
             SCREAMING_SNAKE_CASE__     :				Tuple									=output[0]

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.device(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=randn_tensor(output.shape					,      device=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.nn.functional.mse_loss(__lowercase					,      __lowercase      )
       loss.backward()







'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

a_					= list[list[float | int]]





def  _a(  UpperCamelCase__				:						Matrix,	UpperCamelCase__				:						Matrix							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0 for _ in range(size + 1							)] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				float

      for row in range(UpperCamelCase__							):
            for col in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Tuple									=matrix[row][col]

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=vector[row][0]

      SCREAMING_SNAKE_CASE__     :				Any									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=0
      while row < size and col < size:
            # pivoting
            SCREAMING_SNAKE_CASE__     :				Any									=max((abs(augmented[rowa][col]							), rowa) for rowa in range(UpperCamelCase__,	UpperCamelCase__							)							)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[Any]									=augmented[pivot_row], augmented[row]

            for rowa in range(row + 1,	UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=augmented[rowa][col] / augmented[row][col]
                  SCREAMING_SNAKE_CASE__     :				Tuple									=0
                  for cola in range(col + 1,	size + 1							):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1,	UpperCamelCase__							):
            for row in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=augmented[row][col] / augmented[col][col]
                  for cola in range(UpperCamelCase__,	size + 1							):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row],	1_0							)] for row in range(UpperCamelCase__							)
      ]





def  _a(  UpperCamelCase__				:						list[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0 for _ in range(UpperCamelCase__							)] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				Matrix
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int

      for x_val, y_val in enumerate(UpperCamelCase__							):
            for col in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=(x_val + 1) ** (size - col - 1)
            SCREAMING_SNAKE_CASE__     :				Dict									=y_val

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=solve(UpperCamelCase__,	UpperCamelCase__							)

      def interpolated_func(UpperCamelCase__				:						int							) -> int:
            return sum(
                round(coeffs[x_val][0]							) * (var ** (size - x_val - 1))
                for x_val in range(UpperCamelCase__							)							)

      return interpolated_func





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )





def  _a(  UpperCamelCase__				:						Callable[[int], int] = question_function,	UpperCamelCase__				:						int = 1_0							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				list[int]									=[func(UpperCamelCase__							) for x_val in range(1,	order + 1							)]

      SCREAMING_SNAKE_CASE__     :				list[Callable[[int], int]]									=[
          interpolate(data_points[:max_coeff]							) for max_coeff in range(1,	order + 1							)
      ]

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Callable[[int], int]
      SCREAMING_SNAKE_CASE__     :				int

      for poly in polynomials:
            SCREAMING_SNAKE_CASE__     :				Any									=1
            while func(UpperCamelCase__							) == poly(UpperCamelCase__							):
                  x_val += 1

            ret += poly(UpperCamelCase__							)

      return ret


if __name__ == "__main__":
  print(F'''{solution() = }''')