'''simple docstring'''






import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp







@slow
@require_flax
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Any, UpperCamelCase__      :			List[Any]     )  ->			List[Any]:
        return f'gaussian_noise_s={seed}_shape={"_".join([str(__lowerCAmelCase     ) for s in shape]     )}.npy'
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Dict=0, UpperCamelCase__      :			Union[str, Any]=(4, 4, 64, 64), UpperCamelCase__      :			List[Any]=False     )  ->			Optional[Any]:
        _A    		=  jnp.bfloataa if fpaa else jnp.floataa
        _A    		=  jnp.array(load_hf_numpy(self.get_file_format(__lowerCAmelCase, __lowerCAmelCase     )     ), dtype=__lowerCAmelCase     )
        return image
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			List[str]=False, UpperCamelCase__      :			int="CompVis/stable-diffusion-v1-4"     )  ->			List[Any]:
        _A    		=  jnp.bfloataa if fpaa else jnp.floataa
        _A    		=  """bf16""" if fpaa else None

        _A    		=  FlaxUNetaDConditionModel.from_pretrained(
            __lowerCAmelCase, subfolder='unet', dtype=__lowerCAmelCase, revision=__lowerCAmelCase     )
        return model, params
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			Any=0, UpperCamelCase__      :			Optional[Any]=(4, 77, 7_68), UpperCamelCase__      :			Union[str, Any]=False     )  ->			Union[str, Any]:
        _A    		=  jnp.bfloataa if fpaa else jnp.floataa
        _A    		=  jnp.array(load_hf_numpy(self.get_file_format(__lowerCAmelCase, __lowerCAmelCase     )     ), dtype=__lowerCAmelCase     )
        return hidden_states
   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
           [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
           [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
           [3, 10_00, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
           # fmt: on
       ]     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Any, UpperCamelCase__      :			Any, UpperCamelCase__      :			Optional[int]     )  ->			List[str]:
        _A    		=  self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4', fpaa=__lowerCAmelCase     )
        _A    		=  self.get_latents(__lowerCAmelCase, fpaa=__lowerCAmelCase     )
        _A    		=  self.get_encoder_hidden_states(__lowerCAmelCase, fpaa=__lowerCAmelCase     )

        _A    		=  model.apply(
            {'params': params}, __lowerCAmelCase, jnp.array(__lowerCAmelCase, dtype=jnp.intaa     ), encoder_hidden_states=__lowerCAmelCase, ).sample

        assert sample.shape == latents.shape

        _A    		=  jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     ), dtype=jnp.floataa     )
        _A    		=  jnp.array(__lowerCAmelCase, dtype=jnp.floataa     )

        # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
        assert jnp.allclose(__lowerCAmelCase, __lowerCAmelCase, atol=1e-2     )

   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
           [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
           [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
           [3, 10_00, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
           # fmt: on
       ]     )
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			int     )  ->			str:
        _A    		=  self.get_unet_model(model_id='stabilityai/stable-diffusion-2', fpaa=__lowerCAmelCase     )
        _A    		=  self.get_latents(__lowerCAmelCase, shape=(4, 4, 96, 96), fpaa=__lowerCAmelCase     )
        _A    		=  self.get_encoder_hidden_states(__lowerCAmelCase, shape=(4, 77, 10_24), fpaa=__lowerCAmelCase     )

        _A    		=  model.apply(
            {'params': params}, __lowerCAmelCase, jnp.array(__lowerCAmelCase, dtype=jnp.intaa     ), encoder_hidden_states=__lowerCAmelCase, ).sample

        assert sample.shape == latents.shape

        _A    		=  jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     ), dtype=jnp.floataa     )
        _A    		=  jnp.array(__lowerCAmelCase, dtype=jnp.floataa     )

        # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
        assert jnp.allclose(__lowerCAmelCase, __lowerCAmelCase, atol=1e-2     )











'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






