


'''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 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           return F'gaussian_noise_s={seed}_shape={"_".join([str(_SCREAMING_SNAKE_CASE) for s in shape])}.npy'


    def        A   (  self      :							Any):
           super().tearDown()
           gc.collect()


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[str]=0				,							SCREAMING_SNAKE_CASE      :							str=(4, 4, 64, 64)				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=False):
           _A					:     Union[str, Any]     = jnp.bfloataa if fpaa else jnp.floataa
           _A					:     Optional[int]     = jnp.array(load_hf_numpy(self.get_file_format(_SCREAMING_SNAKE_CASE				,							_SCREAMING_SNAKE_CASE))				,							dtype=_SCREAMING_SNAKE_CASE)
           return image


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Optional[int]=False				,							SCREAMING_SNAKE_CASE      :							Any="CompVis/stable-diffusion-v1-4"):
           _A					:     Optional[Any]     = jnp.bfloataa if fpaa else jnp.floataa
           _A					:     Union[str, Any]     = "bf16" if fpaa else None

           _A					:     List[str]     = FlaxUNetaDConditionModel.from_pretrained(
               _SCREAMING_SNAKE_CASE				,							subfolder='unet'				,							dtype=_SCREAMING_SNAKE_CASE				,							revision=_SCREAMING_SNAKE_CASE)
           return model, params


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple=0				,							SCREAMING_SNAKE_CASE      :							Optional[int]=(4, 77, 768)				,							SCREAMING_SNAKE_CASE      :							int=False):
           _A					:     Dict     = jnp.bfloataa if fpaa else jnp.floataa
           _A					:     str     = jnp.array(load_hf_numpy(self.get_file_format(_SCREAMING_SNAKE_CASE				,							_SCREAMING_SNAKE_CASE))				,							dtype=_SCREAMING_SNAKE_CASE)
           return hidden_states


    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
            [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
            [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
            [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
            # fmt: on
        ])
    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     str     = self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'				,							fpaa=_SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = self.get_latents(_SCREAMING_SNAKE_CASE				,							fpaa=_SCREAMING_SNAKE_CASE)
           _A					:     int     = self.get_encoder_hidden_states(_SCREAMING_SNAKE_CASE				,							fpaa=_SCREAMING_SNAKE_CASE)

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

           assert sample.shape == latents.shape

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

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


    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
            [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
            [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
            [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
            # fmt: on
        ])
    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     Any     = self.get_unet_model(model_id='stabilityai/stable-diffusion-2'				,							fpaa=_SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = self.get_latents(_SCREAMING_SNAKE_CASE				,							shape=(4, 4, 96, 96)				,							fpaa=_SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = self.get_encoder_hidden_states(_SCREAMING_SNAKE_CASE				,							shape=(4, 77, 1024)				,							fpaa=_SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = model.apply(
               {'params': params}				,							_SCREAMING_SNAKE_CASE				,							jnp.array(_SCREAMING_SNAKE_CASE				,							dtype=jnp.intaa)				,							encoder_hidden_states=_SCREAMING_SNAKE_CASE				,							).sample

           assert sample.shape == latents.shape

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

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










'''simple docstring'''







class 			__lowerCamelCase			:  # Public class to implement a graph
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           _A					:     List[Any]     = row
           _A					:     Union[str, Any]     = col
           _A					:     List[str]     = graph


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           # Checking all 8 elements surrounding nth element
           _A					:     Tuple     = [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           _A					:     Dict     = [-1, 0, 1, -1, 1, -1, 0, 1]
           _A					:     List[Any]     = True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE):
                         self.diffs(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):  # And finally, count all islands.
           _A					:     Dict     = [[False for j in range(self.COL)] for i in range(self.ROW)]
           _A					:     Union[str, Any]     = 0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                                count += 1
           return count






