


'''simple docstring'''


import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard



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


  '''simple docstring'''







  def        _a							(							self :       Union[str, Any] ):

     '''simple docstring'''




     super().tearDown()
     gc.collect()






  def        _a							(							self :       Tuple ):

     '''simple docstring'''




     A_   ,					A_						:  Dict          =				FlaxStableDiffusionPipeline.from_pretrained(
         """stabilityai/stable-diffusion-2"""	,revision="""bf16"""	,dtype=jnp.bfloataa	,)

     A_						:  List[Any]          =				"""A painting of a squirrel eating a burger"""
     A_						:  List[str]          =				jax.device_count()
     A_						:  str          =				num_samples * [prompt]
     A_						:  Any          =				sd_pipe.prepare_inputs(__lowerCAmelCase )

     A_						:  int          =				replicate(__lowerCAmelCase )
     A_						:  List[str]          =				shard(__lowerCAmelCase )

     A_						:  List[Any]          =				jax.random.PRNGKey(0 )
     A_						:  int          =				jax.random.split(__lowerCAmelCase	,jax.device_count() )

     A_						:  str          =				sd_pipe(__lowerCAmelCase	,__lowerCAmelCase	,__lowerCAmelCase	,num_inference_steps=25	,jit=__lowerCAmelCase )[0]
     assert images.shape == (jax.device_count(), 1, 768, 768, 3)

     A_						:  Any          =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
     A_						:  Dict          =				images[0, 253:256, 253:256, -1]

     A_						:  str          =				jnp.asarray(jax.device_get(image_slice.flatten() ) )
     A_						:  List[Any]          =				jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512] )
     print(f'output_slice: {output_slice}' )
     assert jnp.abs(output_slice - expected_slice ).max() < 1e-2






  def        _a							(							self :       List[Any] ):

     '''simple docstring'''




     A_						:  Union[str, Any]          =				"""stabilityai/stable-diffusion-2"""
     A_   ,					A_						:  int          =				FlaxDPMSolverMultistepScheduler.from_pretrained(__lowerCAmelCase	,subfolder="""scheduler""" )
     A_   ,					A_						:  Optional[int]          =				FlaxStableDiffusionPipeline.from_pretrained(
         __lowerCAmelCase	,scheduler=__lowerCAmelCase	,revision="""bf16"""	,dtype=jnp.bfloataa	,)
     A_						:  Any          =				scheduler_params

     A_						:  Dict          =				"""A painting of a squirrel eating a burger"""
     A_						:  str          =				jax.device_count()
     A_						:  Any          =				num_samples * [prompt]
     A_						:  Tuple          =				sd_pipe.prepare_inputs(__lowerCAmelCase )

     A_						:  List[Any]          =				replicate(__lowerCAmelCase )
     A_						:  Optional[Any]          =				shard(__lowerCAmelCase )

     A_						:  Optional[int]          =				jax.random.PRNGKey(0 )
     A_						:  str          =				jax.random.split(__lowerCAmelCase	,jax.device_count() )

     A_						:  Union[str, Any]          =				sd_pipe(__lowerCAmelCase	,__lowerCAmelCase	,__lowerCAmelCase	,num_inference_steps=25	,jit=__lowerCAmelCase )[0]
     assert images.shape == (jax.device_count(), 1, 768, 768, 3)

     A_						:  Dict          =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
     A_						:  Optional[Any]          =				images[0, 253:256, 253:256, -1]

     A_						:  Optional[int]          =				jnp.asarray(jax.device_get(image_slice.flatten() ) )
     A_						:  Any          =				jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297] )
     print(f'output_slice: {output_slice}' )
     assert jnp.abs(output_slice - expected_slice ).max() < 1e-2




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]):
                  if adj not in explored:
                        stack.append(lowerCamelCase)
      return explored


__magic_name__			=      {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, 'A'))
