


"""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      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       return F'gaussian_noise_s={seed}_shape={"_".join([str(UpperCamelCase__		) for s in shape]		)}.npy'







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__=0		,      UpperCamelCase__=(4, 4, 64, 64)		,      UpperCamelCase__=False		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Tuple    =       jnp.bfloataa if fpaa else jnp.floataa
       snake_case       : Union[str, Any]    =       jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__		,      UpperCamelCase__		)		)		,      dtype=UpperCamelCase__		)
       return image







   def 					lowerCamelCase						(      self		,      UpperCamelCase__=False		,      UpperCamelCase__="CompVis/stable-diffusion-v1-4"		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : str    =       jnp.bfloataa if fpaa else jnp.floataa
       snake_case       : Tuple    =       'bf16' if fpaa else None

       snake_case       : str    =       FlaxUNetaDConditionModel.from_pretrained(
           UpperCamelCase__		,      subfolder="unet"		,      dtype=UpperCamelCase__		,      revision=UpperCamelCase__		)
       return model, params







   def 					lowerCamelCase						(      self		,      UpperCamelCase__=0		,      UpperCamelCase__=(4, 77, 768)		,      UpperCamelCase__=False		)      ->						str:

       '''simple docstring'''




       snake_case       : Tuple    =       jnp.bfloataa if fpaa else jnp.floataa
       snake_case       : Optional[Any]    =       jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__		,      UpperCamelCase__		)		)		,      dtype=UpperCamelCase__		)
       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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




       snake_case       : Any    =       self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"		,      fpaa=UpperCamelCase__		)
       snake_case       : Any    =       self.get_latents(UpperCamelCase__		,      fpaa=UpperCamelCase__		)
       snake_case       : Tuple    =       self.get_encoder_hidden_states(UpperCamelCase__		,      fpaa=UpperCamelCase__		)

       snake_case       : int    =       model.apply(
           {"params": params}		,      UpperCamelCase__		,      jnp.array(UpperCamelCase__		,      dtype=jnp.intaa		)		,      encoder_hidden_states=UpperCamelCase__		,      ).sample

       assert sample.shape == latents.shape

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

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(UpperCamelCase__		,      UpperCamelCase__		,      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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




       snake_case       : Any    =       self.get_unet_model(model_id="stabilityai/stable-diffusion-2"		,      fpaa=UpperCamelCase__		)
       snake_case       : Any    =       self.get_latents(UpperCamelCase__		,      shape=(4, 4, 96, 96)		,      fpaa=UpperCamelCase__		)
       snake_case       : List[Any]    =       self.get_encoder_hidden_states(UpperCamelCase__		,      shape=(4, 77, 1024)		,      fpaa=UpperCamelCase__		)

       snake_case       : Optional[Any]    =       model.apply(
           {"params": params}		,      UpperCamelCase__		,      jnp.array(UpperCamelCase__		,      dtype=jnp.intaa		)		,      encoder_hidden_states=UpperCamelCase__		,      ).sample

       assert sample.shape == latents.shape

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

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












_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
