"""simple docstring"""






from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class 				snake_case       :




					"""simple docstring"""

					pass





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   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str] )		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Optional[Any]=0    ,						snake_case__		:					Any=(4, 4, 64, 64)    ,						snake_case__		:					List[Any]=False )		->	int:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Optional[int]							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return image



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					List[Any]="CompVis/stable-diffusion-v1-4" )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   str							=      "bf16" if fpaa else None

          snake_case				,					snake_case       :   Optional[int]							=      FlaxUNetaDConditionModel.from_pretrained(
              snake_case__    ,						subfolder="unet"    ,						dtype=snake_case__    ,						revision=snake_case__ )
          return model, params



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Union[str, Any]=0    ,						snake_case__		:					Union[str, Any]=(4, 77, 7_68)    ,						snake_case__		:					Dict=False )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Any							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Any							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=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, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[str]							=      self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_latents(snake_case__    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_encoder_hidden_states(snake_case__    ,						fpaa=snake_case__ )

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

          assert sample.shape == latents.shape

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

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
          assert jnp.allclose(snake_case__    ,						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, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					str    ,						snake_case__		:					Tuple )		->	str:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[Any]							=      self.get_unet_model(model_id="stabilityai/stable-diffusion-2"    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_latents(snake_case__    ,						shape=(4, 4, 96, 96)    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_encoder_hidden_states(snake_case__    ,						shape=(4, 77, 10_24)    ,						fpaa=snake_case__ )

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

          assert sample.shape == latents.shape

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

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





