




"""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 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   str:
       return F'gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_) for s in shape])}.npy'







   def   UpperCAmelCase__			(		self)				->   List[str]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_=0       ,					lowerCamelCase_=(4, 4, 6_4, 6_4)       ,					lowerCamelCase_=False)				->   Union[str, Any]:
       UpperCamelCase              =	jnp.bfloataa if fpaa else jnp.floataa
       UpperCamelCase              =	jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_       ,					lowerCamelCase_))       ,					dtype=lowerCamelCase_)
       return image







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_=False       ,					lowerCamelCase_="CompVis/stable-diffusion-v1-4")				->   Tuple:
       UpperCamelCase              =	jnp.bfloataa if fpaa else jnp.floataa
       UpperCamelCase              =	'''bf16''' if fpaa else None

       UpperCamelCase       ,	UpperCamelCase              =	FlaxUNetaDConditionModel.from_pretrained(
           lowerCamelCase_       ,					subfolder='''unet'''       ,					dtype=lowerCamelCase_       ,					revision=lowerCamelCase_)
       return model, params







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_=0       ,					lowerCamelCase_=(4, 7_7, 7_6_8)       ,					lowerCamelCase_=False)				->   Dict:
       UpperCamelCase              =	jnp.bfloataa if fpaa else jnp.floataa
       UpperCamelCase              =	jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_       ,					lowerCamelCase_))       ,					dtype=lowerCamelCase_)
       return hidden_states







   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [1_7, 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, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ])
   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Union[str, Any]:
       UpperCamelCase       ,	UpperCamelCase              =	self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''       ,					fpaa=lowerCamelCase_)
       UpperCamelCase              =	self.get_latents(lowerCamelCase_       ,					fpaa=lowerCamelCase_)
       UpperCamelCase              =	self.get_encoder_hidden_states(lowerCamelCase_       ,					fpaa=lowerCamelCase_)

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

       assert sample.shape == latents.shape

       UpperCamelCase              =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))       ,					dtype=jnp.floataa)
       UpperCamelCase              =	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
           [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [1_7, 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, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ])
   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   Optional[int]:
       UpperCamelCase       ,	UpperCamelCase              =	self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''       ,					fpaa=lowerCamelCase_)
       UpperCamelCase              =	self.get_latents(lowerCamelCase_       ,					shape=(4, 4, 9_6, 9_6)       ,					fpaa=lowerCamelCase_)
       UpperCamelCase              =	self.get_encoder_hidden_states(lowerCamelCase_       ,					shape=(4, 7_7, 1_0_2_4)       ,					fpaa=lowerCamelCase_)

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

       assert sample.shape == latents.shape

       UpperCamelCase              =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))       ,					dtype=jnp.floataa)
       UpperCamelCase              =	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 ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


