
"""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 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          return F'''gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase_      ) for s in shape]      )}.npy'''



      def  __snake_case							(   self      ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()



      def  __snake_case							(   self     ,	UpperCamelCase_=0     ,	UpperCamelCase_=(4, 4, 64, 64)     ,	UpperCamelCase_=False      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					jnp.bfloataa if fpaa else jnp.floataa
          UpperCAmelCase__	:      Dict     							=					jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase_     ,	UpperCamelCase_      )      )     ,	dtype=UpperCamelCase_      )
          return image



      def  __snake_case							(   self     ,	UpperCamelCase_=False     ,	UpperCamelCase_="CompVis/stable-diffusion-v1-4"      ):
          UpperCAmelCase__	:      int     							=					jnp.bfloataa if fpaa else jnp.floataa
          UpperCAmelCase__	:      List[Any]     							=					'bf16' if fpaa else None

          UpperCAmelCase__							,							UpperCAmelCase__	:      Tuple     							=					FlaxUNetaDConditionModel.from_pretrained(
              UpperCamelCase_     ,	subfolder='unet'     ,	dtype=UpperCamelCase_     ,	revision=UpperCamelCase_      )
          return model, params



      def  __snake_case							(   self     ,	UpperCamelCase_=0     ,	UpperCamelCase_=(4, 77, 768)     ,	UpperCamelCase_=False      ):
          UpperCAmelCase__	:      Tuple     							=					jnp.bfloataa if fpaa else jnp.floataa
          UpperCAmelCase__	:      List[str]     							=					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, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
              # fmt: on
          ]      )
      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'     ,	fpaa=UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					self.get_latents(UpperCamelCase_     ,	fpaa=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					self.get_encoder_hidden_states(UpperCamelCase_     ,	fpaa=UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					model.apply(
              {'params': params}     ,	UpperCamelCase_     ,	jnp.array(UpperCamelCase_     ,	dtype=jnp.intaa      )     ,	encoder_hidden_states=UpperCamelCase_     ,	).sample

          assert sample.shape == latents.shape

          UpperCAmelCase__	:      str     							=					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      )     ,	dtype=jnp.floataa      )
          UpperCAmelCase__	:      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, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
              # fmt: on
          ]      )
      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					self.get_unet_model(model_id='stabilityai/stable-diffusion-2'     ,	fpaa=UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					self.get_latents(UpperCamelCase_     ,	shape=(4, 4, 96, 96)     ,	fpaa=UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					self.get_encoder_hidden_states(UpperCamelCase_     ,	shape=(4, 77, 1_024)     ,	fpaa=UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					model.apply(
              {'params': params}     ,	UpperCamelCase_     ,	jnp.array(UpperCamelCase_     ,	dtype=jnp.intaa      )     ,	encoder_hidden_states=UpperCamelCase_     ,	).sample

          assert sample.shape == latents.shape

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



"""simple docstring"""



import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase__          =	'path-to-your-trained-model'
UpperCamelCase__          =	StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

UpperCamelCase__          =	'A photo of sks dog in a bucket'
UpperCamelCase__          =	pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')

