





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			:   List[str]							,    A			:   int							,    A			:   Optional[Any]		):
         return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A		) for s in shape]		)}.npy'''




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




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




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

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




       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Union[str, Any]=0							,    A			:   Union[str, Any]=(4, 77, 768)							,    A			:   str=False		):
         __snake_case:						List[Any]			  =						jnp.bfloataa if fpaa else jnp.floataa
         __snake_case:						Any			  =						jnp.array(load_hf_numpy(self.get_file_format(A							,    A		)		)							,    dtype=A		)
         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 							UpperCAmelCase__			(  self			:   Any							,    A			:   Optional[Any]							,    A			:   Union[str, Any]							,    A			:   Optional[int]		):
         __snake_case  ,       __snake_case:						str			  =						self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""							,    fpaa=A		)
         __snake_case:						Union[str, Any]			  =						self.get_latents(A							,    fpaa=A		)
         __snake_case:						Optional[Any]			  =						self.get_encoder_hidden_states(A							,    fpaa=A		)

         __snake_case:						List[Any]			  =						model.apply(
             {"""params""": params}							,    A							,    jnp.array(A							,    dtype=jnp.intaa		)							,    encoder_hidden_states=A							,    ).sample

         assert sample.shape == latents.shape

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

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(A							,    A							,    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 							UpperCAmelCase__			(  self			:   Union[str, Any]							,    A			:   Optional[Any]							,    A			:   Tuple							,    A			:   int		):
         __snake_case  ,       __snake_case:						List[Any]			  =						self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""							,    fpaa=A		)
         __snake_case:						Tuple			  =						self.get_latents(A							,    shape=(4, 4, 96, 96)							,    fpaa=A		)
         __snake_case:						Optional[int]			  =						self.get_encoder_hidden_states(A							,    shape=(4, 77, 1_024)							,    fpaa=A		)

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

         assert sample.shape == latents.shape

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

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













import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)






