





"""simple docstring"""




import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard

     from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline



@require_flax
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Tuple					)							->						Any:
     with tempfile.TemporaryDirectory() as tmpdirname:
        # pipeline has Flax weights
        __lowerCAmelCase							     =     FlaxDiffusionPipeline.from_pretrained(
            'hf-internal-testing/tiny-stable-diffusion-pipe' ,  safety_checker=lowerCAmelCase_ ,  cache_dir=lowerCAmelCase_					)

        __lowerCAmelCase							     =     [t[-1] for t in os.walk(os.path.join(lowerCAmelCase_ ,  os.listdir(lowerCAmelCase_					)[0] ,  'snapshots'					)					)]
        __lowerCAmelCase							     =     [item for sublist in all_root_files for item in sublist]

        # None of the downloaded files should be a PyTorch file even if we have some here:
        # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
        assert not any(f.endswith('.bin'					) for f in files					)





@slow
@require_flax
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'hf-internal-testing/tiny-stable-diffusion-pipe' ,  safety_checker=lowerCAmelCase_					)

     __lowerCAmelCase							     =     (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     4

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
     if jax.device_count() == 8:
        assert np.abs(np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 4.1_51_47_45					) < 1e-3
        assert np.abs(np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 4_9_9_4_7.8_7_5					) < 5e-1

     __lowerCAmelCase							     =     pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]					)					)					)
     assert len(lowerCAmelCase_					) == num_samples






  def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='flax' ,  safety_checker=lowerCAmelCase_					)

     __lowerCAmelCase							     =     (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.05_65_24_01)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_3_8_3_8_0_8.2)					) < 5e-1






  def     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase_					)

     __lowerCAmelCase							     =     (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.04_00_39_06)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_3_7_3_5_1_6.7_5)					) < 5e-1






  def     lowercase		(    self			:		int					)							->						List[Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa					)

     __lowerCAmelCase							     =     (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.04_00_39_06)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_3_7_3_5_1_6.7_5)					) < 5e-1






  def     lowercase		(    self			:		Dict					)							->						Dict:
     __lowerCAmelCase							     =     FlaxDDIMScheduler(
         beta_start=0.0_00_85 ,  beta_end=0.0_12 ,  beta_schedule='scaled_linear' ,  set_alpha_to_one=lowerCAmelCase_ ,  steps_offset=1 ,  )

     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  scheduler=lowerCAmelCase_ ,  safety_checker=lowerCAmelCase_ ,  )
     __lowerCAmelCase							     =     scheduler.create_state()

     __lowerCAmelCase							     =     scheduler_state

     __lowerCAmelCase							     =     (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.0_45_04_39_45)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_3_4_7_6_9_3.5)					) < 5e-1






  def     lowercase		(    self			:		int					)							->						Optional[Any]:
     __lowerCAmelCase							     =     (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     jax.random.split(jax.random.PRNGKey(0					) ,  lowerCAmelCase_					)

     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images
     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     images[2, 0, 2_5_6, 1_0:1_7, 1]

     # With memory efficient attention
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase_ ,  use_memory_efficient_attention=lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images
     assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     images[2, 0, 2_5_6, 1_0:1_7, 1]

     # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
     # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
     assert abs(slice_eff - slice					).max() < 1e-2








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

