



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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      with tempfile.TemporaryDirectory() as tmpdirname:
       # pipeline has Flax weights
       __SCREAMING_SNAKE_CASE       :    int       =	FlaxDiffusionPipeline.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-pipe"     ,  safety_checker=UpperCamelCase     ,  cache_dir=UpperCamelCase				)

       __SCREAMING_SNAKE_CASE       :    List[Any]       =	[t[-1] for t in os.walk(os.path.join(UpperCamelCase     ,  os.listdir(UpperCamelCase				)[0]     ,  "snapshots"				)				)]
       __SCREAMING_SNAKE_CASE       :    Any       =	[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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							int				)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	FlaxStableDiffusionPipeline.from_pretrained(
          "hf-internal-testing/tiny-stable-diffusion-pipe"     ,  safety_checker=UpperCamelCase				)

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

      __SCREAMING_SNAKE_CASE       :    Dict       =	jax.random.PRNGKey(0				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	4

      __SCREAMING_SNAKE_CASE       :    List[str]       =	jax.device_count()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_samples * [prompt]
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	pipeline.prepare_inputs(UpperCamelCase				)

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	replicate(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	jax.random.split(UpperCamelCase     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    str       =	shard(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	pipeline(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  jit=UpperCamelCase				).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_5_1_4_7_4_5				) < 1E-3
       assert np.abs(np.abs(UpperCamelCase     ,  dtype=np.floataa				).sum() - 4_9_9_4_7.8_7_5				) < 5E-1

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





     def 	__snake_case    (       self :							Optional[Any]				)->List[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	FlaxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"     ,  revision="flax"     ,  safety_checker=UpperCamelCase				)

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

      __SCREAMING_SNAKE_CASE       :    Dict       =	jax.random.PRNGKey(0				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	5_0

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	jax.device_count()
      __SCREAMING_SNAKE_CASE       :    int       =	num_samples * [prompt]
      __SCREAMING_SNAKE_CASE       :    str       =	pipeline.prepare_inputs(UpperCamelCase				)

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE       :    Any       =	replicate(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	jax.random.split(UpperCamelCase     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	shard(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	pipeline(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  jit=UpperCamelCase				).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_5_6_5_2_4_0_1)				) < 1E-3
       assert np.abs((np.abs(UpperCamelCase     ,  dtype=np.floataa				).sum() - 2_3_8_3_8_0_8.2)				) < 5E-1





     def 	__snake_case    (       self :							str				)->Any:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	FlaxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"     ,  revision="bf16"     ,  dtype=jnp.bfloataa     ,  safety_checker=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	(
          "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
          " field, close up, split lighting, cinematic"
      )

      __SCREAMING_SNAKE_CASE       :    List[str]       =	jax.random.PRNGKey(0				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	5_0

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	jax.device_count()
      __SCREAMING_SNAKE_CASE       :    Dict       =	num_samples * [prompt]
      __SCREAMING_SNAKE_CASE       :    str       =	pipeline.prepare_inputs(UpperCamelCase				)

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE       :    List[str]       =	replicate(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Any       =	jax.random.split(UpperCamelCase     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	shard(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	pipeline(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  jit=UpperCamelCase				).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_4_0_0_3_9_0_6)				) < 1E-3
       assert np.abs((np.abs(UpperCamelCase     ,  dtype=np.floataa				).sum() - 2_3_7_3_5_1_6.7_5)				) < 5E-1





     def 	__snake_case    (       self :							str				)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	FlaxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"     ,  revision="bf16"     ,  dtype=jnp.bfloataa				)

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

      __SCREAMING_SNAKE_CASE       :    Dict       =	jax.random.PRNGKey(0				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	5_0

      __SCREAMING_SNAKE_CASE       :    List[str]       =	jax.device_count()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_samples * [prompt]
      __SCREAMING_SNAKE_CASE       :    int       =	pipeline.prepare_inputs(UpperCamelCase				)

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	replicate(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	jax.random.split(UpperCamelCase     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Any       =	shard(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Any       =	pipeline(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  jit=UpperCamelCase				).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_4_0_0_3_9_0_6)				) < 1E-3
       assert np.abs((np.abs(UpperCamelCase     ,  dtype=np.floataa				).sum() - 2_3_7_3_5_1_6.7_5)				) < 5E-1





     def 	__snake_case    (       self :							Optional[int]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	FlaxDDIMScheduler(
          beta_start=0.0_0_0_8_5     ,  beta_end=0.0_1_2     ,  beta_schedule="scaled_linear"     ,  set_alpha_to_one=UpperCamelCase     ,  steps_offset=1     ,  )

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	FlaxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"     ,  revision="bf16"     ,  dtype=jnp.bfloataa     ,  scheduler=UpperCamelCase     ,  safety_checker=UpperCamelCase     ,  )
      __SCREAMING_SNAKE_CASE       :    List[str]       =	scheduler.create_state()

      __SCREAMING_SNAKE_CASE       :    int       =	scheduler_state

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(
          "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
          " field, close up, split lighting, cinematic"
      )

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	jax.random.PRNGKey(0				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	5_0

      __SCREAMING_SNAKE_CASE       :    Dict       =	jax.device_count()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_samples * [prompt]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	pipeline.prepare_inputs(UpperCamelCase				)

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE       :    int       =	replicate(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Any       =	jax.random.split(UpperCamelCase     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	shard(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	pipeline(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  jit=UpperCamelCase				).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_4_5_0_4_3_9_4_5)				) < 1E-3
       assert np.abs((np.abs(UpperCamelCase     ,  dtype=np.floataa				).sum() - 2_3_4_7_6_9_3.5)				) < 5E-1






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

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	jax.device_count()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_samples * [prompt]
      __SCREAMING_SNAKE_CASE       :    int       =	jax.random.split(jax.random.PRNGKey(0				)     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	FlaxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"     ,  revision="bf16"     ,  dtype=jnp.bfloataa     ,  safety_checker=UpperCamelCase     ,  )

      __SCREAMING_SNAKE_CASE       :    Any       =	replicate(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	pipeline.prepare_inputs(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	shard(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	pipeline(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  jit=UpperCamelCase				).images
      assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	images[2, 0, 2_5_6, 1_0:1_7, 1]

      # With memory efficient attention
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    str       =	FlaxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"     ,  revision="bf16"     ,  dtype=jnp.bfloataa     ,  safety_checker=UpperCamelCase     ,  use_memory_efficient_attention=UpperCamelCase     ,  )

      __SCREAMING_SNAKE_CASE       :    Dict       =	replicate(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	pipeline.prepare_inputs(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	shard(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    str       =	pipeline(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  jit=UpperCamelCase				).images
      assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
      __SCREAMING_SNAKE_CASE       :    Any       =	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 abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Optional[NestedDataStructureLike[PathLike]] = None     ,  UpperCamelCase :							Optional[NamedSplit] = None     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->int:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	path_or_paths
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	split if split or isinstance(UpperCamelCase     ,  UpperCamelCase				) else "train"
      __SCREAMING_SNAKE_CASE       :    Dict       =	features
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Tuple       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_proc
      __SCREAMING_SNAKE_CASE       :    int       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							Optional[int]				)->Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
      pass






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	features
      __SCREAMING_SNAKE_CASE       :    str       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Any       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    int       =	num_proc
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							str				)->Union[Dataset, IterableDataset]:
      pass






