


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     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         with tempfile.TemporaryDirectory() as tmpdirname:
                # pipeline has Flax weights
                UpperCAmelCase__										=     FlaxDiffusionPipeline.from_pretrained(
                    "hf-internal-testing/tiny-stable-diffusion-pipe"		,						safety_checker=_lowercase		,						cache_dir=_lowercase )

                UpperCAmelCase__										=     [t[-1] for t in os.walk(os.path.join(_lowercase		,						os.listdir(_lowercase )[0]		,						"snapshots" ) )]
                UpperCAmelCase__										=     [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     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     FlaxStableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-pipe"		,						safety_checker=_lowercase )

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

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

         UpperCAmelCase__										=     jax.device_count()
         UpperCAmelCase__										=     num_samples * [prompt]
         UpperCAmelCase__										=     pipeline.prepare_inputs(_lowercase )

         # shard inputs and rng
         UpperCAmelCase__										=     replicate(_lowercase )
         UpperCAmelCase__										=     jax.random.split(_lowercase		,						_lowercase )
         UpperCAmelCase__										=     shard(_lowercase )

         UpperCAmelCase__										=     pipeline(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						jit=_lowercase ).images

         assert images.shape == (num_samples, 1, 64, 64, 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(_lowercase		,						dtype=np.floataa ).sum() - 4_9_9_4_7.8_7_5 ) < 5E-1

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






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="flax"		,						safety_checker=_lowercase )

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

         UpperCAmelCase__										=     jax.random.PRNGKey(0 )
         UpperCAmelCase__										=     50

         UpperCAmelCase__										=     jax.device_count()
         UpperCAmelCase__										=     num_samples * [prompt]
         UpperCAmelCase__										=     pipeline.prepare_inputs(_lowercase )

         # shard inputs and rng
         UpperCAmelCase__										=     replicate(_lowercase )
         UpperCAmelCase__										=     jax.random.split(_lowercase		,						_lowercase )
         UpperCAmelCase__										=     shard(_lowercase )

         UpperCAmelCase__										=     pipeline(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						jit=_lowercase ).images

         assert images.shape == (num_samples, 1, 5_12, 5_12, 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(_lowercase		,						dtype=np.floataa ).sum() - 2_3_8_3_8_0_8.2) ) < 5E-1






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="bf16"		,						dtype=jnp.bfloataa		,						safety_checker=_lowercase )

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

         UpperCAmelCase__										=     jax.random.PRNGKey(0 )
         UpperCAmelCase__										=     50

         UpperCAmelCase__										=     jax.device_count()
         UpperCAmelCase__										=     num_samples * [prompt]
         UpperCAmelCase__										=     pipeline.prepare_inputs(_lowercase )

         # shard inputs and rng
         UpperCAmelCase__										=     replicate(_lowercase )
         UpperCAmelCase__										=     jax.random.split(_lowercase		,						_lowercase )
         UpperCAmelCase__										=     shard(_lowercase )

         UpperCAmelCase__										=     pipeline(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						jit=_lowercase ).images

         assert images.shape == (num_samples, 1, 5_12, 5_12, 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(_lowercase		,						dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5E-1






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="bf16"		,						dtype=jnp.bfloataa )

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

         UpperCAmelCase__										=     jax.random.PRNGKey(0 )
         UpperCAmelCase__										=     50

         UpperCAmelCase__										=     jax.device_count()
         UpperCAmelCase__										=     num_samples * [prompt]
         UpperCAmelCase__										=     pipeline.prepare_inputs(_lowercase )

         # shard inputs and rng
         UpperCAmelCase__										=     replicate(_lowercase )
         UpperCAmelCase__										=     jax.random.split(_lowercase		,						_lowercase )
         UpperCAmelCase__										=     shard(_lowercase )

         UpperCAmelCase__										=     pipeline(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						jit=_lowercase ).images

         assert images.shape == (num_samples, 1, 5_12, 5_12, 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(_lowercase		,						dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5E-1






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     FlaxDDIMScheduler(
             beta_start=0.0_0_0_8_5		,						beta_end=0.0_1_2		,						beta_schedule="scaled_linear"		,						set_alpha_to_one=_lowercase		,						steps_offset=1		,						)

         UpperCAmelCase__						,			UpperCAmelCase__										=     FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="bf16"		,						dtype=jnp.bfloataa		,						scheduler=_lowercase		,						safety_checker=_lowercase		,						)
         UpperCAmelCase__										=     scheduler.create_state()

         UpperCAmelCase__										=     scheduler_state

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

         UpperCAmelCase__										=     jax.random.PRNGKey(0 )
         UpperCAmelCase__										=     50

         UpperCAmelCase__										=     jax.device_count()
         UpperCAmelCase__										=     num_samples * [prompt]
         UpperCAmelCase__										=     pipeline.prepare_inputs(_lowercase )

         # shard inputs and rng
         UpperCAmelCase__										=     replicate(_lowercase )
         UpperCAmelCase__										=     jax.random.split(_lowercase		,						_lowercase )
         UpperCAmelCase__										=     shard(_lowercase )

         UpperCAmelCase__										=     pipeline(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						jit=_lowercase ).images

         assert images.shape == (num_samples, 1, 5_12, 5_12, 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(_lowercase		,						dtype=np.floataa ).sum() - 2_3_4_7_6_9_3.5) ) < 5E-1






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







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

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

         UpperCAmelCase__						,			UpperCAmelCase__										=     FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="bf16"		,						dtype=jnp.bfloataa		,						safety_checker=_lowercase		,						)

         UpperCAmelCase__										=     replicate(_lowercase )
         UpperCAmelCase__										=     pipeline.prepare_inputs(_lowercase )
         UpperCAmelCase__										=     shard(_lowercase )
         UpperCAmelCase__										=     pipeline(_lowercase		,						_lowercase		,						_lowercase		,						jit=_lowercase ).images
         assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
         UpperCAmelCase__										=     images[2, 0, 2_56, 10:17, 1]

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

         UpperCAmelCase__										=     replicate(_lowercase )
         UpperCAmelCase__										=     pipeline.prepare_inputs(_lowercase )
         UpperCAmelCase__										=     shard(_lowercase )
         UpperCAmelCase__										=     pipeline(_lowercase		,						_lowercase		,						_lowercase		,						jit=_lowercase ).images
         assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
         UpperCAmelCase__										=     images[2, 0, 2_56, 10:17, 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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A      = {"configuration_mmbt": ["MMBTConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
    from .configuration_mmbt import MMBTConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
    import sys

    A      = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





