



'''simple docstring'''





import gc
import unittest

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


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






@slow
@require_flax
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  def 		_lowercase	(					self :      str  )   ->   Any:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()




  def 		_lowercase	(					self :      Tuple  )   ->   Tuple:
    __lowerCamelCase			:			Optional[int]       			=					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/sd2-inpaint/init_image.png'  )
    __lowerCamelCase			:			Optional[int]       			=					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )

    __lowerCamelCase			:			int       			=					'xvjiarui/stable-diffusion-2-inpainting'
    __lowerCamelCase			,__lowerCamelCase			:			int       			=					FlaxStableDiffusionInpaintPipeline.from_pretrained(_a     ,      safety_checker=_a  )

    __lowerCamelCase			:			List[Any]       			=					'Face of a yellow cat, high resolution, sitting on a park bench'

    __lowerCamelCase			:			List[Any]       			=					jax.random.PRNGKey(0  )
    __lowerCamelCase			:			Dict       			=					50

    __lowerCamelCase			:			Tuple       			=					jax.device_count()
    __lowerCamelCase			:			Dict       			=					num_samples * [prompt]
    __lowerCamelCase			:			List[Any]       			=					num_samples * [init_image]
    __lowerCamelCase			:			Optional[int]       			=					num_samples * [mask_image]
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					pipeline.prepare_inputs(_a     ,      _a     ,      _a  )

    # shard inputs and rng
    __lowerCamelCase			:			Any       			=					replicate(_a  )
    __lowerCamelCase			:			Union[str, Any]       			=					jax.random.split(_a     ,      jax.device_count()  )
    __lowerCamelCase			:			Optional[Any]       			=					shard(_a  )
    __lowerCamelCase			:			Any       			=					shard(_a  )
    __lowerCamelCase			:			List[str]       			=					shard(_a  )

    __lowerCamelCase			:			Dict       			=					pipeline(
        _a     ,      _a     ,      _a     ,      _a     ,      _a     ,      _a     ,      jit=_a  )

    __lowerCamelCase			:			Union[str, Any]       			=					output.images.reshape(_a     ,      512     ,      512     ,      3  )

    __lowerCamelCase			:			Any       			=					images[0, 253:256, 253:256, -1]

    __lowerCamelCase			:			Optional[int]       			=					jnp.asarray(jax.device_get(image_slice.flatten()  )  )
    __lowerCamelCase			:			str       			=					jnp.array(
        [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]  )
    print(f'output_slice: {output_slice}'  )
    assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2











'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






