




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Any:





           '''simple docstring'''

           super().tearDown()
           gc.collect()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->int:





           '''simple docstring'''

           A__			,							A__							=   FlaxControlNetModel.from_pretrained(
               '''lllyasviel/sd-controlnet-canny''' ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__			,							A__							=   FlaxStableDiffusionControlNetPipeline.from_pretrained(
               '''runwayml/stable-diffusion-v1-5''' ,			controlnet=UpperCAmelCase__ ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__							=   controlnet_params

           A__							=   '''bird'''
           A__							=   jax.device_count()
           A__							=   pipe.prepare_text_inputs([prompts] * num_samples)

           A__							=   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''')
           A__							=   pipe.prepare_image_inputs([canny_image] * num_samples)

           A__							=   jax.random.PRNGKey(0)
           A__							=   jax.random.split(UpperCAmelCase__ ,			jax.device_count())

           A__							=   replicate(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)

           A__							=   pipe(
               prompt_ids=UpperCAmelCase__ ,			image=UpperCAmelCase__ ,			params=UpperCAmelCase__ ,			prng_seed=UpperCAmelCase__ ,			num_inference_steps=50 ,			jit=UpperCAmelCase__ ,			).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           A__							=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
           A__							=   images[0, 253:256, 253:256, -1]

           A__							=   jnp.asarray(jax.device_get(image_slice.flatten()))
           A__							=   jnp.array(
               [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078])
           print(f"""output_slice: {output_slice}""")
           assert jnp.abs(output_slice - expected_slice).max() < 1e-2





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__			,							A__							=   FlaxControlNetModel.from_pretrained(
               '''lllyasviel/sd-controlnet-openpose''' ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__			,							A__							=   FlaxStableDiffusionControlNetPipeline.from_pretrained(
               '''runwayml/stable-diffusion-v1-5''' ,			controlnet=UpperCAmelCase__ ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__							=   controlnet_params

           A__							=   '''Chef in the kitchen'''
           A__							=   jax.device_count()
           A__							=   pipe.prepare_text_inputs([prompts] * num_samples)

           A__							=   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png''')
           A__							=   pipe.prepare_image_inputs([pose_image] * num_samples)

           A__							=   jax.random.PRNGKey(0)
           A__							=   jax.random.split(UpperCAmelCase__ ,			jax.device_count())

           A__							=   replicate(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)

           A__							=   pipe(
               prompt_ids=UpperCAmelCase__ ,			image=UpperCAmelCase__ ,			params=UpperCAmelCase__ ,			prng_seed=UpperCAmelCase__ ,			num_inference_steps=50 ,			jit=UpperCAmelCase__ ,			).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           A__							=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
           A__							=   images[0, 253:256, 253:256, -1]

           A__							=   jnp.asarray(jax.device_get(image_slice.flatten()))
           A__							=   jnp.array(
               [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]])
           print(f"""output_slice: {output_slice}""")
           assert jnp.abs(output_slice - expected_slice).max() < 1e-2








from typing import TYPE_CHECKING

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


_lowerCamelCase     :		Union[str, Any]								=       {
    """configuration_swiftformer""": [
        """SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwiftFormerConfig""",
        """SwiftFormerOnnxConfig""",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Union[str, Any]								=       [
      """SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SwiftFormerForImageClassification""",
      """SwiftFormerModel""",
      """SwiftFormerPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_swiftformer import (
      SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SwiftFormerConfig,
      SwiftFormerOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swiftformer import (
        SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwiftFormerForImageClassification,
        SwiftFormerModel,
        SwiftFormerPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		str								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


