'''simple docstring'''






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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Union[str, Any]:
        _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, 7_68, 5_12, 3)

        _A    		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]     )
        _A    		=  images[0, 2_53:2_56, 2_53:2_56, -1]

        _A    		=  jnp.asarray(jax.device_get(image_slice.flatten()     )     )
        _A    		=  jnp.array(
            [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]     )
        print(f'output_slice: {output_slice}'     )
        assert jnp.abs(output_slice - expected_slice     ).max() < 1e-2

   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[str]:
        _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, 7_68, 5_12, 3)

        _A    		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]     )
        _A    		=  images[0, 2_53:2_56, 2_53:2_56, -1]

        _A    		=  jnp.asarray(jax.device_get(image_slice.flatten()     )     )
        _A    		=  jnp.array(
            [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]     )
        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_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase    :     str         =       {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Any         =       ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

   _UpperCAmelCase    :     List[str]         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



