





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        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()



 def 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		FlaxControlNetModel.from_pretrained(
            '''lllyasviel/sd-controlnet-canny''',from_pt=__lowerCamelCase,dtype=jnp.bfloataa					)
        A__   ,      A__		   =		FlaxStableDiffusionControlNetPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5''',controlnet=__lowerCamelCase,from_pt=__lowerCamelCase,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(__lowerCamelCase,jax.device_count()					)

        A__		   =		replicate(__lowerCamelCase					)
        A__		   =		shard(__lowerCamelCase					)
        A__		   =		shard(__lowerCamelCase					)

        A__		   =		pipe(
            prompt_ids=__lowerCamelCase,image=__lowerCamelCase,params=__lowerCamelCase,prng_seed=__lowerCamelCase,num_inference_steps=50,jit=__lowerCamelCase,).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 							UpperCamelCase     (				self					):
        A__   ,      A__		   =		FlaxControlNetModel.from_pretrained(
            '''lllyasviel/sd-controlnet-openpose''',from_pt=__lowerCamelCase,dtype=jnp.bfloataa					)
        A__   ,      A__		   =		FlaxStableDiffusionControlNetPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5''',controlnet=__lowerCamelCase,from_pt=__lowerCamelCase,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(__lowerCamelCase,jax.device_count()					)

        A__		   =		replicate(__lowerCamelCase					)
        A__		   =		shard(__lowerCamelCase					)
        A__		   =		shard(__lowerCamelCase					)

        A__		   =		pipe(
            prompt_ids=__lowerCamelCase,image=__lowerCamelCase,params=__lowerCamelCase,prng_seed=__lowerCamelCase,num_inference_steps=50,jit=__lowerCamelCase,).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


a__: List[Any]							   =						{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Any							   =						[
           'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XCLIPModel',
           'XCLIPPreTrainedModel',
           'XCLIPTextModel',
           'XCLIPVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       a__: Optional[Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





