
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        lowerCAmelCase__      (   unittest.TestCase	):

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


        '''simple docstring'''

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

      def  A_				(			self				)					->      Tuple:


        '''simple docstring'''

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

        _UpperCamelCase						    =	"bird"
        _UpperCamelCase						    =	jax.device_count()
        _UpperCamelCase						    =	pipe.prepare_text_inputs([prompts] * num_samples				)

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

        _UpperCamelCase						    =	jax.random.PRNGKey(0				)
        _UpperCamelCase						    =	jax.random.split(a       ,							jax.device_count()				)

        _UpperCamelCase						    =	replicate(a				)
        _UpperCamelCase						    =	shard(a				)
        _UpperCamelCase						    =	shard(a				)

        _UpperCamelCase						    =	pipe(
            prompt_ids=a       ,							image=a       ,							params=a       ,							prng_seed=a       ,							num_inference_steps=50       ,							jit=a       ,							).images
        assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

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

        _UpperCamelCase						    =	jnp.asarray(jax.device_get(image_slice.flatten()				)				)
        _UpperCamelCase						    =	jnp.array(
            [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]				)
        print(F'output_slice: {output_slice}'				)
        assert jnp.abs(output_slice - expected_slice				).max() < 1e-2







      def  A_				(			self				)					->      Optional[Any]:


        '''simple docstring'''

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

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

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

        _UpperCamelCase						    =	jax.random.PRNGKey(0				)
        _UpperCamelCase						    =	jax.random.split(a       ,							jax.device_count()				)

        _UpperCamelCase						    =	replicate(a				)
        _UpperCamelCase						    =	shard(a				)
        _UpperCamelCase						    =	shard(a				)

        _UpperCamelCase						    =	pipe(
            prompt_ids=a       ,							image=a       ,							params=a       ,							prng_seed=a       ,							num_inference_steps=50       ,							jit=a       ,							).images
        assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

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

        _UpperCamelCase						    =	jnp.asarray(jax.device_get(image_slice.flatten()				)				)
        _UpperCamelCase						    =	jnp.array(
            [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]				)
        print(F'output_slice: {output_slice}'				)
        assert jnp.abs(output_slice - expected_slice				).max() < 1e-2





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



