





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	):
     '''simple docstring'''


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




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

         _A     =							replicate(snake_case_							)
         _A     =							shard(snake_case_							)
         _A     =							shard(snake_case_							)

         _A     =							pipe(
             prompt_ids=snake_case_				,     image=snake_case_				,     params=snake_case_				,     prng_seed=snake_case_				,     num_inference_steps=50				,     jit=snake_case_				,     ).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(
             [0.16_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        lowerCAmelCase__		(    self							):
         _A,			_A     =							FlaxControlNetModel.from_pretrained(
             'lllyasviel/sd-controlnet-openpose'				,     from_pt=snake_case_				,     dtype=jnp.bfloataa							)
         _A,			_A     =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'				,     controlnet=snake_case_				,     from_pt=snake_case_				,     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(snake_case_				,     jax.device_count()							)

         _A     =							replicate(snake_case_							)
         _A     =							shard(snake_case_							)
         _A     =							shard(snake_case_							)

         _A     =							pipe(
             prompt_ids=snake_case_				,     image=snake_case_				,     params=snake_case_				,     prng_seed=snake_case_				,     num_inference_steps=50				,     jit=snake_case_				,     ).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(
             [[0.27_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












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    if not isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        raise ValueError('check_bouncy() accepts only integer arguments'				)
    _A     =							str(_SCREAMING_SNAKE_CASE				)
    _A     =							''.join(sorted(_SCREAMING_SNAKE_CASE				)				)
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 99				)							-> int:




    """simple docstring"""




    if not 0 < percent < 100:
        raise ValueError('solution() only accepts values from 0 to 100'				)
    _A     =							0
    _A     =							1

    while True:
        if check_bouncy(_SCREAMING_SNAKE_CASE				):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print(f"{solution(99)}")





