

"""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 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  def 				lowercase_		(							self						:		Any )		->							str:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()






  def 				lowercase_		(							self						:		str )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   FlaxControlNetModel.from_pretrained(
           '''lllyasviel/sd-controlnet-canny'''     ,       from_pt=__lowercase     ,       dtype=jnp.bfloataa )
       SCREAMING_SNAKE_CASE__   =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
           '''runwayml/stable-diffusion-v1-5'''     ,       controlnet=__lowercase     ,       from_pt=__lowercase     ,       dtype=jnp.bfloataa )
       SCREAMING_SNAKE_CASE__   =   controlnet_params

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

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

       SCREAMING_SNAKE_CASE__   =   jax.random.PRNGKey(0 )
       SCREAMING_SNAKE_CASE__   =   jax.random.split(__lowercase     ,       jax.device_count() )

       SCREAMING_SNAKE_CASE__   =   replicate(__lowercase )
       SCREAMING_SNAKE_CASE__   =   shard(__lowercase )
       SCREAMING_SNAKE_CASE__   =   shard(__lowercase )

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

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

       SCREAMING_SNAKE_CASE__   =   jnp.asarray(jax.device_get(image_slice.flatten() ) )
       SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Dict )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   FlaxControlNetModel.from_pretrained(
           '''lllyasviel/sd-controlnet-openpose'''     ,       from_pt=__lowercase     ,       dtype=jnp.bfloataa )
       SCREAMING_SNAKE_CASE__   =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
           '''runwayml/stable-diffusion-v1-5'''     ,       controlnet=__lowercase     ,       from_pt=__lowercase     ,       dtype=jnp.bfloataa )
       SCREAMING_SNAKE_CASE__   =   controlnet_params

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

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

       SCREAMING_SNAKE_CASE__   =   jax.random.PRNGKey(0 )
       SCREAMING_SNAKE_CASE__   =   jax.random.split(__lowercase     ,       jax.device_count() )

       SCREAMING_SNAKE_CASE__   =   replicate(__lowercase )
       SCREAMING_SNAKE_CASE__   =   shard(__lowercase )
       SCREAMING_SNAKE_CASE__   =   shard(__lowercase )

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

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

       SCREAMING_SNAKE_CASE__   =   jnp.asarray(jax.device_get(image_slice.flatten() ) )
       SCREAMING_SNAKE_CASE__   =   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 __future__ import annotations
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


