
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      __A(    unittest.TestCase			):

  """simple docstring"""







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    FlaxControlNetModel.from_pretrained(
           """lllyasviel/sd-controlnet-canny"""	,      from_pt=SCREAMING_SNAKE_CASE_	,      dtype=jnp.bfloataa						)
       UpperCamelCase__		,	UpperCamelCase__				=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
           """runwayml/stable-diffusion-v1-5"""	,      controlnet=SCREAMING_SNAKE_CASE_	,      from_pt=SCREAMING_SNAKE_CASE_	,      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(SCREAMING_SNAKE_CASE_	,      jax.device_count()						)

       UpperCamelCase__				=    replicate(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    shard(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    shard(SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    replicate(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    shard(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    shard(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    pipe(
           prompt_ids=SCREAMING_SNAKE_CASE_	,      image=SCREAMING_SNAKE_CASE_	,      params=SCREAMING_SNAKE_CASE_	,      prng_seed=SCREAMING_SNAKE_CASE_	,      num_inference_steps=50	,      jit=SCREAMING_SNAKE_CASE_	,      ).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







def 						__magic_name__			(   __a      :	int	,		__a      :	int						):



     '''simple docstring'''




     while a != 0:
          UpperCamelCase__		,	UpperCamelCase__				=    b % a, a
     return b






def 						__magic_name__			(   __a      :	int	,		__a      :	int						):



     '''simple docstring'''




     if gcd(__a	,		__a						) != 1:
          UpperCamelCase__				=    f"mod inverse of {a!r} and {m!r} does not exist"
          raise ValueError(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    1, 0, a
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    0, 1, m
     while va != 0:
          UpperCamelCase__				=    ua // va
          UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m





