


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 A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

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






					def A      (				self						)   ->			str:







								'''simple docstring'''

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

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

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

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

								__lowercase 					=   replicate(__a						)
								__lowercase 					=   shard(__a						)
								__lowercase 					=   shard(__a						)

								__lowercase 					=   pipe(
								    prompt_ids=__a ,       image=__a ,       params=__a ,       prng_seed=__a ,       num_inference_steps=5_0 ,       jit=__a ,       ).images
								assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

								__lowercase 					=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
								__lowercase 					=   images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

								__lowercase 					=   jnp.asarray(jax.device_get(image_slice.flatten()						)						)
								__lowercase 					=   jnp.array(
								    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]						)
								print(F'output_slice: {output_slice}'						)
								assert jnp.abs(output_slice - expected_slice						).max() < 1e-2






					def A      (				self						)   ->			str:







								'''simple docstring'''

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

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

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

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

								__lowercase 					=   replicate(__a						)
								__lowercase 					=   shard(__a						)
								__lowercase 					=   shard(__a						)

								__lowercase 					=   pipe(
								    prompt_ids=__a ,       image=__a ,       params=__a ,       prng_seed=__a ,       num_inference_steps=5_0 ,       jit=__a ,       ).images
								assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

								__lowercase 					=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
								__lowercase 					=   images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

								__lowercase 					=   jnp.asarray(jax.device_get(image_slice.flatten()						)						)
								__lowercase 					=   jnp.array(
								    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]						)
								print(F'output_slice: {output_slice}'						)
								assert jnp.abs(output_slice - expected_slice						).max() < 1e-2






a : Any		=				'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a : Union[str, Any]		=				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a : Union[str, Any]		=				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


