


'''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        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__ ,    UpperCamelCase__       :str							 =			FlaxControlNetModel.from_pretrained(
							    '''lllyasviel/sd-controlnet-canny'''   ,       from_pt=a__   ,       dtype=jnp.bfloataa							)
							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			FlaxStableDiffusionControlNetPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''   ,       controlnet=a__   ,       from_pt=a__   ,       dtype=jnp.bfloataa							)
							UpperCamelCase__       :List[Any]							 =			controlnet_params

							UpperCamelCase__       :Dict							 =			'''bird'''
							UpperCamelCase__       :Optional[int]							 =			jax.device_count()
							UpperCamelCase__       :str							 =			pipe.prepare_text_inputs([prompts] * num_samples							)

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

							UpperCamelCase__       :Optional[int]							 =			jax.random.PRNGKey(0							)
							UpperCamelCase__       :int							 =			jax.random.split(a__   ,       jax.device_count()							)

							UpperCamelCase__       :Union[str, Any]							 =			replicate(a__							)
							UpperCamelCase__       :List[str]							 =			shard(a__							)
							UpperCamelCase__       :Any							 =			shard(a__							)

							UpperCamelCase__       :Optional[int]							 =			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, 768, 512, 3)

							UpperCamelCase__       :str							 =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
							UpperCamelCase__       :List[Any]							 =			images[0, 253:256, 253:256, -1]

							UpperCamelCase__       :Optional[Any]							 =			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
							UpperCamelCase__       :Optional[Any]							 =			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    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__ ,    UpperCamelCase__       :int							 =			FlaxControlNetModel.from_pretrained(
							    '''lllyasviel/sd-controlnet-openpose'''   ,       from_pt=a__   ,       dtype=jnp.bfloataa							)
							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			FlaxStableDiffusionControlNetPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''   ,       controlnet=a__   ,       from_pt=a__   ,       dtype=jnp.bfloataa							)
							UpperCamelCase__       :Optional[Any]							 =			controlnet_params

							UpperCamelCase__       :Dict							 =			'''Chef in the kitchen'''
							UpperCamelCase__       :Optional[Any]							 =			jax.device_count()
							UpperCamelCase__       :Union[str, Any]							 =			pipe.prepare_text_inputs([prompts] * num_samples							)

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

							UpperCamelCase__       :Any							 =			jax.random.PRNGKey(0							)
							UpperCamelCase__       :Optional[Any]							 =			jax.random.split(a__   ,       jax.device_count()							)

							UpperCamelCase__       :List[str]							 =			replicate(a__							)
							UpperCamelCase__       :str							 =			shard(a__							)
							UpperCamelCase__       :Tuple							 =			shard(a__							)

							UpperCamelCase__       :Any							 =			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, 768, 512, 3)

							UpperCamelCase__       :List[Any]							 =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
							UpperCamelCase__       :List[str]							 =			images[0, 253:256, 253:256, -1]

							UpperCamelCase__       :Tuple							 =			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
							UpperCamelCase__       :Optional[Any]							 =			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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case											=					{'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMSNModel''',
		    '''ViTMSNForImageClassification''',
		    '''ViTMSNPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)