



"""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       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	FlaxControlNetModel.from_pretrained(
        """lllyasviel/sd-controlnet-canny"""      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""      ,     controlnet=_SCREAMING_SNAKE_CASE      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	:					List[str]	=	controlnet_params

    UpperCAmelCase	:					int	=	"""bird"""
    UpperCAmelCase	:					Tuple	=	jax.device_count()
    UpperCAmelCase	:					Optional[int]	=	pipe.prepare_text_inputs([prompts] * num_samples						)

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

    UpperCAmelCase	:					List[Any]	=	jax.random.PRNGKey(0						)
    UpperCAmelCase	:					str	=	jax.random.split(_SCREAMING_SNAKE_CASE      ,     jax.device_count()						)

    UpperCAmelCase	:					int	=	replicate(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	shard(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	shard(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	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, 768, 512, 3)

    UpperCAmelCase	:					int	=	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
    UpperCAmelCase	:					Optional[Any]	=	images[0, 253:256, 253:256, -1]

    UpperCAmelCase	:					Tuple	=	jnp.asarray(jax.device_get(image_slice.flatten()						)						)
    UpperCAmelCase	:					Union[str, Any]	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					str	=	FlaxControlNetModel.from_pretrained(
        """lllyasviel/sd-controlnet-openpose"""      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	,		UpperCAmelCase	:					int	=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""      ,     controlnet=_SCREAMING_SNAKE_CASE      ,     from_pt=_SCREAMING_SNAKE_CASE      ,     dtype=jnp.bfloataa						)
    UpperCAmelCase	:					int	=	controlnet_params

    UpperCAmelCase	:					List[Any]	=	"""Chef in the kitchen"""
    UpperCAmelCase	:					Dict	=	jax.device_count()
    UpperCAmelCase	:					Optional[Any]	=	pipe.prepare_text_inputs([prompts] * num_samples						)

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

    UpperCAmelCase	:					Dict	=	jax.random.PRNGKey(0						)
    UpperCAmelCase	:					Any	=	jax.random.split(_SCREAMING_SNAKE_CASE      ,     jax.device_count()						)

    UpperCAmelCase	:					Tuple	=	replicate(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	shard(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	shard(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	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, 768, 512, 3)

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

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




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "vit_mae"


     def __init__(						self ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=2_2_4 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=3 ,						__lowerCamelCase=True ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=8 ,						__lowerCamelCase=2_0_4_8 ,						__lowerCamelCase=0.7_5 ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						)			->				int:
            super().__init__(**__lowerCamelCase)

            _A      :       int				    = hidden_size
            _A      :       List[str]				    = num_hidden_layers
            _A      :       List[Any]				    = num_attention_heads
            _A      :       Optional[Any]				    = intermediate_size
            _A      :       Optional[int]				    = hidden_act
            _A      :       List[Any]				    = hidden_dropout_prob
            _A      :       List[Any]				    = attention_probs_dropout_prob
            _A      :       Union[str, Any]				    = initializer_range
            _A      :       str				    = layer_norm_eps
            _A      :       Any				    = image_size
            _A      :       int				    = patch_size
            _A      :       int				    = num_channels
            _A      :       Dict				    = qkv_bias
            _A      :       Tuple				    = decoder_num_attention_heads
            _A      :       Tuple				    = decoder_hidden_size
            _A      :       List[str]				    = decoder_num_hidden_layers
            _A      :       Optional[Any]				    = decoder_intermediate_size
            _A      :       List[str]				    = mask_ratio
            _A      :       Union[str, Any]				    = norm_pix_loss
