



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    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : str						=			FlaxControlNetModel.from_pretrained(
								    """lllyasviel/sd-controlnet-canny"""							,    from_pt=_lowercase							,    dtype=jnp.bfloataa   )
								snake_case_   : Tuple						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    """runwayml/stable-diffusion-v1-5"""							,    controlnet=_lowercase							,    from_pt=_lowercase							,    dtype=jnp.bfloataa   )
								snake_case_   : Tuple						=			controlnet_params

								snake_case_   : int						=			"""bird"""
								snake_case_   : Any						=			jax.device_count()
								snake_case_   : List[str]						=			pipe.prepare_text_inputs([prompts] * num_samples   )

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

								snake_case_   : List[Any]						=			jax.random.PRNGKey(0   )
								snake_case_   : int						=			jax.random.split(_lowercase							,    jax.device_count()   )

								snake_case_   : List[str]						=			replicate(_lowercase   )
								snake_case_   : Optional[int]						=			shard(_lowercase   )
								snake_case_   : Optional[Any]						=			shard(_lowercase   )

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

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

								snake_case_   : Tuple						=			jnp.asarray(jax.device_get(image_slice.flatten()   )   )
								snake_case_   : Dict						=			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   ) ->						List[Any]:







								'''simple docstring'''

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

								snake_case_   : List[str]						=			"""Chef in the kitchen"""
								snake_case_   : int						=			jax.device_count()
								snake_case_   : int						=			pipe.prepare_text_inputs([prompts] * num_samples   )

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

								snake_case_   : Tuple						=			jax.random.PRNGKey(0   )
								snake_case_   : Optional[Any]						=			jax.random.split(_lowercase							,    jax.device_count()   )

								snake_case_   : Dict						=			replicate(_lowercase   )
								snake_case_   : Union[str, Any]						=			shard(_lowercase   )
								snake_case_   : Optional[int]						=			shard(_lowercase   )

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

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

								snake_case_   : Union[str, Any]						=			jnp.asarray(jax.device_get(image_slice.flatten()   )   )
								snake_case_   : Tuple						=			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






"""simple docstring"""





import math

import tensorflow as tf
from packaging import version
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0    )					, x.dtype    )    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : List[Any]						=			tf.cast(math.pi					, x.dtype    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi    ) * (x + coeff * tf.pow(__UpperCamelCase					, 3    ))    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			tf.convert_to_tensor(__UpperCamelCase    )

						return x * tf.tanh(tf.math.softplus(__UpperCamelCase    )    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : str						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			tf.cast(0.7_978_845_608					, x.dtype    )

						return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)    ))
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : str						=			tf.cast(1.702					, x.dtype    )
						return x * tf.math.sigmoid(coeff * x    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return tf.clip_by_value(_gelu(__UpperCamelCase    )					, -1_0					, 1_0    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[str]=-1    ):


						'''simple docstring'''






						snake_case_			,		snake_case_   : List[Any]						=			tf.split(__UpperCamelCase					, 2					, axis=__UpperCamelCase    )
						return a * tf.math.sigmoid(__UpperCamelCase    )


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
					def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


											'''simple docstring'''






											return tf.keras.activations.gelu(__UpperCamelCase					, approximate=__UpperCamelCase    )

					__lowerCAmelCase       :					int      					=					tf.keras.activations.gelu






					__lowerCAmelCase       :					Optional[Any]      					=					approximate_gelu_wrap
else:
					__lowerCAmelCase       :					List[Any]      					=					_gelu
					__lowerCAmelCase       :					Any      					=					_gelu_new


__lowerCAmelCase       :					Dict      					=					{
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}






def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						if activation_string in ACTaFN:
												return ACTaFN[activation_string]
						else:
												raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()    )}'    )

