





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 		__UpperCAmelCase       (    self			:		Optional[Any]					):





          '''simple docstring'''







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







      def 		__UpperCAmelCase       (    self			:		List[Any]					):





          '''simple docstring'''







          _snake_case :			Optional[int]  						=   FlaxControlNetModel.from_pretrained(
              'lllyasviel/sd-controlnet-canny' ,				from_pt=lowerCamelCase_ ,				dtype=jnp.bfloataa					)
          _snake_case :			Union[str, Any]  						=   FlaxStableDiffusionControlNetPipeline.from_pretrained(
              'runwayml/stable-diffusion-v1-5' ,				controlnet=lowerCamelCase_ ,				from_pt=lowerCamelCase_ ,				dtype=jnp.bfloataa					)
          _snake_case :			str  						=   controlnet_params

          _snake_case :			Optional[Any]  						=   'bird'
          _snake_case :			List[Any]  						=   jax.device_count()
          _snake_case :			Union[str, Any]  						=   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 :			List[str]  						=   pipe.prepare_image_inputs([canny_image] * num_samples					)

          _snake_case :			Optional[Any]  						=   jax.random.PRNGKey(0					)
          _snake_case :			str  						=   jax.random.split(lowerCamelCase_ ,				jax.device_count()					)

          _snake_case :			int  						=   replicate(lowerCamelCase_					)
          _snake_case :			Optional[Any]  						=   shard(lowerCamelCase_					)
          _snake_case :			List[str]  						=   shard(lowerCamelCase_					)

          _snake_case :			List[str]  						=   pipe(
              prompt_ids=lowerCamelCase_ ,				image=lowerCamelCase_ ,				params=lowerCamelCase_ ,				prng_seed=lowerCamelCase_ ,				num_inference_steps=50 ,				jit=lowerCamelCase_ ,				).images
          assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

          _snake_case :			Tuple  						=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
          _snake_case :			List[str]  						=   images[0, 2_53:2_56, 2_53:2_56, -1]

          _snake_case :			int  						=   jnp.asarray(jax.device_get(image_slice.flatten()					)					)
          _snake_case :			int  						=   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			:		Tuple					):





          '''simple docstring'''







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

          _snake_case :			Optional[int]  						=   'Chef in the kitchen'
          _snake_case :			str  						=   jax.device_count()
          _snake_case :			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/pose.png'					)
          _snake_case :			int  						=   pipe.prepare_image_inputs([pose_image] * num_samples					)

          _snake_case :			str  						=   jax.random.PRNGKey(0					)
          _snake_case :			Any  						=   jax.random.split(lowerCamelCase_ ,				jax.device_count()					)

          _snake_case :			Union[str, Any]  						=   replicate(lowerCamelCase_					)
          _snake_case :			Any  						=   shard(lowerCamelCase_					)
          _snake_case :			Any  						=   shard(lowerCamelCase_					)

          _snake_case :			Tuple  						=   pipe(
              prompt_ids=lowerCamelCase_ ,				image=lowerCamelCase_ ,				params=lowerCamelCase_ ,				prng_seed=lowerCamelCase_ ,				num_inference_steps=50 ,				jit=lowerCamelCase_ ,				).images
          assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

          _snake_case :			List[str]  						=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
          _snake_case :			List[Any]  						=   images[0, 2_53:2_56, 2_53:2_56, -1]

          _snake_case :			List[Any]  						=   jnp.asarray(jax.device_get(image_slice.flatten()					)					)
          _snake_case :			Optional[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 math import factorial

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






