





'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable







def       UpperCAmelCase							(     _lowerCamelCase      :  Callable[[int | float], int | float]			,   _lowerCamelCase      :  int | float			,   _lowerCamelCase      :  int | float			,   _lowerCamelCase      :  int = 100			,   ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					x_start
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					fnc(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					0.0
						for _ in range(_lowerCamelCase     ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												SCREAMING_SNAKE_CASE__     :				Any   =					(x_end - x_start) / steps + xa
												SCREAMING_SNAKE_CASE__     :				Tuple   =					fnc(_lowerCamelCase     )
												area += abs(fxa + fxa     ) * (xa - xa) / 2
												# Increment step
												SCREAMING_SNAKE_CASE__     :				Tuple   =					xa
												SCREAMING_SNAKE_CASE__     :				Dict   =					fxa
						return area


if __name__ == "__main__":







				def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]     ):





										'''simple docstring'''


										return x**3 + x**2

				print("f(x) = x^3 + x^2")
				print("The area between the curve, x = -5, x = 5 and the x axis is:")
				__lowercase       :str       =  10
				while i <= 100_000:
								print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
								i *= 10






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    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

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

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

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					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, 7_68, 5_12, 3)

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

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[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      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

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

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

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

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, 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, 7_68, 5_12, 3)

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

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