



'''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  ):





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()





	def       UpperCamelCase_    (					self   :							Optional[int]					):







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

							_UpperCamelCase   :						Any												=  'bird'
							_UpperCamelCase   :						Union[str, Any]												=  jax.device_count()
							_UpperCamelCase   :						Union[str, Any]												=  pipe.prepare_text_inputs([prompts] * num_samples					)

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

							_UpperCamelCase   :						Dict												=  jax.random.PRNGKey(0					)
							_UpperCamelCase   :						Tuple												=  jax.random.split(UpperCAmelCase_					,jax.device_count()					)

							_UpperCamelCase   :						str												=  replicate(UpperCAmelCase_					)
							_UpperCamelCase   :						List[Any]												=  shard(UpperCAmelCase_					)
							_UpperCamelCase   :						List[str]												=  shard(UpperCAmelCase_					)

							_UpperCamelCase   :						List[Any]												=  pipe(
							    prompt_ids=UpperCAmelCase_					,image=UpperCAmelCase_					,params=UpperCAmelCase_					,prng_seed=UpperCAmelCase_					,num_inference_steps=50					,jit=UpperCAmelCase_					,).images
							assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

							_UpperCamelCase   :						str												=  jnp.asarray(jax.device_get(image_slice.flatten()					)					)
							_UpperCamelCase   :						Dict												=  jnp.array(
							    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]					)
							print(F'output_slice: {output_slice}'					)
							assert jnp.abs(output_slice - expected_slice					).max() < 1E-2





	def       UpperCamelCase_    (					self   :							List[Any]					):







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

							_UpperCamelCase   :						List[Any]												=  'Chef in the kitchen'
							_UpperCamelCase   :						str												=  jax.device_count()
							_UpperCamelCase   :						Optional[Any]												=  pipe.prepare_text_inputs([prompts] * num_samples					)

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

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

							_UpperCamelCase   :						List[Any]												=  replicate(UpperCAmelCase_					)
							_UpperCamelCase   :						Any												=  shard(UpperCAmelCase_					)
							_UpperCamelCase   :						Union[str, Any]												=  shard(UpperCAmelCase_					)

							_UpperCamelCase   :						Any												=  pipe(
							    prompt_ids=UpperCAmelCase_					,image=UpperCAmelCase_					,params=UpperCAmelCase_					,prng_seed=UpperCAmelCase_					,num_inference_steps=50					,jit=UpperCAmelCase_					,).images
							assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

							_UpperCamelCase   :						List[str]												=  jnp.asarray(jax.device_get(image_slice.flatten()					)					)
							_UpperCamelCase   :						Any												=  jnp.array(
							    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]					)
							print(F'output_slice: {output_slice}'					)
							assert jnp.abs(output_slice - expected_slice					).max() < 1E-2







'''simple docstring'''





from __future__ import annotations

import math




def 							lowerCamelCase	(					_snake_case	:    float  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__									=    u
    for i in range(1  ,_snake_case							):
        lowercase__									=    temp * (u - i)
    return temp




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    int(input("enter the numbers of values: "							)							)
    lowercase__									=    []
    for _ in range(_snake_case							):
        y.append([]							)
    for i in range(_snake_case							):
        for j in range(_snake_case							):
            y[i].append(_snake_case							)
            lowercase__									=    0

    print("enter the values of parameters in a list: "							)
    lowercase__									=    list(map(_snake_case  ,input().split()							)							)

    print("enter the values of corresponding parameters: "							)
    for i in range(_snake_case							):
        lowercase__									=    float(input()							)

    lowercase__									=    int(input("enter the value to interpolate: "							)							)
    lowercase__									=    (value - x[0]) / (x[1] - x[0])

    # for calculating forward difference table

    for i in range(1  ,_snake_case							):
        for j in range(n - i							):
            lowercase__									=    y[j + 1][i - 1] - y[j][i - 1]

    lowercase__									=    y[0][0]
    for i in range(1  ,_snake_case							):
        summ += (ucal(_snake_case  ,_snake_case							) * y[0][i]) / math.factorial(_snake_case							)

    print(f'''the value at {value} is {summ}'''							)


if __name__ == "__main__":
 main()




