





import pprint

import requests

_lowerCAmelCase				:   Tuple						       =						'''https://zenquotes.io/api'''

def   __snake_case    (       )			->    list:
   return requests.get(API_ENDPOINT_URL + "/today"						).json()

def   __snake_case    (       )			->    list:
   return requests.get(API_ENDPOINT_URL + "/random"						).json()


if __name__ == "__main__":
 _lowerCAmelCase				:   List[Any]						       =						random_quotes()
 pprint.pprint(response)







import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_   ,    A_       :					Optional[Any]        =      FlaxStableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2"      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )

        A_       :					List[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					int        =      jax.device_count()
        A_       :					Union[str, Any]        =      num_samples * [prompt]
        A_       :					Dict        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					List[Any]        =      replicate(snake_case	)
        A_       :					str        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Dict        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					int        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

        A_       :					Dict        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					List[str]        =      jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					str        =      "stabilityai/stable-diffusion-2"
        A_   ,    A_       :					Dict        =      FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case      ,   subfolder="scheduler"	)
        A_   ,    A_       :					Any        =      FlaxStableDiffusionPipeline.from_pretrained(
            snake_case      ,   scheduler=snake_case      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )
        A_       :					Any        =      scheduler_params

        A_       :					Optional[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					Dict        =      jax.device_count()
        A_       :					List[str]        =      num_samples * [prompt]
        A_       :					int        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					Optional[Any]        =      replicate(snake_case	)
        A_       :					Dict        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Union[str, Any]        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					Union[str, Any]        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

        A_       :					Tuple        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					str        =      jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2
