



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 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   FlaxStableDiffusionPipeline.from_pretrained(
						    "stabilityai/stable-diffusion-2"		,       revision="bf16"		,       dtype=jnp.bfloataa		,       )

						a					     =   "A painting of a squirrel eating a burger"
						a					     =   jax.device_count()
						a					     =   num_samples * [prompt]
						a					     =   sd_pipe.prepare_inputs(lowerCamelCase_		)

						a					     =   replicate(lowerCamelCase_		)
						a					     =   shard(lowerCamelCase_		)

						a					     =   jax.random.PRNGKey(0		)
						a					     =   jax.random.split(lowerCamelCase_		,       jax.device_count()		)

						a					     =   sd_pipe(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       num_inference_steps=25		,       jit=lowerCamelCase_		)[0]
						assert images.shape == (jax.device_count(), 1, 768, 768, 3)

						a					     =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
						a					     =   images[0, 253:256, 253:256, -1]

						a					     =   jnp.asarray(jax.device_get(image_slice.flatten()		)		)
						a					     =   jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]		)
						print(F'''output_slice: {output_slice}'''		)
						assert jnp.abs(output_slice - expected_slice		).max() < 1E-2




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "stabilityai/stable-diffusion-2"
						a     ,				a					     =   FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCamelCase_		,       subfolder="scheduler"		)
						a     ,				a					     =   FlaxStableDiffusionPipeline.from_pretrained(
						    lowerCamelCase_		,       scheduler=lowerCamelCase_		,       revision="bf16"		,       dtype=jnp.bfloataa		,       )
						a					     =   scheduler_params

						a					     =   "A painting of a squirrel eating a burger"
						a					     =   jax.device_count()
						a					     =   num_samples * [prompt]
						a					     =   sd_pipe.prepare_inputs(lowerCamelCase_		)

						a					     =   replicate(lowerCamelCase_		)
						a					     =   shard(lowerCamelCase_		)

						a					     =   jax.random.PRNGKey(0		)
						a					     =   jax.random.split(lowerCamelCase_		,       jax.device_count()		)

						a					     =   sd_pipe(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       num_inference_steps=25		,       jit=lowerCamelCase_		)[0]
						assert images.shape == (jax.device_count(), 1, 768, 768, 3)

						a					     =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
						a					     =   images[0, 253:256, 253:256, -1]

						a					     =   jnp.asarray(jax.device_get(image_slice.flatten()		)		)
						a					     =   jnp.array([0.4336, 0.4_2969, 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





_lowercase: Dict      			=		[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]






def     a(      A       :    str       )		->       int:
					"""simple docstring"""





					a					     =   {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
					a					     =   0
					a					     =   0
					while place < len(A       ):
										if (place + 1 < len(A       )) and (vals[roman[place]] < vals[roman[place + 1]]):
															total += vals[roman[place + 1]] - vals[roman[place]]
															place += 2
										else:
															total += vals[roman[place]]
															place += 1
					return total






def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					a					     =   []
					for arabic, roman in ROMAN:
										((a)     ,				(a))					     =   divmod(A ,     A       )
										result.append(roman * factor       )
										if number == 0:
															break
					return "".join(A       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
