



'''simple docstring'''





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        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Optional[int]		):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__		,							snake_case__    :       Union[str, Any]       		= FlaxStableDiffusionPipeline.from_pretrained(
              """stabilityai/stable-diffusion-2"""					,	revision="""bf16"""					,	dtype=jnp.bfloataa					,	)

          snake_case__    :       str       		= """A painting of a squirrel eating a burger"""
          snake_case__    :       int       		= jax.device_count()
          snake_case__    :       str       		= num_samples * [prompt]
          snake_case__    :       Dict       		= sd_pipe.prepare_inputs(snake_case_		)

          snake_case__    :       Optional[int]       		= replicate(snake_case_		)
          snake_case__    :       Tuple       		= shard(snake_case_		)

          snake_case__    :       str       		= jax.random.PRNGKey(0		)
          snake_case__    :       str       		= jax.random.split(snake_case_					,	jax.device_count()		)

          snake_case__    :       Optional[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)

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

          snake_case__    :       int       		= jnp.asarray(jax.device_get(image_slice.flatten()		)		)
          snake_case__    :       Tuple       		= 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 lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Any       		= """stabilityai/stable-diffusion-2"""
          snake_case__		,							snake_case__    :       str       		= FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case_					,	subfolder="""scheduler"""		)
          snake_case__		,							snake_case__    :       Optional[int]       		= FlaxStableDiffusionPipeline.from_pretrained(
              snake_case_					,	scheduler=snake_case_					,	revision="""bf16"""					,	dtype=jnp.bfloataa					,	)
          snake_case__    :       int       		= scheduler_params

          snake_case__    :       str       		= """A painting of a squirrel eating a burger"""
          snake_case__    :       Any       		= jax.device_count()
          snake_case__    :       List[Any]       		= num_samples * [prompt]
          snake_case__    :       List[str]       		= sd_pipe.prepare_inputs(snake_case_		)

          snake_case__    :       int       		= replicate(snake_case_		)
          snake_case__    :       List[str]       		= shard(snake_case_		)

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

          snake_case__    :       List[str]       		= 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)

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

          snake_case__    :       int       		= jnp.asarray(jax.device_get(image_slice.flatten()		)		)
          snake_case__    :       Dict       		= 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





'''simple docstring'''





__a  			=						"Alexander Joslin"

import operator as op

from .stack import Stack





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       str       		= {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

      snake_case__    :       Stack[int]       		= Stack()
      snake_case__    :       Stack[str]       		= Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(_lowerCAmelCase						)						)
            elif i in operators:
                  # RULE 2
                  operator_stack.push(_lowerCAmelCase						)
            elif i == ")":
                  # RULE 4
                  snake_case__    :       Union[str, Any]       		= operator_stack.peek()
                  operator_stack.pop()
                  snake_case__    :       Any       		= operand_stack.peek()
                  operand_stack.pop()
                  snake_case__    :       str       		= operand_stack.peek()
                  operand_stack.pop()

                  snake_case__    :       Optional[Any]       		= operators[opr](_lowerCAmelCase	,  _lowerCAmelCase						)
                  operand_stack.push(_lowerCAmelCase						)

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
       __a  			=						"(5 + ((4 * 2) * (2 + 3)))"
       # answer = 45
       print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
