




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 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






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

 def        a__				(       self      :						Tuple						)     ->			str:

       """simple docstring"""






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

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

       lowerCamelCase_	  =					replicate(A_						)
       lowerCamelCase_	  =					shard(A_						)

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

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

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

       lowerCamelCase_	  =					jnp.asarray(jax.device_get(image_slice.flatten()						)						)
       lowerCamelCase_	  =					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        a__				(       self      :						Any						)     ->			int:

       """simple docstring"""






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

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

       lowerCamelCase_	  =					replicate(A_						)
       lowerCamelCase_	  =					shard(A_						)

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

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

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

       lowerCamelCase_	  =					jnp.asarray(jax.device_get(image_slice.flatten()						)						)
       lowerCamelCase_	  =					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









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(lowercase  )  )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(lowercase  )
            elif i == ")":
                  # RULE 4
                  lowerCamelCase_	  =					operator_stack.peek()
                  operator_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


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



