






"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				str 						=KandinskyVaaPriorPipeline
   a      :				Optional[int] 						=["""prompt"""]
   a      :				str 						=["""prompt""", """negative_prompt"""]
   a      :				Dict 						=[
       """num_images_per_prompt""",
       """generator""",
       """num_inference_steps""",
       """latents""",
       """negative_prompt""",
       """guidance_scale""",
       """output_type""",
       """return_dict""",
   ]
   a      :				Union[str, Any] 						=False

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       return 32

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       return 32

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return self.time_input_dim

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       return self.time_input_dim * 4

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 100

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
       return tokenizer

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       torch.manual_seed(0      )
       UpperCamelCase__         =    CLIPTextConfig(
           bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=self.text_embedder_hidden_size ,				projection_dim=self.text_embedder_hidden_size ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=1000 ,				)
       return CLIPTextModelWithProjection(snake_case_      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       torch.manual_seed(0      )

       UpperCamelCase__         =    {
           'num_attention_heads': 2,
           'attention_head_dim': 12,
           'embedding_dim': self.text_embedder_hidden_size,
           'num_layers': 1,
       }

       UpperCamelCase__         =    PriorTransformer(**snake_case_      )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       UpperCamelCase__         =    nn.Parameter(torch.ones(model.clip_std.shape      )      )
       return model

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       torch.manual_seed(0      )
       UpperCamelCase__         =    CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size ,				image_size=224 ,				projection_dim=self.text_embedder_hidden_size ,				intermediate_size=37 ,				num_attention_heads=4 ,				num_channels=3 ,				num_hidden_layers=5 ,				patch_size=14 ,				)

       UpperCamelCase__         =    CLIPVisionModelWithProjection(snake_case_      )
       return model

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    CLIPImageProcessor(
           crop_size=224 ,				do_center_crop=snake_case_ ,				do_normalize=snake_case_ ,				do_resize=snake_case_ ,				image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] ,				image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] ,				resample=3 ,				size=224 ,				)

       return image_processor

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.dummy_prior
       UpperCamelCase__         =    self.dummy_image_encoder
       UpperCamelCase__         =    self.dummy_text_encoder
       UpperCamelCase__         =    self.dummy_tokenizer
       UpperCamelCase__         =    self.dummy_image_processor

       UpperCamelCase__         =    UnCLIPScheduler(
           variance_type='fixed_small_log' ,				prediction_type='sample' ,				num_train_timesteps=1000 ,				clip_sample=snake_case_ ,				clip_sample_range=10.0 ,				)

       UpperCamelCase__         =    {
           'prior': prior,
           'image_encoder': image_encoder,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'scheduler': scheduler,
           'image_processor': image_processor,
       }

       return components

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=0      )		->       List[Any]:
       if str(snake_case_      ).startswith('mps'      ):
           UpperCamelCase__         =    torch.manual_seed(snake_case_      )
       else:
           UpperCamelCase__         =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
       UpperCamelCase__         =    {
           'prompt': 'horse',
           'generator': generator,
           'guidance_scale': 4.0,
           'num_inference_steps': 2,
           'output_type': 'np',
       }
       return inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    'cpu'

       UpperCamelCase__         =    self.get_dummy_components()

       UpperCamelCase__         =    self.pipeline_class(**snake_case_      )
       UpperCamelCase__         =    pipe.to(snake_case_      )

       pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    pipe(**self.get_dummy_inputs(snake_case_      )      )
       UpperCamelCase__         =    output.image_embeds

       UpperCamelCase__         =    pipe(
           **self.get_dummy_inputs(snake_case_      ) ,				return_dict=snake_case_ ,				)[0]

       UpperCamelCase__         =    image[0, -10:]
       UpperCamelCase__         =    image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       UpperCamelCase__         =    np.array(
           [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2

   @skip_mps
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    torch_device == 'cpu'
       UpperCamelCase__         =    True
       UpperCamelCase__         =    False

       self._test_inference_batch_single_identical(
           test_max_difference=snake_case_ ,				relax_max_difference=snake_case_ ,				test_mean_pixel_difference=snake_case_ ,				)







   @skip_mps
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    torch_device == 'cpu'
       UpperCamelCase__         =    False

       self._test_attention_slicing_forward_pass(
           test_max_difference=snake_case_ ,				test_mean_pixel_difference=snake_case_ ,				)












"""simple docstring"""





A__					: Tuple=	"""Alexander Joslin"""

import operator as op

from .stack import Stack







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

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

    UpperCamelCase__         =    Stack()
    UpperCamelCase__         =    Stack()

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

            UpperCamelCase__         =    operators[opr](SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            operand_stack.push(SCREAMING_SNAKE_CASE					)

    # RULE 5
    return operand_stack.peek()


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




