





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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	KandinskyVaaPriorPipeline
    lowerCamelCase__    :		List[Any]				=	["""prompt"""]
    lowerCamelCase__    :		str				=	["""prompt""", """negative_prompt"""]
    lowerCamelCase__    :		Any				=	[
        """num_images_per_prompt""",
        """generator""",
        """num_inference_steps""",
        """latents""",
        """negative_prompt""",
        """guidance_scale""",
        """output_type""",
        """return_dict""",
    ]
    lowerCamelCase__    :		int				=	False



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 32



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 32



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.time_input_dim



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.time_input_dim * 4



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 1_00



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )
        return tokenizer



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        torch.manual_seed(0     )
        SCREAMING_SNAKE_CASE__											=			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=10_00   ,				)
        return CLIPTextModelWithProjection(UpperCamelCase_     )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        torch.manual_seed(0     )

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

        SCREAMING_SNAKE_CASE__											=			PriorTransformer(**UpperCamelCase_     )
        # 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
        SCREAMING_SNAKE_CASE__											=			nn.Parameter(torch.ones(model.clip_std.shape     )     )
        return model



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        torch.manual_seed(0     )
        SCREAMING_SNAKE_CASE__											=			CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size   ,				image_size=2_24   ,				projection_dim=self.text_embedder_hidden_size   ,				intermediate_size=37   ,				num_attention_heads=4   ,				num_channels=3   ,				num_hidden_layers=5   ,				patch_size=14   ,				)

        SCREAMING_SNAKE_CASE__											=			CLIPVisionModelWithProjection(UpperCamelCase_     )
        return model



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			CLIPImageProcessor(
            crop_size=2_24   ,				do_center_crop=UpperCamelCase_   ,				do_normalize=UpperCamelCase_   ,				do_resize=UpperCamelCase_   ,				image_mean=[0.48145466, 0.4578275, 0.40821073]   ,				image_std=[0.26862954, 0.26130258, 0.27577711]   ,				resample=3   ,				size=2_24   ,				)

        return image_processor



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.dummy_prior
        SCREAMING_SNAKE_CASE__											=			self.dummy_image_encoder
        SCREAMING_SNAKE_CASE__											=			self.dummy_text_encoder
        SCREAMING_SNAKE_CASE__											=			self.dummy_tokenizer
        SCREAMING_SNAKE_CASE__											=			self.dummy_image_processor

        SCREAMING_SNAKE_CASE__											=			UnCLIPScheduler(
            variance_type='''fixed_small_log'''   ,				prediction_type='''sample'''   ,				num_train_timesteps=10_00   ,				clip_sample=UpperCamelCase_   ,				clip_sample_range=10.0   ,				)

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

        return components



    def  lowercase_					(       self   ,				A_   ,				A_=0     ):





        '''simple docstring'''

        if str(UpperCamelCase_     ).startswith('''mps'''     ):
            SCREAMING_SNAKE_CASE__											=			torch.manual_seed(UpperCamelCase_     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.Generator(device=UpperCamelCase_     ).manual_seed(UpperCamelCase_     )
        SCREAMING_SNAKE_CASE__											=			{
            '''prompt''': '''horse''',
            '''generator''': generator,
            '''guidance_scale''': 4.0,
            '''num_inference_steps''': 2,
            '''output_type''': '''np''',
        }
        return inputs



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''cpu'''

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_components()

        SCREAMING_SNAKE_CASE__											=			self.pipeline_class(**UpperCamelCase_     )
        SCREAMING_SNAKE_CASE__											=			pipe.to(UpperCamelCase_     )

        pipe.set_progress_bar_config(disable=UpperCamelCase_     )

        SCREAMING_SNAKE_CASE__											=			pipe(**self.get_dummy_inputs(UpperCamelCase_     )     )
        SCREAMING_SNAKE_CASE__											=			output.image_embeds

        SCREAMING_SNAKE_CASE__											=			pipe(
            **self.get_dummy_inputs(UpperCamelCase_     )   ,				return_dict=UpperCamelCase_   ,				)[0]

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

        assert image.shape == (1, 32)

        SCREAMING_SNAKE_CASE__											=			np.array(
            [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]     )

        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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch_device == '''cpu'''
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			False

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




    @skip_mps
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch_device == '''cpu'''
        SCREAMING_SNAKE_CASE__											=			False

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







'''simple docstring'''




a__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	snake_case__	       =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

	snake_case__	       =   Stack()
	snake_case__	       =   Stack()

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

			snake_case__	       =   operators[opr](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


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



