




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class      UpperCAmelCase_  (						__lowercase,       unittest.TestCase							):
    lowercase__						 =				KandinskyVaaPipeline
    lowercase__						 =				[
        '''image_embeds''',
        '''negative_image_embeds''',
    ]
    lowercase__						 =				['''image_embeds''', '''negative_image_embeds''']
    lowercase__						 =				[
        '''generator''',
        '''height''',
        '''width''',
        '''latents''',
        '''guidance_scale''',
        '''num_inference_steps''',
        '''return_dict''',
        '''guidance_scale''',
        '''num_images_per_prompt''',
        '''output_type''',
        '''return_dict''',
    ]
    lowercase__						 =				False




    @property
    def 							__magic_name__							(   self						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     return 32




    @property
    def 							__magic_name__							(   self						:	Dict				)				->							Any:




     '''simple docstring'''

     return 32




    @property
    def 							__magic_name__							(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     return self.time_input_dim




    @property
    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     return self.time_input_dim * 4




    @property
    def 							__magic_name__							(   self						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     return 100




    @property
    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     torch.manual_seed(0				)

     A__	=    {
         "in_channels": 4,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "image",
         "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
         "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
         "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "layers_per_block": 1,
         "encoder_hid_dim": self.text_embedder_hidden_size,
         "encoder_hid_dim_type": "image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     A__	=    UNetaDConditionModel(**UpperCAmelCase__				)
     return model




    @property
    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[Any]:




     '''simple docstring'''

     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     torch.manual_seed(0				)
     A__	=    VQModel(**self.dummy_movq_kwargs				)
     return model




    def 							__magic_name__							(   self						:	Tuple				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.dummy_unet
     A__	=    self.dummy_movq

     A__	=    DDIMScheduler(
         num_train_timesteps=1_000     ,			beta_schedule="linear"     ,			beta_start=0.0_0085     ,			beta_end=0.012     ,			clip_sample=UpperCAmelCase__     ,			set_alpha_to_one=UpperCAmelCase__     ,			steps_offset=1     ,			prediction_type="epsilon"     ,			thresholding=UpperCAmelCase__     ,			)

     A__	=    {
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }
     return components




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	Any     ,			snake_case_						:	Optional[Any]=0				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    floats_tensor((1, self.text_embedder_hidden_size)     ,			rng=random.Random(UpperCAmelCase__				)				).to(UpperCAmelCase__				)
     A__	=    floats_tensor((1, self.text_embedder_hidden_size)     ,			rng=random.Random(seed + 1				)				).to(
         UpperCAmelCase__				)
     if str(UpperCAmelCase__				).startswith("mps"				):
      A__	=    torch.manual_seed(UpperCAmelCase__				)
     else:
      A__	=    torch.Generator(device=UpperCAmelCase__				).manual_seed(UpperCAmelCase__				)
     A__	=    {
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs




    def 							__magic_name__							(   self						:	str				)				->							str:




     '''simple docstring'''

     A__	=    "cpu"

     A__	=    self.get_dummy_components()

     A__	=    self.pipeline_class(**UpperCAmelCase__				)
     A__	=    pipe.to(UpperCAmelCase__				)

     pipe.set_progress_bar_config(disable=UpperCAmelCase__				)

     A__	=    pipe(**self.get_dummy_inputs(UpperCAmelCase__				)				)
     A__	=    output.images

     A__	=    pipe(
         **self.get_dummy_inputs(UpperCAmelCase__				)     ,			return_dict=UpperCAmelCase__     ,			)[0]

     A__	=    image[0, -3:, -3:, -1]
     A__	=    image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     A__	=    np.array(
         [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]				)

     assert (
         np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
     ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






@slow
@require_torch_gpu
class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




     '''simple docstring'''

     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




    def 							__magic_name__							(   self						:	Optional[int]				)				->							List[str]:




     '''simple docstring'''

     A__	=    load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"				)

     A__	=    KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"     ,			torch_dtype=torch.floataa				)
     pipe_prior.to(UpperCAmelCase__				)

     A__	=    KandinskyVaaPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder"     ,			torch_dtype=torch.floataa				)
     A__	=    pipeline.to(UpperCAmelCase__				)
     pipeline.set_progress_bar_config(disable=UpperCAmelCase__				)

     A__	=    "red cat, 4k photo"

     A__	=    torch.Generator(device="cuda"				).manual_seed(0				)
     A__,				A__	=    pipe_prior(
         UpperCAmelCase__     ,			generator=UpperCAmelCase__     ,			num_inference_steps=5     ,			negative_prompt=""     ,			).to_tuple()

     A__	=    torch.Generator(device="cuda"				).manual_seed(0				)
     A__	=    pipeline(
         image_embeds=UpperCAmelCase__     ,			negative_image_embeds=UpperCAmelCase__     ,			generator=UpperCAmelCase__     ,			num_inference_steps=100     ,			output_type="np"     ,			)

     A__	=    output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(UpperCAmelCase__     ,			UpperCAmelCase__				)











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 A__	=    {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

 A__	=    Stack()
 A__	=    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
   A__	=    operator_stack.peek()
   operator_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()
   A__	=    operand_stack.peek()
   operand_stack.pop()

   A__	=    operators[opr](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # RULE 5
 return operand_stack.peek()


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





