






'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class   lowerCAmelCase__ ( UpperCamelCase__						,UpperCamelCase__						,unittest.TestCase  ):
			A     				= StableDiffusionXLImgaImgPipeline
			A     				= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
			A     				= PipelineTesterMixin.required_optional_params - {"latents"}
			A     				= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			A     				= IMAGE_TO_IMAGE_IMAGE_PARAMS
			A     				= IMAGE_TO_IMAGE_IMAGE_PARAMS

			def 						__UpperCamelCase				(					self					:		Optional[int]	) ->  Optional[int]:







				"""simple docstring"""






				torch.manual_seed(0	)
				lowerCamelCase_					:  Dict						=    UNetaDConditionModel(
				    block_out_channels=(32, 64)     ,		layers_per_block=2     ,		sample_size=32     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,		attention_head_dim=(2, 4)     ,		use_linear_projection=lowercase_     ,		addition_embed_type='''text_time'''     ,		addition_time_embed_dim=8     ,		transformer_layers_per_block=(1, 2)     ,		projection_class_embeddings_input_dim=80     ,		cross_attention_dim=64     ,		)
				lowerCamelCase_					:  int						=    EulerDiscreteScheduler(
				    beta_start=0.0_0085     ,		beta_end=0.012     ,		steps_offset=1     ,		beta_schedule='''scaled_linear'''     ,		timestep_spacing='''leading'''     ,		)
				torch.manual_seed(0	)
				lowerCamelCase_					:  Union[str, Any]						=    AutoencoderKL(
				    block_out_channels=[32, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		sample_size=128     ,		)
				torch.manual_seed(0	)
				lowerCamelCase_					:  int						=    CLIPTextConfig(
				    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-0_5     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_000     ,		hidden_act='''gelu'''     ,		projection_dim=32     ,		)
				lowerCamelCase_					:  str						=    CLIPTextModel(lowercase_	)
				lowerCamelCase_					:  List[Any]						=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     ,		local_files_only=lowercase_	)

				lowerCamelCase_					:  int						=    CLIPTextModelWithProjection(lowercase_	)
				lowerCamelCase_					:  Tuple						=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     ,		local_files_only=lowercase_	)

				lowerCamelCase_					:  str						=    {
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''text_encoder_2''': text_encoder_a,
				    '''tokenizer_2''': tokenizer_a,
				    # "safety_checker": None,
				    # "feature_extractor": None,
				}
				return components

			def 						__UpperCamelCase				(					self					:		Optional[int]     ,		UpperCamelCase_					:		List[str]     ,		UpperCamelCase_					:		Optional[int]=0	) ->  Optional[Any]:







				"""simple docstring"""






				lowerCamelCase_					:  Union[str, Any]						=    floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(lowercase_	)	).to(lowercase_	)
				lowerCamelCase_					:  List[Any]						=    image / 2 + 0.5
				if str(lowercase_	).startswith('''mps'''	):
					lowerCamelCase_					:  Union[str, Any]						=    torch.manual_seed(lowercase_	)
				else:
					lowerCamelCase_					:  Any						=    torch.Generator(device=lowercase_	).manual_seed(lowercase_	)
				lowerCamelCase_					:  List[Any]						=    {
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''image''': image,
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 5.0,
				    '''output_type''': '''numpy''',
				    '''strength''': 0.75,
				}
				return inputs

			def 						__UpperCamelCase				(					self					:		Union[str, Any]	) ->  Dict:







				"""simple docstring"""






				lowerCamelCase_					:  Optional[int]						=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
				lowerCamelCase_					:  Any						=    self.get_dummy_components()
				lowerCamelCase_					:  List[str]						=    StableDiffusionXLImgaImgPipeline(**lowercase_	)
				lowerCamelCase_					:  Tuple						=    sd_pipe.to(lowercase_	)
				sd_pipe.set_progress_bar_config(disable=lowercase_	)

				lowerCamelCase_					:  str						=    self.get_dummy_inputs(lowercase_	)
				lowerCamelCase_					:  Union[str, Any]						=    sd_pipe(**lowercase_	).images
				lowerCamelCase_					:  Any						=    image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)

				lowerCamelCase_					:  str						=    np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]	)

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

			def 						__UpperCamelCase				(					self					:		Union[str, Any]	) ->  Dict:







				"""simple docstring"""






				super().test_attention_slicing_forward_pass(expected_max_diff=3e-3	)

			def 						__UpperCamelCase				(					self					:		int	) ->  Any:







				"""simple docstring"""






				super().test_inference_batch_single_identical(expected_max_diff=3e-3	)

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







				"""simple docstring"""






				pass





			def 						__UpperCamelCase				(					self					:		List[Any]	) ->  List[Any]:







				"""simple docstring"""






				lowerCamelCase_					:  Union[str, Any]						=    self.get_dummy_components()
				lowerCamelCase_					:  Dict						=    StableDiffusionXLImgaImgPipeline(**lowercase_	)
				lowerCamelCase_					:  Any						=    sd_pipe.to(lowercase_	)
				lowerCamelCase_					:  Optional[int]						=    sd_pipe.to(lowercase_	)
				sd_pipe.set_progress_bar_config(disable=lowercase_	)

				# forward without prompt embeds
				lowerCamelCase_					:  str						=    self.get_dummy_inputs(lowercase_	)
				lowerCamelCase_					:  Tuple						=    3 * ['''this is a negative prompt''']
				lowerCamelCase_					:  Dict						=    negative_prompt
				lowerCamelCase_					:  Optional[Any]						=    3 * [inputs['''prompt''']]

				lowerCamelCase_					:  Tuple						=    sd_pipe(**lowercase_	)
				lowerCamelCase_					:  Any						=    output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				lowerCamelCase_					:  Dict						=    self.get_dummy_inputs(lowercase_	)
				lowerCamelCase_					:  List[Any]						=    3 * ['''this is a negative prompt''']
				lowerCamelCase_					:  int						=    3 * [inputs.pop('''prompt'''	)]

				(
				    (
				    lowerCamelCase_      
				)	,				(
				    lowerCamelCase_      
				)	,				(
				    lowerCamelCase_      
				)	,				(
				    lowerCamelCase_      
				)	,				      
				)					:  Any						=    sd_pipe.encode_prompt(lowercase_     ,		negative_prompt=lowercase_	)

				lowerCamelCase_					:  Optional[Any]						=    sd_pipe(
				    **lowercase_     ,		prompt_embeds=lowercase_     ,		negative_prompt_embeds=lowercase_     ,		pooled_prompt_embeds=lowercase_     ,		negative_pooled_prompt_embeds=lowercase_     ,		)
				lowerCamelCase_					:  List[Any]						=    output.images[0, -3:, -3:, -1]

				# make sure that it's equal
				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4






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

			def 						__UpperCamelCase				(					self					:		Any	) ->  List[Any]:







				"""simple docstring"""






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

			def 						__UpperCamelCase				(					self					:		Optional[int]     ,		UpperCamelCase_					:		List[str]     ,		UpperCamelCase_					:		Optional[Any]="cpu"     ,		UpperCamelCase_					:		int=torch.floataa     ,		UpperCamelCase_					:		List[Any]=0	) ->  int:







				"""simple docstring"""






				lowerCamelCase_					:  Tuple						=    torch.Generator(device=lowercase_	).manual_seed(lowercase_	)
				lowerCamelCase_					:  Optional[Any]						=    np.random.RandomState(lowercase_	).standard_normal((1, 4, 64, 64)	)
				lowerCamelCase_					:  Union[str, Any]						=    torch.from_numpy(lowercase_	).to(device=lowercase_     ,		dtype=lowercase_	)
				lowerCamelCase_					:  Optional[Any]						=    {
				    '''prompt''': '''a photograph of an astronaut riding a horse''',
				    '''latents''': latents,
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs





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







				"""simple docstring"""






				lowerCamelCase_					:  Dict						=    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''	)
				pipe.to(lowercase_	)
				pipe.set_progress_bar_config(disable=lowercase_	)

				lowerCamelCase_					:  int						=    self.get_inputs(lowercase_	)
				lowerCamelCase_					:  List[str]						=    pipe(**lowercase_	).images
				lowerCamelCase_					:  Union[str, Any]						=    image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				lowerCamelCase_					:  Union[str, Any]						=    np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]	)
				assert np.abs(image_slice - expected_slice	).max() < 7e-3













'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    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(_lowerCamelCase			)			)
        elif i in operators:
            # RULE 2
            operator_stack.push(_lowerCamelCase			)
        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](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


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



