




import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Optional[Any]:





           '''simple docstring'''

           torch.manual_seed(0)
           A__ 					=       UNetaDModel(
               block_out_channels=(32, 64)   ,					layers_per_block=2   ,					sample_size=32   ,					in_channels=3   ,					out_channels=3   ,					down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,					up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,					)
           return model


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Union[str, Any]:





           '''simple docstring'''

           torch.manual_seed(0)
           A__ 					=       VQModel(
               block_out_channels=[32, 64]   ,					in_channels=3   ,					out_channels=3   ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,					latent_channels=3   ,					)
           return model


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->List[Any]:





           '''simple docstring'''

           torch.manual_seed(0)
           A__ 					=       CLIPTextConfig(
               bos_token_id=0   ,					eos_token_id=2   ,					hidden_size=32   ,					intermediate_size=37   ,					layer_norm_eps=1e-05   ,					num_attention_heads=4   ,					num_hidden_layers=5   ,					pad_token_id=1   ,					vocab_size=1_000   ,					)
           return CLIPTextModel(UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Dict:





           '''simple docstring'''

           A__ 					=       self.dummy_uncond_unet
           A__ 					=       DDIMScheduler()
           A__ 					=       self.dummy_vq_model

           A__ 					=       LDMPipeline(unet=UpperCAmelCase__   ,					vqvae=UpperCAmelCase__   ,					scheduler=UpperCAmelCase__)
           ldm.to(UpperCAmelCase__)
           ldm.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       torch.manual_seed(0)
           A__ 					=       ldm(generator=UpperCAmelCase__   ,					num_inference_steps=2   ,					output_type='''numpy''').images

           A__ 					=       torch.manual_seed(0)
           A__ 					=       ldm(generator=UpperCAmelCase__   ,					num_inference_steps=2   ,					output_type='''numpy'''   ,					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([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
           A__ 					=       1e-2 if torch_device != '''mps''' else 3e-2

           assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance


@slow
@require_torch
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Dict:





           '''simple docstring'''

           A__ 					=       LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256''')
           ldm.to(UpperCAmelCase__)
           ldm.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       torch.manual_seed(0)
           A__ 					=       ldm(generator=UpperCAmelCase__   ,					num_inference_steps=5   ,					output_type='''numpy''').images

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

           assert image.shape == (1, 256, 256, 3)
           A__ 					=       np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
           A__ 					=       1e-2 if torch_device != '''mps''' else 3e-2

           assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance









"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Optional[Any]:






	'''simple docstring'''






	a :						Any      =							[]
	a :						List[str]      =							set({"(", "[", "{"}   )
	a :						int      =							set({")", "]", "}"}   )
	a :						int      =							{"{": "}", "[": "]", "(": ")"}

	for i in range(len(_lowercase   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(_lowercase   ) == 0 or (len(_lowercase   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(_lowercase   ) == 0






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






	a :						Any      =							input("Enter sequence of brackets: "   )
	if is_balanced(_lowercase   ):
		print(_lowercase       ,			"is balanced"   )
	else:
		print(_lowercase       ,			"is not balanced"   )


if __name__ == "__main__":
		main()


