



'''simple docstring'''





import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 				snake_case__ (UpperCamelCase		,      UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					AltDiffusionPipeline
       lowerCAmelCase__  :str						   =					TEXT_TO_IMAGE_PARAMS
       lowerCAmelCase__  :List[str]						   =					TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCAmelCase__  :List[Any]						   =					TEXT_TO_IMAGE_IMAGE_PARAMS
       lowerCAmelCase__  :Any						   =					TEXT_TO_IMAGE_IMAGE_PARAMS



       def 							_a      (				self      )  ->							int:
           torch.manual_seed(0      )
           lowercase__		       =						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")     ,cross_attention_dim=32     ,)
           lowercase__		       =						DDIMScheduler(
               beta_start=0.0_00_85     ,beta_end=0.0_12     ,beta_schedule="scaled_linear"     ,clip_sample=UpperCAmelCase_     ,set_alpha_to_one=UpperCAmelCase_     ,)
           torch.manual_seed(0      )
           lowercase__		       =						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     ,)

           # TODO: address the non-deterministic text encoder (fails for save-load tests)
           # torch.manual_seed(0)
           # text_encoder_config = RobertaSeriesConfig(
           #     hidden_size=32,
           #     project_dim=32,
           #     intermediate_size=37,
           #     layer_norm_eps=1e-05,
           #     num_attention_heads=4,
           #     num_hidden_layers=5,
           #     vocab_size=5002,
           # )
           # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

           torch.manual_seed(0      )
           lowercase__		       =						CLIPTextConfig(
               bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,projection_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=5_002     ,)
           lowercase__		       =						CLIPTextModel(UpperCAmelCase_      )

           lowercase__		       =						XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"      )
           lowercase__		       =						77

           lowercase__		       =						{
               "unet": unet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "safety_checker": None,
               "feature_extractor": None,
           }
           return components



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=0      )  ->							Optional[Any]:
           if str(UpperCAmelCase_      ).startswith("mps"      ):
               lowercase__		       =						torch.manual_seed(UpperCAmelCase_      )
           else:
               lowercase__		       =						torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )
           lowercase__		       =						{
               "prompt": "A painting of a squirrel eating a burger",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
               "output_type": "numpy",
           }
           return inputs



       def 							_a      (				self      )  ->							Any:
           super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )



       def 							_a      (				self      )  ->							Optional[int]:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3      )



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						"cpu"  # ensure determinism for the device-dependent torch.Generator

           lowercase__		       =						self.get_dummy_components()
           torch.manual_seed(0      )
           lowercase__		       =						RobertaSeriesConfig(
               hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5_002     ,)
           # TODO: remove after fixing the non-deterministic text encoder
           lowercase__		       =						RobertaSeriesModelWithTransformation(UpperCAmelCase_      )
           lowercase__		       =						text_encoder

           lowercase__		       =						AltDiffusionPipeline(**UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						self.get_dummy_inputs(UpperCAmelCase_      )
           lowercase__		       =						"A photo of an astronaut"
           lowercase__		       =						alt_pipe(**UpperCAmelCase_      )
           lowercase__		       =						output.images
           lowercase__		       =						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           lowercase__		       =						np.array(
               [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]      )

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







       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						"cpu"  # ensure determinism for the device-dependent torch.Generator

           lowercase__		       =						self.get_dummy_components()
           lowercase__		       =						PNDMScheduler(skip_prk_steps=UpperCAmelCase_      )
           torch.manual_seed(0      )
           lowercase__		       =						RobertaSeriesConfig(
               hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5_002     ,)
           # TODO: remove after fixing the non-deterministic text encoder
           lowercase__		       =						RobertaSeriesModelWithTransformation(UpperCAmelCase_      )
           lowercase__		       =						text_encoder
           lowercase__		       =						AltDiffusionPipeline(**UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						self.get_dummy_inputs(UpperCAmelCase_      )
           lowercase__		       =						alt_pipe(**UpperCAmelCase_      )
           lowercase__		       =						output.images
           lowercase__		       =						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           lowercase__		       =						np.array(
               [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]      )

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





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



       def 							_a      (				self      )  ->							List[str]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       def 							_a      (				self      )  ->							Optional[int]:
           # make sure here that pndm scheduler skips prk
           lowercase__		       =						AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,safety_checker=UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						"A painting of a squirrel eating a burger"
           lowercase__		       =						torch.manual_seed(0      )
           lowercase__		       =						alt_pipe([prompt]     ,generator=UpperCAmelCase_     ,guidance_scale=6.0     ,num_inference_steps=20     ,output_type="np"      )

           lowercase__		       =						output.images

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

           assert image.shape == (1, 512, 512, 3)
           lowercase__		       =						np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]      )

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







       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						DDIMScheduler.from_pretrained("BAAI/AltDiffusion"     ,subfolder="scheduler"      )

           lowercase__		       =						AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,scheduler=UpperCAmelCase_     ,safety_checker=UpperCAmelCase_      )
           lowercase__		       =						alt_pipe.to(UpperCAmelCase_      )
           alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

           lowercase__		       =						"A painting of a squirrel eating a burger"
           lowercase__		       =						torch.manual_seed(0      )

           lowercase__		       =						alt_pipe([prompt]     ,generator=UpperCAmelCase_     ,num_inference_steps=2     ,output_type="numpy"      )
           lowercase__		       =						output.images

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

           assert image.shape == (1, 512, 512, 3)
           lowercase__		       =						np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]      )

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









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]       ):  # noqa: E741





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    lowercase__		       =						0
    lowercase__		       =						[0] * n
    lowercase__		       =						[False] * n
    lowercase__		       =						[False] * n

    def dfs(_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						List[str]     ,_snake_case  :						str       ):
        if parent == root:
            out_edge_count += 1
        lowercase__		       =						True
        lowercase__		       =						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                lowercase__		       =						dfs(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                lowercase__		       =						min(low[at]     ,low[to]       )

                # AP found via bridge
                if at < low[to]:
                    lowercase__		       =						True
                # AP found via cycle
                if at == low[to]:
                    lowercase__		       =						True
            else:
                lowercase__		       =						min(low[at]     ,_snake_case       )
        return out_edge_count

    for i in range(_snake_case       ):
        if not visited[i]:
            lowercase__		       =						0
            lowercase__		       =						dfs(_snake_case     ,_snake_case     ,-1     ,_snake_case       )
            lowercase__		       =						out_edge_count > 1

    for x in range(len(_snake_case       )       ):
        if is_art[x] is True:
            print(_snake_case       )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__      					=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)




