



'''simple docstring'''






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()



@skip_mps
class 							lowercase       (  _lowerCamelCase					,					_lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  StableDiffusionPanoramaPipeline
 UpperCAmelCase							=  TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase							=  TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase							=  TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase							=  TEXT_TO_IMAGE_IMAGE_PARAMS







 def _snake_case (							self							)		->   List[str]:
        torch.manual_seed(0							)
        _UpperCAmelCase  :						List[Any]			     = UNetaDConditionModel(
            block_out_channels=(32, 64)  ,layers_per_block=1  ,sample_size=32  ,in_channels=4  ,out_channels=4  ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,cross_attention_dim=32  ,)
        _UpperCAmelCase  :						Tuple			     = DDIMScheduler()
        torch.manual_seed(0							)
        _UpperCAmelCase  :						Tuple			     = 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  ,)
        torch.manual_seed(0							)
        _UpperCAmelCase  :						Dict			     = 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  ,)
        _UpperCAmelCase  :						Any			     = CLIPTextModel(a_							)
        _UpperCAmelCase  :						Optional[Any]			     = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

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







 def _snake_case (							self  ,a_  ,a_=0							)		->   List[str]:
        _UpperCAmelCase  :						List[str]			     = torch.manual_seed(a_							)
        _UpperCAmelCase  :						Optional[int]			     = {
            """prompt""": """a photo of the dolomites""",
            """generator""": generator,
            # Setting height and width to None to prevent OOMs on CPU.
            """height""": None,
            """width""": None,
            """num_inference_steps""": 1,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						str			     = """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase  :						List[str]			     = self.get_dummy_components()
        _UpperCAmelCase  :						List[str]			     = StableDiffusionPanoramaPipeline(**a_							)
        _UpperCAmelCase  :						Optional[int]			     = sd_pipe.to(a_							)
        sd_pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = self.get_dummy_inputs(a_							)
        _UpperCAmelCase  :						Tuple			     = sd_pipe(**a_							).images
        _UpperCAmelCase  :						Union[str, Any]			     = image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        _UpperCAmelCase  :						Optional[int]			     = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]							)

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







 def _snake_case (							self							)		->   Tuple:
        super().test_inference_batch_consistent(batch_sizes=[1, 2]							)







 def _snake_case (							self							)		->   Tuple:
        super().test_inference_batch_single_identical(batch_size=2  ,expected_max_diff=3.2_5E-3							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase  :						List[str]			     = self.get_dummy_components()
        _UpperCAmelCase  :						int			     = StableDiffusionPanoramaPipeline(**a_							)
        _UpperCAmelCase  :						Optional[int]			     = sd_pipe.to(a_							)
        sd_pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						Any			     = self.get_dummy_inputs(a_							)
        _UpperCAmelCase  :						Dict			     = """french fries"""
        _UpperCAmelCase  :						Any			     = sd_pipe(**a_  ,negative_prompt=a_							)
        _UpperCAmelCase  :						Optional[int]			     = output.images
        _UpperCAmelCase  :						Optional[Any]			     = image[0, -3:, -3:, -1]

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

        _UpperCAmelCase  :						Dict			     = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]							)

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







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Union[str, Any]			     = """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase  :						Optional[int]			     = self.get_dummy_components()
        _UpperCAmelCase  :						List[Any]			     = StableDiffusionPanoramaPipeline(**a_							)
        _UpperCAmelCase  :						List[Any]			     = sd_pipe.to(a_							)
        sd_pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						Optional[int]			     = self.get_dummy_inputs(a_							)
        _UpperCAmelCase  :						int			     = sd_pipe(**a_  ,view_batch_size=2							)
        _UpperCAmelCase  :						Optional[Any]			     = output.images
        _UpperCAmelCase  :						Optional[Any]			     = image[0, -3:, -3:, -1]

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

        _UpperCAmelCase  :						int			     = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]							)

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







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						List[str]			     = """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase  :						int			     = self.get_dummy_components()
        _UpperCAmelCase  :						str			     = EulerAncestralDiscreteScheduler(
            beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule="""scaled_linear"""							)
        _UpperCAmelCase  :						Dict			     = StableDiffusionPanoramaPipeline(**a_							)
        _UpperCAmelCase  :						Optional[Any]			     = sd_pipe.to(a_							)
        sd_pipe.set_progress_bar_config(disable=a_							)

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

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

        _UpperCAmelCase  :						Tuple			     = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]							)

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







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						List[str]			     = """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase  :						Any			     = self.get_dummy_components()
        _UpperCAmelCase  :						List[str]			     = PNDMScheduler(
            beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule="""scaled_linear"""  ,skip_prk_steps=a_							)
        _UpperCAmelCase  :						Tuple			     = StableDiffusionPanoramaPipeline(**a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = sd_pipe.to(a_							)
        sd_pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						str			     = self.get_dummy_inputs(a_							)
        _UpperCAmelCase  :						Any			     = sd_pipe(**a_							).images
        _UpperCAmelCase  :						Optional[Any]			     = image[0, -3:, -3:, -1]

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

        _UpperCAmelCase  :						Optional[Any]			     = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]							)

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







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


 """simple docstring"""


 def _snake_case (							self							)		->   Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case (							self  ,a_=0							)		->   Any:
        _UpperCAmelCase  :						Optional[Any]			     = torch.manual_seed(a_							)
        _UpperCAmelCase  :						str			     = {
            """prompt""": """a photo of the dolomites""",
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						Optional[Any]			     = """stabilityai/stable-diffusion-2-base"""
        _UpperCAmelCase  :						List[str]			     = DDIMScheduler.from_pretrained(a_  ,subfolder="""scheduler"""							)
        _UpperCAmelCase  :						List[Any]			     = StableDiffusionPanoramaPipeline.from_pretrained(a_  ,scheduler=a_  ,safety_checker=a_							)
        pipe.to(a_							)
        pipe.set_progress_bar_config(disable=a_							)
        pipe.enable_attention_slicing()

        _UpperCAmelCase  :						Tuple			     = self.get_inputs()
        _UpperCAmelCase  :						Union[str, Any]			     = pipe(**a_							).images
        _UpperCAmelCase  :						List[Any]			     = image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2_048, 3)

        _UpperCAmelCase  :						Dict			     = np.array(
            [
                0.3696_8392,
                0.2702_5372,
                0.3244_6766,
                0.2837_9387,
                0.3636_3274,
                0.3073_3347,
                0.2710_0027,
                0.2705_4125,
                0.2553_6096,
            ]							)

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







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[str]			     = StableDiffusionPanoramaPipeline.from_pretrained(
            """stabilityai/stable-diffusion-2-base"""  ,safety_checker=a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
        pipe.to(a_							)
        pipe.set_progress_bar_config(disable=a_							)
        pipe.enable_attention_slicing()

        _UpperCAmelCase  :						Optional[int]			     = self.get_inputs()
        _UpperCAmelCase  :						Union[str, Any]			     = pipe(**a_							).images
        _UpperCAmelCase  :						Optional[Any]			     = image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2_048, 3)

        _UpperCAmelCase  :						Dict			     = np.array(
            [
                [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                ]
            ]							)

        assert np.abs(expected_slice - image_slice							).max() < 1E-3







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						List[Any]			     = 0

        def callback_fn(a_  ,a_  ,a_							) -> None:
               _UpperCAmelCase  :						Optional[int]			     = True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                      _UpperCAmelCase  :						List[Any]			     = latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 256)
                      _UpperCAmelCase  :						Any			     = latents[0, -3:, -3:, -1]

                      _UpperCAmelCase  :						Optional[Any]			     = np.array(
                          [
                              0.1868_1869,
                              0.3390_7816,
                              0.536_1276,
                              0.1443_2865,
                              -0.0285_6611,
                              -0.7394_1123,
                              0.2339_7987,
                              0.4732_2682,
                              -0.3782_3164,
                          ]							)
                      assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
               elif step == 2:
                      _UpperCAmelCase  :						Any			     = latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 256)
                      _UpperCAmelCase  :						Tuple			     = latents[0, -3:, -3:, -1]

                      _UpperCAmelCase  :						Union[str, Any]			     = np.array(
                          [
                              0.1853_9645,
                              0.3398_7248,
                              0.537_8559,
                              0.1443_7142,
                              -0.0245_5261,
                              -0.733_8317,
                              0.2399_0755,
                              0.4735_6272,
                              -0.378_6505,
                          ]							)

                      assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2

        _UpperCAmelCase  :						Dict			     = False

        _UpperCAmelCase  :						Tuple			     = """stabilityai/stable-diffusion-2-base"""
        _UpperCAmelCase  :						List[str]			     = DDIMScheduler.from_pretrained(a_  ,subfolder="""scheduler"""							)
        _UpperCAmelCase  :						Tuple			     = StableDiffusionPanoramaPipeline.from_pretrained(a_  ,scheduler=a_  ,safety_checker=a_							)
        _UpperCAmelCase  :						Optional[Any]			     = pipe.to(a_							)
        pipe.set_progress_bar_config(disable=a_							)
        pipe.enable_attention_slicing()

        _UpperCAmelCase  :						int			     = self.get_inputs()
        pipe(**a_  ,callback=a_  ,callback_steps=1							)
        assert callback_fn.has_been_called
        assert number_of_steps == 3







 def _snake_case (							self							)		->   Any:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCAmelCase  :						Tuple			     = """stabilityai/stable-diffusion-2-base"""
        _UpperCAmelCase  :						Tuple			     = DDIMScheduler.from_pretrained(a_  ,subfolder="""scheduler"""							)
        _UpperCAmelCase  :						Optional[Any]			     = StableDiffusionPanoramaPipeline.from_pretrained(a_  ,scheduler=a_  ,safety_checker=a_							)
        _UpperCAmelCase  :						Dict			     = pipe.to(a_							)
        pipe.set_progress_bar_config(disable=a_							)
        pipe.enable_attention_slicing(1							)
        pipe.enable_sequential_cpu_offload()

        _UpperCAmelCase  :						int			     = self.get_inputs()
        _UpperCAmelCase  :						List[str]			     = pipe(**a_							)

        _UpperCAmelCase  :						List[Any]			     = torch.cuda.max_memory_allocated()
        # make sure that less than 5.2 GB is allocated
        assert mem_bytes < 5.5 * 10**9








'''simple docstring'''






# using dfs for finding eulerian path traversal




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=None		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = (path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = True, True
                     _UpperCAmelCase  :						List[Any]			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       return path




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = 0
       _UpperCAmelCase  :						Optional[int]			     = -1
       for i in range(lowerCAmelCase_		):
              if i not in graph.keys():
                     continue
              if len(graph[i]		) % 2 == 1:
                     odd_degree_nodes += 1
                     _UpperCAmelCase  :						Optional[int]			     = i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = [[False for _ in range(max_node + 1		)] for _ in range(max_node + 1		)]
       _UpperCAmelCase     ,_UpperCAmelCase  :						int			     = check_circuit_or_path(lowerCAmelCase_       ,   lowerCAmelCase_		)
       if check == 3:
              print("""graph is not Eulerian"""		)
              print("""no path"""		)
              return
       _UpperCAmelCase  :						Dict			     = 1
       if check == 2:
              _UpperCAmelCase  :						Dict			     = odd_node
              print("""graph has a Euler path"""		)
       if check == 1:
              print("""graph has a Euler cycle"""		)
       _UpperCAmelCase  :						Dict			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       print(lowerCAmelCase_		)




def     snake_case_					(     )->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _UpperCAmelCase  :						int			     = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _UpperCAmelCase  :						Tuple			     = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _UpperCAmelCase  :						List[Any]			     = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _UpperCAmelCase  :						List[str]			     = {
           1: [],
           2: []
           # all degree is zero
       }
       _UpperCAmelCase  :						Union[str, Any]			     = 10
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   main()



