
'''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       _lowerCAmelCase   (   __snake_case					,							__snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						StableDiffusionPanoramaPipeline
							lowerCAmelCase_ 						=						TEXT_TO_IMAGE_PARAMS
							lowerCAmelCase_ 						=						TEXT_TO_IMAGE_BATCH_PARAMS
							lowerCAmelCase_ 						=						TEXT_TO_IMAGE_IMAGE_PARAMS
							lowerCAmelCase_ 						=						TEXT_TO_IMAGE_IMAGE_PARAMS

							def 	lowercase							(self  )					->       List[Any]:
														torch.manual_seed(0  )
														_snake_case       =  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  ,  )
														_snake_case       =  DDIMScheduler()
														torch.manual_seed(0  )
														_snake_case       =  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  )
														_snake_case       =  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=1000  ,  )
														_snake_case       =  CLIPTextModel(UpperCAmelCase  )
														_snake_case       =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

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

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       Tuple:
														_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """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 	lowercase							(self  )					->       Tuple:
														_snake_case       =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
														_snake_case       =  self.get_dummy_components()
														_snake_case       =  StableDiffusionPanoramaPipeline(**UpperCAmelCase  )
														_snake_case       =  sd_pipe.to(UpperCAmelCase  )
														sd_pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  sd_pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]
														assert image.shape == (1, 64, 64, 3)

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

							def 	lowercase							(self  )					->       Any:
														super().test_inference_batch_single_identical(batch_size=2  ,  expected_max_diff=3.2_5e-3  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
														_snake_case       =  self.get_dummy_components()
														_snake_case       =  StableDiffusionPanoramaPipeline(**UpperCAmelCase  )
														_snake_case       =  sd_pipe.to(UpperCAmelCase  )
														sd_pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  """french fries"""
														_snake_case       =  sd_pipe(**UpperCAmelCase  ,  negative_prompt=UpperCAmelCase  )
														_snake_case       =  output.images
														_snake_case       =  image[0, -3:, -3:, -1]

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

														_snake_case       =  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 	lowercase							(self  )					->       str:
														_snake_case       =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
														_snake_case       =  self.get_dummy_components()
														_snake_case       =  StableDiffusionPanoramaPipeline(**UpperCAmelCase  )
														_snake_case       =  sd_pipe.to(UpperCAmelCase  )
														sd_pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  sd_pipe(**UpperCAmelCase  ,  view_batch_size=2  )
														_snake_case       =  output.images
														_snake_case       =  image[0, -3:, -3:, -1]

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

														_snake_case       =  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 	lowercase							(self  )					->       Tuple:
														_snake_case       =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
														_snake_case       =  self.get_dummy_components()
														_snake_case       =  EulerAncestralDiscreteScheduler(
														    beta_start=0.0_0085  ,  beta_end=0.012  ,  beta_schedule="""scaled_linear"""  )
														_snake_case       =  StableDiffusionPanoramaPipeline(**UpperCAmelCase  )
														_snake_case       =  sd_pipe.to(UpperCAmelCase  )
														sd_pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  sd_pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

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

														_snake_case       =  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 	lowercase							(self  )					->       str:
														_snake_case       =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
														_snake_case       =  self.get_dummy_components()
														_snake_case       =  PNDMScheduler(
														    beta_start=0.0_0085  ,  beta_end=0.012  ,  beta_schedule="""scaled_linear"""  ,  skip_prk_steps=UpperCAmelCase  )
														_snake_case       =  StableDiffusionPanoramaPipeline(**UpperCAmelCase  )
														_snake_case       =  sd_pipe.to(UpperCAmelCase  )
														sd_pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  sd_pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

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

														_snake_case       =  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       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[Any]:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

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

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

														_snake_case       =  self.get_inputs()
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 2048, 3)

														_snake_case       =  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 	lowercase							(self  )					->       str:
														_snake_case       =  StableDiffusionPanoramaPipeline.from_pretrained(
														    """stabilityai/stable-diffusion-2-base"""  ,  safety_checker=UpperCAmelCase  )
														_snake_case       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )
														pipe.enable_attention_slicing()

														_snake_case       =  self.get_inputs()
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 2048, 3)

														_snake_case       =  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 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  0

														def callback_fn(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ) -> None:
																					_snake_case       =  True
																					nonlocal number_of_steps
																					number_of_steps += 1
																					if step == 1:
																												_snake_case       =  latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 256)
																												_snake_case       =  latents[0, -3:, -3:, -1]

																												_snake_case       =  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:
																												_snake_case       =  latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 256)
																												_snake_case       =  latents[0, -3:, -3:, -1]

																												_snake_case       =  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

														_snake_case       =  False

														_snake_case       =  """stabilityai/stable-diffusion-2-base"""
														_snake_case       =  DDIMScheduler.from_pretrained(UpperCAmelCase  ,  subfolder="""scheduler"""  )
														_snake_case       =  StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase  ,  scheduler=UpperCAmelCase  ,  safety_checker=UpperCAmelCase  )
														_snake_case       =  pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )
														pipe.enable_attention_slicing()

														_snake_case       =  self.get_inputs()
														pipe(**UpperCAmelCase  ,  callback=UpperCAmelCase  ,  callback_steps=1  )
														assert callback_fn.has_been_called
														assert number_of_steps == 3





							def 	lowercase							(self  )					->       List[Any]:
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														_snake_case       =  """stabilityai/stable-diffusion-2-base"""
														_snake_case       =  DDIMScheduler.from_pretrained(UpperCAmelCase  ,  subfolder="""scheduler"""  )
														_snake_case       =  StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase  ,  scheduler=UpperCAmelCase  ,  safety_checker=UpperCAmelCase  )
														_snake_case       =  pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )
														pipe.enable_attention_slicing(1  )
														pipe.enable_sequential_cpu_offload()

														_snake_case       =  self.get_inputs()
														_snake_case       =  pipe(**UpperCAmelCase  )

														_snake_case       =  torch.cuda.max_memory_allocated()
														# make sure that less than 5.2 GB is allocated
														assert mem_bytes < 5.5 * 10**9

'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														if dst_width < 0 or dst_height < 0:
																					raise ValueError("""Destination width/height should be > 0"""  )

														_snake_case       =  img
														_snake_case       =  img.shape[1]
														_snake_case       =  img.shape[0]
														_snake_case       =  dst_width
														_snake_case       =  dst_height

														_snake_case       =  self.src_w / self.dst_w
														_snake_case       =  self.src_h / self.dst_h

														_snake_case       =  _snake_case       =  (
														    np.ones((self.dst_h, self.dst_w, 3)  ,  np.uinta  ) * 255
														)

							def 	lowercase							(self  )					->       List[Any]:
														for i in range(self.dst_h  ):
																					for j in range(self.dst_w  ):
																												_snake_case       =  self.img[self.get_y(UpperCAmelCase  )][self.get_x(UpperCAmelCase  )]

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_x * x  )





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_y * y  )


if __name__ == "__main__":
				__lowerCAmelCase							,    __lowerCAmelCase												=	800, 600
				__lowerCAmelCase												=	imread('image_data/lena.jpg', 1)
				__lowerCAmelCase												=	NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
				)
				waitKey(0)
				destroyAllWindows()