




"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			DDIMPipeline
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			lowerCamelCase__      =			PipelineTesterMixin.required_optional_params - {
			    """num_images_per_prompt""",
			    """latents""",
			    """callback""",
			    """callback_steps""",
			}
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			List[Any]    =    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')  ,      )
							_lowerCamelCase     :			List[str]    =    DDIMScheduler()
							_lowerCamelCase     :			Optional[int]    =    {'unet': unet, 'scheduler': scheduler}
							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Dict    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			List[str]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			Tuple    =    {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()
							_lowerCamelCase     :			Optional[Any]    =    self.pipeline_class(**lowercase  )
							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			str    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			int    =    pipe(**lowercase  ).images
							_lowerCamelCase     :			Any    =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,      (1, 32, 32, 3)  )
							_lowerCamelCase     :			Tuple    =    np.array(
							    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]  )
							_lowerCamelCase     :			str    =    np.abs(image_slice.flatten() - expected_slice  ).max()
							self.assertLessEqual(lowercase  ,      1E-3  )
			def 					A_    (  self  ):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_local(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_optional_components(expected_max_difference=3E-3  )






			def 					A_    (  self  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






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






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    'google/ddpm-cifar10-32'

							_lowerCamelCase     :			Optional[Any]    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			Dict    =    DDIMScheduler()

							_lowerCamelCase     :			Dict    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddim.to(lowercase  )
							ddim.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[str]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    ddim(generator=lowercase  ,      eta=0.0  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			List[Any]    =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]  )

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






			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    'google/ddpm-ema-bedroom-256'

							_lowerCamelCase     :			str    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			str    =    DDIMScheduler.from_pretrained(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddpm.to(lowercase  )
							ddpm.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			Tuple    =    torch.manual_seed(0  )
							_lowerCamelCase     :			int    =    ddpm(generator=lowercase  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							_lowerCamelCase     :			str    =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]  )

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





"""simple docstring"""






# Imports
import numpy as np






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							self.set_matricies(red=lowercase  ,      green=lowercase  ,      blue=lowercase  ,      red_edge=lowercase  ,      nir=lowercase  )
			def 					A_    (  self  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							if red is not None:
											_lowerCamelCase     :			Optional[int]    =    red
							if green is not None:
											_lowerCamelCase     :			Optional[Any]    =    green
							if blue is not None:
											_lowerCamelCase     :			Tuple    =    blue
							if red_edge is not None:
											_lowerCamelCase     :			Optional[Any]    =    red_edge
							if nir is not None:
											_lowerCamelCase     :			Union[str, Any]    =    nir
							return True
			def 					A_    (  self  ,      lowercase=""  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							self.set_matricies(red=lowercase  ,      green=lowercase  ,      blue=lowercase  ,      red_edge=lowercase  ,      nir=lowercase  )
							_lowerCamelCase     :			str    =    {
							    'ARVI2': self.arvaa,
							    'CCCI': self.ccci,
							    'CVI': self.cvi,
							    'GLI': self.gli,
							    'NDVI': self.ndvi,
							    'BNDVI': self.bndvi,
							    'redEdgeNDVI': self.red_edge_ndvi,
							    'GNDVI': self.gndvi,
							    'GBNDVI': self.gbndvi,
							    'GRNDVI': self.grndvi,
							    'RBNDVI': self.rbndvi,
							    'PNDVI': self.pndvi,
							    'ATSAVI': self.atsavi,
							    'BWDRVI': self.bwdrvi,
							    'CIgreen': self.ci_green,
							    'CIrededge': self.ci_rededge,
							    'CI': self.ci,
							    'CTVI': self.ctvi,
							    'GDVI': self.gdvi,
							    'EVI': self.evi,
							    'GEMI': self.gemi,
							    'GOSAVI': self.gosavi,
							    'GSAVI': self.gsavi,
							    'Hue': self.hue,
							    'IVI': self.ivi,
							    'IPVI': self.ipvi,
							    'I': self.i,
							    'RVI': self.rvi,
							    'MRVI': self.mrvi,
							    'MSAVI': self.m_savi,
							    'NormG': self.norm_g,
							    'NormNIR': self.norm_nir,
							    'NormR': self.norm_r,
							    'NGRDI': self.ngrdi,
							    'RI': self.ri,
							    'S': self.s,
							    'IF': self._if,
							    'DVI': self.dvi,
							    'TVI': self.tvi,
							    'NDRE': self.ndre,
							}

							try:
											return funcs[index]()
							except KeyError:
											print('Index not in the list!'  )
											return False
			def 					A_    (  self  ):
							return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
			def 					A_    (  self  ):
							return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
							    (self.nir - self.red) / (self.nir + self.red)
							)
			def 					A_    (  self  ):
							return self.nir * (self.red / (self.green**2))
			def 					A_    (  self  ):
							return (2 * self.green - self.red - self.blue) / (
							    2 * self.green + self.red + self.blue
							)
			def 					A_    (  self  ):
							return (self.nir - self.red) / (self.nir + self.red)
			def 					A_    (  self  ):
							return (self.nir - self.blue) / (self.nir + self.blue)
			def 					A_    (  self  ):
							return (self.redEdge - self.red) / (self.redEdge + self.red)
			def 					A_    (  self  ):
							return (self.nir - self.green) / (self.nir + self.green)
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.blue)) / (
							    self.nir + (self.green + self.blue)
							)
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.red)) / (
							    self.nir + (self.green + self.red)
							)
			def 					A_    (  self  ):
							return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.red + self.blue)) / (
							    self.nir + (self.green + self.red + self.blue)
							)
			def 					A_    (  self  ,      lowercase=0.08  ,      lowercase=1.22  ,      lowercase=0.03  ):
							return a * (
							    (self.nir - a * self.red - b)
							    / (a * self.nir + self.red - a * b + x * (1 + a**2))
							)
			def 					A_    (  self  ):
							return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
			def 					A_    (  self  ):
							return (self.nir / self.green) - 1
			def 					A_    (  self  ):
							return (self.nir / self.redEdge) - 1
			def 					A_    (  self  ):
							return (self.red - self.blue) / self.red
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    self.ndvi()
							return ((ndvi + 0.5) / (abs(ndvi + 0.5  ))) * (abs(ndvi + 0.5  ) ** (1 / 2))
			def 					A_    (  self  ):
							return self.nir - self.green
			def 					A_    (  self  ):
							return 2.5 * (
							    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
							)
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
							    self.nir + self.red + 0.5
							)
							return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)
			def 					A_    (  self  ,      lowercase=0.16  ):
							return (self.nir - self.green) / (self.nir + self.green + y)
			def 					A_    (  self  ,      lowercase=0.5  ):
							return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
			def 					A_    (  self  ):
							return np.arctan(
							    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)  )
			def 					A_    (  self  ,      lowercase=None  ,      lowercase=None  ):
							return (self.nir - b) / (a * self.red)
			def 					A_    (  self  ):
							return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
			def 					A_    (  self  ):
							return (self.red + self.green + self.blue) / 30.5
			def 					A_    (  self  ):
							return self.nir / self.red
			def 					A_    (  self  ):
							return (self.rvi() - 1) / (self.rvi() + 1)
			def 					A_    (  self  ):
							return (
							    (2 * self.nir + 1)
							    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
							) / 2
			def 					A_    (  self  ):
							return self.green / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return self.nir / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return self.red / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return (self.green - self.red) / (self.green + self.red)
			def 					A_    (  self  ):
							return (self.red - self.green) / (self.red + self.green)
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    np.max([np.max(self.red  ), np.max(self.green  ), np.max(self.blue  )]  )
							_lowerCamelCase     :			Dict    =    np.min([np.min(self.red  ), np.min(self.green  ), np.min(self.blue  )]  )
							return (max_value - min_value) / max_value
			def 					A_    (  self  ):
							return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
			def 					A_    (  self  ):
							return self.nir / self.red
			def 					A_    (  self  ):
							return (self.ndvi() + 0.5) ** (1 / 2)






			def 					A_    (  self  ):
							return (self.nir - self.redEdge) / (self.nir + self.redEdge)