



"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






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

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Dict:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"		)
				A__											=					sd_pipe.to(SCREAMING_SNAKE_CASE__		)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

				sd_pipe.set_scheduler("sample_euler"		)

				A__											=					"A painting of a squirrel eating a burger"
				A__											=					torch.manual_seed(0		)
				A__											=					sd_pipe([prompt]						,	generator=SCREAMING_SNAKE_CASE__						,	guidance_scale=9.0						,	num_inference_steps=20						,	output_type="np"		)

				A__											=					output.images

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

				assert image.shape == (1, 512, 512, 3)
				A__											=					np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]		)

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


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"		)
				A__											=					sd_pipe.to(SCREAMING_SNAKE_CASE__		)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

				sd_pipe.set_scheduler("sample_euler"		)

				A__											=					"A painting of a squirrel eating a burger"
				A__											=					torch.manual_seed(0		)
				A__											=					sd_pipe([prompt]						,	generator=SCREAMING_SNAKE_CASE__						,	guidance_scale=9.0						,	num_inference_steps=20						,	output_type="np"		)

				A__											=					output.images

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

				assert image.shape == (1, 512, 512, 3)
				A__											=					np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-1







			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"		)
				A__											=					sd_pipe.to(SCREAMING_SNAKE_CASE__		)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

				sd_pipe.set_scheduler("sample_dpmpp_2m"		)

				A__											=					"A painting of a squirrel eating a burger"
				A__											=					torch.manual_seed(0		)
				A__											=					sd_pipe(
				    [prompt]						,	generator=SCREAMING_SNAKE_CASE__						,	guidance_scale=7.5						,	num_inference_steps=15						,	output_type="np"						,	use_karras_sigmas=SCREAMING_SNAKE_CASE__						,	)

				A__											=					output.images

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

				assert image.shape == (1, 512, 512, 3)
				A__											=					np.array(
				    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]		)

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









"""simple docstring"""







import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)






class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					["a", "b", "c"]

				# Defaults to last layer if both are None
				A__							,							A__											=					get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[2]		)

				# Out indices set to match out features
				A__							,							A__											=					get_aligned_output_features_output_indices(["a", "c"]						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["a", "c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[0, 2]		)

				# Out features set to match out indices
				A__							,							A__											=					get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__						,	[0, 2]						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["a", "c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[0, 2]		)

				# Out features selected from negative indices
				A__							,							A__											=					get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__						,	[-3, -1]						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["a", "c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[-3, -1]		)


			def   snake_case__	(      self		)      ->	Dict:
				# Stage names must be set
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(["a", "b"]						,	(0, 1)						,	SCREAMING_SNAKE_CASE__		)

				# Out features must be a list
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(("a", "b")						,	(0, 1)						,	["a", "b"]		)

				# Out features must be a subset of stage names
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(["a", "b"]						,	(0, 1)						,	["a"]		)

				# Out indices must be a list or tuple
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(SCREAMING_SNAKE_CASE__						,	0						,	["a", "b"]		)

				# Out indices must be a subset of stage names
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(SCREAMING_SNAKE_CASE__						,	(0, 1)						,	["a"]		)

				# Out features and out indices must be the same length
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(["a", "b"]						,	(0,)						,	["a", "b", "c"]		)

				# Out features should match out indices
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(["a", "b"]						,	(0, 2)						,	["a", "b", "c"]		)

				# Out features and out indices should be in order
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(["b", "a"]						,	(0, 1)						,	["a", "b"]		)

				# Check passes with valid inputs
				verify_out_features_out_indices(["a", "b", "d"]						,	(0, 1, -1)						,	["a", "b", "c", "d"]		)







			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					BackboneMixin()

				A__											=					["a", "b", "c"]
				A__											=					["a", "c"]
				A__											=					[0, 2]

				# Check that the output features and indices are set correctly
				self.assertEqual(backbone.out_features						,	["a", "c"]		)
				self.assertEqual(backbone.out_indices						,	[0, 2]		)

				# Check out features and indices are updated correctly
				A__											=					["a", "b"]
				self.assertEqual(backbone.out_features						,	["a", "b"]		)
				self.assertEqual(backbone.out_indices						,	[0, 1]		)

				A__											=					[-3, -1]
				self.assertEqual(backbone.out_features						,	["a", "c"]		)
				self.assertEqual(backbone.out_indices						,	[-3, -1]		)




