
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@nightly
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"      ,				torch_dtype=torch.floataa )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )

								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe.dual_guided(
								    prompt="first prompt"      ,				image=snake_case__      ,				text_to_image_strength=0.7_5      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=2      ,				output_type="numpy"      ,				).images

								with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(snake_case__ )
									__lowerCAmelCase         =							VersatileDiffusionPipeline.from_pretrained(snake_case__      ,				torch_dtype=torch.floataa )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							generator.manual_seed(0 )
								__lowerCAmelCase         =							pipe.dual_guided(
								    prompt="first prompt"      ,				image=snake_case__      ,				text_to_image_strength=0.7_5      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=2      ,				output_type="numpy"      ,				).images

								assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"      ,				torch_dtype=torch.floataa )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							"cyberpunk 2077"
								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe.dual_guided(
								    prompt=snake_case__      ,				image=snake_case__      ,				text_to_image_strength=0.7_5      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1] )

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

								__lowerCAmelCase         =							"A painting of a squirrel eating a burger "
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe.text_to_image(
								    prompt=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy" ).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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

								__lowerCAmelCase         =							pipe.image_variation(snake_case__      ,				generator=snake_case__      ,				output_type="numpy" ).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6] )

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








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint

@require_torch
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 4], [1, 2, 3, 4]]
								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )
								self.assertTrue(isinstance(dc.token_ids      ,				snake_case__ ) )

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint(snake_case__ )  # fails here







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								__lowerCAmelCase         =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								__lowerCAmelCase         =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(3 )
								__lowerCAmelCase         =							stepped is True and completed is True and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3] )







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(4 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2, 4] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(5 )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

								dc.reset()

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.remaining() == 3 )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.remaining() == 2 )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(5 )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.remaining() == 0 )
								self.assertTrue(dc.current_seq == [1, 2, 5] )






