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 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Dict			  =		StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
        __UpperCamelCase     :List[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        sd_pipe.set_scheduler('''sample_euler''')

        __UpperCamelCase     :Tuple			  =		'''A painting of a squirrel eating a burger'''
        __UpperCamelCase     :Union[str, Any]			  =		torch.manual_seed(0)
        __UpperCamelCase     :List[str]			  =		sd_pipe([prompt] , generator=__lowercase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''')

        __UpperCamelCase     :str			  =		output.images

        __UpperCamelCase     :List[Any]			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCamelCase     :Optional[int]			  =		np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39])

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







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Union[str, Any]			  =		StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
        __UpperCamelCase     :Optional[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        sd_pipe.set_scheduler('''sample_euler''')

        __UpperCamelCase     :Tuple			  =		'''A painting of a squirrel eating a burger'''
        __UpperCamelCase     :List[str]			  =		torch.manual_seed(0)
        __UpperCamelCase     :List[Any]			  =		sd_pipe([prompt] , generator=__lowercase , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''')

        __UpperCamelCase     :int			  =		output.images

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

        assert image.shape == (1, 512, 512, 3)
        __UpperCamelCase     :str			  =		np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12])

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







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Union[str, Any]			  =		StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
        __UpperCamelCase     :str			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        sd_pipe.set_scheduler('''sample_dpmpp_2m''')

        __UpperCamelCase     :Optional[int]			  =		'''A painting of a squirrel eating a burger'''
        __UpperCamelCase     :str			  =		torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		sd_pipe(
            [prompt] , generator=__lowercase , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=__lowercase , )

        __UpperCamelCase     :List[str]			  =		output.images

        __UpperCamelCase     :Union[str, Any]			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCamelCase     :Tuple			  =		np.array(
            [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48])

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     [
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       List[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





