




"""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 lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'  )
							_lowerCamelCase     :			Any    =    sd_pipe.to(lowercase  )
							sd_pipe.set_progress_bar_config(disable=lowercase  )

							sd_pipe.set_scheduler('sample_euler'  )

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

							_lowerCamelCase     :			Union[str, Any]    =    output.images

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

							assert image.shape == (1, 512, 512, 3)
							_lowerCamelCase     :			Dict    =    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 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'  )
							_lowerCamelCase     :			Any    =    sd_pipe.to(lowercase  )
							sd_pipe.set_progress_bar_config(disable=lowercase  )

							sd_pipe.set_scheduler('sample_euler'  )

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

							_lowerCamelCase     :			Optional[int]    =    output.images

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

							assert image.shape == (1, 512, 512, 3)
							_lowerCamelCase     :			List[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 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'  )
							_lowerCamelCase     :			int    =    sd_pipe.to(lowercase  )
							sd_pipe.set_progress_bar_config(disable=lowercase  )

							sd_pipe.set_scheduler('sample_dpmpp_2m'  )

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

							_lowerCamelCase     :			List[Any]    =    output.images

							_lowerCamelCase     :			Optional[int]    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_lowerCamelCase     :			Any    =    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


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



