

"""simple docstring"""






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class       _UpperCAmelCase     (						unittest.TestCase):
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   1
				_UpperCAmelCase      :							List[Any]       =   3
				_UpperCAmelCase      :							List[Any]       =   (32, 32)

				_UpperCAmelCase      :							Dict       =   floats_tensor((batch_size, num_channels) + sizes ,						rng=random.Random(0       )       ).to(_A       )
				return image
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							str       =   UNetaDConditionModel(
				    block_out_channels=(32, 64) ,						layers_per_block=2 ,						sample_size=32 ,						in_channels=4 ,						out_channels=4 ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,						cross_attention_dim=32 ,						)
				return model
			@property
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							Optional[Any]       =   AutoencoderKL(
				    block_out_channels=[32, 64] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,						latent_channels=4 ,						)
				return model
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							List[str]       =   CLIPTextConfig(
				    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=10_00 ,						)
				return CLIPTextModel(_A       )
			@property
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				def extract(*_A ,						**_A       ):






					class       _UpperCAmelCase     :
								def __init__(						self       )      ->      Union[str, Any]:






									'''simple docstring'''







									_UpperCAmelCase      :							Union[str, Any]       =   torch.ones([0]       )
								def 							__snake_case	(						self ,						_A       )      ->      Optional[Any]:






									'''simple docstring'''







									self.pixel_values.to(_A       )
									return self
					return Out()

				return extract
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
				_UpperCAmelCase      :							int       =   self.dummy_cond_unet
				_UpperCAmelCase      :							str       =   DDIMScheduler(
				    beta_start=0.00085 ,						beta_end=0.012 ,						beta_schedule="""scaled_linear""" ,						clip_sample=_A ,						set_alpha_to_one=_A ,						)

				_UpperCAmelCase      :							Union[str, Any]       =   self.dummy_vae
				_UpperCAmelCase      :							List[Any]       =   self.dummy_text_encoder
				_UpperCAmelCase      :							Tuple       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

				# make sure here that pndm scheduler skips prk
				_UpperCAmelCase      :							int       =   StableDiffusionPipeline(
				    unet=_A ,						scheduler=_A ,						vae=_A ,						text_encoder=_A ,						tokenizer=_A ,						safety_checker=_A ,						feature_extractor=self.dummy_extractor ,						)
				_UpperCAmelCase      :							Union[str, Any]       =   sd_pipe.to(_A       )
				sd_pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							Optional[int]       =   """A painting of a squirrel eating a burger"""

				_UpperCAmelCase      :							List[str]       =   torch.Generator(device=_A       ).manual_seed(0       )
				_UpperCAmelCase      :							str       =   sd_pipe([prompt] ,						generator=_A ,						guidance_scale=6.0 ,						num_inference_steps=2 ,						output_type="""np"""       )
				_UpperCAmelCase      :							Tuple       =   output.images

				_UpperCAmelCase      :							List[str]       =   torch.Generator(device=_A       ).manual_seed(0       )
				_UpperCAmelCase      :							Optional[int]       =   sd_pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=6.0 ,						num_inference_steps=2 ,						output_type="""np""" ,						return_dict=_A ,						)[0]

				_UpperCAmelCase      :							Any       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							Optional[int]       =   image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				_UpperCAmelCase      :							str       =   np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
				_UpperCAmelCase      :							List[Any]       =   self.dummy_cond_unet
				_UpperCAmelCase      :							str       =   PNDMScheduler(skip_prk_steps=_A       )
				_UpperCAmelCase      :							List[Any]       =   self.dummy_vae
				_UpperCAmelCase      :							Union[str, Any]       =   self.dummy_text_encoder
				_UpperCAmelCase      :							Dict       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

				# make sure here that pndm scheduler skips prk
				_UpperCAmelCase      :							Union[str, Any]       =   StableDiffusionPipeline(
				    unet=_A ,						scheduler=_A ,						vae=_A ,						text_encoder=_A ,						tokenizer=_A ,						safety_checker=_A ,						feature_extractor=self.dummy_extractor ,						)
				_UpperCAmelCase      :							List[Any]       =   sd_pipe.to(_A       )
				sd_pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							int       =   """A painting of a squirrel eating a burger"""
				_UpperCAmelCase      :							Optional[int]       =   torch.Generator(device=_A       ).manual_seed(0       )
				_UpperCAmelCase      :							Optional[int]       =   sd_pipe([prompt] ,						generator=_A ,						guidance_scale=6.0 ,						num_inference_steps=2 ,						output_type="""np"""       )

				_UpperCAmelCase      :							Tuple       =   output.images

				_UpperCAmelCase      :							Any       =   torch.Generator(device=_A       ).manual_seed(0       )
				_UpperCAmelCase      :							Dict       =   sd_pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=6.0 ,						num_inference_steps=2 ,						output_type="""np""" ,						return_dict=_A ,						)[0]

				_UpperCAmelCase      :							str       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							Optional[int]       =   image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				_UpperCAmelCase      :							List[Any]       =   np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   StableDiffusionPipeline.from_pretrained(
				    """hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,						safety_checker=_A       )
				assert isinstance(_A ,						_A       )
				assert isinstance(pipe.scheduler ,						_A       )
				assert pipe.safety_checker is None

				_UpperCAmelCase      :							Any       =   pipe("""example prompt""" ,						num_inference_steps=2       ).images[0]
				assert image is not None

				# check that there's no error when saving a pipeline with one of the models being None
				with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(_A       )
					_UpperCAmelCase      :							Optional[Any]       =   StableDiffusionPipeline.from_pretrained(_A       )

				# sanity check that the pipeline still works
				assert pipe.safety_checker is None
				_UpperCAmelCase      :							Union[str, Any]       =   pipe("""example prompt""" ,						num_inference_steps=2       ).images[0]
				assert image is not None
			@unittest.skipIf(torch_device != """cuda""" ,						"""This test requires a GPU"""       )
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   self.dummy_cond_unet
				_UpperCAmelCase      :							Dict       =   PNDMScheduler(skip_prk_steps=_A       )
				_UpperCAmelCase      :							List[str]       =   self.dummy_vae
				_UpperCAmelCase      :							Tuple       =   self.dummy_text_encoder
				_UpperCAmelCase      :							List[str]       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

				# put models in fp16
				_UpperCAmelCase      :							Optional[Any]       =   unet.half()
				_UpperCAmelCase      :							List[str]       =   vae.half()
				_UpperCAmelCase      :							Dict       =   bert.half()

				# make sure here that pndm scheduler skips prk
				_UpperCAmelCase      :							str       =   StableDiffusionPipeline(
				    unet=_A ,						scheduler=_A ,						vae=_A ,						text_encoder=_A ,						tokenizer=_A ,						safety_checker=_A ,						feature_extractor=self.dummy_extractor ,						)
				_UpperCAmelCase      :							str       =   sd_pipe.to(_A       )
				sd_pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							Optional[int]       =   """A painting of a squirrel eating a burger"""
				_UpperCAmelCase      :							str       =   sd_pipe([prompt] ,						num_inference_steps=2 ,						output_type="""np"""       ).images

				assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class       _UpperCAmelCase     (						unittest.TestCase):
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   """cuda"""
				_UpperCAmelCase      :							List[Any]       =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       )
				_UpperCAmelCase      :							List[str]       =   pipe.to(_A       )
				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							str       =   """a photo of a cat"""
				_UpperCAmelCase      :							Optional[int]       =   {
				    """editing_prompt""": ["""sunglasses"""],
				    """reverse_editing_direction""": [False],
				    """edit_warmup_steps""": 10,
				    """edit_guidance_scale""": 6,
				    """edit_threshold""": 0.95,
				    """edit_momentum_scale""": 0.5,
				    """edit_mom_beta""": 0.6,
				}

				_UpperCAmelCase      :							Tuple       =   3
				_UpperCAmelCase      :							Dict       =   7

				# no sega enabled
				_UpperCAmelCase      :							str       =   torch.Generator(_A       )
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							int       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						)

				_UpperCAmelCase      :							Optional[Any]       =   output.images
				_UpperCAmelCase      :							Optional[Any]       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							Any       =   [
				    0.34673113,
				    0.38492733,
				    0.37597352,
				    0.34086335,
				    0.35650748,
				    0.35579205,
				    0.3384763,
				    0.34340236,
				    0.3573271,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							int       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						**_A ,						)

				_UpperCAmelCase      :							Dict       =   output.images
				_UpperCAmelCase      :							Dict       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							List[str]       =   [
				    0.41887826,
				    0.37728766,
				    0.30138272,
				    0.41416335,
				    0.41664985,
				    0.36283392,
				    0.36191246,
				    0.43364465,
				    0.43001732,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   """cuda"""
				_UpperCAmelCase      :							Tuple       =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       )
				_UpperCAmelCase      :							str       =   pipe.to(_A       )
				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							str       =   """an image of a crowded boulevard, realistic, 4k"""
				_UpperCAmelCase      :							str       =   {
				    """editing_prompt""": """crowd, crowded, people""",
				    """reverse_editing_direction""": True,
				    """edit_warmup_steps""": 10,
				    """edit_guidance_scale""": 8.3,
				    """edit_threshold""": 0.9,
				    """edit_momentum_scale""": 0.5,
				    """edit_mom_beta""": 0.6,
				}

				_UpperCAmelCase      :							Optional[int]       =   9
				_UpperCAmelCase      :							Tuple       =   7

				# no sega enabled
				_UpperCAmelCase      :							Dict       =   torch.Generator(_A       )
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							Any       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						)

				_UpperCAmelCase      :							Union[str, Any]       =   output.images
				_UpperCAmelCase      :							str       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							Tuple       =   [
				    0.43497998,
				    0.91814065,
				    0.7540739,
				    0.55580205,
				    0.8467265,
				    0.5389691,
				    0.62574506,
				    0.58897763,
				    0.50926757,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							int       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						**_A ,						)

				_UpperCAmelCase      :							Union[str, Any]       =   output.images
				_UpperCAmelCase      :							Union[str, Any]       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							str       =   [
				    0.3089719,
				    0.30500144,
				    0.29016042,
				    0.30630964,
				    0.325687,
				    0.29419225,
				    0.2908091,
				    0.28723598,
				    0.27696294,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   """cuda"""
				_UpperCAmelCase      :							Union[str, Any]       =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       )
				_UpperCAmelCase      :							Tuple       =   pipe.to(_A       )
				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							Optional[Any]       =   """a castle next to a river"""
				_UpperCAmelCase      :							str       =   {
				    """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
				    """reverse_editing_direction""": False,
				    """edit_warmup_steps""": [15, 18],
				    """edit_guidance_scale""": 6,
				    """edit_threshold""": [0.9, 0.8],
				    """edit_momentum_scale""": 0.5,
				    """edit_mom_beta""": 0.6,
				}

				_UpperCAmelCase      :							List[str]       =   48
				_UpperCAmelCase      :							Optional[Any]       =   7

				# no sega enabled
				_UpperCAmelCase      :							Any       =   torch.Generator(_A       )
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						)

				_UpperCAmelCase      :							Dict       =   output.images
				_UpperCAmelCase      :							Any       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							int       =   [
				    0.75163555,
				    0.76037145,
				    0.61785,
				    0.9189673,
				    0.8627701,
				    0.85189694,
				    0.8512813,
				    0.87012076,
				    0.8312857,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							Any       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						**_A ,						)

				_UpperCAmelCase      :							List[Any]       =   output.images
				_UpperCAmelCase      :							List[str]       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							Any       =   [
				    0.73553365,
				    0.7537271,
				    0.74341905,
				    0.66480356,
				    0.6472925,
				    0.63039416,
				    0.64812905,
				    0.6749717,
				    0.6517102,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   """cuda"""
				_UpperCAmelCase      :							Optional[Any]       =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,						torch_dtype=torch.floataa       )
				_UpperCAmelCase      :							int       =   pipe.to(_A       )
				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							Dict       =   """a photo of a cat"""
				_UpperCAmelCase      :							Optional[int]       =   {
				    """editing_prompt""": ["""sunglasses"""],
				    """reverse_editing_direction""": [False],
				    """edit_warmup_steps""": 10,
				    """edit_guidance_scale""": 6,
				    """edit_threshold""": 0.95,
				    """edit_momentum_scale""": 0.5,
				    """edit_mom_beta""": 0.6,
				}

				_UpperCAmelCase      :							int       =   3
				_UpperCAmelCase      :							str       =   7

				# no sega enabled
				_UpperCAmelCase      :							List[Any]       =   torch.Generator(_A       )
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						)

				_UpperCAmelCase      :							List[str]       =   output.images
				_UpperCAmelCase      :							int       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							Dict       =   [
				    0.34887695,
				    0.3876953,
				    0.375,
				    0.34423828,
				    0.3581543,
				    0.35717773,
				    0.3383789,
				    0.34570312,
				    0.359375,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

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

				# with sega enabled
				# generator = torch.manual_seed(seed)
				generator.manual_seed(_A       )
				_UpperCAmelCase      :							Optional[Any]       =   pipe(
				    [prompt] ,						generator=_A ,						guidance_scale=_A ,						num_inference_steps=50 ,						output_type="""np""" ,						width=5_12 ,						height=5_12 ,						**_A ,						)

				_UpperCAmelCase      :							str       =   output.images
				_UpperCAmelCase      :							Dict       =   image[0, -3:, -3:, -1]
				_UpperCAmelCase      :							Tuple       =   [
				    0.42285156,
				    0.36914062,
				    0.29077148,
				    0.42041016,
				    0.41918945,
				    0.35498047,
				    0.3618164,
				    0.4423828,
				    0.43115234,
				]

				assert image.shape == (1, 5_12, 5_12, 3)

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








"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase__			:					Union[str, Any]							  =					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					List[str]							  =					[
						    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechSatForAudioFrameClassification''',
						    '''UniSpeechSatForCTC''',
						    '''UniSpeechSatForPreTraining''',
						    '''UniSpeechSatForSequenceClassification''',
						    '''UniSpeechSatForXVector''',
						    '''UniSpeechSatModel''',
						    '''UniSpeechSatPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

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





