





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__  :	Optional[int]     ={
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =[
							    '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Wav2Vec2ConformerForAudioFrameClassification''',
							    '''Wav2Vec2ConformerForCTC''',
							    '''Wav2Vec2ConformerForPreTraining''',
							    '''Wav2Vec2ConformerForSequenceClassification''',
							    '''Wav2Vec2ConformerForXVector''',
							    '''Wav2Vec2ConformerModel''',
							    '''Wav2Vec2ConformerPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_wavaveca_conformer import (
							    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    WavaVecaConformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavaveca_conformer import (
														    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavaVecaConformerForAudioFrameClassification,
														    WavaVecaConformerForCTC,
														    WavaVecaConformerForPreTraining,
														    WavaVecaConformerForSequenceClassification,
														    WavaVecaConformerForXVector,
														    WavaVecaConformerModel,
														    WavaVecaConformerPreTrainedModel,
														)

else:
							import sys

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












from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available

@dataclass
class 					__A   (  a			):
				__A            = 42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
							from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
							from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline





