


'''simple docstring'''

from typing import TYPE_CHECKING

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


_UpperCAmelCase					:			Tuple				     =					{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			Union[str, Any]				     =					["""SegformerFeatureExtractor"""]
	_UpperCAmelCase					:			str				     =					["""SegformerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			List[str]				     =					[
	    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SegformerDecodeHead""",
	    """SegformerForImageClassification""",
	    """SegformerForSemanticSegmentation""",
	    """SegformerLayer""",
	    """SegformerModel""",
	    """SegformerPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase					:			List[str]				     =					[
	    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSegformerDecodeHead""",
	    """TFSegformerForImageClassification""",
	    """TFSegformerForSemanticSegmentation""",
	    """TFSegformerModel""",
	    """TFSegformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_segformer import SegformerFeatureExtractor
		from .image_processing_segformer import SegformerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_segformer import (
		    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SegformerDecodeHead,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerLayer,
		    SegformerModel,
		    SegformerPreTrainedModel,
		)
	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_segformer import (
		    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSegformerDecodeHead,
		    TFSegformerForImageClassification,
		    TFSegformerForSemanticSegmentation,
		    TFSegformerModel,
		    TFSegformerPreTrainedModel,
		)

else:
	import sys

	_UpperCAmelCase					:			List[str]				     =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''simple docstring'''

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

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

