





from typing import TYPE_CHECKING

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


UpperCamelCase__   : Optional[int]					  = {
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : Dict					  = ["""FunnelTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : Tuple					  = [
				    """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """FunnelBaseModel""",
				    """FunnelForMaskedLM""",
				    """FunnelForMultipleChoice""",
				    """FunnelForPreTraining""",
				    """FunnelForQuestionAnswering""",
				    """FunnelForSequenceClassification""",
				    """FunnelForTokenClassification""",
				    """FunnelModel""",
				    """FunnelPreTrainedModel""",
				    """load_tf_weights_in_funnel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : Any					  = [
				    """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFFunnelBaseModel""",
				    """TFFunnelForMaskedLM""",
				    """TFFunnelForMultipleChoice""",
				    """TFFunnelForPreTraining""",
				    """TFFunnelForQuestionAnswering""",
				    """TFFunnelForSequenceClassification""",
				    """TFFunnelForTokenClassification""",
				    """TFFunnelModel""",
				    """TFFunnelPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
				from .tokenization_funnel import FunnelTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_funnel_fast import FunnelTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_funnel import (
								    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FunnelBaseModel,
								    FunnelForMaskedLM,
								    FunnelForMultipleChoice,
								    FunnelForPreTraining,
								    FunnelForQuestionAnswering,
								    FunnelForSequenceClassification,
								    FunnelForTokenClassification,
								    FunnelModel,
								    FunnelPreTrainedModel,
								    load_tf_weights_in_funnel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_funnel import (
								    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFFunnelBaseModel,
								    TFFunnelForMaskedLM,
								    TFFunnelForMultipleChoice,
								    TFFunnelForPreTraining,
								    TFFunnelForQuestionAnswering,
								    TFFunnelForSequenceClassification,
								    TFFunnelForTokenClassification,
								    TFFunnelModel,
								    TFFunnelPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase__   : str					  = _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 PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

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


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipeline_cycle_diffusion import CycleDiffusionPipeline
				from .pipeline_stable_diffusion import StableDiffusionPipeline
				from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
				from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
				from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
				from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
				from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
				from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
				from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
				from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
				from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
				from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
				from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
				from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
				from .pipeline_stable_unclip import StableUnCLIPPipeline
				from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
				from .safety_checker import StableDiffusionSafetyChecker
				from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
				from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import (
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				)
else:
				from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
				from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
				from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
				if not (
				    is_torch_available()
				    and is_transformers_available()
				    and is_k_diffusion_available()
				    and is_k_diffusion_version(""">=""", """0.0.12""")
				):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not (is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_onnx_objects import *  # noqa F403
else:
				from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
				from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
				from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
				from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
				from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
				import flax







				@flax.struct.dataclass
				class   _UpperCamelCase  (       A_  ):







									'''simple docstring'''

									lowerCamelCase     :     np.ndarray
									lowerCamelCase     :     List[bool]


				from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
				from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
				from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
				from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
				from .safety_checker_flax import FlaxStableDiffusionSafetyChecker


