'''simple docstring'''
from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_pt_objects import *  # noqa F403
else:
  from .consistency_models import ConsistencyModelPipeline
  from .dance_diffusion import DanceDiffusionPipeline
  from .ddim import DDIMPipeline
  from .ddpm import DDPMPipeline
  from .dit import DiTPipeline
  from .latent_diffusion import LDMSuperResolutionPipeline
  from .latent_diffusion_uncond import LDMPipeline
  from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
  from .pndm import PNDMPipeline
  from .repaint import RePaintPipeline
  from .score_sde_ve import ScoreSdeVePipeline
  from .stochastic_karras_ve import KarrasVePipeline

try:
  if not (is_torch_available() and is_librosa_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
  from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
  if not (is_torch_available() and is_transformers_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
  from .audioldm import AudioLDMPipeline
  from .controlnet import (
      StableDiffusionControlNetImgaImgPipeline,
      StableDiffusionControlNetInpaintPipeline,
      StableDiffusionControlNetPipeline,
  )
  from .deepfloyd_if import (
      IFImgaImgPipeline,
      IFImgaImgSuperResolutionPipeline,
      IFInpaintingPipeline,
      IFInpaintingSuperResolutionPipeline,
      IFPipeline,
      IFSuperResolutionPipeline,
  )
  from .kandinsky import (
      KandinskyImgaImgPipeline,
      KandinskyInpaintPipeline,
      KandinskyPipeline,
      KandinskyPriorPipeline,
  )
  from .kandinskya_a import (
      KandinskyVaaControlnetImgaImgPipeline,
      KandinskyVaaControlnetPipeline,
      KandinskyVaaImgaImgPipeline,
      KandinskyVaaInpaintPipeline,
      KandinskyVaaPipeline,
      KandinskyVaaPriorEmbaEmbPipeline,
      KandinskyVaaPriorPipeline,
  )
  from .latent_diffusion import LDMTextToImagePipeline
  from .paint_by_example import PaintByExamplePipeline
  from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
  from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
  from .stable_diffusion import (
      CycleDiffusionPipeline,
      StableDiffusionAttendAndExcitePipeline,
      StableDiffusionDepthaImgPipeline,
      StableDiffusionDiffEditPipeline,
      StableDiffusionImageVariationPipeline,
      StableDiffusionImgaImgPipeline,
      StableDiffusionInpaintPipeline,
      StableDiffusionInpaintPipelineLegacy,
      StableDiffusionInstructPixaPixPipeline,
      StableDiffusionLatentUpscalePipeline,
      StableDiffusionLDMaDPipeline,
      StableDiffusionModelEditingPipeline,
      StableDiffusionPanoramaPipeline,
      StableDiffusionParadigmsPipeline,
      StableDiffusionPipeline,
      StableDiffusionPixaPixZeroPipeline,
      StableDiffusionSAGPipeline,
      StableDiffusionUpscalePipeline,
      StableUnCLIPImgaImgPipeline,
      StableUnCLIPPipeline,
  )
  from .stable_diffusion_safe import StableDiffusionPipelineSafe
  from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
  from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
  from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
  from .versatile_diffusion import (
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
  )
  from .vq_diffusion import VQDiffusionPipeline


try:
  if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
  from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
  if not is_onnx_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_onnx_objects import *  # noqa F403
else:
  from .onnx_utils import OnnxRuntimeModel

try:
  if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
  from .stable_diffusion import (
      OnnxStableDiffusionImgaImgPipeline,
      OnnxStableDiffusionInpaintPipeline,
      OnnxStableDiffusionInpaintPipelineLegacy,
      OnnxStableDiffusionPipeline,
      OnnxStableDiffusionUpscalePipeline,
      StableDiffusionOnnxPipeline,
  )

try:
  if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
  from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_flax_objects import *  # noqa F403
else:
  from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
  if not (is_flax_available() and is_transformers_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
  from .controlnet import FlaxStableDiffusionControlNetPipeline
  from .stable_diffusion import (
      FlaxStableDiffusionImgaImgPipeline,
      FlaxStableDiffusionInpaintPipeline,
      FlaxStableDiffusionPipeline,
  )
try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A	:   List[Any]	=					logging.get_logger(__name__)

__A	:   Tuple	=					{
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Tuple	=		"electra"







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=3_0522     ,						_SCREAMING_SNAKE_CASE=128     ,						_SCREAMING_SNAKE_CASE=256     ,						_SCREAMING_SNAKE_CASE=12     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=1024     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=512     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=0.0_2     ,						_SCREAMING_SNAKE_CASE=1E-12     ,						_SCREAMING_SNAKE_CASE="first"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE="absolute"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[int]:
													super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=vocab_size
													lowerCamelCase_								=embedding_size
													lowerCamelCase_								=hidden_size
													lowerCamelCase_								=num_hidden_layers
													lowerCamelCase_								=num_attention_heads
													lowerCamelCase_								=intermediate_size
													lowerCamelCase_								=hidden_act
													lowerCamelCase_								=hidden_dropout_prob
													lowerCamelCase_								=attention_probs_dropout_prob
													lowerCamelCase_								=max_position_embeddings
													lowerCamelCase_								=type_vocab_size
													lowerCamelCase_								=initializer_range
													lowerCamelCase_								=layer_norm_eps

													lowerCamelCase_								=summary_type
													lowerCamelCase_								=summary_use_proj
													lowerCamelCase_								=summary_activation
													lowerCamelCase_								=summary_last_dropout
													lowerCamelCase_								=position_embedding_type
													lowerCamelCase_								=use_cache
													lowerCamelCase_								=classifier_dropout


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):







						@property
						def        _snake_case				(			self							)->							Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																				lowerCamelCase_								={0: """batch""", 1: """choice""", 2: """sequence"""}
													else:
																				lowerCamelCase_								={0: """batch""", 1: """sequence"""}
													return OrderedDict(
													    [
													        ("""input_ids""", dynamic_axis),
													        ("""attention_mask""", dynamic_axis),
													        ("""token_type_ids""", dynamic_axis),
													    ]							)
