




"""simple docstring"""

a         =      '''0.18.2'''

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_pt_objects import *  # noqa F403
else:
       from .models import (
           AutoencoderKL,
           ControlNetModel,
           ModelMixin,
           PriorTransformer,
           TaFilmDecoder,
           TransformeraDModel,
           UNetaDModel,
           UNetaDConditionModel,
           UNetaDModel,
           UNetaDConditionModel,
           VQModel,
       )
       from .optimization import (
           get_constant_schedule,
           get_constant_schedule_with_warmup,
           get_cosine_schedule_with_warmup,
           get_cosine_with_hard_restarts_schedule_with_warmup,
           get_linear_schedule_with_warmup,
           get_polynomial_decay_schedule_with_warmup,
           get_scheduler,
       )
       from .pipelines import (
           AudioPipelineOutput,
           ConsistencyModelPipeline,
           DanceDiffusionPipeline,
           DDIMPipeline,
           DDPMPipeline,
           DiffusionPipeline,
           DiTPipeline,
           ImagePipelineOutput,
           KarrasVePipeline,
           LDMPipeline,
           LDMSuperResolutionPipeline,
           PNDMPipeline,
           RePaintPipeline,
           ScoreSdeVePipeline,
       )
       from .schedulers import (
           CMStochasticIterativeScheduler,
           DDIMInverseScheduler,
           DDIMParallelScheduler,
           DDIMScheduler,
           DDPMParallelScheduler,
           DDPMScheduler,
           DEISMultistepScheduler,
           DPMSolverMultistepInverseScheduler,
           DPMSolverMultistepScheduler,
           DPMSolverSinglestepScheduler,
           EulerAncestralDiscreteScheduler,
           EulerDiscreteScheduler,
           HeunDiscreteScheduler,
           IPNDMScheduler,
           KarrasVeScheduler,
           KDPMaAncestralDiscreteScheduler,
           KDPMaDiscreteScheduler,
           PNDMScheduler,
           RePaintScheduler,
           SchedulerMixin,
           ScoreSdeVeScheduler,
           UnCLIPScheduler,
           UniPCMultistepScheduler,
           VQDiffusionScheduler,
       )
       from .training_utils import EMAModel

try:
       if not (is_torch_available() and is_scipy_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
       from .schedulers import LMSDiscreteScheduler

try:
       if not (is_torch_available() and is_torchsde_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
       from .schedulers import DPMSolverSDEScheduler

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 .pipelines import (
           AltDiffusionImgaImgPipeline,
           AltDiffusionPipeline,
           AudioLDMPipeline,
           CycleDiffusionPipeline,
           IFImgaImgPipeline,
           IFImgaImgSuperResolutionPipeline,
           IFInpaintingPipeline,
           IFInpaintingSuperResolutionPipeline,
           IFPipeline,
           IFSuperResolutionPipeline,
           ImageTextPipelineOutput,
           KandinskyImgaImgPipeline,
           KandinskyInpaintPipeline,
           KandinskyPipeline,
           KandinskyPriorPipeline,
           KandinskyVaaControlnetImgaImgPipeline,
           KandinskyVaaControlnetPipeline,
           KandinskyVaaImgaImgPipeline,
           KandinskyVaaInpaintPipeline,
           KandinskyVaaPipeline,
           KandinskyVaaPriorEmbaEmbPipeline,
           KandinskyVaaPriorPipeline,
           LDMTextToImagePipeline,
           PaintByExamplePipeline,
           SemanticStableDiffusionPipeline,
           ShapEImgaImgPipeline,
           ShapEPipeline,
           StableDiffusionAttendAndExcitePipeline,
           StableDiffusionControlNetImgaImgPipeline,
           StableDiffusionControlNetInpaintPipeline,
           StableDiffusionControlNetPipeline,
           StableDiffusionDepthaImgPipeline,
           StableDiffusionDiffEditPipeline,
           StableDiffusionImageVariationPipeline,
           StableDiffusionImgaImgPipeline,
           StableDiffusionInpaintPipeline,
           StableDiffusionInpaintPipelineLegacy,
           StableDiffusionInstructPixaPixPipeline,
           StableDiffusionLatentUpscalePipeline,
           StableDiffusionLDMaDPipeline,
           StableDiffusionModelEditingPipeline,
           StableDiffusionPanoramaPipeline,
           StableDiffusionParadigmsPipeline,
           StableDiffusionPipeline,
           StableDiffusionPipelineSafe,
           StableDiffusionPixaPixZeroPipeline,
           StableDiffusionSAGPipeline,
           StableDiffusionUpscalePipeline,
           StableUnCLIPImgaImgPipeline,
           StableUnCLIPPipeline,
           TextToVideoSDPipeline,
           TextToVideoZeroPipeline,
           UnCLIPImageVariationPipeline,
           UnCLIPPipeline,
           UniDiffuserModel,
           UniDiffuserPipeline,
           UniDiffuserTextDecoder,
           VersatileDiffusionDualGuidedPipeline,
           VersatileDiffusionImageVariationPipeline,
           VersatileDiffusionPipeline,
           VersatileDiffusionTextToImagePipeline,
           VideoToVideoSDPipeline,
           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 .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

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 .pipelines import StableDiffusionKDiffusionPipeline

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 .pipelines import (
           OnnxStableDiffusionImgaImgPipeline,
           OnnxStableDiffusionInpaintPipeline,
           OnnxStableDiffusionInpaintPipelineLegacy,
           OnnxStableDiffusionPipeline,
           OnnxStableDiffusionUpscalePipeline,
           StableDiffusionOnnxPipeline,
       )

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 .pipelines import AudioDiffusionPipeline, Mel

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 .pipelines import SpectrogramDiffusionPipeline

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_flax_objects import *  # noqa F403
else:
       from .models.controlnet_flax import FlaxControlNetModel
       from .models.modeling_flax_utils import FlaxModelMixin
       from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
       from .models.vae_flax import FlaxAutoencoderKL
       from .pipelines import FlaxDiffusionPipeline
       from .schedulers import (
           FlaxDDIMScheduler,
           FlaxDDPMScheduler,
           FlaxDPMSolverMultistepScheduler,
           FlaxKarrasVeScheduler,
           FlaxLMSDiscreteScheduler,
           FlaxPNDMScheduler,
           FlaxSchedulerMixin,
           FlaxScoreSdeVeScheduler,
       )


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 .pipelines import (
           FlaxStableDiffusionControlNetPipeline,
           FlaxStableDiffusionImgaImgPipeline,
           FlaxStableDiffusionInpaintPipeline,
           FlaxStableDiffusionPipeline,
       )

try:
       if not (is_note_seq_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_note_seq_objects import *  # noqa F403
else:
       from .pipelines import MidiProcessor







"""simple docstring"""

import argparse
from collections import defaultdict

import yaml


a         =      '''docs/source/en/_toctree.yml'''






def 						_snake_case					( _snake_case							: List[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       defaultdict(_snake_case							)
      _A        =       []
      _A        =       []
      for doc in doc_list:
            if "local" in doc:
                  counts[doc["local"]] += 1

            if doc["title"].lower() == "overview":
                  overview_doc.append({'local': doc['local'], 'title': doc['title']}							)
            else:
                  new_doc_list.append(_snake_case							)

      _A        =       new_doc_list
      _A        =       [key for key, value in counts.items() if value > 1]

      _A        =       []
      for duplicate_key in duplicates:
            _A        =       list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}							)
            if len(_snake_case							) > 1:
                  raise ValueError(
                      F'''{duplicate_key} is present several times in the documentation table of content at '''
                      '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                      'others.'							)
            # Only add this once
            new_doc.append({'local': duplicate_key, 'title': titles[0]}							)

      # Add none duplicate-keys
      new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]							)
      _A        =       sorted(_snake_case    ,  key=lambda _snake_case							: s["title"].lower()							)

      # "overview" gets special treatment and is always first
      if len(_snake_case							) > 1:
            raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'							)

      overview_doc.extend(_snake_case							)

      # Sort
      return overview_doc






def 						_snake_case					( _snake_case							: Tuple=False							)   -> List[Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[scheduler_idx]["title"] != "Schedulers":
            scheduler_idx += 1

      _A        =       api_doc[scheduler_idx]['sections']
      _A        =       clean_doc_toc(_snake_case							)

      _A        =       False
      if new_scheduler_doc != scheduler_doc:
            _A        =       True
            if overwrite:
                  _A        =       new_scheduler_doc

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)






def 						_snake_case					( _snake_case							: str=False							)   -> Union[str, Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[pipeline_idx]["title"] != "Pipelines":
            pipeline_idx += 1

      _A        =       False
      _A        =       api_doc[pipeline_idx]['sections']
      _A        =       []

      # sort sub pipeline docs
      for pipeline_doc in pipeline_docs:
            if "section" in pipeline_doc:
                  _A        =       pipeline_doc['section']
                  _A        =       clean_doc_toc(_snake_case							)
                  if overwrite:
                        _A        =       new_sub_pipeline_doc
            new_pipeline_docs.append(_snake_case							)

      # sort overall pipeline doc
      _A        =       clean_doc_toc(_snake_case							)

      if new_pipeline_docs != pipeline_docs:
            _A        =       True
            if overwrite:
                  _A        =       new_pipeline_docs

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       a         =      parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

