



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








"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



