





"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  OmegaConf.load(_UpperCAmelCase					)
  A_  :       List[Any]										=  torch.load(_UpperCAmelCase ,					map_location='cpu'					)['model']
  A_  :       List[Any]										=  list(state_dict.keys()					)

  # extract state_dict for VQVAE
  A_  :       Optional[int]										=  {}
  A_  :       Tuple										=  'first_stage_model.'
  for key in keys:
    if key.startswith(_UpperCAmelCase					):
      A_  :       List[Any]										=  state_dict[key]

    # extract state_dict for UNetLDM
  A_  :       Optional[Any]										=  {}
  A_  :       Tuple										=  'model.diffusion_model.'
  for key in keys:
    if key.startswith(_UpperCAmelCase					):
      A_  :       int										=  state_dict[key]

  A_  :       Optional[int]										=  config.model.params.first_stage_config.params
  A_  :       Dict										=  config.model.params.unet_config.params

  A_  :       Tuple										=  VQModel(**_UpperCAmelCase					).eval()
  vqvae.load_state_dict(_UpperCAmelCase					)

  A_  :       List[str]										=  UNetLDMModel(**_UpperCAmelCase					).eval()
  unet.load_state_dict(_UpperCAmelCase					)

  A_  :       Any										=  DDIMScheduler(
      timesteps=config.model.params.timesteps ,					beta_schedule='scaled_linear' ,					beta_start=config.model.params.linear_start ,					beta_end=config.model.params.linear_end ,					clip_sample=_UpperCAmelCase ,					)

  A_  :       str										=  LDMPipeline(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  pipeline.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						Tuple						=					argparse.ArgumentParser()
   parser.add_argument('--checkpoint_path', type=str, required=True)
   parser.add_argument('--config_path', type=str, required=True)
   parser.add_argument('--output_path', type=str, required=True)
   lowerCamelCase_     :						Dict						=					parser.parse_args()

   convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)






"""simple docstring"""




from typing import TYPE_CHECKING

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


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)