import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCamelCase__    							= version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A,  __A,  __A=False,  )					->    Optional[int]:







      '''simple docstring'''


      output_path.parent.mkdir(parents=__A,  exist_ok=__A							)
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                __A,  __A,  f=output_path.as_posix(),  input_names=__A,  output_names=__A,  dynamic_axes=__A,  do_constant_folding=__A,  use_external_data_format=__A,  enable_onnx_checker=__A,  opset_version=__A,  )
      else:
            export(
                __A,  __A,  f=output_path.as_posix(),  input_names=__A,  output_names=__A,  dynamic_axes=__A,  do_constant_folding=__A,  opset_version=__A,  )




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A,  __A,  __A = False							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            UpperCAmelCase__								=  "cuda"
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("`float16` model export is only supported on GPUs with CUDA"							)
      else:
            UpperCAmelCase__								=  "cpu"
      UpperCAmelCase__								=  Path(__A							)

      # VAE DECODER
      UpperCAmelCase__								=  AutoencoderKL.from_pretrained(model_path + "/vae"							)
      UpperCAmelCase__								=  vae_decoder.config.latent_channels
      # forward only through the decoder part
      UpperCAmelCase__								=  vae_decoder.decode
      onnx_export(
          __A,  model_args=(
              torch.randn(1,  __A,  25,  25							).to(device=__A,  dtype=__A							),
              False,
          ),  output_path=output_path / "vae_decoder" / "model.onnx",  ordered_input_names=["latent_sample", "return_dict"],  output_names=["sample"],  dynamic_axes={
              "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
          },  opset=__A,  )
      del vae_decoder


if __name__ == "__main__":
      UpperCamelCase__    							= argparse.ArgumentParser()

      parser.add_argument(
          '--model_path',
          type=str,
          required=True,
          help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
      )

      parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
      parser.add_argument(
          '--opset',
          default=1_4,
          type=int,
          help='The version of the ONNX operator set to use.',
      )
      parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')

      UpperCamelCase__    							= parser.parse_args()
      print(args.output_path)
      convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
      print('SD: Done: ONNX')




from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  [self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase__								=  [tuple(__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else key for key in keys]
         UpperCAmelCase__								=  Counter(__UpperCAmelCase		)
         UpperCAmelCase__								=  [key for key in counter if counter[key] > 1]
         if duplicate_keys:
               raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''simple docstring'''


      UpperCAmelCase__								=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCAmelCase__								=  full_content[1:].index("---"							) + 1
            UpperCAmelCase__								=  "\n".join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

      return None, "\n".join(__A							)





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(readme_file.read()		)
         if yaml_string is not None:
               return cls.from_yaml_string(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



         if path.exists():
               with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
                     UpperCAmelCase__								=  readme_file.read()
         else:
               UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self._to_readme(__UpperCAmelCase		)
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"		) as readme_file:
               readme_file.write(__UpperCAmelCase		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



         if readme_content is not None:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(__UpperCAmelCase		)
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n" + content
         else:
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n"
         return full_content


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase__								=  {
             (key.replace("-"	,  "_"		) if key.replace("-"	,  "_"		) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         return yaml.safe_dump(
             {
                 (key.replace("_"	,  "-"		) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }	,  sort_keys=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)



