




'''simple docstring'''





import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_SCREAMING_SNAKE_CASE							:   int      		= logging.get_logger(__name__)




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      Optional[int]						       =    "AutoTokenizer"
			lowerCAmelCase_     :      int						       =    ["tokenizer"]

			lowerCAmelCase_     :      List[Any]						       =    {
			    "semantic_prompt": 1,
			    "coarse_prompt": 2,
			    "fine_prompt": 2,
			}




			def __init__(					self				,							a__				,							a__=None							)				->				Optional[Any]:

										'''simple docstring'''




										super().__init__(a__							)

										snake_case_						=      speaker_embeddings




			@classmethod
			def    lowerCAmelCase__				(					cls				,							a__				,							a__="speaker_embeddings_path.json"				,							**a__							)				->				Any:

										'''simple docstring'''




										if speaker_embeddings_dict_path is not None:
																	snake_case_						=      get_file_from_repo(
																	    a__				,							a__				,							subfolder=kwargs.pop("subfolder"				,							a__							)				,							cache_dir=kwargs.pop("cache_dir"				,							a__							)				,							force_download=kwargs.pop("force_download"				,							a__							)				,							proxies=kwargs.pop("proxies"				,							a__							)				,							resume_download=kwargs.pop("resume_download"				,							a__							)				,							local_files_only=kwargs.pop("local_files_only"				,							a__							)				,							use_auth_token=kwargs.pop("use_auth_token"				,							a__							)				,							revision=kwargs.pop("revision"				,							a__							)				,							)
																	if speaker_embeddings_path is None:
																								logger.warning(
																								    F'`{os.path.join(a__				,							a__							)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'							)
																								snake_case_						=      None
																	else:
																								with open(a__							) as speaker_embeddings_json:
																															snake_case_						=      json.load(a__							)
										else:
																	snake_case_						=      None

										snake_case_						=      AutoTokenizer.from_pretrained(a__				,							**a__							)

										return cls(tokenizer=a__				,							speaker_embeddings=a__							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__="speaker_embeddings_path.json"				,							a__="speaker_embeddings"				,							a__ = False				,							**a__				,							)				->				Tuple:

										'''simple docstring'''




										if self.speaker_embeddings is not None:
																	os.makedirs(os.path.join(a__				,							a__				,							"v2"							)				,							exist_ok=a__							)

																	snake_case_						=      {}

																	snake_case_						=      save_directory

																	for prompt_key in self.speaker_embeddings:
																								if prompt_key != "repo_or_path":
																															snake_case_						=      self._load_voice_preset(a__							)

																															snake_case_						=      {}
																															for key in self.speaker_embeddings[prompt_key]:
																																						np.save(
																																						    os.path.join(
																																						        embeddings_dict["repo_or_path"]				,							a__				,							F'{prompt_key}_{key}'							)				,							voice_preset[key]				,							allow_pickle=a__				,							)
																																						snake_case_						=      os.path.join(a__				,							F'{prompt_key}_{key}.npy'							)

																															snake_case_						=      tmp_dict

																	with open(os.path.join(a__				,							a__							)				,							"w"							) as fp:
																								json.dump(a__				,							a__							)

										super().save_pretrained(a__				,							a__				,							**a__							)




			def    lowerCAmelCase__				(					self				,							a__ = None				,							**a__							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      self.speaker_embeddings[voice_preset]

										snake_case_						=      {}
										for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																	if key not in voice_preset_paths:
																								raise ValueError(
																								    F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'							)

																	snake_case_						=      get_file_from_repo(
																	    self.speaker_embeddings.get("repo_or_path"				,							"/"							)				,							voice_preset_paths[key]				,							subfolder=kwargs.pop("subfolder"				,							a__							)				,							cache_dir=kwargs.pop("cache_dir"				,							a__							)				,							force_download=kwargs.pop("force_download"				,							a__							)				,							proxies=kwargs.pop("proxies"				,							a__							)				,							resume_download=kwargs.pop("resume_download"				,							a__							)				,							local_files_only=kwargs.pop("local_files_only"				,							a__							)				,							use_auth_token=kwargs.pop("use_auth_token"				,							a__							)				,							revision=kwargs.pop("revision"				,							a__							)				,							)
																	if path is None:
																								raise ValueError(
																								    F'`{os.path.join(self.speaker_embeddings.get("repo_or_path"				,							"/"							)				,							voice_preset_paths[key]							)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'							)

																	snake_case_						=      np.load(a__							)

										return voice_preset_dict




			def    lowerCAmelCase__				(					self				,							a__ = None							)				->				Dict:

										'''simple docstring'''




										for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																	if key not in voice_preset:
																								raise ValueError(F'Voice preset unrecognized, missing {key} as a key.'							)

																	if not isinstance(voice_preset[key]				,							np.ndarray							):
																								raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]							)}D ndarray.'							)

																	if len(voice_preset[key].shape							) != self.preset_shape[key]:
																								raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]							)}D ndarray.'							)




			def __call__(					self				,							a__=None				,							a__=None				,							a__="pt"				,							a__=256				,							a__=False				,							a__=True				,							a__=False				,							**a__				,							)				->				List[str]:

										'''simple docstring'''




										if voice_preset is not None and not isinstance(a__				,							a__							):
																	if (
																	    isinstance(a__				,							a__							)
																	    and self.speaker_embeddings is not None
																	    and voice_preset in self.speaker_embeddings
																	):
																								snake_case_						=      self._load_voice_preset(a__							)

																	else:
																								if isinstance(a__				,							a__							) and not voice_preset.endswith(".npz"							):
																															snake_case_						=      voice_preset + ".npz"

																								snake_case_						=      np.load(a__							)

										if voice_preset is not None:
																	self._validate_voice_preset_dict(a__				,							**a__							)
																	snake_case_						=      BatchFeature(data=a__				,							tensor_type=a__							)

										snake_case_						=      self.tokenizer(
										    a__				,							return_tensors=a__				,							padding="max_length"				,							max_length=a__				,							return_attention_mask=a__				,							return_token_type_ids=a__				,							add_special_tokens=a__				,							**a__				,							)

										if voice_preset is not None:
																	snake_case_						=      voice_preset

										return encoded_text







'''simple docstring'''






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__=False     ,				):






 """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 		_A     (    A__     ,				A__     ,				A__     ,				A__ = False      ):






 """simple docstring"""



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

 # VAE DECODER
 __lowercase							  = AutoencoderKL.from_pretrained(model_path + '''/vae'''      )
 __lowercase							  = vae_decoder.config.latent_channels
 # forward only through the decoder part
 __lowercase							  = 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__":
 lowerCAmelCase__											=	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=14,
     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''')

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

