



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a__      :				List[str]   	=			version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__=False						,  ):
      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	):
      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__":
       a__      :				Optional[int]   	=			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")

       a__      :				Any   	=			parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("SD: Done: ONNX")





from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__      :				Dict   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  int       				=    "speech_to_text"
   A    :  Optional[Any]       				=    ["past_key_values"]
   A    :  Optional[int]       				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple=1_00_00			,				lowerCAmelCase  :			int=12			,				lowerCAmelCase  :			int=20_48			,				lowerCAmelCase  :			Union[str, Any]=4			,				lowerCAmelCase  :			str=6			,				lowerCAmelCase  :			Dict=20_48			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Optional[int]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.0			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict="relu"			,				lowerCAmelCase  :			Tuple=2_56			,				lowerCAmelCase  :			Dict=0.1			,				lowerCAmelCase  :			Optional[Any]=0.0			,				lowerCAmelCase  :			List[Any]=0.0			,				lowerCAmelCase  :			Any=0.02			,				lowerCAmelCase  :			List[Any]=2			,				lowerCAmelCase  :			Tuple=True			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			List[str]=0			,				lowerCAmelCase  :			Union[str, Any]=2			,				lowerCAmelCase  :			Any=60_00			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Optional[Any]=2			,				lowerCAmelCase  :			Optional[Any]=(5, 5)			,				lowerCAmelCase  :			Union[str, Any]=10_24			,				lowerCAmelCase  :			List[Any]=80			,				lowerCAmelCase  :			List[str]=1			,				**lowerCAmelCase  :			List[str]			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  encoder_ffn_dim
         lowercase__     =  encoder_layers
         lowercase__     =  encoder_attention_heads
         lowercase__     =  decoder_ffn_dim
         lowercase__     =  decoder_layers
         lowercase__     =  decoder_attention_heads
         lowercase__     =  dropout
         lowercase__     =  attention_dropout
         lowercase__     =  activation_dropout
         lowercase__     =  activation_function
         lowercase__     =  init_std
         lowercase__     =  encoder_layerdrop
         lowercase__     =  decoder_layerdrop
         lowercase__     =  use_cache
         lowercase__     =  encoder_layers
         lowercase__     =  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__     =  max_source_positions
         lowercase__     =  max_target_positions
         lowercase__     =  num_conv_layers
         lowercase__     =  list(lowerCAmelCase)
         lowercase__     =  conv_channels
         lowercase__     =  input_feat_per_channel
         lowercase__     =  input_channels

         if len(self.conv_kernel_sizes) != self.num_conv_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                   f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '''
                   f'''`config.num_conv_layers = {self.num_conv_layers}`.''')

         super().__init__(
             pad_token_id=lowerCAmelCase			,				bos_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				is_encoder_decoder=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
