


"""simple docstring"""


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase      :Tuple	    =		version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')




def   lowerCAmelCase		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						tuple			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Dict=False			,				)   ->      Union[str, Any]:
      output_path.parent.mkdir(parents=UpperCamelCase__			,				exist_ok=UpperCamelCase__    )
      # 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(
                UpperCamelCase__			,				UpperCamelCase__			,				f=output_path.as_posix()			,				input_names=UpperCamelCase__			,				output_names=UpperCamelCase__			,				dynamic_axes=UpperCamelCase__			,				do_constant_folding=UpperCamelCase__			,				use_external_data_format=UpperCamelCase__			,				enable_onnx_checker=UpperCamelCase__			,				opset_version=UpperCamelCase__			,				)
      else:
            export(
                UpperCamelCase__			,				UpperCamelCase__			,				f=output_path.as_posix()			,				input_names=UpperCamelCase__			,				output_names=UpperCamelCase__			,				dynamic_axes=UpperCamelCase__			,				do_constant_folding=UpperCamelCase__			,				opset_version=UpperCamelCase__			,				)




@torch.no_grad()
def   lowerCAmelCase		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						bool = False    )   ->      Dict:
      lowerCamelCase					:					Tuple        =	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowerCamelCase					:					str        =	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""    )
      else:
            lowerCamelCase					:					Dict        =	"""cpu"""
      lowerCamelCase					:					Optional[Any]        =	Path(UpperCamelCase__    )

      # VAE DECODER
      lowerCamelCase					:					List[Any]        =	AutoencoderKL.from_pretrained(model_path + """/vae"""    )
      lowerCamelCase					:					Optional[int]        =	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowerCamelCase					:					Dict        =	vae_decoder.decode
      onnx_export(
          UpperCamelCase__			,				model_args=(
              torch.randn(1			,				UpperCamelCase__			,				25			,				25    ).to(device=UpperCamelCase__			,				dtype=UpperCamelCase__    ),
              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=UpperCamelCase__			,				)
      del vae_decoder


if __name__ == "__main__":
    __lowerCamelCase      :List[str]	    =		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      :int	    =		parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('SD: Done: ONNX')








"""simple docstring"""


from typing import List, Optional, Union

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


__lowerCamelCase      :str	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													='''time_series_transformer'''
     snake_case__	:					List[Any]													={
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }

     def __init__(	self:						List[str]     ,		__a:						Optional[int] = None     ,		__a:						Optional[int] = None     ,		__a:						str = "student_t"     ,		__a:						str = "nll"     ,		__a:						int = 1     ,		__a:						List[int] = [1, 2, 3, 4, 5, 6, 7]     ,		__a:						Optional[Union[str, bool]] = "mean"     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						Optional[List[int]] = None     ,		__a:						Optional[List[int]] = None     ,		__a:						int = 32     ,		__a:						int = 32     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						bool = True     ,		__a:						str = "gelu"     ,		__a:						int = 64     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						int = 100     ,		__a:						float = 0.02     ,		__a:						Tuple=True     ,		**__a:						str     ,		)->    Any:
           # time series specific configuration
           lowerCamelCase					:					str        =	prediction_length
           lowerCamelCase					:					Optional[Any]        =	context_length or prediction_length
           lowerCamelCase					:					Tuple        =	distribution_output
           lowerCamelCase					:					Any        =	loss
           lowerCamelCase					:					List[Any]        =	input_size
           lowerCamelCase					:					int        =	num_time_features
           lowerCamelCase					:					Dict        =	lags_sequence
           lowerCamelCase					:					Optional[int]        =	scaling
           lowerCamelCase					:					int        =	num_dynamic_real_features
           lowerCamelCase					:					Tuple        =	num_static_real_features
           lowerCamelCase					:					Any        =	num_static_categorical_features
           if cardinality and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					int        =	cardinality
           else:
                 lowerCamelCase					:					Dict        =	[0]
           if embedding_dimension and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					str        =	embedding_dimension
           else:
                 lowerCamelCase					:					str        =	[min(50     ,		(cat + 1) // 2	) for cat in self.cardinality]
           lowerCamelCase					:					Any        =	num_parallel_samples

           # Transformer architecture configuration
           lowerCamelCase					:					Any        =	input_size * len(__a	) + self._number_of_features
           lowerCamelCase					:					List[str]        =	d_model
           lowerCamelCase					:					Tuple        =	encoder_attention_heads
           lowerCamelCase					:					Optional[int]        =	decoder_attention_heads
           lowerCamelCase					:					Union[str, Any]        =	encoder_ffn_dim
           lowerCamelCase					:					str        =	decoder_ffn_dim
           lowerCamelCase					:					str        =	encoder_layers
           lowerCamelCase					:					Any        =	decoder_layers

           lowerCamelCase					:					Optional[int]        =	dropout
           lowerCamelCase					:					List[str]        =	attention_dropout
           lowerCamelCase					:					Tuple        =	activation_dropout
           lowerCamelCase					:					Optional[int]        =	encoder_layerdrop
           lowerCamelCase					:					int        =	decoder_layerdrop

           lowerCamelCase					:					Optional[int]        =	activation_function
           lowerCamelCase					:					Optional[Any]        =	init_std

           lowerCamelCase					:					Optional[Any]        =	use_cache

           super().__init__(is_encoder_decoder=__a     ,		**__a	)







     @property
     def 		a__						(	self:						int	)->    int:
           return (
               sum(self.embedding_dimension	)
               + self.num_dynamic_real_features
               + self.num_time_features
               + self.num_static_real_features
               + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
           )




