'''simple docstring'''






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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






def    __UpperCAmelCase			(  a_:       str,					a_:       str,					a_:       Any,					a_:       Dict,					a_:       Optional[Any],					a_:       List[str],					a_:       Optional[int],					a_:       List[str]=False,					):
   output_path.parent.mkdir(parents=_lowerCAmelCase,					exist_ok=_lowerCAmelCase							)
   # 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(
          _lowerCAmelCase,					_lowerCAmelCase,					f=output_path.as_posix(),					input_names=_lowerCAmelCase,					output_names=_lowerCAmelCase,					dynamic_axes=_lowerCAmelCase,					do_constant_folding=_lowerCAmelCase,					use_external_data_format=_lowerCAmelCase,					enable_onnx_checker=_lowerCAmelCase,					opset_version=_lowerCAmelCase,					)
   else:
      export(
          _lowerCAmelCase,					_lowerCAmelCase,					f=output_path.as_posix(),					input_names=_lowerCAmelCase,					output_names=_lowerCAmelCase,					dynamic_axes=_lowerCAmelCase,					do_constant_folding=_lowerCAmelCase,					opset_version=_lowerCAmelCase,					)






@torch.no_grad()
def    __UpperCAmelCase			(  a_:       Union[str, Any],					a_:       Optional[int],					a_:       int,					a_:       str = False							):
   _UpperCAmelCase							:	Optional[Any]							=   torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      _UpperCAmelCase							:	str							=   "cuda"
   elif fpaa and not torch.cuda.is_available():
      raise ValueError("`float16` model export is only supported on GPUs with CUDA"							)
   else:
      _UpperCAmelCase							:	Optional[Any]							=   "cpu"
   _UpperCAmelCase							:	Dict							=   Path(_lowerCAmelCase							)

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


if __name__ == "__main__":
     __a										=					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										=					parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print('SD: Done: ONNX')






import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCamelCase       :     Union[str, Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     Any   =     {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Dict						    =   'bart'
      _UpperCAmelCase	:str						    =   ['past_key_values']
      _UpperCAmelCase	:Any						    =   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__( self       ,							A_=5_0265       ,							A_=1024       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=0.0       ,							A_=0.0       ,							A_="gelu"       ,							A_=1024       ,							A_=0.1       ,							A_=0.0       ,							A_=0.0       ,							A_=0.02       ,							A_=0.0       ,							A_=False       ,							A_=True       ,							A_=3       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_=True       ,							A_=2       ,							A_=2       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   vocab_size
       UpperCamelCase	:					List[Any]       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   d_model
       UpperCamelCase	:					Optional[Any]       	=   encoder_ffn_dim
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   encoder_attention_heads
       UpperCamelCase	:					Optional[int]       	=   decoder_ffn_dim
       UpperCamelCase	:					List[str]       	=   decoder_layers
       UpperCamelCase	:					Optional[int]       	=   decoder_attention_heads
       UpperCamelCase	:					int       	=   dropout
       UpperCamelCase	:					int       	=   attention_dropout
       UpperCamelCase	:					Tuple       	=   activation_dropout
       UpperCamelCase	:					Tuple       	=   activation_function
       UpperCamelCase	:					int       	=   init_std
       UpperCamelCase	:					List[Any]       	=   encoder_layerdrop
       UpperCamelCase	:					List[str]       	=   decoder_layerdrop
       UpperCamelCase	:					Dict       	=   classifier_dropout
       UpperCamelCase	:					Optional[int]       	=   use_cache
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           num_labels=A_       ,							pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							is_encoder_decoder=A_       ,							decoder_start_token_id=A_       ,							forced_eos_token_id=A_       ,							**A_       ,							)

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"       ,							A_   ):
        UpperCamelCase	:					int       	=   self.bos_token_id
        warnings.warn(
            F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
            "The config can simply be saved and uploaded again to be fixed."   )



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )

        if self.use_past:
         UpperCamelCase	:					List[str]       	=   {0: "batch"}
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "decoder_sequence"}
         UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
         self.fill_with_past_key_values_(A_       ,							direction="inputs"   )
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        UpperCamelCase	:					Any       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					Optional[Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
       else:
        UpperCamelCase	:					Optional[Any]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
            ]   )

       return common_inputs





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Tuple       	=   super().outputs
       else:
        UpperCamelCase	:					Dict       	=   super(A_       ,							self   ).outputs
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					int       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					int       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Tuple       	=   {0: "batch", 2: "past_sequence + sequence"}
       return common_outputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       # Generate decoder inputs
       UpperCamelCase	:					List[Any]       	=   seq_length if not self.use_past else 1
       UpperCamelCase	:					Tuple       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )
       UpperCamelCase	:					Optional[int]       	=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase	:					List[Any]       	=   dict(**A_       ,							**A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   common_inputs["input_ids"].shape
        UpperCamelCase	:					List[Any]       	=   common_inputs["decoder_input_ids"].shape[1]
        UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   self.num_attention_heads
        UpperCamelCase	:					int       	=   (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        UpperCamelCase	:					List[Any]       	=   decoder_seq_length + 3
        UpperCamelCase	:					str       	=   (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["decoder_attention_mask"], torch.ones(A_       ,							A_   )]       ,							dim=1   )

        UpperCamelCase	:					int       	=   []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   self.num_layers
        UpperCamelCase	:					Any       	=   min(A_       ,							A_   )
        UpperCamelCase	:					List[str]       	=   max(A_       ,							A_   ) - min_num_layers
        UpperCamelCase	:					Dict       	=   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(A_   ):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
             )   )
        # TODO: test this.
        UpperCamelCase	:					Optional[Any]       	=   encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(A_       ,							A_   ):
         common_inputs["past_key_values"].append((torch.zeros(A_   ), torch.zeros(A_   ))   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   common_inputs["input_ids"].shape
        # Not using the same length for past_key_values
        UpperCamelCase	:					Optional[Any]       	=   seqlen + 2
        UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.num_layers
        UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_attention_heads
        UpperCamelCase	:					str       	=   (
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        UpperCamelCase	:					Optional[Any]       	=   common_inputs["attention_mask"].dtype
        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["attention_mask"], torch.ones(A_       ,							A_       ,							dtype=A_   )]       ,							dim=1   )
        UpperCamelCase	:					Optional[Any]       	=   [
            (torch.zeros(A_   ), torch.zeros(A_   )) for _ in range(A_   )
        ]
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_batch       ,							num_token_to_add=0   )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase	:					Union[str, Any]       	=   tokenizer.num_special_tokens_to_add(A_   )
       UpperCamelCase	:					int       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=A_   )

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase	:					int       	=   [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
       UpperCamelCase	:					Dict       	=   dict(tokenizer(A_       ,							return_tensors=A_   )   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       elif self.task == "causal-lm":
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_causal_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )
       else:
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[Any]       	=   super()._flatten_past_key_values_(A_       ,							A_       ,							A_       ,							A_   )
       else:
        UpperCamelCase	:					Optional[Any]       	=   super(A_       ,							self   )._flatten_past_key_values_(
            A_       ,							A_       ,							A_       ,							A_   )
