





"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE__					=   version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						tuple							,						SCREAMING_SNAKE_CASE     :						Path							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						Optional[Any]=False							,						):







      '''simple docstring'''




      output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE							,						exist_ok=SCREAMING_SNAKE_CASE			)
      # 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(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						f=output_path.as_posix()							,						input_names=SCREAMING_SNAKE_CASE							,						output_names=SCREAMING_SNAKE_CASE							,						dynamic_axes=SCREAMING_SNAKE_CASE							,						do_constant_folding=SCREAMING_SNAKE_CASE							,						use_external_data_format=SCREAMING_SNAKE_CASE							,						enable_onnx_checker=SCREAMING_SNAKE_CASE							,						opset_version=SCREAMING_SNAKE_CASE							,						)
      else:
            export(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						f=output_path.as_posix()							,						input_names=SCREAMING_SNAKE_CASE							,						output_names=SCREAMING_SNAKE_CASE							,						dynamic_axes=SCREAMING_SNAKE_CASE							,						do_constant_folding=SCREAMING_SNAKE_CASE							,						opset_version=SCREAMING_SNAKE_CASE							,						)


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						bool = False			):







      '''simple docstring'''




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

      # VAE DECODER
      lowerCAmelCase  						=       AutoencoderKL.from_pretrained(model_path + """/vae"""			)
      lowerCAmelCase  						=       vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowerCAmelCase  						=       vae_decoder.decode
      onnx_export(
          SCREAMING_SNAKE_CASE							,						model_args=(
              torch.randn(1							,						SCREAMING_SNAKE_CASE							,						25							,						25			).to(device=SCREAMING_SNAKE_CASE							,						dtype=SCREAMING_SNAKE_CASE			),
              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=SCREAMING_SNAKE_CASE							,						)
      del vae_decoder


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   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")

       SCREAMING_SNAKE_CASE__					=   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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'longformer'





      def __init__(       self      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 1      ,						lowercase = 0      ,						lowercase = 2      ,						lowercase = 30_522      ,						lowercase = 768      ,						lowercase = 12      ,						lowercase = 12      ,						lowercase = 3_072      ,						lowercase = "gelu"      ,						lowercase = 0.1      ,						lowercase = 0.1      ,						lowercase = 512      ,						lowercase = 2      ,						lowercase = 0.02      ,						lowercase = 1e-12      ,						lowercase = False      ,						**lowercase      ,						)		->     Optional[int]:
            super().__init__(pad_token_id=lowercase      ,						**lowercase )

            lowerCAmelCase  						=       attention_window
            lowerCAmelCase  						=       sep_token_id
            lowerCAmelCase  						=       bos_token_id
            lowerCAmelCase  						=       eos_token_id
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       type_vocab_size
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       layer_norm_eps
            lowerCAmelCase  						=       onnx_export







class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase = "default"      ,						lowercase = None )		->     Tuple:
            super().__init__(lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       True





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                  lowerCAmelCase  						=       {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""global_attention_mask""", dynamic_axis),
                ] )





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            lowerCAmelCase  						=       super().outputs
            if self.task == "default":
                  lowerCAmelCase  						=       {0: """batch"""}
            return outputs





      @property
      def     _snake_case			(       self )		->     float:
            return 1e-4





      @property
      def     _snake_case			(       self )		->     int:
            # needs to be >= 14 to support tril operator
            return max(super().default_onnx_opset      ,						14 )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = -1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       super().generate_dummy_inputs(
                preprocessor=lowercase      ,						batch_size=lowercase      ,						seq_length=lowercase      ,						is_pair=lowercase      ,						framework=lowercase )
            import torch

            # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
            # makes the export fail randomly
            lowerCAmelCase  						=       torch.zeros_like(inputs["""input_ids"""] )
            # make every second token global
            lowerCAmelCase  						=       1

            return inputs






