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


lowerCAmelCase			:							Optional[Any]     =logging.get_logger(__name__)

lowerCAmelCase			:							str     ={
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}




class 	__snake_case	( lowerCAmelCase__		):





      '''simple docstring'''






      _snake_case    		=       "vivit"



      def __init__(					self     :				int    ,  _UpperCamelCase     :				Tuple=224    ,  _UpperCamelCase     :				Optional[int]=32    ,  _UpperCamelCase     :				List[Any]=[2, 16, 16]    ,  _UpperCamelCase     :				Any=3    ,  _UpperCamelCase     :				List[Any]=768    ,  _UpperCamelCase     :				List[Any]=12    ,  _UpperCamelCase     :				Tuple=12    ,  _UpperCamelCase     :				Tuple=3072    ,  _UpperCamelCase     :				List[str]="gelu_fast"    ,  _UpperCamelCase     :				Optional[Any]=0.0    ,  _UpperCamelCase     :				int=0.0    ,  _UpperCamelCase     :				List[Any]=0.0_2    ,  _UpperCamelCase     :				Tuple=1E-0_6    ,  _UpperCamelCase     :				Any=True    ,  **_UpperCamelCase     :				Dict    ,  )				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						hidden_size
          _lowerCamelCase     :		Any 						=						num_hidden_layers
          _lowerCamelCase     :		Tuple 						=						num_attention_heads
          _lowerCamelCase     :		Dict 						=						intermediate_size
          _lowerCamelCase     :		List[Any] 						=						hidden_act
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		List[str] 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Any 						=						initializer_range
          _lowerCamelCase     :		Optional[Any] 						=						layer_norm_eps

          _lowerCamelCase     :		Optional[int] 						=						image_size
          _lowerCamelCase     :		Union[str, Any] 						=						num_frames
          _lowerCamelCase     :		List[Any] 						=						tubelet_size
          _lowerCamelCase     :		Optional[Any] 						=						num_channels
          _lowerCamelCase     :		Optional[int] 						=						qkv_bias

          super().__init__(**_lowerCamelCase)





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 						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'''
    _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     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						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			:							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")

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




