





"""simple docstring"""






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False,)		->       int:






       """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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = False							)		->       Dict:






       """simple docstring"""




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

       # VAE DECODER
       _UpperCAmelCase     =     AutoencoderKL.from_pretrained(model_path + '/vae'							)
       _UpperCAmelCase     =     vae_decoder.config.latent_channels
       # forward only through the decoder part
       _UpperCAmelCase     =     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__":
    lowerCAmelCase_			=							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_			=							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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """trocr"""
       lowerCAmelCase__						 =  ["""past_key_values"""]
       lowerCAmelCase__						 =  {
           """num_attention_heads""": """decoder_attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """decoder_layers""",
       }






       def __init__(   self    ,  a__=5_02_65    ,  a__=10_24    ,  a__=12    ,  a__=16    ,  a__=40_96    ,  a__="gelu"    ,  a__=5_12    ,  a__=0.1    ,  a__=0.0    ,  a__=0.0    ,  a__=2    ,  a__=0.02    ,  a__=0.0    ,  a__=True    ,  a__=False    ,  a__=True    ,  a__=True    ,  a__=1    ,  a__=0    ,  a__=2    ,  **a__    ,  ):
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     d_model
              _UpperCAmelCase     =     decoder_layers
              _UpperCAmelCase     =     decoder_attention_heads
              _UpperCAmelCase     =     decoder_ffn_dim
              _UpperCAmelCase     =     activation_function
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     dropout
              _UpperCAmelCase     =     attention_dropout
              _UpperCAmelCase     =     activation_dropout
              _UpperCAmelCase     =     init_std
              _UpperCAmelCase     =     decoder_layerdrop
              _UpperCAmelCase     =     use_cache
              _UpperCAmelCase     =     scale_embedding
              _UpperCAmelCase     =     use_learned_position_embeddings
              _UpperCAmelCase     =     layernorm_embedding

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






