"""simple docstring"""


import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


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







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=False						,					)				->      Optional[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 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase = False					)				->      Tuple:
     snake_case_					       =					torch.floataa if fpaa else torch.floataa
     if fpaa and torch.cuda.is_available():
          snake_case_					       =					'cuda'
     elif fpaa and not torch.cuda.is_available():
          raise ValueError('`float16` model export is only supported on GPUs with CUDA'					)
     else:
          snake_case_					       =					'cpu'
     snake_case_					       =					StableDiffusionPipeline.from_pretrained(UpperCAmelCase						,					torch_dtype=UpperCAmelCase					).to(UpperCAmelCase					)
     snake_case_					       =					Path(UpperCAmelCase					)

     # TEXT ENCODER
     snake_case_					       =					pipeline.text_encoder.config.max_position_embeddings
     snake_case_					       =					pipeline.text_encoder.config.hidden_size
     snake_case_					       =					pipeline.tokenizer(
         'A sample prompt'						,					padding='max_length'						,					max_length=pipeline.tokenizer.model_max_length						,					truncation=UpperCAmelCase						,					return_tensors='pt'						,					)
     onnx_export(
         pipeline.text_encoder						,					model_args=(text_input.input_ids.to(device=UpperCAmelCase						,					dtype=torch.intaa					))						,					output_path=output_path / 'text_encoder' / 'model.onnx'						,					ordered_input_names=['input_ids']						,					output_names=['last_hidden_state', 'pooler_output']						,					dynamic_axes={
             'input_ids': {0: 'batch', 1: 'sequence'},
         }						,					opset=UpperCAmelCase						,					)
     del pipeline.text_encoder

     # UNET
     snake_case_					       =					pipeline.unet.config.in_channels
     snake_case_					       =					pipeline.unet.config.sample_size
     snake_case_					       =					output_path / 'unet' / 'model.onnx'
     onnx_export(
         pipeline.unet						,					model_args=(
             torch.randn(2						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					).to(device=UpperCAmelCase						,					dtype=UpperCAmelCase					),
             torch.randn(2					).to(device=UpperCAmelCase						,					dtype=UpperCAmelCase					),
             torch.randn(2						,					UpperCAmelCase						,					UpperCAmelCase					).to(device=UpperCAmelCase						,					dtype=UpperCAmelCase					),
             False,
         )						,					output_path=UpperCAmelCase						,					ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict']						,					output_names=['out_sample']						,					dynamic_axes={
             'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
             'timestep': {0: 'batch'},
             'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
         }						,					opset=UpperCAmelCase						,					use_external_data_format=UpperCAmelCase						,					)
     snake_case_					       =					str(unet_path.absolute().as_posix()					)
     snake_case_					       =					os.path.dirname(UpperCAmelCase					)
     snake_case_					       =					onnx.load(UpperCAmelCase					)
     # clean up existing tensor files
     shutil.rmtree(UpperCAmelCase					)
     os.mkdir(UpperCAmelCase					)
     # collate external tensor files into one
     onnx.save_model(
         UpperCAmelCase						,					UpperCAmelCase						,					save_as_external_data=UpperCAmelCase						,					all_tensors_to_one_file=UpperCAmelCase						,					location='weights.pb'						,					convert_attribute=UpperCAmelCase						,					)
     del pipeline.unet

     # VAE ENCODER
     snake_case_					       =					pipeline.vae
     snake_case_					       =					vae_encoder.config.in_channels
     snake_case_					       =					vae_encoder.config.sample_size
     # need to get the raw tensor output (sample) from the encoder
     snake_case_					       =					lambda UpperCAmelCase						,					UpperCAmelCase					: vae_encoder.encode(UpperCAmelCase						,					UpperCAmelCase					)[0].sample()
     onnx_export(
         UpperCAmelCase						,					model_args=(
             torch.randn(1						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					).to(device=UpperCAmelCase						,					dtype=UpperCAmelCase					),
             False,
         )						,					output_path=output_path / 'vae_encoder' / 'model.onnx'						,					ordered_input_names=['sample', 'return_dict']						,					output_names=['latent_sample']						,					dynamic_axes={
             'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
         }						,					opset=UpperCAmelCase						,					)

     # VAE DECODER
     snake_case_					       =					pipeline.vae
     snake_case_					       =					vae_decoder.config.latent_channels
     snake_case_					       =					vae_decoder.config.out_channels
     # forward only through the decoder part
     snake_case_					       =					vae_encoder.decode
     onnx_export(
         UpperCAmelCase						,					model_args=(
             torch.randn(1						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					).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 pipeline.vae

     # SAFETY CHECKER
     if pipeline.safety_checker is not None:
          snake_case_					       =					pipeline.safety_checker
          snake_case_					       =					safety_checker.config.vision_config.num_channels
          snake_case_					       =					safety_checker.config.vision_config.image_size
          snake_case_					       =					safety_checker.forward_onnx
          onnx_export(
              pipeline.safety_checker						,					model_args=(
                  torch.randn(
                      1						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					).to(device=UpperCAmelCase						,					dtype=UpperCAmelCase					),
                  torch.randn(1						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					).to(device=UpperCAmelCase						,					dtype=UpperCAmelCase					),
              )						,					output_path=output_path / 'safety_checker' / 'model.onnx'						,					ordered_input_names=['clip_input', 'images']						,					output_names=['out_images', 'has_nsfw_concepts']						,					dynamic_axes={
                  'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
                  'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
              }						,					opset=UpperCAmelCase						,					)
          del pipeline.safety_checker
          snake_case_					       =					OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'					)
          snake_case_					       =					pipeline.feature_extractor
     else:
          snake_case_					       =					None
          snake_case_					       =					None

     snake_case_					       =					OnnxStableDiffusionPipeline(
         vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder'					)						,					vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder'					)						,					text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder'					)						,					tokenizer=pipeline.tokenizer						,					unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet'					)						,					scheduler=pipeline.scheduler						,					safety_checker=UpperCAmelCase						,					feature_extractor=UpperCAmelCase						,					requires_safety_checker=safety_checker is not None						,					)

     onnx_pipeline.save_pretrained(UpperCAmelCase					)
     print('ONNX pipeline saved to'						,					UpperCAmelCase					)

     del pipeline
     del onnx_pipeline
     snake_case_					       =					OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase						,					provider='CPUExecutionProvider'					)
     print('ONNX pipeline is loadable'					)


if __name__ == "__main__":
    __UpperCamelCase									=			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''')

    __UpperCamelCase									=			parser.parse_args()

    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)








import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.transformer(input_ids=lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					int										=(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
        return self.LinearTransformation(lowerCAmelCase__							), embs






