






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__				   =		version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_a    (      a :Optional[int]	,							a :tuple	,							a :Path	,							a :int	,							a :Optional[Any]	,							a :Any	,							a :Union[str, Any]	,							a :int=False	,							)							->			Union[str, Any]:
 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 :str	,							a :str	,							a :int	,							a :bool = False )							->			List[Any]:
 a		  =      torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  a		  =      '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
 else:
  a		  =      '''cpu'''
 a		  =      Path(a )

 # VAE DECODER
 a		  =      AutoencoderKL.from_pretrained(model_path + '''/vae''' )
 a		  =      vae_decoder.config.latent_channels
 # forward only through the decoder part
 a		  =      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__":
     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()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")










import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  KandinskyVaaPriorPipeline
      __snake_case       =  ['''prompt''']
      __snake_case       =  ['''prompt''', '''negative_prompt''']
      __snake_case       =  [
          '''num_images_per_prompt''',
          '''generator''',
          '''num_inference_steps''',
          '''latents''',
          '''negative_prompt''',
          '''guidance_scale''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case       =  False







      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						int  )    ->List[str]:




       """simple docstring"""


       return self.time_input_dim







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def       __lowerCAmelCase					(      self      :						Any  )    ->List[Any]:




       """simple docstring"""


       return 100







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->str:




       """simple docstring"""


       a		  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
       return tokenizer







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      CLIPTextConfig(
           bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=self.text_embedder_hidden_size ,					projection_dim=self.text_embedder_hidden_size ,					intermediate_size=37 ,					layer_norm_eps=1e-0_5 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_000 ,					)
       return CLIPTextModelWithProjection(__UpperCAmelCase  )







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       torch.manual_seed(0  )

       a		  =      {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       a		  =      PriorTransformer(**__UpperCAmelCase  )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       a		  =      nn.Parameter(torch.ones(model.clip_std.shape  )  )
       return model







      @property
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size ,					image_size=224 ,					projection_dim=self.text_embedder_hidden_size ,					intermediate_size=37 ,					num_attention_heads=4 ,					num_channels=3 ,					num_hidden_layers=5 ,					patch_size=14 ,					)

       a		  =      CLIPVisionModelWithProjection(__UpperCAmelCase  )
       return model







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       a		  =      CLIPImageProcessor(
           crop_size=224 ,					do_center_crop=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					do_resize=__UpperCAmelCase ,					image_mean=[0.48145466, 0.4578275, 0.40821073] ,					image_std=[0.26862954, 0.26130258, 0.27577711] ,					resample=3 ,					size=224 ,					)

       return image_processor







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.dummy_prior
       a		  =      self.dummy_image_encoder
       a		  =      self.dummy_text_encoder
       a		  =      self.dummy_tokenizer
       a		  =      self.dummy_image_processor

       a		  =      UnCLIPScheduler(
           variance_type='''fixed_small_log''' ,					prediction_type='''sample''' ,					num_train_timesteps=1_000 ,					clip_sample=__UpperCAmelCase ,					clip_sample_range=10.0 ,					)

       a		  =      {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						str=0  )    ->int:




       """simple docstring"""


       if str(__UpperCAmelCase  ).startswith('''mps'''  ):
        a		  =      torch.manual_seed(__UpperCAmelCase  )
       else:
        a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
       a		  =      {
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs







      def       __lowerCAmelCase					(      self      :						str  )    ->Tuple:




       """simple docstring"""


       a		  =      '''cpu'''

       a		  =      self.get_dummy_components()

       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       a		  =      pipe.to(__UpperCAmelCase  )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
       a		  =      output.image_embeds

       a		  =      pipe(
           **self.get_dummy_inputs(__UpperCAmelCase  ) ,					return_dict=__UpperCAmelCase ,					)[0]

       a		  =      image[0, -10:]
       a		  =      image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       a		  =      np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2







      @skip_mps
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      True
       a		  =      False

       self._test_inference_batch_single_identical(
           test_max_difference=__UpperCAmelCase ,					relax_max_difference=__UpperCAmelCase ,					test_mean_pixel_difference=__UpperCAmelCase ,					)







      @skip_mps
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      False

       self._test_attention_slicing_forward_pass(
           test_max_difference=__UpperCAmelCase ,					test_mean_pixel_difference=__UpperCAmelCase ,					)


