

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 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=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 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = False		):


       """simple docstring"""




       __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       	=							Path(__A		)

       # VAE DECODER
       __snake_case       	=							AutoencoderKL.from_pretrained(model_path + """/vae"""		)
       __snake_case       	=							vae_decoder.config.latent_channels
       # forward only through the decoder part
       __snake_case       	=							vae_decoder.decode
       onnx_export(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							tempfile.mkdtemp()

              # fmt: off
              __snake_case       	=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
              # fmt: on
              __snake_case       	=							dict(zip(_UpperCamelCase      , range(len(_UpperCamelCase   )   )   )   )
              __snake_case       	=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
              __snake_case       	=							{"""unk_token""": """<unk>"""}

              __snake_case       	=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["""vocab_file"""]   )
              __snake_case       	=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["""merges_file"""]   )
              with open(self.vocab_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     fp.write(json.dumps(_UpperCamelCase   ) + """\n"""   )
              with open(self.merges_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     fp.write("""\n""".join(_UpperCamelCase   )   )

              __snake_case       	=							{
                  """do_resize""": True,
                  """size""": 20,
                  """do_center_crop""": True,
                  """crop_size""": 18,
                  """do_normalize""": True,
                  """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
                  """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
              }
              __snake_case       	=							os.path.join(self.tmpdirname      , _UpperCamelCase   )
              with open(self.image_processor_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     json.dump(_UpperCamelCase      , _UpperCamelCase   )


       def 			a		(		self      , **_UpperCamelCase   )      ->							Optional[int]:






              """simple docstring"""


              return CLIPTokenizer.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


       def 			a		(		self      , **_UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              return CLIPTokenizerFast.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


       def 			a		(		self      , **_UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              return CLIPImageProcessor.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              shutil.rmtree(self.tmpdirname   )


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							[np.random.randint(2_55      , size=(3, 30, 4_00)      , dtype=np.uinta   )]

              __snake_case       	=							[Image.fromarray(np.moveaxis(_UpperCamelCase      , 0      , -1   )   ) for x in image_inputs]

              return image_inputs


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.get_tokenizer()
              __snake_case       	=							self.get_rust_tokenizer()
              __snake_case       	=							self.get_image_processor()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )
              processor_slow.save_pretrained(self.tmpdirname   )
              __snake_case       	=							CLIPProcessor.from_pretrained(self.tmpdirname      , use_fast=_UpperCamelCase   )

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )
              processor_fast.save_pretrained(self.tmpdirname   )
              __snake_case       	=							CLIPProcessor.from_pretrained(self.tmpdirname   )

              self.assertEqual(processor_slow.tokenizer.get_vocab()      , tokenizer_slow.get_vocab()   )
              self.assertEqual(processor_fast.tokenizer.get_vocab()      , tokenizer_fast.get_vocab()   )
              self.assertEqual(tokenizer_slow.get_vocab()      , tokenizer_fast.get_vocab()   )
              self.assertIsInstance(processor_slow.tokenizer      , _UpperCamelCase   )
              self.assertIsInstance(processor_fast.tokenizer      , _UpperCamelCase   )

              self.assertEqual(processor_slow.image_processor.to_json_string()      , image_processor.to_json_string()   )
              self.assertEqual(processor_fast.image_processor.to_json_string()      , image_processor.to_json_string()   )
              self.assertIsInstance(processor_slow.image_processor      , _UpperCamelCase   )
              self.assertIsInstance(processor_fast.image_processor      , _UpperCamelCase   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							CLIPProcessor(tokenizer=self.get_tokenizer()      , image_processor=self.get_image_processor()   )
              processor.save_pretrained(self.tmpdirname   )

              __snake_case       	=							self.get_tokenizer(bos_token="""(BOS)"""      , eos_token="""(EOS)"""   )
              __snake_case       	=							self.get_image_processor(do_normalize=_UpperCamelCase      , padding_value=1.0   )

              __snake_case       	=							CLIPProcessor.from_pretrained(
                  self.tmpdirname      , bos_token="""(BOS)"""      , eos_token="""(EOS)"""      , do_normalize=_UpperCamelCase      , padding_value=1.0   )

              self.assertEqual(processor.tokenizer.get_vocab()      , tokenizer_add_kwargs.get_vocab()   )
              self.assertIsInstance(processor.tokenizer      , _UpperCamelCase   )

              self.assertEqual(processor.image_processor.to_json_string()      , image_processor_add_kwargs.to_json_string()   )
              self.assertIsInstance(processor.image_processor      , _UpperCamelCase   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							image_processor(_UpperCamelCase      , return_tensors="""np"""   )
              __snake_case       	=							processor(images=_UpperCamelCase      , return_tensors="""np"""   )

              for key in input_image_proc.keys():
                     self.assertAlmostEqual(input_image_proc[key].sum()      , input_processor[key].sum()      , delta=1E-2   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""

              __snake_case       	=							processor(text=_UpperCamelCase   )

              __snake_case       	=							tokenizer(_UpperCamelCase   )

              for key in encoded_tok.keys():
                     self.assertListEqual(encoded_tok[key]      , encoded_processor[key]   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""
              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							processor(text=_UpperCamelCase      , images=_UpperCamelCase   )

              self.assertListEqual(list(inputs.keys()   )      , ["""input_ids""", """attention_mask""", """pixel_values"""]   )

              # test if it raises when no input is passed
              with pytest.raises(_UpperCamelCase   ):
                     processor()


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              __snake_case       	=							processor.batch_decode(_UpperCamelCase   )
              __snake_case       	=							tokenizer.batch_decode(_UpperCamelCase   )

              self.assertListEqual(_UpperCamelCase      , _UpperCamelCase   )






       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""
              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							processor(text=_UpperCamelCase      , images=_UpperCamelCase   )

              self.assertListEqual(list(inputs.keys()   )      , processor.model_input_names   )

