





'''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


__lowerCAmelCase							:	Dict			=version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def 						UpperCamelCase				(							_lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					tuple						,       _lowerCamelCase					:					Path						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					List[Any]=False						,       ):
    output_path.parent.mkdir(parents=_lowerCamelCase						,       exist_ok=_lowerCamelCase       )
    # 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(
            _lowerCamelCase						,       _lowerCamelCase						,       f=output_path.as_posix()						,       input_names=_lowerCamelCase						,       output_names=_lowerCamelCase						,       dynamic_axes=_lowerCamelCase						,       do_constant_folding=_lowerCamelCase						,       use_external_data_format=_lowerCamelCase						,       enable_onnx_checker=_lowerCamelCase						,       opset_version=_lowerCamelCase						,       )
    else:
        export(
            _lowerCamelCase						,       _lowerCamelCase						,       f=output_path.as_posix()						,       input_names=_lowerCamelCase						,       output_names=_lowerCamelCase						,       dynamic_axes=_lowerCamelCase						,       do_constant_folding=_lowerCamelCase						,       opset_version=_lowerCamelCase						,       )





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					bool = False       ):
    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__				=			StableDiffusionPipeline.from_pretrained(_lowerCamelCase						,       torch_dtype=_lowerCamelCase       ).to(_lowerCamelCase       )
    A__				=			Path(_lowerCamelCase       )

    # TEXT ENCODER
    A__				=			pipeline.text_encoder.config.max_position_embeddings
    A__				=			pipeline.text_encoder.config.hidden_size
    A__				=			pipeline.tokenizer(
        "A sample prompt"						,       padding="max_length"						,       max_length=pipeline.tokenizer.model_max_length						,       truncation=_lowerCamelCase						,       return_tensors="pt"						,       )
    onnx_export(
        pipeline.text_encoder						,       model_args=(text_input.input_ids.to(device=_lowerCamelCase						,       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=_lowerCamelCase						,       )
    del pipeline.text_encoder

    # UNET
    A__				=			pipeline.unet.config.in_channels
    A__				=			pipeline.unet.config.sample_size
    A__				=			output_path / "unet" / "model.onnx"
    onnx_export(
        pipeline.unet						,       model_args=(
            torch.randn(2						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       ).to(device=_lowerCamelCase						,       dtype=_lowerCamelCase       ),
            torch.randn(2       ).to(device=_lowerCamelCase						,       dtype=_lowerCamelCase       ),
            torch.randn(2						,       _lowerCamelCase						,       _lowerCamelCase       ).to(device=_lowerCamelCase						,       dtype=_lowerCamelCase       ),
            False,
        )						,       output_path=_lowerCamelCase						,       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=_lowerCamelCase						,       use_external_data_format=_lowerCamelCase						,       )
    A__				=			str(unet_path.absolute().as_posix()       )
    A__				=			os.path.dirname(_lowerCamelCase       )
    A__				=			onnx.load(_lowerCamelCase       )
    # clean up existing tensor files
    shutil.rmtree(_lowerCamelCase       )
    os.mkdir(_lowerCamelCase       )
    # collate external tensor files into one
    onnx.save_model(
        _lowerCamelCase						,       _lowerCamelCase						,       save_as_external_data=_lowerCamelCase						,       all_tensors_to_one_file=_lowerCamelCase						,       location="weights.pb"						,       convert_attribute=_lowerCamelCase						,       )
    del pipeline.unet

    # VAE ENCODER
    A__				=			pipeline.vae
    A__				=			vae_encoder.config.in_channels
    A__				=			vae_encoder.config.sample_size
    # need to get the raw tensor output (sample) from the encoder
    A__				=			lambda _lowerCamelCase						,       _lowerCamelCase       : vae_encoder.encode(_lowerCamelCase						,       _lowerCamelCase       )[0].sample()
    onnx_export(
        _lowerCamelCase						,       model_args=(
            torch.randn(1						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       ).to(device=_lowerCamelCase						,       dtype=_lowerCamelCase       ),
            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=_lowerCamelCase						,       )

    # VAE DECODER
    A__				=			pipeline.vae
    A__				=			vae_decoder.config.latent_channels
    A__				=			vae_decoder.config.out_channels
    # forward only through the decoder part
    A__				=			vae_encoder.decode
    onnx_export(
        _lowerCamelCase						,       model_args=(
            torch.randn(1						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       ).to(device=_lowerCamelCase						,       dtype=_lowerCamelCase       ),
            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=_lowerCamelCase						,       )
    del pipeline.vae

    # SAFETY CHECKER
    if pipeline.safety_checker is not None:
        A__				=			pipeline.safety_checker
        A__				=			safety_checker.config.vision_config.num_channels
        A__				=			safety_checker.config.vision_config.image_size
        A__				=			safety_checker.forward_onnx
        onnx_export(
            pipeline.safety_checker						,       model_args=(
                torch.randn(
                    1						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       ).to(device=_lowerCamelCase						,       dtype=_lowerCamelCase       ),
                torch.randn(1						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       ).to(device=_lowerCamelCase						,       dtype=_lowerCamelCase       ),
            )						,       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=_lowerCamelCase						,       )
        del pipeline.safety_checker
        A__				=			OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"       )
        A__				=			pipeline.feature_extractor
    else:
        A__				=			None
        A__				=			None

    A__				=			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=_lowerCamelCase						,       feature_extractor=_lowerCamelCase						,       requires_safety_checker=safety_checker is not None						,       )

    onnx_pipeline.save_pretrained(_lowerCamelCase       )
    print("ONNX pipeline saved to"						,       _lowerCamelCase       )

    del pipeline
    del onnx_pipeline
    A__				=			OnnxStableDiffusionPipeline.from_pretrained(_lowerCamelCase						,       provider="CPUExecutionProvider"       )
    print("ONNX pipeline is loadable"       )


if __name__ == "__main__":
  __lowerCAmelCase							:	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							:	Any			=parser.parse_args()

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










'''simple docstring'''

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    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     A__				=			tempfile.mkdtemp()

     # fmt: off
     A__				=			["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
     A__				=			dict(zip(lowercase_						,       range(len(lowercase_							)							)							)							)
     A__				=			["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
     A__				=			{"unk_token": "<unk>"}

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

     A__				=			{
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
         "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
     }
     A__				=			os.path.join(self.tmpdirname						,       lowercase_							)
     with open(self.image_processor_file						,       "w"						,       encoding="utf-8"							) as fp:
         json.dump(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Union[str, Any]							)->							Tuple:
     return CLIPTokenizer.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       **lowercase_      :Tuple							)->							Dict:
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Dict						,       **lowercase_      :Union[str, Any]							)->							Any:
     return CLIPImageProcessor.from_pretrained(self.tmpdirname						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							int:
     shutil.rmtree(self.tmpdirname							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Optional[int]:
     A__				=			[np.random.randint(2_55						,       size=(3, 30, 4_00)						,       dtype=np.uinta							)]

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

     return image_inputs
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     A__				=			self.get_tokenizer()
     A__				=			self.get_rust_tokenizer()
     A__				=			self.get_image_processor()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_slow.save_pretrained(self.tmpdirname							)
     A__				=			CLIPProcessor.from_pretrained(self.tmpdirname						,       use_fast=lowercase_							)

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)
     processor_fast.save_pretrained(self.tmpdirname							)
     A__				=			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						,       lowercase_							)
     self.assertIsInstance(processor_fast.tokenizer						,       lowercase_							)

     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						,       lowercase_							)
     self.assertIsInstance(processor_fast.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Optional[int]:
     A__				=			CLIPProcessor(tokenizer=self.get_tokenizer()						,       image_processor=self.get_image_processor()							)
     processor.save_pretrained(self.tmpdirname							)

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

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

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

     self.assertEqual(processor.image_processor.to_json_string()						,       image_processor_add_kwargs.to_json_string()							)
     self.assertIsInstance(processor.image_processor						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			self.prepare_image_inputs()

     A__				=			image_processor(lowercase_						,       return_tensors="np"							)
     A__				=			processor(images=lowercase_						,       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      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

     A__				=			"lower newer"

     A__				=			processor(text=lowercase_							)

     A__				=			tokenizer(lowercase_							)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]						,       encoded_processor[key]							)
 def      UpperCAmelCase_							(				self      :str							)->							Any:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

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

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

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

     # test if it raises when no input is passed
     with pytest.raises(lowercase_							):
         processor()
 def      UpperCAmelCase_							(				self      :Tuple							)->							Tuple:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

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

     A__				=			processor.batch_decode(lowercase_							)
     A__				=			tokenizer.batch_decode(lowercase_							)

     self.assertListEqual(lowercase_						,       lowercase_							)



 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     A__				=			self.get_image_processor()
     A__				=			self.get_tokenizer()

     A__				=			CLIPProcessor(tokenizer=lowercase_						,       image_processor=lowercase_							)

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

     A__				=			processor(text=lowercase_						,       images=lowercase_							)

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



