



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


SCREAMING_SNAKE_CASE_:Optional[int]					=						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase=False				,					)	->  List[Any]:



    """simple docstring"""






    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				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase = False		)	->  Optional[Any]:



    """simple docstring"""






    A			:	Dict										=       torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A			:	int										=       'cuda'
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
    else:
        A			:	List[str]										=       'cpu'
    A			:	Dict										=       StableDiffusionPipeline.from_pretrained(lowerCamelCase_				,					torch_dtype=lowerCamelCase_		).to(lowerCamelCase_		)
    A			:	Any										=       Path(lowerCamelCase_		)

    # TEXT ENCODER
    A			:	List[str]										=       pipeline.text_encoder.config.max_position_embeddings
    A			:	str										=       pipeline.text_encoder.config.hidden_size
    A			:	List[Any]										=       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			:	Any										=       pipeline.unet.config.in_channels
    A			:	Tuple										=       pipeline.unet.config.sample_size
    A			:	Optional[int]										=       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			:	Tuple										=       str(unet_path.absolute().as_posix()		)
    A			:	Optional[int]										=       os.path.dirname(lowerCamelCase_		)
    A			:	Any										=       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			:	Any										=       pipeline.vae
    A			:	Tuple										=       vae_encoder.config.in_channels
    A			:	Tuple										=       vae_encoder.config.sample_size
    # need to get the raw tensor output (sample) from the encoder
    A			:	str										=       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			:	List[Any]										=       pipeline.vae
    A			:	int										=       vae_decoder.config.latent_channels
    A			:	str										=       vae_decoder.config.out_channels
    # forward only through the decoder part
    A			:	List[Any]										=       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			:	List[Any]										=       pipeline.safety_checker
        A			:	Optional[int]										=       safety_checker.config.vision_config.num_channels
        A			:	str										=       safety_checker.config.vision_config.image_size
        A			:	List[str]										=       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			:	str										=       OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""		)
        A			:	int										=       pipeline.feature_extractor
    else:
        A			:	Any										=       None
        A			:	Tuple										=       None

    A			:	Optional[Any]										=       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			:	Optional[Any]										=       OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase_				,					provider="""CPUExecutionProvider"""		)
    print("""ONNX pipeline is loadable"""		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:str					=						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""")

     SCREAMING_SNAKE_CASE_:Optional[Any]					=						parser.parse_args()

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




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 OwlViTImageProcessor, OwlViTProcessor






@require_vision
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	str										=       tempfile.mkdtemp()

        # fmt: off
        A			:	List[Any]										=       ["""""", """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			:	Optional[int]										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[Any]										=       ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
        A			:	Union[str, Any]										=       {"""unk_token""": """<unk>"""}

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

        A			:	int										=       {
            """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],
        }
        A			:	List[Any]										=       os.path.join(self.tmpdirname,		lowerCamelCase__		)
        with open(self.image_processor_file,		"""w""",		encoding="""utf-8"""		) as fp:
            json.dump(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizer.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return OwlViTImageProcessor.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        shutil.rmtree(self.tmpdirname		)






    def       _lowerCAmelCase (	self		):
        A			:	str										=       [np.random.randint(255,		size=(3, 30, 400),		dtype=np.uinta		)]

        A			:	Optional[int]										=       [Image.fromarray(np.moveaxis(lowerCamelCase__,		0,		-1		)		) for x in image_inputs]

        return image_inputs






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       self.get_tokenizer()
        A			:	Optional[Any]										=       self.get_rust_tokenizer()
        A			:	Optional[int]										=       self.get_image_processor()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_slow.save_pretrained(self.tmpdirname		)
        A			:	Optional[int]										=       OwlViTProcessor.from_pretrained(self.tmpdirname,		use_fast=lowerCamelCase__		)

        A			:	Tuple										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_fast.save_pretrained(self.tmpdirname		)
        A			:	str										=       OwlViTProcessor.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,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.tokenizer,		lowerCamelCase__		)

        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,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.image_processor,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       OwlViTProcessor(tokenizer=self.get_tokenizer(),		image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

        A			:	Optional[int]										=       self.get_tokenizer(bos_token="""(BOS)""",		eos_token="""(EOS)"""		)
        A			:	Tuple										=       self.get_image_processor(do_normalize=lowerCamelCase__		)

        A			:	Optional[Any]										=       OwlViTProcessor.from_pretrained(
            self.tmpdirname,		bos_token="""(BOS)""",		eos_token="""(EOS)""",		do_normalize=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.get_image_processor()
        A			:	str										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	Optional[Any]										=       image_processor(lowerCamelCase__,		return_tensors="""np"""		)
        A			:	Any										=       processor(images=lowerCamelCase__,		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       _lowerCAmelCase (	self		):
        A			:	int										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	Optional[int]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       """lower newer"""

        A			:	Union[str, Any]										=       processor(text=lowerCamelCase__,		return_tensors="""np"""		)

        A			:	str										=       tokenizer(lowerCamelCase__,		return_tensors="""np"""		)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key][0].tolist(),		encoded_processor[key][0].tolist()		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	int										=       self.get_tokenizer()

        A			:	str										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	List[str]										=       """lower newer"""
        A			:	Any										=       self.prepare_image_inputs()

        A			:	Tuple										=       processor(text=lowerCamelCase__,		images=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	str										=       """google/owlvit-base-patch32"""
        A			:	Dict										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[int]										=       processor(text=lowerCamelCase__		)

        A			:	Any										=       16
        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(2, seq_length)		)

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






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       """google/owlvit-base-patch32"""
        A			:	Any										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	int										=       [["""cat""", """nasa badge"""], ["""person"""]]
        A			:	List[Any]										=       processor(text=lowerCamelCase__		)

        A			:	Dict										=       16
        A			:	List[str]										=       len(lowerCamelCase__		)
        A			:	List[str]										=       max([len(lowerCamelCase__		) for texts in input_texts]		)

        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(batch_size * num_max_text_queries, seq_length)		)

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






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       """google/owlvit-base-patch32"""
        A			:	int										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[Any]										=       processor(text=lowerCamelCase__		)

        A			:	int										=       16
        A			:	Optional[Any]										=       inputs["""input_ids"""]
        A			:	Optional[int]										=       [
            [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ]

        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(2, seq_length)		)
        self.assertListEqual(list(input_ids[0]		),		predicted_ids[0]		)
        self.assertListEqual(list(input_ids[1]		),		predicted_ids[1]		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	Dict										=       self.get_tokenizer()

        A			:	Optional[Any]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       self.prepare_image_inputs()
        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	List[str]										=       processor(images=lowerCamelCase__,		query_images=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A			:	Optional[Any]										=       processor.batch_decode(lowerCamelCase__		)
        A			:	Union[str, Any]										=       tokenizer.batch_decode(lowerCamelCase__		)

        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)


