



"""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


A:   Tuple								=		version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

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

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		str			,		UpperCamelCase						:		int			,		UpperCamelCase						:		bool = False						):
 UpperCAmelCase	:					Tuple	=	torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  UpperCAmelCase	:					List[str]	=	"cuda"
 elif fpaa and not torch.cuda.is_available():
  raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""						)
 else:
  UpperCAmelCase	:					Tuple	=	"cpu"
 UpperCAmelCase	:					Optional[Any]	=	StableDiffusionPipeline.from_pretrained(_snake_case			,		torch_dtype=_snake_case						).to(_snake_case						)
 UpperCAmelCase	:					List[str]	=	Path(_snake_case						)

 # TEXT ENCODER
 UpperCAmelCase	:					Tuple	=	pipeline.text_encoder.config.max_position_embeddings
 UpperCAmelCase	:					List[str]	=	pipeline.text_encoder.config.hidden_size
 UpperCAmelCase	:					List[Any]	=	pipeline.tokenizer(
     """A sample prompt"""			,		padding="""max_length"""			,		max_length=pipeline.tokenizer.model_max_length			,		truncation=_snake_case			,		return_tensors="""pt"""			,		)
 onnx_export(
     pipeline.text_encoder			,		model_args=(text_input.input_ids.to(device=_snake_case			,		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=_snake_case			,		)
 del pipeline.text_encoder

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

 # VAE ENCODER
 UpperCAmelCase	:					int	=	pipeline.vae
 UpperCAmelCase	:					Tuple	=	vae_encoder.config.in_channels
 UpperCAmelCase	:					List[Any]	=	vae_encoder.config.sample_size
 # need to get the raw tensor output (sample) from the encoder
 UpperCAmelCase	:					Union[str, Any]	=	lambda UpperCamelCase			,		UpperCamelCase						: vae_encoder.encode(_snake_case			,		_snake_case						)[0].sample()
 onnx_export(
     _snake_case			,		model_args=(
         torch.randn(1			,		_snake_case			,		_snake_case			,		_snake_case						).to(device=_snake_case			,		dtype=_snake_case						),
         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=_snake_case			,		)

 # VAE DECODER
 UpperCAmelCase	:					Dict	=	pipeline.vae
 UpperCAmelCase	:					Tuple	=	vae_decoder.config.latent_channels
 UpperCAmelCase	:					Optional[Any]	=	vae_decoder.config.out_channels
 # forward only through the decoder part
 UpperCAmelCase	:					str	=	vae_encoder.decode
 onnx_export(
     _snake_case			,		model_args=(
         torch.randn(1			,		_snake_case			,		_snake_case			,		_snake_case						).to(device=_snake_case			,		dtype=_snake_case						),
         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=_snake_case			,		)
 del pipeline.vae

 # SAFETY CHECKER
 if pipeline.safety_checker is not None:
  UpperCAmelCase	:					List[Any]	=	pipeline.safety_checker
  UpperCAmelCase	:					Optional[int]	=	safety_checker.config.vision_config.num_channels
  UpperCAmelCase	:					str	=	safety_checker.config.vision_config.image_size
  UpperCAmelCase	:					List[Any]	=	safety_checker.forward_onnx
  onnx_export(
      pipeline.safety_checker			,		model_args=(
          torch.randn(
              1			,		_snake_case			,		_snake_case			,		_snake_case			,		).to(device=_snake_case			,		dtype=_snake_case						),
          torch.randn(1			,		_snake_case			,		_snake_case			,		_snake_case						).to(device=_snake_case			,		dtype=_snake_case						),
      )			,		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=_snake_case			,		)
  del pipeline.safety_checker
  UpperCAmelCase	:					Optional[int]	=	OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""						)
  UpperCAmelCase	:					Tuple	=	pipeline.feature_extractor
 else:
  UpperCAmelCase	:					Optional[Any]	=	None
  UpperCAmelCase	:					Dict	=	None

 UpperCAmelCase	:					Optional[int]	=	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=_snake_case			,		feature_extractor=_snake_case			,		requires_safety_checker=safety_checker is not None			,		)

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

 del pipeline
 del onnx_pipeline
 UpperCAmelCase	:					int	=	OnnxStableDiffusionPipeline.from_pretrained(_snake_case			,		provider="""CPUExecutionProvider"""						)
 print("""ONNX pipeline is loadable"""						)


if __name__ == "__main__":
      A:   List[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=1_4,
          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")

      A:   List[Any]								=		parser.parse_args()

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


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class       _snake_case					:







							@staticmethod
							def 			SCREAMING_SNAKE_CASE    (				*_a						,   **_a		):
								pass






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     hashlib.mda(image.tobytes()   )
	return m.hexdigest()[:10]






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     np.array(_snake_case   )
	__magic_name__		:		Optional[int]	   =     npimg.shape
	return {"hash": hashimage(_snake_case   ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
			UpperCamelCase__          =    dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     MaskGenerationPipeline(model=_a						,   image_processor=_a		)
				return image_segmenter, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				pass







			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     pipeline("mask-generation"						,   model="facebook/sam-vit-huge"		)

				__magic_name__		:		str	   =     image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Dict	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				# fmt: off
				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
				        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
				        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
				        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
				        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
				        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
				        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
				        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
				        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
				        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
				        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
				        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
				        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
				        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
				        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
				        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
				        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
				        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
				        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
				        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
				        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
				        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
				        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
				    ]						,   )
				# fmt: on







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "facebook/sam-vit-huge"
				__magic_name__		:		str	   =     pipeline("mask-generation"						,   model=_a		)

				__magic_name__		:		Tuple	   =     image_segmenter(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"						,   pred_iou_thresh=1						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Any	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				    ]						,   )
