

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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=False					, )	->	Union[str, Any]:
		output_path.parent.mkdir(parents=__SCREAMING_SNAKE_CASE					, exist_ok=__SCREAMING_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(
				    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, f=output_path.as_posix()					, input_names=__SCREAMING_SNAKE_CASE					, output_names=__SCREAMING_SNAKE_CASE					, dynamic_axes=__SCREAMING_SNAKE_CASE					, do_constant_folding=__SCREAMING_SNAKE_CASE					, use_external_data_format=__SCREAMING_SNAKE_CASE					, enable_onnx_checker=__SCREAMING_SNAKE_CASE					, opset_version=__SCREAMING_SNAKE_CASE					, )
		else:
				export(
				    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, f=output_path.as_posix()					, input_names=__SCREAMING_SNAKE_CASE					, output_names=__SCREAMING_SNAKE_CASE					, dynamic_axes=__SCREAMING_SNAKE_CASE					, do_constant_folding=__SCREAMING_SNAKE_CASE					, opset_version=__SCREAMING_SNAKE_CASE					, )






@torch.no_grad()
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE = False )	->	List[Any]:
		__lowerCAmelCase:       Dict          =			torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				__lowerCAmelCase:       Any          =			"cuda"
		elif fpaa and not torch.cuda.is_available():
				raise ValueError("`float16` model export is only supported on GPUs with CUDA" )
		else:
				__lowerCAmelCase:       List[str]          =			"cpu"
		__lowerCAmelCase:       Dict          =			StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					, torch_dtype=__SCREAMING_SNAKE_CASE ).to(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Dict          =			Path(__SCREAMING_SNAKE_CASE )

		# TEXT ENCODER
		__lowerCAmelCase:       Optional[int]          =			pipeline.text_encoder.config.max_position_embeddings
		__lowerCAmelCase:       Union[str, Any]          =			pipeline.text_encoder.config.hidden_size
		__lowerCAmelCase:       Dict          =			pipeline.tokenizer(
		    "A sample prompt"					, padding="max_length"					, max_length=pipeline.tokenizer.model_max_length					, truncation=__SCREAMING_SNAKE_CASE					, return_tensors="pt"					, )
		onnx_export(
		    pipeline.text_encoder					, model_args=(text_input.input_ids.to(device=__SCREAMING_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=__SCREAMING_SNAKE_CASE					, )
		del pipeline.text_encoder

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

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

		# VAE DECODER
		__lowerCAmelCase:       List[str]          =			pipeline.vae
		__lowerCAmelCase:       Optional[int]          =			vae_decoder.config.latent_channels
		__lowerCAmelCase:       List[str]          =			vae_decoder.config.out_channels
		# forward only through the decoder part
		__lowerCAmelCase:       int          =			vae_encoder.decode
		onnx_export(
		    __SCREAMING_SNAKE_CASE					, model_args=(
		        torch.randn(1					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ).to(device=__SCREAMING_SNAKE_CASE					, dtype=__SCREAMING_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=__SCREAMING_SNAKE_CASE					, )
		del pipeline.vae

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

		__lowerCAmelCase:       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=__SCREAMING_SNAKE_CASE					, feature_extractor=__SCREAMING_SNAKE_CASE					, requires_safety_checker=safety_checker is not None					, )

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

		del pipeline
		del onnx_pipeline
		__lowerCAmelCase:       List[Any]          =			OnnxStableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					, provider="CPUExecutionProvider" )
		print("ONNX pipeline is loadable" )


if __name__ == "__main__":
			__A						   =			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")

			__A						   =			parser.parse_args()

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





"""simple docstring"""


import pytest

from datasets import inspect_metric, list_metrics, load_metric






@pytest.fixture
def     a__    (    __SCREAMING_SNAKE_CASE )	->	Union[str, Any]:
		monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"					, set() )






@pytest.fixture
def     a__    (    __SCREAMING_SNAKE_CASE )	->	str:





		class     snake_case	:

						def __init__(	self							:				int     ,  UpperCamelCase__							:				Optional[int])->   Union[str, Any]:



								'''simple docstring'''


								__lowerCAmelCase:       str          =			metric_id





		class     snake_case	:
						SCREAMING_SNAKE_CASE_   :      List[Any]         =  [MetricMock(__snake_case  ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]

						def 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



								'''simple docstring'''


								return self._metrics


		monkeypatch.setattr("datasets.inspect.huggingface_hub"					, HfhMock() )






@pytest.mark.parametrize(
    "func, args"					, [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		if "tmp_path" in args:
				__lowerCAmelCase:       Tuple          =			tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
		with pytest.warns(__SCREAMING_SNAKE_CASE					, match="https://huggingface.co/docs/evaluate" ):
				func(*__SCREAMING_SNAKE_CASE )


