



'''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	:		List[str]				=   version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						tuple				,		lowerCamelCase   :						Path				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						Any				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						str=False				,		):






	'''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   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						str				,		lowerCamelCase   :						int				,		lowerCamelCase   :						bool = False					):






	'''simple docstring'''




	__lowerCAmelCase												=  torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		__lowerCAmelCase												=  "cuda"
	elif fpaa and not torch.cuda.is_available():
		raise ValueError("`float16` model export is only supported on GPUs with CUDA"					)
	else:
		__lowerCAmelCase												=  "cpu"
	__lowerCAmelCase												=  StableDiffusionPipeline.from_pretrained(lowerCamelCase				,		torch_dtype=lowerCamelCase					).to(lowerCamelCase					)
	__lowerCAmelCase												=  Path(lowerCamelCase					)

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

	__lowerCAmelCase												=  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
	__lowerCAmelCase												=  OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase				,		provider="CPUExecutionProvider"					)
	print("ONNX pipeline is loadable"					)


if __name__ == "__main__":
	lowerCAmelCase	:		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''')

	lowerCAmelCase	:		Any				=   parser.parse_args()

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




'''simple docstring'''



import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def   __lowerCAmelCase					(							lowerCamelCase   :						bytes				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''{sampling_rate}'''
	__lowerCAmelCase												=  "1"
	__lowerCAmelCase												=  "f32le"
	__lowerCAmelCase												=  [
	    "ffmpeg",
	    "-i",
	    "pipe:0",
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]

	try:
		with subprocess.Popen(lowerCamelCase				,		stdin=subprocess.PIPE				,		stdout=subprocess.PIPE					) as ffmpeg_process:
			__lowerCAmelCase												=  ffmpeg_process.communicate(lowerCamelCase					)
	except FileNotFoundError as error:
		raise ValueError("ffmpeg was not found but is required to load audio files from filename"					) from error
	__lowerCAmelCase												=  output_stream[0]
	__lowerCAmelCase												=  np.frombuffer(lowerCamelCase				,		np.floataa					)
	if audio.shape[0] == 0:
		raise ValueError("Malformed soundfile"					)
	return audio



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						float				,		lowerCamelCase   :						str = "f32le"				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''{sampling_rate}'''
	__lowerCAmelCase												=  "1"
	if format_for_conversion == "s16le":
		__lowerCAmelCase												=  2
	elif format_for_conversion == "f32le":
		__lowerCAmelCase												=  4
	else:
		raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

	__lowerCAmelCase												=  platform.system()
	if system == "Linux":
		__lowerCAmelCase												=  "alsa"
		__lowerCAmelCase												=  "default"
	elif system == "Darwin":
		__lowerCAmelCase												=  "avfoundation"
		__lowerCAmelCase												=  ":0"
	elif system == "Windows":
		__lowerCAmelCase												=  "dshow"
		__lowerCAmelCase												=  "default"

	__lowerCAmelCase												=  [
	    "ffmpeg",
	    "-f",
	    format_,
	    "-i",
	    input_,
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-fflags",
	    "nobuffer",
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]
	__lowerCAmelCase												=  int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
	__lowerCAmelCase												=  _ffmpeg_stream(lowerCamelCase				,		lowerCamelCase					)
	for item in iterator:
		yield item



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						float				,		lowerCamelCase   :						Optional[int] = None				,		lowerCamelCase   :						Optional[Union[Tuple[float, float], float]] = None				,		lowerCamelCase   :						str = "f32le"				,		):






	'''simple docstring'''




	if stream_chunk_s is not None:
		__lowerCAmelCase												=  stream_chunk_s
	else:
		__lowerCAmelCase												=  chunk_length_s

	__lowerCAmelCase												=  ffmpeg_microphone(lowerCamelCase				,		lowerCamelCase				,		format_for_conversion=lowerCamelCase					)
	if format_for_conversion == "s16le":
		__lowerCAmelCase												=  np.intaa
		__lowerCAmelCase												=  2
	elif format_for_conversion == "f32le":
		__lowerCAmelCase												=  np.floataa
		__lowerCAmelCase												=  4
	else:
		raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

	if stride_length_s is None:
		__lowerCAmelCase												=  chunk_length_s / 6
	__lowerCAmelCase												=  int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
	if isinstance(lowerCamelCase				,		(int, float)					):
		__lowerCAmelCase												=  [stride_length_s, stride_length_s]

	__lowerCAmelCase												=  int(round(sampling_rate * stride_length_s[0]					)					) * size_of_sample
	__lowerCAmelCase												=  int(round(sampling_rate * stride_length_s[1]					)					) * size_of_sample
	__lowerCAmelCase												=  datetime.datetime.now()
	__lowerCAmelCase												=  datetime.timedelta(seconds=lowerCamelCase					)
	for item in chunk_bytes_iter(lowerCamelCase				,		lowerCamelCase				,		stride=(stride_left, stride_right)				,		stream=lowerCamelCase					):
		# Put everything back in numpy scale
		__lowerCAmelCase												=  np.frombuffer(item["raw"]				,		dtype=lowerCamelCase					)
		__lowerCAmelCase												=  (
		    item["stride"][0] // size_of_sample,
		    item["stride"][1] // size_of_sample,
		)
		__lowerCAmelCase												=  sampling_rate
		audio_time += delta
		if datetime.datetime.now() > audio_time + 10 * delta:
			# We're late !! SKIP
			continue
		yield item



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						int				,		lowerCamelCase   :						Tuple[int, int]				,		lowerCamelCase   :						bool = False					):






	'''simple docstring'''




	__lowerCAmelCase												=  B""
	__lowerCAmelCase							,       __lowerCAmelCase												=  stride
	if stride_left + stride_right >= chunk_len:
		raise ValueError(
		    f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''					)
	__lowerCAmelCase												=  0
	for raw in iterator:
		acc += raw
		if stream and len(lowerCamelCase					) < chunk_len:
			__lowerCAmelCase												=  (_stride_left, 0)
			yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
		else:
			while len(lowerCamelCase					) >= chunk_len:
				# We are flushing the accumulator
				__lowerCAmelCase												=  (_stride_left, stride_right)
				__lowerCAmelCase												=  {"raw": acc[:chunk_len], "stride": stride}
				if stream:
					__lowerCAmelCase												=  False
				yield item
				__lowerCAmelCase												=  stride_left
				__lowerCAmelCase												=  acc[chunk_len - stride_left - stride_right :]
    # Last chunk
	if len(lowerCamelCase					) > stride_left:
		__lowerCAmelCase												=  {"raw": acc, "stride": (_stride_left, 0)}
		if stream:
			__lowerCAmelCase												=  False
		yield item




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  2**24  # 16Mo
	try:
		with subprocess.Popen(lowerCamelCase				,		stdout=subprocess.PIPE				,		bufsize=lowerCamelCase					) as ffmpeg_process:
			while True:
				__lowerCAmelCase												=  ffmpeg_process.stdout.read(lowerCamelCase					)
				if raw == b"":
					break
				yield raw
	except FileNotFoundError as error:
		raise ValueError("ffmpeg was not found but is required to stream audio files from filename"					) from error