
'''simple docstring'''
import os
import string
import sys


lowercase__											=1 << 8

lowercase__											={
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 27,
    'up': 65 + ARROW_KEY_FLAG,
    'down': 66 + ARROW_KEY_FLAG,
    'right': 67 + ARROW_KEY_FLAG,
    'left': 68 + ARROW_KEY_FLAG,
    'mod_int': 91,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 50,
    'delete': 51,
    'pg_up': 53,
    'pg_down': 54,
}

lowercase__											=KEYMAP['up']
lowercase__											=KEYMAP['left']

if sys.platform == "win32":
		lowercase__											=[]
		lowercase__											={
		    b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
		    b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
		}

for i in range(10):
		lowercase__											=ord(str(i))






def      UpperCamelCase_				(      ):
	if os.name == "nt":
		import msvcrt

		a_      				=						"""mbcs"""
		# Flush the keyboard buffer
		while msvcrt.kbhit():
			msvcrt.getch()
		if len(A__						) == 0:
			# Read the keystroke
			a_      				=						msvcrt.getch()

			# If it is a prefix char, get second part
			if ch in (b"\x00", b"\xe0"):
				a_      				=						ch + msvcrt.getch()
				# Translate actual Win chars to bullet char types
				try:
					a_      				=						chr(WIN_KEYMAP[cha]						)
					WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]						)						)
					WIN_CH_BUFFER.append(A__						)
					if ord(A__						) in (
					    KEYMAP["insert"] - 1 << 9,
					    KEYMAP["delete"] - 1 << 9,
					    KEYMAP["pg_up"] - 1 << 9,
					    KEYMAP["pg_down"] - 1 << 9,
					):
						WIN_CH_BUFFER.append(chr(1_26						)						)
					a_      				=						chr(KEYMAP["""esc"""]						)
				except KeyError:
					a_      				=						cha[1]
			else:
				a_      				=						ch.decode(A__						)
		else:
			a_      				=						WIN_CH_BUFFER.pop(0						)
	elif os.name == "posix":
		import termios
		import tty

		a_      				=						sys.stdin.fileno()
		a_      				=						termios.tcgetattr(A__						)
		try:
			tty.setraw(A__						)
			a_      				=						sys.stdin.read(1						)
		finally:
			termios.tcsetattr(A__  ,      termios.TCSADRAIN  ,      A__						)
	return ch






def      UpperCamelCase_				(      ):
	a_      				=						get_raw_chars()
	if ord(A__						) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
		return char

	elif ord(A__						) == KEYMAP["esc"]:
		a_      				=						get_raw_chars()
		if ord(A__						) == KEYMAP["mod_int"]:
			a_      				=						get_raw_chars()
			if ord(A__						) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(A__						) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
				return chr(ord(A__						) + ARROW_KEY_FLAG						)
			else:
				return KEYMAP["undefined"]
		else:
			return get_raw_chars()

	else:
		if char in string.printable:
			return char
		else:
			return KEYMAP["undefined"]


'''simple docstring'''
import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowercase__											=version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__=False  ,      ):
	output_path.parent.mkdir(parents=A__  ,      exist_ok=A__						)
	# 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(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      use_external_data_format=A__  ,      enable_onnx_checker=A__  ,      opset_version=A__  ,      )
	else:
		export(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      opset_version=A__  ,      )






@torch.no_grad()
def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__ = 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_      				=						Path(A__						)

	# VAE DECODER
	a_      				=						AutoencoderKL.from_pretrained(model_path + """/vae"""						)
	a_      				=						vae_decoder.config.latent_channels
	# forward only through the decoder part
	a_      				=						vae_decoder.decode
	onnx_export(
	    A__  ,      model_args=(
	        torch.randn(1  ,      A__  ,      25  ,      25						).to(device=A__  ,      dtype=A__						),
	        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=A__  ,      )
	del vae_decoder


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

		lowercase__											=parser.parse_args()
		print(args.output_path)
		convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
		print('SD: Done: ONNX')
