

import os
import string
import sys


A_	:			Any  	=      1 << 8

A_	:			int  	=      {
    '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,
}

A_	:			Union[str, Any]  	=      KEYMAP['up']
A_	:			List[str]  	=      KEYMAP['left']

if sys.platform == "win32":
	A_	:			Dict  	=      []
	A_	:			Dict  	=      {
	    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):
	A_	:			str  	=      ord(str(i))

def 						UpperCamelCase ()					->				str:
	if os.name == "nt":
		import msvcrt

		A__							:   int					       =		"""mbcs"""
		# Flush the keyboard buffer
		while msvcrt.kbhit():
			msvcrt.getch()
		if len(lowercase_ ) == 0:
			# Read the keystroke
			A__							:   List[Any]					       =		msvcrt.getch()

			# If it is a prefix char, get second part
			if ch in (b"\x00", b"\xe0"):
				A__							:   List[str]					       =		ch + msvcrt.getch()
				# Translate actual Win chars to bullet char types
				try:
					A__							:   List[str]					       =		chr(WIN_KEYMAP[cha] )
					WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
					WIN_CH_BUFFER.append(lowercase_ )
					if ord(lowercase_ ) in (
					    KEYMAP["insert"] - 1 << 9,
					    KEYMAP["delete"] - 1 << 9,
					    KEYMAP["pg_up"] - 1 << 9,
					    KEYMAP["pg_down"] - 1 << 9,
					):
						WIN_CH_BUFFER.append(chr(126 ) )
					A__							:   Tuple					       =		chr(KEYMAP["""esc"""] )
				except KeyError:
					A__							:   str					       =		cha[1]
			else:
				A__							:   Dict					       =		ch.decode(lowercase_ )
		else:
			A__							:   Optional[int]					       =		WIN_CH_BUFFER.pop(0 )
	elif os.name == "posix":
		import termios
		import tty

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


def 						UpperCamelCase ()					->				Any:
	A__							:   Dict					       =		get_raw_chars()
	if ord(lowercase_ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
		return char

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

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






import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


A_	:			Union[str, Any]  	=      logging.get_logger(__name__)

A_	:			str  	=      {
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__=None   ,	**A__      ):
						logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""      )
						A__							:   Dict					       =		model
						A__							:   Any					       =		kwargs.get("""model_save_dir"""   ,	A__      )
						A__							:   Optional[int]					       =		kwargs.get("""latest_model_name"""   ,	A__      )






					def __call__(			self   ,	**A__      ):
						A__							:   int					       =		{k: np.array(A__      ) for k, v in kwargs.items()}
						return self.model.run(A__   ,	A__      )






					@staticmethod
					def   __A      (			A__   ,	A__=None   ,	A__=None      ):
						if provider is None:
							logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""      )
							A__							:   List[Any]					       =		"""CPUExecutionProvider"""

						return ort.InferenceSession(A__   ,	providers=[provider]   ,	sess_options=A__      )






					def   __A      (			self   ,	A__   ,	A__ = None   ,	**A__      ):
						A__							:   List[Any]					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A__							:   List[Any]					       =		self.model_save_dir.joinpath(self.latest_model_name      )
						A__							:   Optional[int]					       =		Path(A__      ).joinpath(A__      )
						try:
							shutil.copyfile(A__   ,	A__      )
						except shutil.SameFileError:
							pass

						# copy external weights (for models >2GB)
						A__							:   str					       =		self.model_save_dir.joinpath(A__      )
						if src_path.exists():
							A__							:   List[str]					       =		Path(A__      ).joinpath(A__      )
							try:
								shutil.copyfile(A__   ,	A__      )
							except shutil.SameFileError:
								pass






					def   __A      (			self   ,	A__   ,	**A__   ,	):
						if os.path.isfile(A__      ):
							logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
							return

						os.makedirs(A__   ,	exist_ok=A__      )

						# saving model weights/files
						self._save_pretrained(A__   ,	**A__      )






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = False   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   str					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(A__      ):
							A__							:   Dict					       =		OnnxRuntimeModel.load_model(
							    os.path.join(A__   ,	A__      )   ,	provider=A__   ,	sess_options=A__      )
							A__							:   Optional[Any]					       =		Path(A__      )
						# load model from hub
						else:
							# download model
							A__							:   Union[str, Any]					       =		hf_hub_download(
							    repo_id=A__   ,	filename=A__   ,	use_auth_token=A__   ,	revision=A__   ,	cache_dir=A__   ,	force_download=A__   ,	)
							A__							:   List[str]					       =		Path(A__      ).parent
							A__							:   str					       =		Path(A__      ).name
							A__							:   Optional[int]					       =		OnnxRuntimeModel.load_model(A__   ,	provider=A__   ,	sess_options=A__      )
						return cls(model=A__   ,	**A__      )






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[Any]					       =		None
						if len(str(A__      ).split("""@"""      )      ) == 2:
							A__       ,       A__							:   Union[str, Any]					       =		model_id.split("""@"""      )

						return cls._from_pretrained(
						    model_id=A__   ,	revision=A__   ,	cache_dir=A__   ,	force_download=A__   ,	use_auth_token=A__   ,	**A__   ,	)



