





'''simple docstring'''






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   : Optional[Any]             =			logging.get_logger(__name__)

A   : List[Any]             =			{
    '''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 			lowerCamelCase					:


					def __init__(					self	:  List[str]       ,     __snake_case	:  Optional[Any]=None       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'       )
											_snake_case:   Tuple  					=		model
											_snake_case:   List[str]  					=		kwargs.get('model_save_dir'       ,     _A       )
											_snake_case:   Any  					=		kwargs.get('latest_model_name'       ,     _A       )


					def __call__(					self	:  List[str]       ,     **__snake_case	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		{k: np.array(_A       ) for k, v in kwargs.items()}
											return self.model.run(_A       ,     _A       )


					@staticmethod
					def    SCREAMING_SNAKE_CASE_		(					__snake_case	:  List[str]       ,     __snake_case	:  List[str]=None       ,     __snake_case	:  Optional[int]=None       ):





											'''simple docstring'''





											if provider is None:
																	logger.info('No onnxruntime provider specified, using CPUExecutionProvider'       )
																	_snake_case:   List[Any]  					=		'CPUExecutionProvider'

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  str       ,     __snake_case	:  List[Any] = None       ,     **__snake_case	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		file_name if file_name is not None else ONNX_WEIGHTS_NAME

											_snake_case:   Union[str, Any]  					=		self.model_save_dir.joinpath(self.latest_model_name       )
											_snake_case:   List[Any]  					=		Path(_A       ).joinpath(_A       )
											try:
																	shutil.copyfile(_A       ,     _A       )
											except shutil.SameFileError:
																	pass

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  int       ,     **__snake_case	:  Dict       ,     ):





											'''simple docstring'''





											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    SCREAMING_SNAKE_CASE_		(					cls	:  List[Any]       ,     __snake_case	:  List[str]       ,     __snake_case	:  Dict = None       ,     __snake_case	:  Dict = None       ,     __snake_case	:  Tuple = False       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  List[str] = None       ,     __snake_case	:  Optional[Any] = None       ,     __snake_case	:  Tuple = None       ,     **__snake_case	:  int       ,     ):





											'''simple docstring'''





											_snake_case:   str  					=		file_name if file_name is not None else ONNX_WEIGHTS_NAME
											# load model from local directory
											if os.path.isdir(_A       ):
																	_snake_case:   Tuple  					=		OnnxRuntimeModel.load_model(
																	    os.path.join(_A       ,     _A       )       ,     provider=_A       ,     sess_options=_A       )
																	_snake_case:   Any  					=		Path(_A       )
											# load model from hub
											else:
																	# download model
																	_snake_case:   Tuple  					=		hf_hub_download(
																	    repo_id=_A       ,     filename=_A       ,     use_auth_token=_A       ,     revision=_A       ,     cache_dir=_A       ,     force_download=_A       ,     )
																	_snake_case:   str  					=		Path(_A       ).parent
																	_snake_case:   Tuple  					=		Path(_A       ).name
																	_snake_case:   Tuple  					=		OnnxRuntimeModel.load_model(_A       ,     provider=_A       ,     sess_options=_A       )
											return cls(model=_A       ,     **_A       )




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Tuple       ,     __snake_case	:  Any       ,     __snake_case	:  List[str] = True       ,     __snake_case	:  Union[str, Any] = None       ,     __snake_case	:  List[str] = None       ,     **__snake_case	:  Optional[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		None
											if len(str(_A       ).split('@'       )       ) == 2:
																	_snake_case:   Optional[Any]  					=		model_id.split('@'       )

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












'''simple docstring'''






A   : str             =			{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


A   : Union[str, Any]             =			{value: key for key, value in encode_dict.items()}





def       lowercase_      (		lowercase__						)   ->str:
						_snake_case:   Union[str, Any]  					=		''
						for letter in word.lower():
												if letter.isalpha() or letter == " ":
																		encoded += encode_dict[letter]
												else:
																		raise Exception('encode() accepts only letters of the alphabet and spaces'						)
						return encoded





def       lowercase_      (		lowercase__						)   ->str:
						if set(lowercase__						) - {"A", "B", " "} != set():
												raise Exception('decode() accepts only \'A\', \'B\' and spaces'						)
						_snake_case:   List[Any]  					=		''
						for word in coded.split():
												while len(lowercase__						) != 0:
																		decoded += decode_dict[word[:5]]
																		_snake_case:   Optional[int]  					=		word[5:]
												decoded += " "
						return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()





